Conversational Intelligence

Meeting customer needs is one of the biggest challenges for the rapidly evolving banking industry. Financial services customers expect faster, smoother, and more personalized journeys. According to a 2023 Accenture survey of banking customers, 72% said that personalization influences their choice of bank.

These growing expectations call for AI-driven financial CX that delivers actionable insights for banks. Conversational intelligence offers such a solution that analyzes customer interactions at scale to enable smarter decision-making for banking CX teams.

The current CX challenges in banking

With 71% of consumers preferring two-way messaging with businesses, there is a growing expectation for smooth and frictionless experiences. However, many banks struggle to meet this expectation. Here are four common issues that erode trust and satisfaction for banking customers.

Long wait times and poor call routing

When customers need help, delays in reaching the right agent can quickly sour the experience. Long hold times, multiple transfers, or being routed to the wrong department signal inefficiency and a lack of empathy. 

As a result, customers find it difficult to trust the bank’s ability to meet their needs. 61% of consumers are willing to leave a brand after just one negative experience, further increasing the impact of each CX misstep.

Generic or repetitive interactions

The last thing customers want to hear or see is a template response to their issues. They expect quick and empathetic communication that makes them feel heard. Without personalization or context, interactions feel disengaging, resulting in decreased customer loyalty.

Fragmented communication across channels

With multiple communication channels and siloed experience data, banks often struggle to build comprehensive views of customer experiences. Customers have to repeat themselves on different channels, since agents typically lack the full context. This disjointed experience leads to friction, longer resolution times, and a perception that the bank isn’t truly listening.

Inability to proactively identify and resolve pain points

The modern financial services customer expects proactive support every step of the way. For example, if there are banking products that can address their unique needs, they expect to be aware of them right away. This level of personalized service requires the use of analytical tools to spot emerging friction points and resolve them before it’s too late.

5 ways conversational Intelligence enhances CX in banking 

Conversational Intelligence (CI) provides an AI-powered approach to enhancing customer experiences across financial institution touchpoints. It processes textual and audio data from multiple channels to provide comprehensive insights into customer behavior. Here are five powerful ways that effective data collection and analysis help improve CX in banking.

1. Reduces wait times and resolves issues faster

CI tools help triage inquiries in real time by identifying customer intent and urgency. Their ability to connect experience data from various channels saves time for businesses during customer interactions. For example, InMoment provides CI capabilities as part of its omnichannel customer experience platform to enable better customer understanding and first-contact resolution.

2. Delivers personalized and contextual interactions

CI analyzes historical data and conversation patterns to help the human agents tailor their responses to each customer’s unique needs and situation. The result is a more empathetic and personalized interaction, whether for checking account balances or resolving complex disputes.

For example, if a customer calls her bank to ask about mortgage refinance, CI helps the support agent access the necessary context to help her. Perhaps she’s reached out via chat in the past or has expressed certain feelings in her reviews. 

These prior questions, preferences, and sentiments make up the context that lets the agent pick up the conversation seamlessly. Instead of starting from scratch, agents leverage the AI-driven capabilities of CI to get straight to the point. As a result, customers feel heard and valued, improving their overall user experience.

3. Enables proactive support

CI tools detect early signals of dissatisfaction by leveraging sentiment analysis for customer feedback. This Natural Language Processing (NLP) technique uncovers intent, effort, and emotions in text to help financial institutions identify recurring issues. As a result, banks step in before problems escalate, improving retention and demonstrating attentiveness.

4. Increases agent effectiveness 

CI boosts agent productivity by equipping them with all the information and context to make effective decisions. With real-time guidance, CI tools can suggest ideal responses, surface relevant knowledge articles, and provide compliance prompts as conversations unfold.

The availability of this information reduces the burden on customer support agents, allowing them to focus on improving customer engagement. Without worrying about fetching data or second-guessing protocols, agents lead smoother conversations and provide consistent service.

5. Provides insights to continually improve CX

Each customer interaction holds valuable insight for banks. Therefore, collecting reviews and saving chat transcripts isn’t enough; it’s far more helpful to understand what they represent. 

CI supports this goal by continuously analyzing volumes of banking experience data to reveal sentiment trends, pain points, and emerging opportunities. These findings guide everything from product strategy to agent coaching programs, helping banks confidently evolve their CX strategy.

The use cases for conversational intelligence in banking

From contact centers to compliance teams, CI enables financial institutions to work smarter, respond faster, and create more cohesive customer journeys. Below are key use cases where conversational banking delivers value to agents, managers, and customers.

Contact center optimization

CI helps optimize contact center operations by automating repetitive tasks, identifying bottlenecks, and surfacing actionable insights for improvement.

For example, if there is a surge in customer queries about credit card reward programs, CI detects this trend in real-time and alerts CX teams. It also analyzes customer-agent interactions to reveal areas for improvement in response time and quality. Beyond this analysis, CI tools leverage automation to access customer data, suggest troubleshooting scripts, and route calls to the right agents.

As a result, agents don’t have to second-guess their next steps or spend valuable time on routine tasks. Instead, they can focus on delivering effective customer service that improves call center metrics like average handle time and repeat call rate.

Compliance and risk monitoring

Regulatory non-compliance has serious consequences for banks, including substantial penalties and reputational damage. CI helps generate evidence for proving compliance by capturing and maintaining accurate logs of all customer interactions. It also automatically detects regulatory red flags like failure to add a required disclaimer when talking to a customer. This proactive monitoring prevents the need for intensive manual review, making it easier for banks to ensure compliance and audit readiness.

Voice of the Customer (VoC) analysis

Understanding customer sentiment is crucial for building long-term trust and loyalty. With 80% of service organizations expected to use some form of AI by 2026, traditional surveys are no longer viable for feedback collection and analysis. 

The way forward is conversational analytics, which captures emotion, intent, and recurring themes across volumes of online interactions. InMoment’s core NLP engine, for instance, offers low-latency text extraction and analytics that process over five social media posts per second to provide real-time updates on customer sentiment.

Banks that use CI for VoC analysis gain a richer understanding of the customer journey and respond faster to changes in sentiment. This analysis boosts satisfaction and helps align decisions with what truly matters to customers.

Training agents and managing performance  

A key application of conversational analytics is managing customer support agent performance. The tool digs into customer-agent interactions to highlight wins as well as areas for improvement. 

The data-driven insights provide excellent agent coaching opportunities. For example, agents exhibiting low empathy during high-stress interactions likely don’t work well under pressure. As a result, managers can tailor a unique training program for them without relying on a one-size-fits-all approach.

Automating how calls are categorized

Manual tagging for call categorization is time-consuming, error-prone, and inconsistent across agents. CI streamlines the tagging process by automatically detecting the topic, intent, and emotional tone of each conversation. It applies machine learning models trained on historical interaction data to assign categories in real-time.

Automating this process improves data quality, saves valuable agent time, and enables banks to generate more reliable reports. It also enhances trend analysis, making it easier to identify high-priority categories for CX improvements.

Identifying and resolving the root cause behind issues 

Conversational analytics supports proactive customer service by unifying insights across channels. From virtual assistants and AI chatbots to mobile banking apps and phone calls, each channel features customer experience signals that CI connects to uncover systemic patterns.

For example, if there is a pattern of frustration regarding delayed transactions, CI surfaces this insight so the bank can investigate. This process uncovers underlying issues for the bank to address before it’s too late. As a result, financial institutions proactively meet customer expectations while driving product improvements.

Quality assurance

CI transforms quality assurance (QA) by automatically evaluating each customer-agent interaction. The tool scores conversations against pre-defined metrics like tone, compliance, and empathy to provide an objective view of performance trends. This automation ensures consistent service quality across teams by eliminating human oversight or bias.

Conversation continuity across channels

According to the 2023 Zendesk CX Trends Report, 70% of banking customers expect everyone they interact with at the bank to have full context. This expectation highlights the importance of establishing conversation continuity across multiple channels. If a customer voices a complaint via support chat, they expect the agent to know their issue when they pick up the phone, too.

CI ensures this continuity by working within an omnichannel system like the InMoment XI Platform. It connects and collects interactions across channels to provide a comprehensive view of each customer journey. As a result, agents can pick up right where the last conversation left off, saving time and reducing friction.

Getting started with conversational intelligence in banking

Conversational AI is the next step forward for banks looking to stay competitive and improve service quality. Here’s a step-by-step guide for moving from strategy to impact:

1. Define your objectives

Start by outlining your specific CX goals. Are you aiming to improve response times, enhance compliance, reduce call volume, or streamline agent coaching? Clear goals will guide your platform selection and rollout strategy.

2. Select the right CI platform

The right CI platform will save you valuable time and effort without straining your existing resources. InMoment’s conversational analytics platform checks that box by seamlessly integrating with your existing system to offer a range of capabilities, from omnichannel analysis to automated conversation tagging. These features support your CX efforts by taking care of the analytical heavy lifting, allowing you to focus on the human touch.

3. Integrate it into your banking workflow

The right tool will also offer robust CX integrations to smoothly connect with your CRM or automation platforms. This feature enables unified data capture and allows insights to flow freely across teams and touchpoints.

4. Focus on key metrics

With the volume of data at your disposal, it’s crucial to determine the metrics that matter most to your team. Without this focus, you will feel overwhelmed, and your agents will lack a clear definition of success. 

Start by considering the goals you set at the beginning. If your priority is reducing operational cost, metrics like cost per call, average handle time, and first call resolution are key focus areas. Once you’ve determined the right metrics for your goals, you can better adjust your CX strategies.

5. Train your customer support team

Your CI tool of choice will deliver real value when your team uses it the right way. Beyond explaining the tool’s features, train them on surfacing insights from it and using the information to deliver CX improvements. For example, show them how sentiment analysis of call transcripts reveals intent and emotional state. Guide them on transforming this insight into action that boosts customer satisfaction in future calls.

6. Refine and scale

Continue to improve and scale your platform by tracking progress against your initial objectives. Once you start seeing the desired outcomes, you will feel more confident about expanding your use case or evolving your CX strategy. 

Stay up-to-date on changing customer expectations with the help of artificial intelligence and automation. From trend analysis to keyword alerts, leverage CI features to continuously improve and amplify the impact of your CX efforts.

Elevate your bank’s CX strategy with InMoment

Conversational intelligence offers an incredible competitive advantage to banks in the modern age. From using generative AI for empathetic responses to surfacing insights from volumes of customer interaction data, the tool is a game-changer for customer-centric financial institutions.

InMoment’s conversational analytics platform brings together all the features necessary to exceed customer expectations. It captures, connects, and analyzes experience data from every relevant channel to highlight actionable insights. The result is a data-driven approach to customer service that improves agent performance and key CX metrics.

Schedule a demo today to see how InMoment’s conversational intelligence software can help enhance your bank’s reputation and customer loyalty!

Financial institutions are dealing with growing customer expectations while staying compliant with stricter data privacy laws. With over 50 percent of customers willing to switch banks after one negative experience, organizations can’t afford to rest on their laurels. 

It’s increasingly important to meet modern expectations with data-driven tools. Conversational intelligence presents a powerful solution for financial service providers in this regard.

How does conversational intelligence enhance customer experience in financial services?

Personalization, speed, and trust are non-negotiables for the modern financial services customer. According to the 2023 Zendesk CX Trends Report, 72% of banking customers demand immediate service, while 62% agree that personalized interactions are better than general ones.

Conversational Intelligence (CI) moves beyond traditional customer support to provide analytical insights into evolving customer behaviors. Here are three key benefits of conversational AI for financial services:

Deeper CX insights

With thousands of conversations happening across customer support channels, financial service providers have a wealth of data containing untapped insight. CI empowers them to build rich datasets from these interactions and analyze them for real-time signals about pain points and expectations.

For example, conversational artificial intelligence uses algorithms trained on transaction histories to predict future engagement levels. This insight can help identify potential cases of churn, highlight the creditworthiness of account holders, and surface products that should be recommended to customers. This predictive analysis supports proactive service, leading to an enhanced customer experience.

Increasing first-call resolution (FCR)

Conversation banking also provides insight into recurring customer queries. From credit card complaints to payment disputes, customers bring forth a range of pain points that agents are expected to resolve. Digging into their conversations highlights common issues, allowing agents to update their knowledge bases for faster resolution.

As a result, agents succeed in improving key call center metrics like first-call resolution (FCR), which tracks the percentage of customer inquiries that agents resolve after the first call. The downstream effect of fewer repeat calls is lower operational costs, enabling financial services to achieve profitability while boosting trust.

Improving agent performance and consistency

Even the most skilled human agents struggle with message consistency, empathy, and real-time recall under pressure. CI supports them by providing real-time guidance during live interactions, helping agents maintain compliance and deliver consistent experiences.

For instance, if an agent forgets to mention a required disclosure during a loan application discussion, CI can prompt them right away. Over time, aggregated insights support personalized training and performance reviews, empowering agents to put their best foot forward in each interaction.

Key use cases of conversational intelligence in finance

Financial institutions leverage conversational AI to improve operational efficiency and deliver better CX outcomes. Here are seven use cases demonstrating the impact of AI in financial services:

Improving call center and support interactions

Modern contact centers in the banking industry face immense pressure to reduce costs while delivering frictionless customer service. A tool like InMoment conversation analytics helps achieve these twin goals through intelligent routing, automating responses to routine tasks, and highlighting areas for improvement across large call volumes. The result is a streamlined experience that boosts customer satisfaction and loyalty.

Training and coaching financial advisors or agents

Conversational data analysis also empowers managers to create effective agent coaching programs. From flagging behavioral patterns and skill gaps to suggesting next-best steps, the analytical insights help improve financial advice quality and agent confidence.

Personalizing client experiences at scale.

Customers expect their banks to understand their unique goals and pain points. When they present a query, they look forward to a response that makes them feel heard and valued. 

It’s no wonder, then, that personalization drives positive experiences, with financial services starting to sit up and take notice. Over 85% of financial brands highlighted personalization as their strategic North Star in a 2023 survey by Dynamic Yield.

CI supports personalization efforts by leveraging sentiment analysis on customer data, including feedback and past interactions. The insights into customer emotion and intent help agents tailor their responses, offers, and advice to individual needs. 

With InMoment’s patented, AI-driven Active Listening™, you can go one step further by simplifying the response generation process. The tool leverages generative AI to intelligently respond to customers during the feedback process, resulting in richer insights.

Driving proactive client engagement

CI ensures proactive customer engagement through deep analysis of behavioral signals and recurring queries. For example, it can detect frequently asked questions about topics like mortgage eligibility in conversational data. The tool uses the information to signal upsell opportunities and prompt timely follow-ups from agents. Agents can then take immediate action to address the pressing customer need, boosting conversion and retention rates.

Ensuring compliance and risk management

Risk management is a top priority for financial institutions, with rising scrutiny on data privacy and disclosure accuracy. CI helps ensure regulatory compliance by monitoring conversations for flagged language, missing disclosures, and script deviations. This automation prevents the need for manually reviewing thousands of interactions, which is error-prone and unproductive. Instead, compliance teams can confidently detect potential violations, audit conversations, and reduce legal exposure.

Automating how calls are categorized

Accurate call categorization helps teams analyze trends, prioritize resources, and allocate follow-up. Conversational AI tools use machine learning to auto-detect customer intent and apply the correct categories in real time. Just like with compliance, CI eliminates manual work in call categorization, improving reporting accuracy and reducing agent workload.

Identifying and resolving root cause behind issues

Instead of simply chasing metrics, financial institutions must surface insights from the numbers. For instance, a low average handle time looks good on paper until you realize agents are rushing their calls! The best way to stay competitive and take insight-driven action is by addressing the root cause behind customer service issues.

CI supports this important step by aggregating interaction data across your channels to unveil recurring issues. This visibility helps CX teams identify the underlying issue, whether it’s low agent confidence, a training gap, or a systemic flaw. The outcome is better experiences, fewer support calls, and significant cost savings.

How to implement conversational intelligence in financial services

The right approach empowers financial services providers to smoothly integrate CI into their workflows. The following steps are key to unlocking powerful insights, improving customer experiences, and enhancing operational efficiency.

1. Define objectives and map customer journeys

Start by identifying the specific goals your CI tool should support. For instance, improving customer satisfaction is a primary goal for customer-centric organizations, but maybe you’ve been hitting the mark there. Perhaps a bigger problem for you is the rising cost of contact center operations. Or maybe you want to ensure stricter compliance due to recent policy changes.

The next step is to map out key customer journeys where valuable conversations occur. These journeys could include onboarding, loan processing, or any customer interaction with your bank. The idea is to identify high-impact moments where you can focus your CI efforts.

2. Use the right conversational intelligence platform

The right CI platform offers omnichannel coverage, tried-and-tested Natural Language Processing (NLP) models, and seamless integration with your existing systems. 

InMoment’s conversational intelligence software goes beyond these features to support your regulatory compliance goals as well. It leverages accurate in-house transcription to safely capture the entirety of each agent-customer interaction for audit purposes. The tool uses machine learning to flag missing disclosures, suspicious recommendations, and other areas where the agent may not be acting in the customer’s best interests.

As a result, you don’t have to invest valuable hours into unproductive manual audits. You free up more time for building stronger customer relationships and improving key aspects of the banking experience.

3. Educate and train team members

Effective CI implementation depends on adoption. Ensure agents, advisors, analysts, and managers understand how to use the new tools and interpret insights correctly.

Offer hands-on training sessions that explain how CI empowers agents in their daily tasks. Highlight the tool’s ability to support real-time decision-making and ensure compliance in each interaction. Educated agents not only perform better with CI but also become internal advocates, which helps streamline CI adoption across other teams.

4. Operationalize insights across teams and touchpoints

The true value of AI in financial CX lies in transforming analytical insights into action. Establish cross-functional workflows to ensure that customer data and feedback loops inform everything from chatbot training to script updates and escalation paths. 

For example, if CI highlights growing dissatisfaction with a product release, communicate it to your developers and marketing team. Work with them to implement the necessary fixes and inform customers with timely and effective updates. Don’t let insights sit on the dashboard—embed them into workflows to stay competitive and customer-centric.

Power smarter, more personalized experiences in finance with InMoment

With personalization and smoother banking influencing customer decisions, an AI-powered CX strategy is more important than ever before. It takes care of the analytical workload for thousands of customer interactions, providing valuable insights so you don’t have to guess at what works.

With InMoment’s conversational intelligence tool for financial services, you can track and improve key contact center metrics to move ahead of the pack. Schedule a demo today to see how our analytics and automation help you meet modern customer needs while reducing risk exposure!

From geopolitical instability to a shifting regulatory landscape, the insurance industry is experiencing significant transformation. These massive shifts, combined with rising policyholder expectations for smooth and personalized service, require a data-driven approach to providing insurance.

Conversational Intelligence (CI) is emerging as a game-changer in this space. It leverages artificial intelligence to analyze agent-customer interactions, streamline workflows, and improve outcomes across claims, service, and compliance.

Benefits of conversational intelligence for insurance providers

With the insurance industry navigating growing expectations and complexity, the need for efficient and personalized services is greater than ever. Here are four major ways in which CI improves account health for insurance providers.

Faster, more accurate claims processing

Delays in claims processing erode customer trust and drain resources. Conversational intelligence helps by analyzing automated call summaries to flag common issues, uncover friction in workflows, and guide agents toward smoother resolution.

For example, if multiple policyholders are confused about required documentation, conversational AI technology surfaces those patterns across channels. This allows insurers to clarify policy details, reduce wait times, and even automate repetitive tasks like follow-up reminders. As a result, the claims process is more efficient and consistent.

Improved customer experience and personalization

Policyholders expect empathy and transparency when interacting with their insurer. With AI-driven insights, insurance companies are better able to identify unique customer needs, concerns, and preferences. 

CI also supports effective location-based campaigns by highlighting how policyholders respond in different regions. This information is useful for delivering tailored communication across the entire customer journey. Customers feel heard and valued, which is key for retention and satisfaction.

Agent coaching and performance optimization

Even top-performing agents require targeted coaching to put their best foot forward. CI helps achieve this goal by giving supervisors visibility into agent behavior. It surfaces patterns from customer queries, tone, and resolution quality. 

Unlike manual review, the automated nature of CI allows managers to evaluate 100% of agent-customer interactions without breaking a sweat! As a result, contact center leaders can use real call data to monitor human agent behavior, pinpoint best practices, and identify areas for improvement.

Better compliance monitoring and risk reduction

Organizations are increasingly keen on evolving their compliance programs to avoid penalties and reputational damage. According to Drata’s 2023 Compliance Trends Report, 87% of organizations indicated negative outcomes due to low compliance maturity. With the insurance business being such a heavily regulated space, insurers are under immense pressure to comply.

The ability of CI to automatically monitor and record conversations across channels is invaluable here. Besides tracking 100% of conversations for audit purposes, it helps ensure regulatory adherence by detecting missed disclosures and suspicious agent behavior.

Therefore, compliance and QA teams no longer need to manually review every interaction. Instead, they can focus their efforts where the risk is highest, significantly improving risk management and ensuring that the entire organization remains audit-ready.

Key use cases of conversational intelligence in insurance

The ability to analyze customer interactions at scale is crucial for improving policyholder experiences and internal operations. The following use cases show how CI helps insurance providers deliver proactive and personalized service across the entire customer journey.

Claims management and fraud detection

Few processes are as critical in the insurance industry as claims processing. Delays, miscommunication, or oversight at this stage result in lost trust, increased operational costs, and reputational damage. CI equips insurance providers with the tools to analyze claims-related conversations in real-time, helping them accelerate resolutions while also detecting potential fraud.

The tool uses natural language processing (NLP) to sift through thousands of call transcripts and identify patterns in claim inquiries. For instance, if it spots inconsistencies across different policyholder accounts, it flags them for further review. This feature is key for early fraud detection without requiring exhaustive manual analysis.

Beyond fraud, CI tools help streamline the claims journey by identifying common pain points. They highlight instances of frustration and confusion amongst policyholders, so that insurers can adjust their scripts or update internal workflows for more efficient service.

Enhancing call center operations

Modern contact centers in the insurance sector handle an overwhelming volume of inquiries, from simple policy questions to complex claims. With rising expectations for speed and accuracy, operational performance can’t be left to guesswork.

Conversational AI helps by analyzing every interaction, uncovering performance trends, and supporting informed decision-making. For instance, it processes call summaries in real-time to surface actionable insights like recurring complaints, long silences, or script deviations. This information helps leaders understand why calls take longer to resolve or which agents require additional coaching.

CI also supports smarter staffing decisions by revealing call volume patterns and common call categories. The result is a more responsive and efficient operation that reduces wait times, improves consistency, and allows teams to focus on high-value engagements.

Improving customer onboarding and policy renewals

The first impression sets the tone for the entire customer journey. Additionally, policy renewals are a make-or-break moment for retention. CI improves both processes by equipping insurance providers with instant insight into friction points, common complaints, and missed opportunities for customer engagement.

For example, if many policyholders ask the same questions about coverage limits or pricing during onboarding, CI surfaces those trends for contact center teams. Agents and managers are then able to optimize scripts, educational content, and self-service flows. During renewals, the tool can help by flagging sentiment shifts in real time. This allows agents to address concerns proactively before a policy lapses.

Boosting compliance and QA processes

Ensuring regulatory compliance across high-volume customer interactions is both critical and resource-intensive. Conversational AI platforms help through continuous call monitoring and analysis. These processes are key for detecting missed disclosures or suspicious agent behavior.

QA teams benefit from CI through automatic analysis of each agent-customer interaction. This ensures regulatory accuracy, improves agent consistency, and drastically reduces the effort and cost of manual review. It also supports audit readiness, helping insurers stay aligned with industry standards without sacrificing operational efficiency.

Identifying customer sentiment and service gaps

Understanding how policyholders feel is essential to delivering standout service. CI leverages sentiment analysis to track tone, emotion, and intent in real-time across all customer queries and channels like chatbots and social media.

This enables insurers to detect cases of confusion, frustration, or disengagement among customers. These emotional signals highlight emerging service gaps, so that teams can prioritize follow-up and refine customer support strategies for better outcomes.

Automating how calls are categorized

Accurate conversation tagging is essential for tracking issue trends, reporting, and process improvement. But manual categorization is time-consuming and often inconsistent.

Conversational AI removes this friction by automatically detecting intent and applying relevant categories to every interaction. For instance, InMoment’s CI tool uses machine learning to auto-detect customer intent and apply the right category in real time. This automation reduces repetitive tasks for agents and ensures consistent data collection for QA, product, and operations teams.

Identifying and resolving root cause behind issues

Surface-level issues like repeated customer support calls or frequent clarifications on insurance policy terms often point to deeper problems. CI helps uncover the root cause by aggregating and analyzing interaction data across every communication channel.

By connecting these dots, insurers can detect recurring breakdowns in communication, process bottlenecks, or knowledge gaps that drive dissatisfaction. These CI insights empower teams to take strategic action, including agent training and removing operational bottlenecks. The result is a smoother customer experience and long-term cost savings.

Steps for implementing conversational intelligence in insurance

Integrating CI into your daily operations involves more than just investing in the tool. It requires a strategic rollout, from identifying your goals to training your team. Here are four key steps for leveraging CI to transform conversations into actionable insights.

1. Identify use cases and goals

The first step towards successful conversational AI implementation is knowing exactly where it can deliver the most value. For insurance providers, this means identifying the high-impact customer interactions or internal workflows that would benefit most from CI insights.

Start by involving key stakeholders and aligning on mission-critical outcomes. These objectives could include increasing customer satisfaction, reducing claims processing times, or boosting compliance.

The next step is to identify corresponding use cases where CI can play a key role. For example, InMoment’s conversation analytics platform helps reduce legal exposure by automatically monitoring conversations for missed disclosures.

2. Choose the right conversational intelligence platform

Choose a platform that goes beyond the technology to assist you with professional services, including data collection, analysis, and selecting the right metrics to track. 

As a leader in customer experience and conversational intelligence, InMoment helps large insurance providers turn unstructured data into action. Its conversational AI platform offers:

• Accurate transcription and NLP tailored for industry-specific terminology
  
• Multilingual support for diverse policyholder bases
  
• Built-in data security and compliance features to protect sensitive customer data
  
• Seamless integration with existing CRM, claims management systems, and contact center tools
  
• Omnichannel capabilities to unify conversations across voice, chat, email, and apps

These features help surface opportunities to improve service quality, reduce risk, and boost operational agility at scale. As a result, you can simplify decision-making and drive positive customer experience outcomes by working with a proven partner.

3. Integrate with existing systems

CI delivers maximum value when it works in concert with your existing technology stack. Seamless integration with tools like CRM platforms and claims management systems enables CI to analyze key datasets and provide valuable insights.

InMoment leverages robust CX integrations to enable the smooth flow of data across systems. These integrations empower teams to use shared insights and access a unified view of each policyholder’s history and preferences. By eliminating information silos, integrations allow for smoother experience management.

4. Train teams and set usage guidelines

Train your customer support agents and managers on how to make the most of conversational AI features. Besides enhancing adoption across teams, this step is key for ensuring consistent value for policyholders.

When teams understand how to track metrics, analyze automated call summaries, and trigger post-call surveys, they are better able to adopt a proactive approach to work. Similarly, managers can improve their coaching programs and ensure compliance by learning how to interpret customer interactions.

It’s also crucial to establish clear usage guidelines for your CI tool. This practice ensures teams use the tool responsibly and consistently. As a result, you can integrate AI into daily operations without compromising the human touch that customers value the most.

Drive performance and progress with InMoment’s Conversational Intelligence

From faster claims processing to surfacing opportunities for agent improvement, CI is key for gaining a competitive advantage in the insurance industry. It turns everyday interactions into strategic insight, enabling insurers to meet the rising expectations of modern policyholders.

InMoment empowers insurance providers with comprehensive data analysis, automated QA and compliance review, and reliable fraud detection. Beyond the cutting-edge technology, you get access to a professional team of CX experts when you partner with us. From building custom datasets to selecting the right metrics to track, we set you up for long-term operational and customer success.
Schedule a demo today to see how you can modernize your insurance operations with AI-powered insights!

In today’s consumer-led economy, your ability to improve the customer experience directly impacts whether you close the deal or lose it. If sales teams can’t resolve issues or respond to concerns fast enough, buyers won’t stick around.

That’s why conversational intelligence (CI) is such a powerful tool for sales-focused organizations. It gives you a clear view into your customers’ minds, surfacing what they care about, their pain points, and what ultimately drives their decisions. 

With this level of insight, sales teams and contact centers can fine-tune their approach, strengthen messaging, and close more deals with less guesswork. Think of it as a backstage pass into customers’ motivations delivered in real time. 

Below, we break down the top benefits of conversational intelligence and share best practices to maximize its value.

What Is Conversational Intelligence?

Conversational intelligence involves collecting and analyzing sales conversations to uncover actionable insights, improve experiences, and build stronger customer relationships. And while it may sound like a heavy lift, especially with the volume of calls sales teams handle daily, it’s actually the opposite.

Using innovations like artificial intelligence (AI), natural language processing (NLP), and machine learning, conversational intelligence tools break down customer conversations at scale to deliver meaningful findings. This might mean identifying gaps in messaging, highlighting missed opportunities, or revealing exactly where prospects disengage. 

With these insights in hand, your team can focus on high-value tasks, like refining pitches, building relationships, and closing deals.

How Can Conversational Intelligence Benefit Sales Teams?

If you’re like many companies right now, you’re keeping a close eye on spending. It makes sense: Budgets are tighter, and every investment needs to earn its place. However, conversational intelligence may be the missing link between your team and stronger sales performance. 

From speeding up issue resolution to delivering sharper customer insights, conversational intelligence helps teams move faster, sell smarter, and adapt in real time. It’s more than just a CX tool—it’s a competitive edge for businesses that want to rise above the noise and stay there. 

Here’s how it helps sales teams thrive at every stage of the sales cycle:

Speed Up Customer Issue Resolution

Sales reps deal with a steady stream of questions, concerns, and objections before closing deals. Keeping track of them all isn’t easy, unless they’ve got the memory of an elephant. 

That’s where conversational intelligence earns its keep. It monitors sales conversations in real time, identifies common problem areas, and flags patterns before they derail. With these insights, reps can resolve issues faster and keep momentum going. Sometimes, that’s the difference between a sales cycle that takes weeks and one that drags on for months. 

Boost Agent Performance and Job Satisfaction

Trying to close a deal without understanding your customers is like taking a shot in the dark. You might get lucky, but more often than not, you’ll miss the mark. 

Conversational intelligence reduces that risk. It analyzes customer interactions to highlight what drives conversions and where prospects tend to drop off in the sales cycle. These conversation insights can be used to train sales teams and support agents more effectively, boosting their confidence, sharpening their skills, and improving overall performance. 

The ripple effect? Greater job satisfaction and higher retention. And considering that the cost of replacing a team member can be three to four times their salary, that’s a win worth investing in.

Lower Service Costs and Streamline Response Times

Depending on your industry, sales costs can eat up 15–30% of total revenue. But they don’t have to. 

When you invest in solutions that help you shorten the sales cycle, like conversational intelligence, you cut the guesswork and reduce the cost per sale. Instead of chasing dead ends or repeating the same ineffective tactics, reps can focus on what actually moves deals forward. Fewer wasted sales calls. Fewer missed opportunities. Lower service costs. 

CI also helps your team move faster by pinpointing common customer questions and proven resolutions. The result is faster, more consistent reply times that keep customers engaged and momentum high.

Increase Customer Lifetime Value

Top-performing sales teams don’t just aim for one-time conversions—they build relationships that last.

Conversational intelligence helps by providing valuable insights into what inspires trust, loyalty, and long-term engagement. It can spotlight standout messaging, high-performing reps, and service tactics that turn first-time buyers into brand advocates. With these insights, you can strengthen connections and unlock meaningful revenue growth over time.

Think of it as a playbook for improving customer retention, repeat business, and referrals without having to guess what works.

Prove the Value of Customer Service Efforts

It’s not always easy to link customer service to business results, but conversational intelligence makes it possible.

By turning customer interactions into measurable data, CI allows you to track critical metrics like resolution speed, customer satisfaction score (CSAT), and follow-up success. These insights help connect your team’s efforts to real-world outcomes, like increased sales, reduced churn, and higher retention. 

And in today’s “Era of Less,” when decision-makers are tightening budgets, that kind of return on investment (ROI) clarity can make all the difference.

Best Practices for Implementing Conversational Intelligence in Sales Strategies

The best tools only deliver value when they’re implemented intentionally. Here are a few best practices to help you get the most out of your conversational intelligence platform and ensure it’s fully integrated into your sales strategies:

1. Choose the Right Platform

The wrong tool won’t just underdeliver: It might drain resources that are better used elsewhere. That’s why choosing the right conversational intelligence tool is critical.

Start by clarifying your team’s goals. Looking to strengthen sales coaching? Choose a platform that goes beyond summarizing call recordings and includes agent scorecards and coaching workflows. Need real-time insights to improve sales processes on the fly? Prioritize tools with live conversation analysis.

Equally important: integration. Your platform should connect seamlessly with your existing tools, like customer relationship management (CRM) software, to support consistent workflows. It also needs to be intuitive and scalable—something your team can grow with, not just test and abandon after a quarter.

2. Train Your Sales Team

Conversational intelligence is powerful, but only when your team knows how to use it.

Training shouldn’t stop at platform features. Reps need to understand why the tool matters, what kind of insights it delivers, and how to turn those insights into action. For example, if you’re introducing a new insight or behavior to focus on, explain how it connects to outcomes your team cares about, like stronger conversions or a shorter sales cycle.

Take a hands-on approach during sales coaching. Give agents opportunities to explore the new platform, review real call recordings, and practice applying conversational data to actual conversations. The more real it feels, the more likely it’ll stick.

3. Focus on Key Metrics 

Conversational intelligence tools can deliver a lot of data. Without focus, it’s easy to get lost in details that don’t drive outcomes.

Start by identifying the metrics that matter most to your team. These might include talk-to-listen ratios, objection-handling success, competitor mentions, or deal progressions. Once you know what to watch, use those insights to shape sales strategies and boost rep performance. 

For example, if a data trend shows that reps are dominating the conversation, it may be time to coach more active listening. The goal is to connect the data back to what actually moves deals forward.

4. Integrate into the Sales Workflow

Conversational intelligence shouldn’t feel like extra work. If it does, adoption will drop fast.

Make it part of the natural flow by integrating it with your CRM (for easier follow-ups) and collaboration tools (to streamline insight sharing). Automate message and call uploads wherever possible so reps can stay focused on conversations, not admin work.

The most effective teams embed CI into every stage, from prospecting to post-sale check-ins. When it’s baked into every step, it consistently delivers value and supports customer progression through the sales cycle. 

5. Celebrate Wins and Socialize Insights

It’s human nature to fixate on what’s not working. But highlighting what is working builds momentum and motivation.

Celebrate standout moments by recognizing team members who consistently handle calls well. Something as simple as a “Rep of the Week” spotlight for top performers can boost morale, strengthen team culture, and encourage others to level up.

Just as important: socialize your CI insights. Share winning conversations from high-performing reps during team meetings or through collaboration tools, so the whole team can learn from what’s working in real time.

6. Listen to the Voice of the Market

Your customers are already telling you what they need—you just have to listen.

Conversational intelligence tools help you do exactly that by analyzing customer interactions at scale. They reveal emerging needs, market trends, and concerns that are essential to staying competitive. It can also capture customer expectations, making it easier for your team to align messaging with what you can actually deliver and guide future development decisions.

To get even more value, set up keyword alerts for things like feature mentions or pricing concerns. Then review the results with your broader team (sales, management, product development, and marketing teams) to turn raw insights into actionable strategies. 

Key Features of Conversational Intelligence for Sales 

The right conversational intelligence analytics software can transform how your sales team sells, but only if it has the features to back it up. If you’re evaluating tools, prioritize ones that are purpose-built for performance, insight, and scale:

• AI text and call summaries: Automatically condense long conversations into short, digestible snippets, making it easier to review and act on insights quickly.
• Conversational analytics: Analyzes the flow, tone, and context of conversations to uncover customer sentiment, buying signals, and opportunities to improve the experience.
• Text analytics: Makes sense of unstructured data and highlights trends, anomalies, and recurring themes in customer feedback. 
• Transcription capabilities: Converts recordings to text for easier call analysis and coaching 
• Impact prediction: Forecasts how a rep’s actions are likely to influence outcomes, using historical data to support faster, smarter decision-making.
• Agent and coach scorecards: Provides a clear view of team performance, spotlighting strengths and opportunities for improvement. 

Transform Your Sales Outcomes With InMoment’s Conversational Intelligence

Conversational intelligence has shifted from nice-to-have to a competitive necessity. It empowers sales teams to improve performance, boost job satisfaction, and better understand and respond to customers’ needs and expectations. With the right insights, you can reduce friction in the sales process, build stronger relationships, and unlock long-term growth.

InMoment’s customer experience platform brings it all together. Our conversational intelligence tools analyze phone calls, chat logs, email threads, and survey responses—turning complex data into clear, actionable insights. Your team gets a complete view of what’s working, what’s not, and where to focus next to drive real results.

See how InMoment’s conversational intelligence software can help your team close more deals and elevate the customer experience—schedule a demo today.

You can’t fully predict customer behavior from day one. People are unpredictable, and what resonates with one group might fall flat with another. 

That’s where A/B testing shines. It helps you test messaging, support strategies, marketing campaigns, and experience variations to optimize interactions, streamline support processes, and improve the overall user experience. 

But while traditional A/B testing tells you what performs better, conversational intelligence reveals why. It adds context to the data, uncovering customer sentiment, intent, and pain points that quantitative metrics alone can’t explain—fueling more targeted optimization and data-driven decision-making.

Let’s explore how conversational intelligence enhances the A/B testing process and how to implement it effectively.

Why Traditional A/B Testing Falls Short Without Conversational Intelligence

A/B testing (also known as split testing) is essential for customer experience optimization, but on its own, it can only take you so far in aligning CX efforts with broader business goals. Over time, traditional A/B testing tends to hit a wall for a few key reasons:

• It focuses on surface-level metrics like conversion rates, response times, customer satisfaction score (CSAT), and Net Promoter Score (NPS) but it lacks the qualitative context behind them. It doesn’t tell you why a customer felt frustrated, delighted, confused, or compelled to stay.
• It can be misleading when it fails to capture emotional responses. For example, a customer might complete a purchase or subscribe (boosting your conversion metrics), yet still feel dissatisfied with the experience. Traditional A/B testing would mark that as a win, even if that frustration puts long-term retention at risk.

How Conversational Intelligence Enhances A/B Testing

Conversational analytics software brings critical context to A/B testing. It goes beyond numbers to explain why customers behave a certain way, making it a powerful addition to your conversion analytics toolkit. When you understand the reasons behind user choices, you can refine experiences with greater precision.

Let’s break down how conversational intelligence strengthens A/B tests.

Uncovers Customer Pain Points and Friction Areas

If you’re testing different versions of your messaging, support strategy, or landing page, traditional A/B testing tools can tell you which variation performs better than the control group, but not explain the reasons behind it. That’s helpful, but it won’t show you where customers are getting stuck or what’s turning them away.

Conversational intelligence fills in those gaps. Analyzing service chats, voice transcripts, and chatbot interactions reveals the pain points and friction areas in the version that underperforms, giving you clear direction on what needs to be improved. 

Example: An A/B test for a new support chatbot shows that Version A is the clear winner. CI analysis reveals that customers find Version B confusing because it lacks clear instructions and key functionality—insight that wouldn’t show up in standard performance metrics.

Automates Feedback Collection and Analysis

Let’s be honest: manual data collection from live chats, emails, call center logs, surveys, and support tickets is tedious, time-consuming, and nearly impossible to scale. 

CI tools automate this process by using machine learning and natural language processing (NLP) to scan through large volumes of customer communication. They turn unstructured feedback into in-depth, actionable insights, helping you make smarter, faster decisions about what to run tests on next.

Example: CI detects complaints about hard-to-find contact options in one variation, highlighting a usability issue that shapes your next A/B test. 

Identifies Customer Sentiment and Satisfaction Drivers

Metrics like click-through rates (CTRs) can tell you which A/B test version performs better, but they won’t tell you how customers actually feel about the experience. That’s where customer conversations come in.

CI tools use sentiment analysis to evaluate word choice, tone, and emotion across interactions. This helps you identify the experiences that generate positive sentiment and, just as importantly, the ones that don’t. With these insights from real user conversations, you can fine-tune future A/B tests to focus on what truly resonates. 

Example: A brand tests two versions of a customer onboarding flow. CI sentiment analysis reveals that Version A causes frustration due to overly complex steps, even though completion rates are the same. Armed with this insight, the brand simplifies the experience in the next test to boost satisfaction. 

Enhances Personalization and User Engagement

With 81% of consumers preferring brands that deliver personalized experiences, understanding your customers on a deeper level isn’t optional—it’s essential. Conversational intelligence helps you get there.

By analyzing how customers speak and what they care about, CI helps personalize the experience and sharpen your marketing strategy—from content and messaging to segmentation by behavior, preferences, and demographics. For example, you can apply CI insights to location-based campaigns to uncover what’s not working for users in a specific region and tailor the testing process to address that user group’s pain points directly.

These insights improve your next test and enable quick, meaningful adjustments that make customers feel heard and understood. 

Example: One chatbot variation offers proactive solutions based on prior customer pain points, leading to higher engagement and fewer escalations.

Detects Emotional Reactions to Service Changes

Customer behavior isn’t always a reliable indicator of satisfaction. People may use a new feature or follow a script simply because they’re in a hurry or don’t want the hassle of switching providers. Traditional A/B testing might interpret this engagement as a success, even if the experience causes frustration.

CI fills in the emotional gaps. It uncovers patterns of confusion, irritation, or delight that typical metrics miss, helping you avoid rolling out changes that create hidden friction or long-term dissatisfaction.

Example: A new automated refund process is A/B tested. Both Version A and Version B receive similar engagement, but CI finds that Version B generates fewer complaints and more positive sentiment in support interactions.

Refines Customer Messaging and Support Strategies

Conversational intelligence takes the guesswork out of A/B tests by highlighting the exact words, requests, phrases, expressions customers use. 

Instead of testing random messaging variations or support strategies, you can focus on the language and guidance that actually matters, like the phrasing of a call-to-action (CTA), chatbot guidance, or self-service instructions.

Example: CI reveals that customers repeatedly ask for a “talk to an agent” option. That insight leads to a clearer escalation pathway in the winning variation, improving both satisfaction and resolution rates.

Steps to Implement Conversational Intelligence in A/B Testing

Conversational intelligence is the lens that sharpens your A/B testing vision. It provides context (the why behind customer choices) that helps fine-tune tests and accelerate CX improvements. But to unlock its full value, it’s important to implement it thoughtfully and avoid common mistakes, like relying only on quantitative data or overlooking emotional drivers in customer conversations.

Here’s how to get started:

1. Identify Key Conversational Data Sources (Chatbots, Customer Support Logs, Social Media Comments)

Capturing valuable insights starts with assessing a variety of customer touchpoints. The more channels you analyze, the more complete your view will be of customer preferences, concerns, and questions.

Collect conversational data from multiple sources (like social media comments, chatbot logs, support transcripts, call recordings, and feedback forms) to ensure your A/B testing process reflects the full spectrum of the customer experience. 

2. Use AI-Powered Conversational Intelligence Tools to Analyze Themes, Sentiment, and User Intent

Manually reviewing thousands of customer interactions isn’t realistic, and limiting your insights to a small sample size can lead to skewed or incomplete conclusions. That’s where AI-powered tools come in.

Conversational intelligence platforms act as advanced analytics tools, helping you move beyond raw feedback to spot actionable trends. They can detect user sentiment, recurring themes, and user intent in real time. These tools help you uncover patterns, like frustrations around your checkout process, that directly inform A/B test decisions and future CX refinements.

3. Integrate Conversational Insights With A/B Test Results

Once you’ve implemented your AI-powered CI tool, the next step is to combine its insights with your existing A/B test metrics. For example, if traditional A/B testing shows that customers prefer Version B, conversational intelligence can help uncover the why behind that preference, like clearer messaging, better support tone, or fewer points of friction.

Blending these insights moves your A/B testing beyond surface-level performance data, giving you a clearer view of how to optimize the experience based on customer perception, not just user behavior.

4. Iterate and Refine A/B Test Variations Based on These Insights

Conversational intelligence insights are only as valuable as the actions you take on them. Use what you learn to continuously refine your A/B test variations—whether that means updating the messaging, adjusting design elements on a webpage, or simplifying processes. 

For example, if CI data shows that customers describe your pricing page in Version B as “confusing,” revise the structure and run another test. Continue iterating until customer sentiment improves and the data confirms you’ve found a version that truly works.

Gain Deeper Customer Insights With InMoment

A/B testing shows what performs better, but conversational intelligence reveals why. Together, they help your team move beyond surface metrics to uncover what truly drives customer behavior, improves the user experience, and accelerates CX optimization.  

To get the most from your A/B testing process, invest in a CI tool with AI-powered analytics. These tools help you quickly identify patterns, sentiment, and intent across high volumes of customer interactions. 

InMoment turns conversation data into a competitive edge. Our AI-powered customer experience platform analyzes interactions across contact center calls, chats, surveys, and more—highlighting preferences, friction points, and emotional responses. These insights show why one A/B test variation outperforms another so you can iterate faster, improve with confidence, and deliver experiences that truly resonate.

Schedule a demo to see how InMoment’s AI-powered CX platform can help you elevate your A/B testing strategy!

Businesses deliver seamless customer experiences when their sales and marketing teams are on the same page. Research from Gartner suggests that sales teams prioritizing alignment with marketing are nearly three times more likely to exceed new customer acquisition targets. 

However, as a result of data silos and a lack of common goals, companies often struggle to achieve this alignment. Conversational intelligence offers a data-driven solution by enabling both teams to access shared insights into customer expectations.

Understanding Conversational Intelligence (CI)

Conversation intelligence (CI) is an AI-driven approach to analyzing and optimizing customer conversations. It leverages natural language processing (NLP) to extract actionable insights from speech and text data. These insights guide strategy for delivering positive experiences that boost customer retention and satisfaction.

CI extracts information across multiple channels, including phone calls, emails, social media feedback, and chat transcripts. It analyzes sentiment, intent, and tone to highlight recurring issues and areas for improvement in customer relationships. For example, CI can identify the most frequent category of complaints during support calls to flag common pain points.

These analytical capabilities also support marketing and sales efforts to convert prospects. Sales teams use CI to understand communication strategies tailored to their audience. Meanwhile, marketing teams leverage the insights to fine-tune messaging and campaigns.

The Consequences of Misaligned Sales and Marketing Teams

Misalignment between sales and marketing teams creates a negative ripple effect. It results in inefficient operations and poor customer experiences. For example, this disconnect causes marketing teams to generate leads that sales teams can’t convert. Prospects receive inconsistent messaging that doesn’t work for them, resulting in lost opportunities to add to the bottom line.

How Can You Tell if Your Marketing and Sales Teams are Misaligned?

Siloed operations make it difficult to identify misalignment between marketing and sales teams. When both departments have separate goals, fragmented data, and no cross-team communication, you’ll struggle to see how they affect each other. Key misalignment indicators to look for include:

• Inconsistent Messaging: Customers experience conflicting messaging because of misalignment. For example, your marketing team will push your product’s standout feature, while your sales representatives will focus on discounts. It’s unclear whether you’re competing on value or price. This inconsistency breeds confusion and mistrust, affecting your brand reputation management efforts.
• Lack of Shared Goals: A clear sign of misalignment is when sales and marketing departments have separate objectives. Consider a case in which marketing prioritizes lead volume while the sales team cares more about the closing revenue. Without a single common metric, productive collaboration between both teams is difficult.
• Poor Lead Quality: Do your sales and marketing teams agree on what constitutes a qualified lead? If they don’t see eye-to-eye on lead scoring, your business will struggle to target the most valuable prospects. As a result, your customer acquisition efforts will be ineffective.
• Communication Gaps: A lack of cross-team communication results in missed opportunities. If your marketing executives launch a social media campaign without informing sales, there will be confusion when leads start engaging. Responding to customers within 24-48 hours boosts retention by 8.5%, so any delay from salespeople will be costly for your business.
• Conflicting Strategies: Misaligned strategies also indicate a disconnect between sales and marketing. Internal friction occurs when, for instance, sales pushes certain products to meet quotas while marketing campaigns promote entirely different services. This approach creates a jarring experience for customers, making it difficult for them to trust you.

How CI Facilitates Sales and Marketing Alignment

According to an Accenture study, businesses using AI to align sales and marketing achieve a 34% boost in revenue growth. Here are four key ways CI connects both teams.

Provides Real-Time Insights Across Teams

Immediate awareness of customer sentiment gives businesses a competitive edge by enabling quick and effective action. Tools like InMoment’s customer intelligence platform pull experience data from multiple channels and perform real-time analysis for informed decision-making.

Marketing teams use these insights to hit the right notes with their target audience. Sales teams use the same information for closing deals and acquiring ideal customers. Since both departments have the same, up-to-date data, they can work with the right insights to streamline the overall customer experience.

Improves Customer Understanding

CI allows businesses to explore customer goals and pain points in detail. The comprehensive insights empower marketing and sales teams to better address customer needs. Each team relies on key data points like entity, intent, and sentiment to guide their strategies.

If CI identifies a recurring “negative” sentiment associated with the “product delivery” entity, the marketing team can react by highlighting faster delivery options in its campaigns. The sales team can take proactive steps to assure potential customers of timely delivery during outreach.

The shared understanding of a major customer pain point allows both teams to work on consistent messaging and engagement. Customers feel heard and valued, increasing their brand loyalty over time.

You can simplify this process by leveraging an industry-standard text analytics tool. InMoment’s conversation analytics software lets you extract user intent and emotions from conversational data. This quick insight into experiences helps you improve your services and customer relationships.

Unifies Brand Messaging and Strategy

Consistent messaging is crucial for brand perception and trust. Your marketing team should produce content that resonates with your audience while addressing common concerns to support sales processes. Both teams should receive training on using the same content strategy to complete their tasks.

CI enables this alignment by looking at conversations across sales and marketing channels. It highlights differences in how both teams communicate product features, for instance. 

Businesses can address these discrepancies by rallying their teams around a shared North Star metric. This approach provides a common goal for each department, making it easier to track progress and drive improvements.

For example, if Net Promoter Score (NPS) is the North Star metric, both teams understand that the goal is to enhance brand advocacy. Marketing highlights the glowing reviews shared by promoters. Meanwhile, sales can tailor its pitches to include elements that worked well for existing promoters. The result is a smooth and trustworthy customer journey that boosts satisfaction.

Tracks and Measures Team Effectiveness

The data-driven insights from CI also help track team performance. Conversation analysis indicates customer satisfaction levels when interacting with marketing or sales teams. It measures metrics like response times and sentiment for this purpose.

If CI suggests that sales reps struggle with quick issue resolution, managers can correct this by providing training or effective scripts. As a result, businesses understand areas for improvement in their outreach efforts. These insights help guide teams, address performance bottlenecks, and meet customer expectations.

Steps to Implement Conversational Intelligence for Alignment

1. Identify the Right CI Tools for Your Organization’s Needs

2. Partner with Your CI Provider for Proper Customization

3. Integrate CI With Your Existing Sales and Marketing Systems

4. Train Both Teams on How to Use the Platform Effectively

5. Set Measurable Goals and Track Performance Over Time

The role of CI in facilitating marketing and sales alignment is clear. It organizes valuable information like customer intent and sentiment in one place to encourage collaborative outreach. The same insights also help managers track and improve team members’ performance. Now that you understand the value of CI, here are five key steps for implementing it in your business.

1. Identify the Right CI Tools for Your Organization’s Needs

Investing in a comprehensive CI tool is the first step toward alignment between your teams. Besides offering a rich set of features, including omnichannel data collection, text analytics, and reporting, the tool should be customizable.

Start by asking yourself what you want to achieve with conversational intelligence. What is the exact metric you want to improve? Are you focusing on customer churn or lead generation? A common understanding of business goals results in customer-centric marketing content that drives sales enablement.

Evaluate and select your CI tool based on the features that are most relevant to your goals. For example, if you want to improve call center performance, you should leverage real-time call transcription from tools like InMoment’s conversational intelligence.

Involve your sales and marketing executives in the decision-making process. Their input will help you identify the tool that best aligns with their respective goals and needs. This step also allows both stakeholders to enhance alignment between their teams.

2. Partner with Your CI Provider for Proper Customization

Off-the-shelf solutions won’t be able to provide the rich insights you need to bridge the gap between sales and marketing. As a result, you must partner with a competent CI provider to ensure the tool fits your unique requirements.

A reliable CI provider works with you to understand your customer profiles, expectations, and team performance. For example, InMoment’s professional CI experts will help ensure your tool reflects your metric of choice in its data collection and analysis.

Another key benefit of working with a CI provider is readily available support. Your CI tool should evolve and adapt to any new challenges you face as a business. This process requires regular updates and hands-on assistance from an expert. Therefore, working with the right provider helps you maximize customer experience ROI while aligning your teams.

3. Integrate CI With Your Existing Sales and Marketing Systems

Seamless integration is a non-negotiable for your CI tool of choice. You shouldn’t have to rethink your workflows or adjust existing systems to make way for conversational analytics. The goal is to provide actionable insights to teams without creating friction.

For example, integrating CI with your CRM allows sales and marketing teams to create strong buyer personas. It supports personalization by highlighting the unique needs of each individual. Similarly, connecting CI with marketing automation tools helps segment customers for targeted outreach.

Additionally, these integrations foster collaboration as they enable the smooth flow of data across systems. Your teams are more likely to use shared insights when there are no information silos. InMoment’s robust CX integrations empower businesses to connect insights with every existing system for smoother experience management.

4. Train Both Teams on How to Use the Platform Effectively

Even the best CI tool for your business will be ineffective if your teams don’t understand it. Proper training is necessary for sales and marketing teams to leverage CI for their daily tasks. They should know how to use features like sentiment analysis to generate high-quality leads or retain customers.

Sales reps could receive training on using CI insights to personalize their sales calls. Meanwhile, marketing teams will benefit from learning trend analysis and campaign optimization. Dedicated training programs and guides will help both teams unlock the full potential of CI.

5. Set Measurable Goals and Track Performance Over Time

A successful CI initiative needs to be results-oriented. Defining clear key performance indicators (KPIs) helps you set measurable goals and motivate teams to work toward them. Common KPIs include conversion rates, churn rates, and customer service metrics like NPS.

Regularly monitoring progress toward your goals helps you identify weaknesses and performance issues. It also enables you to highlight quick wins to motivate similar customer-centric efforts from your teams. As a result, you ensure your CI tool drives meaningful improvement in alignment and outcomes.

Achieve Better Sales and Marketing Alignment with InMoment

Sales and marketing alignment is key to business growth. Shared goals, consistent messaging, and common KPIs allow both teams to generate and convert high-quality leads. On the other hand, a disconnect between both teams results in a lack of brand awareness and trust.

InMoment’s conversation intelligence software helps you close the feedback loop with customers by supporting alignment. It leverages omnichannel data collection and text analytics to provide a comprehensive view of the customer experience in one place. The real-time insights enable a collaborative effort that improves engagement and drives revenue. If you’re looking for a reliable CI provider, schedule a demo today!

The path to real improvement starts with understanding.

Effective communication is the foundation of strong teams, enabling them to collaborate, solve problems, and build better relationships with customers and stakeholders. Nowhere is this more critical than in call centers and customer experience teams, where every interaction shapes customer satisfaction, brand perception, and business outcomes.

Conversational Intelligence (CI) is transforming how teams communicate and grow, particularly in environments where clear, real-time interactions are essential—such as call centers. By analyzing conversation patterns, CI helps businesses understand customer needs more deeply, improve team communication, and identify opportunities for training and development. 

As a result, teams can build stronger relationships, improve collaboration, and continuously refine their approach to service and engagement.

What Does “Conversational Intelligence” Mean?

Conversational intelligence is the technological ability to understand, analyze, and extract meaning and insights from human dialog (and any other conversational text). 

It uses natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) to interpret the sense, tone, content, and even outcome of a conversation. Then CI tools catalog and correlate those observations at scale, revealing patterns in conversational data that businesses can use to improve their operations, performance, and customer satisfaction. 

Benefits of Conversational Intelligence for Team Development

Businesses use conversational intelligence for a variety of purposes. It shows up often in marketing and the sales process (such as with location-based campaigns) and retail (in the form of customer feedback)—but it’s also a great tool for developing contact center teams. 

Here’s how:

1. Better Coaching and Learning Opportunities

CI gives managers and leaders a clearer sense of how well their CX or contact center teams are performing, even identifying specific topics and responses that frequently lead to bad results. 

For example, CI software might identify an unusual number of repeat callers with a similar issue, all of whom got the incorrect answer on call #1. This is a problem every manager wants to solve, but this type of problem could go unnoticed for a long time if you’re using manual methods. 

By identifying learning opportunities and coaching needs early, CI software empowers leaders like you to provide more effective coaching, facilitate knowledge sharing, and promote continuous learning within teams.

2. More Effective Meetings

Conversational intelligence tools are also at the heart of the latest generation of smart meeting tools. We’ve had automatic transcription of what was said in a video meeting for some time now, but CI takes that transcription data and makes it even more useful in several ways. 

CI tools can intelligently summarize meetings (think smart meeting minutes, but more granular if you need more detail), create action items, advise on next steps, and perform other similar smart tasks. Some CI tools can also automatically translate meeting transcripts into other languages.

Together these capabilities enhance the value and effectiveness of meetings, helping to make sure all team members are aligned on action items and next steps.

3. Better Feedback

When feedback is subjective, left up to the interpretation of individual managers, team members may disagree, resist, or even understand the opposite of what the manager was trying to communicate! 

CI helps managers and teams deliver and receive feedback that’s not just opinion—it’s backed up with unbiased evidence that connects behaviors and responses to outcomes.

With CI, feedback isn’t just one person’s point of view anymore. It’s grounded in cold, hard facts. Communication based on facts is clearer, more actionable, and growth-oriented (because it constantly points to outcomes). It helps to grow self-awareness with less defensiveness, reframing issues as growth opportunities requiring follow-up rather than as personal attacks.

4. Greater Productivity and Employee Engagement

CI isn’t just about fixing problems—it also helps businesses lean into what they’re already doing well. 

Organizations can use conversational analytics to identify what kinds of conversational approaches are working well and quickly. Then leadership can communicate those successful, productive tactics to entire teams. 

As others adopt high-performing approaches and leave behind middling and low-performing ones, productivity ticks upward. What’s more, employees grow more involved because success breeds stronger engagement.

5. Better Conflict Resolution

Conversational intelligence tools also help teams navigate conflict more smoothly. Spotting and addressing problems earlier can tamp down the tension that would otherwise build up. 

The neutral, unbiased nature of data helps to lower the temperature, too. Using data to show the connection between behaviors and outcomes keeps things constructive and respectful so they don’t feel quite so personal.

6. Faster Onboarding

When new employees join the team, CI makes it possible to access past customer conversations and see key information in real time. Team members can understand company expectations and culture faster and more fully, and they can also quickly absorb effective ways to interact with customers.

7. Stronger Leadership 

Last, CI can make senior leadership more effective in their work. Leaders can more effectively communicate vision and inspire trust when they have an accurate understanding of workplace performance. Leading by example with CI can help transform company culture into one that prioritizes collaboration, motivation, and shared success.

How To Implement Conversational Intelligence Effectively in Your Team

CI adoption starts with software, but doing it right takes more than just purchasing and setting up software. Follow these steps to start gleaning intelligent insights the right way.

Analyze Features and Choose the Right Software

Start by inventorying your needs. What specifically does conversational intelligence software need to accomplish for your business? List these out and start comparing your needs against feature sets from leading CX and CI tools.

Key features to look for:

• Real-time transcription
• Sentiment analysis
• Reporting capabilities
• Translation functionality

If you’re new to CI or moving on from an older solution, don’t limit yourself during this phase. New features and capabilities may meet needs or solve problems that you don’t yet know are fixable.

Ensure Integration With Your Tools

By the time an organization is ready for an enterprise-grade Integrated CX suite, the business already has a complex tech stack in place. A good solution can replace some of the hardware and software you’re already using to run your business, but you’ll still rely on other tools from other providers for some business functions. 

Select CI tools that integrate seamlessly with the other tools in your tech stack (like collaboration platforms, CRM systems, and communication software) or that have webhooks or API access so you can build your own integrations. By integrating your CI solution with the rest of your tech stack, you’ll avoid workflow disruptions and duplicate data entry.

Customize for Your Team’s Needs

Every business (and even teams within a business) has its own set of goals, unique outcomes they want to achieve through analytics. With many tools the out-of-the-box settings and reports cover the basics for most customers, but you can unlock much richer and more detailed insights by customizing your CI settings to align with specific departmental or organizational goals.

Train and Onboard Team Members

CI tools can deliver powerful insights, but they can also be daunting—and maybe even frightening to contact center agents who worry about performance oversight or “big brother” listening to their work.

Training is key here. When team members understand the power and value that CI tools offer—including the power to perform better as a call center agent—fear melts away and enthusiasm grows in its place. 

And don’t forget an additional layer of practical training or onboarding for the team members and leaders who use your CI suite in a more hands-on way. 

Leverage Insights for Actionable Improvements

CI insights are all about improving. Just about any conversational form of data can be studied, and teams can refine and improve their processes based on what they find.

• Refine communication strategies: Repeat successful tactics and eliminate unsuccessful ones.
• Improve coaching: Make it outcome-focused and data-backed rather than personal and opinion-based.
• Enhance collaboration: Identify communication gaps or points of conflict in internal comms.

By acting on trends and feedback you identify using your CI tool, you can lead your team and organization to steady, ongoing growth and improvement.

Empower Your Team’s Growth With InMoment’s Conversational Intelligence

Conversational intelligence is a truly transformational technology that helps organizations gain a deeper understanding of what’s happening in customer interactions. It’s a powerful tool for unlocking the full potential of your workforce, helping reps refocus on tactics that work.

InMoment is an industry leader in all things customer experience (CX), including conversational intelligence. InMoment helps contact centers understand conversational unstructured data at scale, identifying both effective behaviors and business problems to be solved. 

Start unlocking your conversational data today: Start your InMoment demo now.

Marketing your B2B product without first defining your ideal customer profile (ICP) is like shooting in the dark—you may have all the tools you need to hit your target, but that won’t help much when you have no idea where it is. 

An ICP is a detailed description of the best-fit customer from your target audience. It defines attributes like industry, company, and pain points, making it easier to determine which businesses in your target market would make the best customers. However, creating an accurate ICP is no easy feat, especially if you rely on old-school methods or broad assumptions. 

For a reliable profile, you need modern solutions that go beyond standard firmographics. Enter conversational intelligence (CI)—AI-driven technology that analyzes customer interactions.

Let’s look at how CI can help you refine your ICP. 

What Role Does Conversational Intelligence Play in Refining an ICP?

Conversational intelligence involves using machine learning (ML), artificial intelligence (AI), and natural language processing (NLP) to analyze human conversations. 

It pulls data from touchpoints like social media, chatbots, emails, customer feedback, customer relationship management (CRM) tools, and interactions with customer support, marketing, and sales teams, providing insights into customer intent, sentiment, pain points, and patterns.

Through these insights, you can create highly accurate customer profiles and focus your resources and marketing efforts on the right leads. This is more important than ever since marketing budgets have been on a decline since 2022—you want to make the most of what you have. 

Key Insights from Conversational Intelligence to Refine an ICP

Conversational analytics provide “cheat codes” for building an accurate ICP by unlocking insights beyond obvious firmographics like your customers’ industries. They uncover sentiment, engagement, buying intent, and competitor mentions, giving you a well-rounded view of which target customers are likely to convert. 

1. Identifying Pain Points and Buying Intent

Conversational intelligence uncovers recurring challenges in customer conversations, which can help you determine what your target market struggles with most. It can look for phrases that highlight an unmet need, like “we’re frustrated with” or “we can’t figure out how to.” 

This technology also picks up subtle buying intent signals in text or audio conversations. For example, it can point to highly engaged customers—those actively asking questions about your product in conversations—to help you create an ICP that includes their attributes. 

2. Analyzing Sentiment and Engagement Patterns

Conversational intelligence provides conversation breakdowns that can help you segment customers based on how they feel about your brand—positive, neutral, or negative. 

It highlights customers who use positive language when interacting with your brand, like “This may be just what we need,” helping you focus on the most engaged prospects when creating your ICP. 

3. Extracting Key Demographic and Firmographic Data

CI uses real-world conversation data to spotlight high-converting market segments. If startups use the most positive language and tone, it can point you toward this group, allowing you to refine your ICP to include their specific industry, company age, and company size data. 

CI can also highlight demographic info disclosed in conversations, such as team size, the job titles of key decision-makers, and customer locations. This can help you work on your buyer persona and allow for well-informed location-based campaigns. 

4. Tracking Competitor Mentions and Market Positioning

Conversational intelligence goes beyond basic firmographic data to analyze how potential customers compare your business to competitor brands. 

It tracks competitor mentions in conversations, highlighting which brands potential customers talk about the most, as well as the specific features or offerings they value or, conversely, don’t like. This helps you determine how you stack up against the competition and uncovers who your perfect customers are. 

For example, if companies looking for a particular SaaS solution frequently mention a competitor because it provides specific integrations that your solution doesn’t, you can refine your ICP to focus on businesses that don’t need those integrations. 

5. Optimizing Lead Scoring and Qualification

CI uses qualitative insights from conversations to help teams refine ICP criteria and prioritize high-value leads. It detects signals that point to high interest levels and urgency, such as questions about pricing or phrases like “We need to go to market in the next X months,” pointing to the customer segments that should be on your high-priority list. 

CI also refines lead scoring and qualification by tracking potential customers’ levels of engagement. It highlights those who engage frequently with your team, which can help you refine your ICP to focus on ready-to-buy target accounts. 

6. Uncovering Emerging Trends and Evolving Needs

Customer needs and preferences aren’t static—your ICP shouldn’t be either. 

CI can help you create a dynamic customer profile by detecting shifts in customer behavior, expectations, and industry trends. For example, based on customers’ conversations with your sales team, it can offer insight into new pain points you can solve, helping you expand your current profile. 

How to Leverage Conversational Intelligence to Refine Your ICP

Now that you know the value of CI in profile refinement, how do you leverage it to refine your ICP and fuel your marketing strategies? We’ll show you with this step-by-step guide.

Step 1: Choose a CI Tool and Collect Conversational Data from Various Sources

Your choice of CI tool will impact the value of your data and the accuracy of your ICP. So don’t just pick the first tool you come across. Take time to assess each option, prioritizing features like:

• Transcription capabilities to convert audio recordings to text for seamless AI analytics.
• Conversation summaries to glean key takeaways from potential customers’ interactions with your brand, saving your sales and marketing teams time
• Advanced text analytics to find common themes, pain points, and patterns to streamline the development process
• Real-time insights from analyzing conversations as they occur, ensuring your teams always have access to the latest information

Once you find a tool that ticks all your boxes, collect conversational data from multiple touchpoints, including chatbots, messaging tools, customer interviews, sales calls, and surveys. This gives you a comprehensive understanding of customer needs and behaviors, which can help you develop a basic ICP. 

Step 2: Analyze Conversations for Patterns, Keywords, and Sentiment

Once you’ve collected data and formed a basic ICP, use your CI tool to further analyze conversations for recurring patterns, sentiment, trends, and frequently mentioned keywords. This can reveal customer interests, concerns, and intent, helping you refine your ICP based on what potential customers are actually looking for. 

Step 3: Identify Key Themes to Distinguish High-Value Customers

Narrow your ICP further by leaning on your CI tool to identify characteristics that define the most engaged and profitable customers. Look for commonalities (such as common pain points, product preferences, and buying motivations) in conversations, and then segment customers based on your findings. This differentiates high-intent customers from the rest, making it easier to identify their attributes. 

Step 4: Refine ICP Criteria Using Conversational Insights 

Once you’ve segmented high-value targets from the broader market, look for common characteristics to understand what makes them your best customers. Are they in the same industry? Do they have similar needs? Do they share the same customer base? Do they have a comparable decision-making process? 

If you find common ground, feed the characteristics into your ICP to get a refined demographic, firmographic, and behavioral view of your ideal customer. 

Step 5: Continuously Update ICP with Ongoing CI Insights

You probably don’t have the same needs you did a couple of years ago, and your customers are the same—their needs and expectations will evolve with time. 

Prepare your business for these changes by regularly using conversational intelligence data to understand shifting market trends, customer expectations, and business growth strategies. Then revisit and refine your ICP based on the findings to make sure your marketing campaigns and sales strategies stay relevant. 

Transform Customer Data into Meaningful Insights with InMoment

If you’re targeting businesses that fit your current ICP—but your conversion metrics say otherwise—maybe it’s time to reevaluate who your perfect customer actually is. This time, invite conversational intelligence software to the party. It can help fine-tune your ICP by pointing to high-intent customers based on conversations with your brand. 

InMoment simplifies ICP development by pulling data from every customer touchpoint and uncovering common themes and patterns. 

Our platform goes beyond analyzing conversations to provide a fully integrated customer experience (CX) system that can pull signals from every medium, including CRM tools, to refine your ICP—so you can sell to those who are ready to buy. 

Schedule a demo with InMoment today to learn how our platform can help you create a well-defined ICP that boosts your conversion rates!

Want to improve your account health? Keeping customers happy and engaged is an excellent first step. 

But, if we’re being honest, keeping track of customer relationships with your brand—especially when you have thousands of interactions—isn’t exactly a walk in the park. Some issues may inadvertently slip through the cracks, causing customers to look elsewhere. 

That’s where conversational intelligence (CI) becomes so valuable. It leverages artificial intelligence (AI) to analyze customer conversations, giving you real-time insights into customer sentiment and satisfaction with your brand. 

Great businesses aren’t built on sales alone—they’re built on relationships. Spotting pain points helps you connect, solve problems, and earn trust. If that sounds like a win-win scenario, let’s look at how CI enhances account health efforts.

What Is Conversational Intelligence (CI)?

Conversational intelligence involves analyzing customer interactions with your business to assess engagement, intent, and sentiment. CI leverages technologies like machine learning (ML) and natural language processing (NLP) to assess customers’ language patterns, tones, and response dynamics, eliminating the need to manually sift through thousands of calls, chats, and emails. 

With research showing that a great customer experience (CX) can improve profit margins by 1% to 2%—a considerable amount for large businesses—you need every tool in your toolbox to better understand customers and their needs. 

That’s why it’s important to invest in the right conversational analytics software, one with powerful features that fuel a variety of use cases, such as:

• Automated summaries: Leverages conversational AI technology to highlight key takeaways from customer interactions—so you don’t have to do it manually 
• Transcription capabilities: Converts calls and other audio recordings into text for seamless AI-powered analysis
• Text analytics: Assesses unstructured conversational data and picks up on patterns to highlight common pain points and opportunities 
• Scoring capabilities: Analyzes customer support performance, highlighting strengths and areas for improvement 
• Omnichannel data integration: Monitors customer interactions across every touchpoint (social media, chatbots, messages, and phone calls) for a comprehensive view of customer engagement 

The Connection Between CI and Account Health

CI can significantly impact account health efforts for CX teams by offering valuable insights on how to improve interactions. Here’s a deeper look at how the two relate.

Capturing Customer Sentiment and Satisfaction Trends

CI assesses customer conversations across different mediums, analyzing everything from language to tone in order to detect when customers are happy, frustrated, or dissatisfied—even before their sentiment appears in survey results and support tickets. 

If your CX team notices sentiment and satisfaction are starting to dip, they can proactively engage at-risk customers before minor issues escalate and affect account health. 

Identifying Customer Pain Points and Opportunities for Expansion

CI picks up on commonalities, gathering data that can help you identify concerns, frustrations, and customer intent. 

Are a large swath of customers experiencing a common issue with your product or brand? What would they like you to do better? What do they say drives them toward completing purchases? 

These are all questions CI can answer, helping account managers determine what consumers value most. Identifying—and subsequently addressing—these pain points and opportunities can lead to higher engagement and satisfaction. 

Measuring Engagement Levels to Assess Account Health

While traditional static metrics like service renewals and the number of support requests submitted can offer some insight into account health, they may not provide the full picture. 

For example, service renewals may not always indicate customer happiness, as some customers may be renewing their subscriptions while actively looking for something better. (Hey, we’ve all done this at some point.)

CI goes the extra mile, tracking more reliable factors like response times, interaction frequency, and conversational sentiment. This allows customer success teams to segment accounts by engagement risk, promoting proactive intervention where warranted. 

If a once-active customer starts taking longer to engage with your team, or a once-positive customer begins to express frustration, you can categorize them as high-risk and amp up your engagement efforts to retain interest.  

Key Ways Conversational Intelligence Improves Account Health

It’s not feasible or practical for businesses to invest in every solution on the market, so if you’re wondering whether or not a conversation intelligence platform is worthwhile—we get it. If you still have hesitations about CI’s value, let’s dig deeper into the benefits as they relate to account health.

Enhancing Account Monitoring with Real-Time Analytics

With CI, gone are the days of relying on lagging indicators for decision-making. You can say goodbye to relying on periodic surveys, renewal rates, or gut feelings that never seem to improve customer retention. 

CI continually analyzes customer interactions with your brand across channels to assess sentiment and engagement, providing accurate, actionable insights into your account health. 

Detecting Early Signs of Churn and Dissatisfaction

While customer acquisition is an important investment, customer retention should be a priority. The cost of retaining existing customers is lower than that of acquiring new ones, and it’s also quicker to sell to existing customers.

CI helps nurture relationships with current customers by identifying and highlighting signals of frustration, such as drops in engagement and negative sentiment trends. This way, you can step in with proactive interventions to minimize churn risks and nip dissatisfaction in the bud. 

Automating and Refining Account Health Scoring Models

CI eliminates the need for manual account health tracking by dynamically updating scores based on real-time customer conversations. 

This promotes contact center optimization by easing the burden on your team and providing an evolving and more accurate picture of customer satisfaction. And a dynamic view of customer engagement and satisfaction helps your team prioritize high-risk accounts before it’s too late. 

Reducing Subjective Bias in Account Assessments

Human-based assessments are subjective. Where you see a happy and engaged customer, someone else may see a customer who’s about to jump ship. Unfortunately, relying on gut feelings and inconsistent account reviews could make it challenging to follow up with the right customers on time. 

CI eliminates bias by relying on actual data rather than personal opinions. This can make all the difference when trying to boost customer retention. 

InMoment’s Smart Summaries solution, for example, analyzes conversations and provides data-driven, objective insights into account health. Using our AI-powered technology, contact centers can reduce average handle time (AHT) by 33% by eliminating time-consuming administrative tasks that human agents would typically do. 

Improving Customer Engagement and Personalization

The value of personalization is undeniable. Companies that offer personalized experiences are 71% more likely to register improved customer loyalty, and CI can help your success team personalize experiences by identifying customers’ preferences. 

It looks at changes in customer sentiment under different circumstances, highlighting what your audience values the most. This helps you make well-informed personalization decisions, potentially improving customer relationships by meeting customer needs as they emerge. 

Strengthen Your Account Health Strategy with Conversational Intelligence from InMoment

As customer needs evolve and competition grows, it’s become increasingly important for brands to monitor their account health. CI helps by analyzing interactions across all business channels and providing real-time insights into customers’ sentiment and engagement. 

CI takes the guesswork out of account health management and helps customer success teams be more proactive, reducing churn and improving the customer experience. 

With InMoment’s conversation analytics software, you get a holistic view of customer interactions across all touchpoints. Our conversation intelligence tools not only help you improve customer experiences by showing you what matters most, but also promote superior customer service by providing insights into agent performance for continual improvements. 

Get started with InMoment today to improve your account health with actionable, data-driven insights!

Competition for customers’ attention is fiercer than ever. A wrong move on your part—be it failing to solve customers’ issues or offering subpar support—could see them looking elsewhere, affecting your retention numbers. 

To reduce churn, you need to elevate the customer experience (CX). And we know just the solution to help you: conversational Intelligence (CI). 

CI leverages artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to analyze customers’ interactions with your brand. It provides real-time insights on everything from their pain points to their purchasing triggers, helping you improve experiences and increase sales. 

What Are Conversational Intelligence Insights?

Conversational intelligence insights are findings derived from assessing customer conversations with or about your brand. 

Conversational analytics software monitors interactions across various touchpoints and channels, including phone call recordings, chatbot transcripts, emails, social media platforms, and business messaging apps, providing a comprehensive view of how customers communicate and what they say about you. 

These insights can help you understand customer intent, sentiment, and engagement patterns, improving decision-making, brand-customer communication, and overall customer experiences. 

With 37% of consumers leaving brands solely because of poor experiences, the value of these insights can’t be ignored. Think of them as the secret sauce for building customer loyalty. 

The Key Insights Gained from Conversational Intelligence

Conversational intelligence is the gift that keeps on giving. It provides a wide range of insights—customer sentiment, pain points, common concerns, market trends, and even agent performance. So you never have to guess what customers want, what drives them to action, or how well your teams meet their needs. 

Customer Sentiment and Emotional Tone

With conversational intelligence, you can identify customers who are about to jump ship and focus on retention efforts. 

The technology analyzes conversation tones, word choices, and contexts, highlighting the telltale signs of unhappy, dissatisfied, and disengaged customers early on. Based on these findings, you can segment at-risk customers and take proactive steps to minimize the risk of attrition. 

Customer Pain Points and Needs

Customers often voice their unmet needs, frustrations, and recurring challenges with customer support and sales teams. CI software detects these concerns in real time, giving you the info you need to refine your brand’s offerings and messaging. 

It can show your product development team which features to focus on when creating or updating your offerings, as well as help marketing and sales reps craft messaging that speaks to customers’ frustrations to boost conversion rates. 

Buying Intent and Decision Triggers

Conversational intelligence picks up on both apparent and subtle buying signals by analyzing every aspect of your team’s interactions with potential customers. 

It can pinpoint direct signals, like customers asking when they can get started with your solution, and the not-so-direct ones, like pricing questions. This can help you qualify leads more effectively, allowing you to focus on high-intent potential customers. 

CI technology can also highlight the factors influencing purchase decisions by assessing the kinds of questions customers ask before committing. This can facilitate well-informed development decisions and show you what to highlight in marketing campaigns. 

Customer Preferences and Behavioral Patterns

Communication intelligence reveals customers’ preferred communication styles and channels by assessing how they engage with your brand and their most used mediums. This helps you tailor future communications to align with their preferences. 

The technology also breaks down customers’ product or service preferences, purchasing behaviors, and decision-making processes, which can facilitate better personalization. For example, it can identify customers who frequently reach out to your sales team to better understand products before committing, so you can be proactive in future interactions. 

Objections and Barriers to Conversion

It can be difficult to pinpoint what makes customers hesitate or say no to your offerings, especially if you have many teams working in silos. CI software provides insights by integrating data from sales conversations and inquiries and looking for common concerns among customers who say no. 

These insights can help marketing and sales leaders refine their messaging and pitches for better deal closure rates in future interactions. For example, if your tool reports that customers say no because they’re unfamiliar with your brand, teams can incorporate social proof elements, like customer testimonials and case studies, into future pitches to put potential customers’ minds at ease. 

Emerging Market Trends and Popular Topics

Conversation intelligence software analyzes large data sets, looking for frequently mentioned keywords and phrases. These terms can provide insight into evolving customer needs and industry trends, allowing you to adjust your strategy accordingly. 

If customers ask about sustainability, for example, you can start to budget for eco-friendly initiatives or highlight them in your interactions if you already have some running. Staying ahead of shifting customer needs and expectations helps you stay competitive. 

Competitor Mentions and Market Perception

Conversational intelligence analyzes competitor mentions in interactions, showing you who your biggest competitors are and how customers compare your offerings. This information can help you identify areas where competitors are outperforming you and uncover differentiation opportunities. 

In other words, if customers mention your brand and competitor brands in price comparison contexts, you’ll know to focus on your affordability (if you’re cheaper) or your product’s value (if you’re more expensive) in future conversations. 

Customer Journey Insights and Engagement Patterns

Conversational intelligence tracks customer interactions across different touchpoints, from the point of first contact to post-purchase. By doing so, it can help you identify when customers are the most engaged and where in the sales funnel they drop off—so you know what to improve to boost engagement and conversions. 

It can also highlight customers’ questions or concerns at every stage of their purchase journeys, helping you create the right content for each one. 

Sales and Upsell Opportunities

CI tools detect signals for cross-selling and upselling by analyzing conversations for high-interest phrases and concerns. For instance, if you’re a SaaS brand with tiered plans, it can look for questions about accessing more advanced features or increasing the number of platform users and recommend upselling opportunities. 

These insights can help you capitalize on selling opportunities and maximize your revenue potential. If you offer bundled products, they can also help you better package your offerings to meet customers’ needs. 

Agent Performance and Training Needs

Conversational intelligence doesn’t just monitor the customer’s end. It also evaluates how customer-facing teams handle interactions, pinpointing both high-performing agents and areas that need improvement. 

These insights can inform agent appreciation initiatives and show you what to focus on in training to improve customer satisfaction and sentiment. 

Compliance and Risk Management

CI assesses conversation content and shared information against regulatory requirements, flagging compliance violations that can cause legal risks. This allows you to take appropriate steps to minimize your risk exposure, like engaging legal professionals early. 

The AI-driven technology also analyzes interactions against internal company guidelines and highlights violations, letting you know when policy refreshers or updates are needed. 

Predictive Analytics and Future Behavior Trends

CI can also help you prepare for future challenges or opportunities by predicting customers’ actions based on historical conversation data. 

Say your data shows that customers frequently talk about competitors before they abandon ship. It can look for this red flag in current interactions, letting you know when there’s a high churn risk so you can take proactive retention strategies. 

Similarly, if it determines that potential customers start engaging more frequently just before they’re about to convert, it can look for this signal in current conversations, helping you prepare for increased product or service demand. 

How Different Teams Can Use CI Insights to Their Advantage

Conversational intelligence insights take the guesswork out of many teams’ interactions with customers, empowering them with the information they need to promote positive outcomes. Here’s a quick look at the teams that benefit most and how they can leverage these insights effectively.

Customer Support and Success Teams

CI insights help customer support and success teams protect account health by identifying frustrated and dissatisfied customers. They can then prioritize them for immediate follow-ups and support, potentially reducing churn. 

These insights also highlight recurring concerns, trends, and team performance, which can inform response quality improvement and skill development efforts. 

Example: Contact center agents in the telecom industry use CI to identify customers at risk of churning based on sentiment and engagement patterns, passing them over to retention to proactively address their issues and find a resolution.

Marketing Teams

With most companies cutting marketing budgets first in periods of market uncertainty, marketing teams can’t afford to make unfounded guesses when running campaigns. 

CI insights promote well-informed marketing campaigns by helping teams refine their ideal customer profiles (ICP). They highlight both demographic and firmographic data, as well as areas like pain points and decision triggers—all vital for ICP development. Well-defined ICPs minimize the risk of wasting resources on target markets that are unlikely to convert. 

Further, these insights also reveal customer communication preferences and motivations, which can help teams refine messaging and campaign strategies to resonate better with their target audiences. They also pinpoint high-intent customers and help teams identify them within each geographic area, allowing for well-informed location-based campaigns. 

Example: A B2B marketing team’s CI software detects that customers frequently ask about product integration capabilities. To encourage conversions, the team highlights its solution’s capabilities and mentions the kinds of tools it integrates with in marketing campaigns. 

Sales Teams

The days of pitching to every potential customer and hoping they’ll convert are over—thanks to CI technology. It differentiates hot and cold leads by looking for buying signals like “we’re in the market for something like X,” allowing sales teams to prioritize high-intent segments. 

CI insights also highlight customer pain points and the reasons for hesitation or objections in the sales process, helping teams develop more effective pitches and overall sales strategies. 

Example: A health and wellness sales team uses CI to analyze its interactions with customers and finds that they hesitate to book services because they aren’t sure what’s included. Based on this insight, the team revises its sales pitch to clearly lay out what’s offered in each package and what customers can expect if they sign up.

Customer Insights Teams

Manually combing through mountains of data to understand customer behavior, needs, and preferences can feel like punishment, even for the hardest-working employees. Plus, the process takes a lot of time, which doesn’t cut it in the world of constantly evolving customer needs. 

Luckily, customer insights teams can always use CI. It analyzes large datasets to identify themes, patterns, pain points, and trends that could improve customer understanding. 

Take Discover, one of InMoment’s latest products. It analyzes billions of data points to identify CX insights and sends you notifications in real time, keeping you informed on dynamic customer needs and industry trends. 

Example: Thanks to CI, a customer insights team in the automotive industry determines that sustainability is becoming a growing concern for current customers. Therefore, it recommends that the brand consider adding new models and trim packages to its limited line of electric vehicles. 

Product Teams

CI insights help product teams prioritize feature developments based on direct customer feedback and real-world demand, aligning products with user needs. 

These insights also highlight usability issues that impact the customer experience and product gaps (based on comparisons with competitors), helping product teams focus on developments that positively impact customer retention and brand positioning. 

Example: A software development product team finds that customers frequently struggle to navigate their solution’s interface. To reduce the risk of churn, it prioritizes improving its product’s ease of use over adding new features. 

Ecommerce

CI helps ecommerce teams improve customer service and personalize shopping experiences by analyzing customer interactions related to product inquiries, shipping concerns, and purchase behavior. By addressing deficiencies in these areas, teams can improve conversion rates and minimize cart abandonment. 

Example: CI insights help an ecommerce brand segment customers by the products they show the most interest in, facilitating personalized messaging and offers. 

How To Implement Conversational Intelligence for Actionable Insights

Implementing conversational intelligence into customer interactions is relatively straightforward. Use this step-by-step guide to start leveraging CI for actionable insights in your business.

Step 1: Choose the Right CI Tool

The first step is to choose a reliable conversation intelligence tool that aligns with your needs. Your top priority should be finding one that goes beyond transcribing interactions, providing actionable insights into things like customer engagement levels and pain points. 

That said, transcription capabilities are still pretty great, as they convert audio recordings to text for easier analysis. Other factors to consider include:

• Sentiment analysis: Your tool should be able to gauge whether customers’ feelings toward your brand are positive, neutral, or negative based on their tone and word choice. 
• Summarization: This feature eliminates the need to sift through heaps of customer conversations to gather hidden gems.
• Agent performance metrics: Scorecards can help you identify both high-performing agents and areas for improvement. 
• Categorization capabilities: The ability to classify customer conversations by sentiment, buying intent, or specific keywords can streamline analyses. 

Beyond these factors, you also need to assess a conversation intelligence platform’s ease of use, scalability, security, and integration capabilities before committing to make sure it’s the right fit. 

Step 2: Integrate CI with your Existing CRM, Marketing, and Analytics Platforms

After finding the right tool, integrate it with your current customer interaction systems, including: 

• Customer relationship management (CRM) tools like Salesforce and HubSpot
• Data management solutions like Zapier and Oracle
• Communication platforms like Slack and Gmail
• Review platforms like Outreach and Google Reviews
• Analytics platforms like Google Analytics 

Integrating CI with existing systems promotes a smooth flow of conversation data, making it easier for teams to track insights across customer interactions and marketing campaigns. 

Step 3: Define the Metrics and KPIs your Team Will Track With CI Insights

While less overwhelming than actual conversation data, CI insights can seem like a lot if you don’t know what to focus on. So set clear objectives and define key metrics and KPIs upfront. 

Depending on your objectives, you might track metrics like call resolution rates, engagement scores, lead conversion rates, and customer sentiment scores. If your main reason for investing in CI software is to improve agent performance, for example, call resolution rate would be an excellent metric to monitor. 

Step 4: Use CI Data to Iterate and Refine Strategies Continuously

The final step is to use your CI insights to refine communication tactics, customer support processes, and decision-making across sales, marketing, and product development teams. Focus on the most pertinent or pressing action items first to get a lot of value upfront, then you can start tackling the smaller or less impactful items on your list. 

However, this shouldn’t be a one-time process. Continually leverage CI data to identify patterns, trends, and shifts in customer needs over time, and adjust your approaches to optimize business strategies as markets change. 

Unlock Powerful Conversational Insights With InMoment 

Conversational intelligence unlocks a world of valuable data across many business departments—sales, marketing, customer support, product development, ecommerce, and customer insights. 

Whether you want to help your sales team elevate their pitches, get your development team to work on what customers actually want, or reduce cart abandonment rates by catering to customers’ unique needs, this technology has you covered. 

With InMoment’s conversational intelligence technology, you’ll get insights into everything from customer sentiment to industry trends, facilitating smarter decision-making. Our integrated CX platform monitors and analyzes customer interactions across every medium, ensuring you never miss a beat. 

Learn how InMoment’s CI and analytics technology and centralized CX platform can fuel data-driven improvements throughout your business!

If your business operates a contact center as a part of its customer support or customer success strategy, chances are you’re in a constant quest for improvement. There’s always room to make customer interactions more efficient and more successful, and the effort is almost always worth it: any positive change accomplishes cost savings, better customer relationships, or both.

The trick is determining what needs to change, along with which changes will deliver the most benefit. For most call centers, the answer is right in front of them—it just needs to be untangled. 

Contact centers handle high volumes of customer feedback across multiple channels. And with effective contact center analytics practices, customer feedback can be the roadmap to happier customers and efficient operations.

What Are Contact Center Analytics?

Contact center analytics is the end-to-end process of collecting, measuring, analyzing, and implementing data generated during customer interactions. Businesses can collect this data from multiple sources, including phone calls, emails, online chats (both virtual and human), and social media interactions. 

Using contact center analytics, businesses can leverage this data to better understand many facets of customer interactions and the contact center’s operations. Businesses can use analytics data to track key customer experience KPIs, understand customer sentiment, identify recurring issues, and train and coach staff. 

Types of Contact Center Analytics

Contact centers can generate many different types of analytics. These are seven of the most important analytics categories for most call center operations.

Conversational Analytics

First up and perhaps most important is conversational analytics. Contact centers generate tons of unstructured data: text-based conversations and call recordings are rich with information, but this information is extremely difficult to get using older analytics methods.

Conversational analytics is a newer approach that leverages the power of natural language processing (NLP) and machine learning to analyze threaded conversations between agents and customers. This form of analytics can identify numerous elements within conversations, including sentiment, patterns in questions asked or complaints received, accuracy of agent responses, and more.

Through conversational analytics, businesses can improve their call center interactions by identifying what does and doesn’t get the desired response, at scale. They can also identify recurring issues and complaints, which can help product and service teams solve relevant customer experience difficulties—which can even lead to reduced call volume.

Text Analytics

Text analytics processes numerous forms of written communication, such as emails and chat messages, looking for patterns in those communications (such as frequently mentioned problems or products). It’s similar to conversational analytics but with less focus on sentiment. 

The goal of text analytics is to pull actionable insights out of unstructured data, identifying issues or methods of improvement that aren’t otherwise obvious. 

Thanks to the power and accuracy of modern transcription tools, businesses can perform text analytics on audio content by converting audio to text, and then plugging it into text analytics like any other text source.

Predictive Analytics

Predictive analytics evaluates historical data, such as performance data or sales trends, and uses it to make predictions about future events. Contact centers can use predictive analytics to understand likely spikes in call volume related to seasonality, product launches, or other factors. 

Key Driver Analytics

Key driver analytics looks at the biggest factors that drive behavior. What data points or performance metrics tend to result in the behaviors you want from stakeholders (loyalty, retention, conversion), and what data points or events tend to lead to undesirable behaviors (churn, poor reviews, returns)? 

By identifying and then analyzing the data points that drive customer behavior, businesses can better prioritize where to focus and determine how to push more consumers toward desired behaviors (and away from undesirable behaviors). 

Customer Journey Insights

Many businesses, especially those with long-term customer relationships such as software/SaaS, put concerted effort into understanding the customer journey. This concept recognizes that many customer relationships are much more complex than convincing a consumer to buy something in a single moment in time. 

Customer relationships ideally go on for long stretches of time (that’s the “journey” element), and customers may relate to the company differently along that journey.

Customer journey insights help organizations understand what happens along that customer journey. These insights help businesses find problems and weak points in the customer journey, such as points where churn is unusually high. They also help to break down silos, identify voice-of-the-customer gaps (VoC gaps), and measure opportunities for improving the overall customer experience.

Sentiment Analytics

Related to conversational analytics, sentiment analysis finds indicators of how a customer is feeling during a text or voice interaction. Evaluating customer emotions and opinions can help businesses understand what’s causing positive and negative reactions at scale. This is a key tool for understanding and improving brand perception, as well as for identifying the solvable issues underlying negative sentiment.

Cross-Channel Analytics

Businesses interact with customers and prospects across numerous customer support and marketing channels, and it’s easy for this data to get siloed or obscured. Data often comes in different formats, and cleaning this up into something useful can be time-consuming and difficult.

Cross-channel analytics provides a unified view of customer interactions across multiple communication platforms. The best contact center solutions employ machine learning algorithms to pull disparate data sets together without significant manual work or formatting. This generates seamless insights that give businesses a clear view of how customers are responding across all channels.

What Are Important Metrics in Contact Center Analytics?

In today’s customer-centric business landscape, contact center analytics play a pivotal role in understanding and improving customer interactions. 

By focusing on these key metrics, organizations can gain valuable insights into the efficiency and effectiveness of their contact center operations. 

Here are some of the most important metrics in call center analytics that contribute to enhancing overall customer experience and operational performance:

• Call center performance metrics: Call center performance metrics are critical for assessing and enhancing the efficiency and effectiveness of call center operations. Key metrics like Average Handle Time (AHT), First Call Resolution (FCR), and Service Level provide insights into agent performance, operational efficiency, and customer satisfaction. 

By continuously monitoring and optimizing these metrics, businesses can improve service quality, boost customer loyalty, and drive overall success.

• Agent performance metrics: Behind every successful contact center are the agents who shape the customer journey. Tracking their performance is essential. Analytics provide insights into response times and resolution rates, enabling targeted training and personalized feedback.
• Customer experience metrics: Businesses measure a contact center’s effectiveness by the experiences it creates. These customer experiences are the currency of success in today’s customer-centric world. 

By focusing on metrics like Customer Satisfaction Score (CSAT), Net Promoter Score (NPS), and Customer Effort Score (CES), businesses can understand and address customer needs. Continuously refining these metrics allows businesses to create experiences that delight customers.

Why Are Contact Center Analytics Important?

Contact center analytics are one of the most important ways to improve because they can help to identify unique avenues for improvement. No other method can find patterns and anomalies that are otherwise hidden in your call center data.

Using contact center analytics, organizational leaders can:

• Optimize customer interactions by identifying tactics that work
• Improve operational efficiency by identifying areas of waste 
• Drive data-informed decisions by arming decision-makers with clearer insights

All of these enhance customer satisfaction and improve business outcomes—and it’s all thanks to the data your contact center already collects, now made useful through analytics. 

The Benefits of Contact Center Analytics

Implementing contact center analytics will reap multiple benefits for your organization. Not only will these benefits help with contact center optimization, but they also aid in optimizing the entire business process. Here are some of the benefits you can expect from utilizing contact center analytics: 

• Increased Efficiency: Analytics streamline processes, reduce wait times, and ensure customer inquiries are handled promptly and efficiently.
• Cost Savings: Organizations can achieve significant cost savings by optimizing staffing levels, implementing self-service options, and leveraging automation.
• Improved Agent Productivity: Providing agents with the necessary tools, training, and technology enhances productivity, reduces handling times, and improves customer service.
• Enhanced Scalability: Optimized contact centers can more effectively handle fluctuations in call volumes, seasonal variations, and unexpected surges in customer inquiries.
• Competitive Advantage: Superior customer service, driven by analytics, can differentiate organizations in the market and increase customer loyalty.

Challenges of Contact Center Analytics

Analytics can be the key to unlocking new levels of performance and customer satisfaction, but these outcomes aren’t automatic. Contact centers face certain challenges in getting the most out of their analytics.

• Data Overload: Without the right analytics tools, you may end up with too much of a good thing (data) and not enough capacity to work with it.
• Integration of Multiple Data Sources: Data comes from numerous sources, each with its own quirks with formatting and presentation. Plus, contact centers deal with tons of unstructured data, which is especially difficult to integrate.
• Lack of Real-Time Insights: Some analytics approaches are great at scrutinizing what’s happened in the past, but not what’s happening right now. They can’t process data in real time or provide up-to-the-minute insights.
• Lack of Skilled Personnel: Working with data requires a technical skill set for which there’s a significant talent shortage. However, choosing the right contact center tools can ease this challenge.

How Contact Center Analytics Shape the Future of Customer Experience

Organizations can use data from contact center analytics to enhance the customer experience. By providing insights into customer interactions, and identifying patterns and trends, these insights can uncover areas for improvement. 

With call data, customer feedback, and agent performance, businesses can increase personalization, improve multi-channel communication, and address issues proactively, leading to increased customer satisfaction and loyalty.

Creating Personalized Customer Journeys

In a world teeming with generic interactions, personalized experiences stand out. Contact center analytics serves as the bridge between businesses and their customers, decoding the myriad signals that customers send out. By diving deep into these signals, businesses can craft journeys that resonate with individual customer personas. 

It’s not just about addressing needs; it’s about anticipating them, understanding the unspoken desires, and weaving experiences that are as unique as fingerprints. Personalization, powered by analytics, transforms customers from mere statistics to cherished partners in a shared journey. 

And, as we always emphasize at InMoment, when businesses take the time to truly know their customers, they unlock the potential to create moments that linger long after the interaction ends. 

Enhanced Multi-Channel Communication

The digital age has ushered in a plethora of communication channels, from social media to chatbots. But how does a business discern which channel resonates most with its audience? 

Enter analytics. 

By meticulously analyzing customer interactions across various touchpoints, businesses can discern patterns, preferences, and propensities. This knowledge empowers them to streamline their communication strategies, ensuring that every message is not just heard, but felt.

InMoment champions the cause of meaningful communication, and with the insights from contact center analytics, businesses can ensure that every conversation is a step towards building lasting relationships.

Solving Problems Proactively Before They Arise

The future of customer experience lies in anticipation. Predictive analytics, with its ability to sift through vast data sets and discern patterns, offers businesses a crystal ball. Instead of merely reacting to issues, businesses can now proactively address them, often even before the customer is aware. 

This shift from reactive to proactive problem-solving is transformative. It signifies a business that doesn’t just listen but understands, one that values its customers enough to stay a step ahead, ensuring smooth and delightful experiences. 

At InMoment, we’ve always believed in the power of foresight, and with predictive analytics, businesses can turn insights into foresight, crafting a future where every customer feels valued, understood, and cherished. 

How To Use Analytics To Improve Your Contact Center: 6 Best Practices

Whether you’re just beginning your analytics journey or are seeking to make your analytics more useful and effective, these 6 best practices will help your contact center improve.

1. Define Clear Objectives

Start by establishing specific, measurable goals for what you want to achieve with analytics. Perhaps your main objective is to improve customer satisfaction or reduce average handle time. Maybe it’s reducing churn or increasing the number of cases solved on first contact. Whatever your objectives, defining them is the first step toward using analytics software to achieve specific business outcomes.

2. Leverage Contact Center Analytics Software

Second, to get the most out of your analytics, you need analytics software that was built with contact centers in view. The way your business or business unit operates has unique concerns, and you need software that focuses on those concerns, not just on general business analytics.

Contact center analytics software also helps reduce the need for highly skilled technical employees, as the software does much of the work for you.

3. Integrate Multiple Data Sources

You have access to data from a wide range of channels. Taken together, this data can provide a comprehensive view of both customer behavior and contact center performance. The best analytics software solutions can do much of this work automatically, reducing your reliance on manual data entry and aggregation.

Contact centers that don’t centralize these efforts risk missing out on insights from some data sources. They may also fail to fully understand the big picture, and they risk getting left behind by competitors who have these insights.

4. Regularly Review and Update Metrics

To succeed in your contact center analytics journey, you need to review the data and metrics you receive regularly. Continuously checking the validity of your data helps ensure its accuracy and relevance, enabling you to make informed decisions based on reliable insights. This practice not only enhances the quality of your analysis but also allows you to promptly identify and address any discrepancies or anomalies. 

5. Invest in Training

Ensure your staff is well-trained in using analytics tools and interpreting data to make informed decisions. This involves providing comprehensive training programs that cover the functionalities of the analytics software and the principles of data analysis and interpretation. 

Regular workshops, hands-on training sessions, and continuous learning opportunities can keep your team updated on the latest analytics techniques and tools. 

By empowering your staff with these skills, you enable them to leverage data effectively to optimize processes, enhance customer engagement and experiences, and drive business growth. 

Features To Look for in Contact Center Analytics Software

Contact center analytics software is crucial for getting the best insights from your customer data. It can take different forms: Some solutions may be very structured, while others may be lighter in implementation and only used for specific use cases, such as exporting reports. 

Regardless, there are some key features you need to consider in your contact center analytics solution, including: 

Multi-Channel Integration

First, be sure to choose a solution that allows you to aggregate data from various communication channels, including phone, email, chat, and social media reviews. 

Without this capability, you’re essentially working on a puzzle with half the pieces missing. Or, worse, you’re dealing with a dozen different puzzles all suggesting slightly different things, and you’re stuck trying to figure out what to make of it all. Only with multi-channel integration can you sort it all out into one cohesive picture.

Performance Monitoring and Reporting Capabilities

Being able to capture data is one key facet of effective analytics, but there’s more to the story. You’ll also need effective tools to monitor performance and a way to present analytics digestibly. 

With these, you’ll be able to better identify changes in performance (both good and bad) and more easily identify the most critical insights. Effective data visualization techniques (such as charts, graphs, and heatmaps) highlight key metrics and trends, allowing decision-makers to grasp the most essential information quickly.

Speech and Text Analytics

Manually reviewing all the data running through your contact center may be nearly or entirely impossible, so choose a solution that uses speech analytics and text analytics to automate the process of data extraction and analysis. 

Speech analytics can automatically transcribe and analyze phone conversations, capturing key phrases, sentiments, and emotional cues that provide deep insights into customer experiences and agent performance. Similarly, text analytics can process written communications, such as emails, chat transcripts, and social media interactions, to identify common themes, sentiments, and emerging issues.

By leveraging these advanced analytics tools, you can efficiently sift through vast amounts of data to uncover critical patterns and trends without manual intervention.

Predictive Analytics

Furthermore, ensure your chosen contact center analytics software has predictive analytics capabilities. Predictive analytics leverages historical data and advanced algorithms to forecast outcomes, such as call volumes, customer satisfaction levels, and agent performance. 

By providing these insights, the software enables you to make data-driven decisions that improve resource allocation, enhance customer service strategies, and optimize overall contact center operations.

Professional Services and Support

Contact center analytics is a complex business function, and the software supporting it often doesn’t work as an entirely DIY or self-service solution. So look for software providers who also offer professional services and support for their contact center analytics tools.

InMoment is a leader in professional services and support. We become a true strategic partner with our clients, not just another vendor. Our unparalleled professional services and support are flexible and customizable depending on your needs. 

We employ product specialists, data scientists, and advanced-degree researchers so that we can support our customers with high-level professional services, including (but not limited to):

• Program administration
• Proactive best-practice guidance 
• Ad-hoc research initiatives

Customizable Dashboards

Contact centers don’t all universally prioritize the same analytics elements, so make sure to choose a solution that allows you to customize your dashboards to fit the way you want to interact with your data.

By choosing a solution with a user-friendly interface that allows you to customize dashboards and tailor reports, you’ll gain better visibility into your operations.

Centralize Your Contact Center Analytics With InMoment

Contact center analytics can transform what’s possible at your organization in terms of operational efficiency, customer satisfaction, and numerous other priorities and growth metrics. 

But to see results that are truly transformative, you need a contact center analytics platform with the right set of technical capabilities and features. You need a partner that enables you to draw insights from your data rather than spend all your resources wrangling it.

InMoment’s conversation analytics software allows businesses to use structured and unstructured customer feedback to make the most informed decisions about their business. 

Schedule a demo today to see what insights you can unlock!

Conversational intelligence (CI) is making a big difference for countless growing and large businesses, helping them understand customer interactions at scale with a level of precision that seemed impossible just a few years ago.

Most people think of contact centers, such as customer support call centers and help desks, as the main use case for conversation intelligence software. It’s true that CI software drives positive results for call centers, reducing response times, improving outcomes, and producing data-driven insights for agent coaching. But CI is also making inroads in numerous other business areas. 

Location-based marketing is one use case that’s gaining a significant amount of traction. More and more businesses are seeing the value in leveraging conversational intelligence in their location-based marketing, helping them better target those marketing efforts through greater understanding of customer sentiment.

What Is Conversational Intelligence, and Why Use It For Location-Based Campaigns?

Conversation intelligence is a technology that collects, interprets, and analyzes conversational interactions, typically between customers and businesses. These interactions can be text-based (email, chat) or voice-based (phone, where the conversation is recorded and transcribed) and can originate from many different channels. 

CI software collects and analyzes that conversation data using both natural language processing and machine learning algorithms. Then it reports on sentiment, drawing out powerful insights that can help organizations improve the customer experience. 

So what does this have to do with location-based marketing? CI technology enables real-time insights into customers not just as a whole but also divided into locations. With clearer insights into how users in each specific location are responding, brands can target them with ever more personalized marketing.  

Key Benefits of Using Conversational Intelligence for Local Campaigns

Using conversational intelligence tools for your local marketing efforts can help you achieve these tangible business outcomes.

Improved Local Targeting and Personalization

The tools you’re already using for local marketing give you the ability to segment users by specific location, but CI helps you take this capability to a far more personalized level. With CI tools, you can identify precise audience segments within each geographic area.

In other words, without CI, you can personalize a campaign to a location (using geo-targeting, geo-fencing for mobile devices, IP addresses, and the like), but you can’t identify or target specific demographics within that location or region. With CI, you can hyper-personalize campaigns to target only frustrated users in a specific location, or only satisfied customers, and so on. 

For example, a “give us another chance” message feels bizarre to happy users but might be just the ticket to regain someone with a negative sentiment. The opposite may be true of a “The quality you know and love” message, which could be seriously off-putting to negative sentiment customers but highly converting among those with positive user experiences.

Increased Customer Engagement and Satisfaction

Any opportunity to make your marketing efforts more meaningful is something worth pursuing, and CI is a powerful way to do exactly that. Customers who feel like a business is addressing their specific, local needs generally walk away from a marketing interaction with a more positive view. 

But to get the tone of a hyper-local campaign right, you need to understand what customers in that area are feeling. Otherwise, attempts may come off as dull or ingenuine, especially when attempted at scale.

CI helps businesses tune in to the feelings and sentiments of local customer segments, leading to more focused location-based campaigns that boost engagement and improve satisfaction.

Better Campaign ROI

CI-enhanced marketing can also improve your campaign ROI. Let’s walk through the steps of why:

• Every ad view/impression costs money (rates vary widely, but between $0.11 and $0.50 per click is a common range).
• Not every viewer will convert.
• Some ad views go to viewers with low or no potential to convert.

That last step is the frustrating one. Spending money on ads with zero potential is a lot like setting dollar bills on fire. Using CI data to understand sentiment allows you to cater your advertising messaging and targeting for better conversions and a greater return on your investment.

Data-Driven Decision Making

Marketers know that there are plenty of vibes at play in the industry. Highly market-tested, million-dollar commercials fall flat or even negatively impact a brand based on, well, vibes.

But the savviest, most experienced marketers will tell you that behind any real vibe is a very real set of data. Too much vibe-reading (without looking into the data behind the vibes) can become problematic.

CI software unlocks richer, more granular audience data, including how those local audiences are feeling. Businesses can use this rich data to inform their decision-making, refine their marketing strategies, and balance resource allocation in response to hard numbers—not just vibes. 

Use Cases of Conversational Intelligence in Location-Based Campaigns

We’ve covered why conversational intelligence makes sense in local marketing, so now it’s time to examine the how. Consider these five specific use cases where businesses are using CI to accomplish more with their location-based campaigns. 

1. Enhancing Geo-Targeted Advertising

With CI, businesses can better tailor their ads to local target audiences based on conversational trends. For example, say a community discusses an upcoming neighborhood-wide event on social media. A retailer might pick up on this via CI and run a hyper-local ad campaign with digital marketing messages highlighting products that people might want for that event. 

2. Improving Local Customer Engagement

Large brands can also use CI to deliver more authentic local engagement at scale, leading customers to see individual business locations as part of the neighborhood or community, not just a faceless brand.

A grocery chain might identify that customers in a specific neighborhood frequently mention a product on social media or complain to customer support that they can never find a particular product. Neighborhood stores could then adjust both their targeted ads (using location targeting to reach potential customers in proximity to a specific physical location) and in-store placement, showing a responsive attitude to local concerns. 

3. Optimizing Multi-Location Campaigns

CI also enables businesses to identify differences in responses and performance across multiple geographic locations. Using conversational analytics, businesses can identify what is and isn’t working in different locations and make campaign adjustments where needed. 

Along the same lines, businesses can make more informed decisions about resource allocation. Is a retail store campaign performing especially well in location A, but especially poorly in location C? Increasing ad spend in location A’s region makes sense, while dialing back (or reworking the campaign entirely) may be the better approach in location C’s area.

4. Driving Real-Time Personalization

When paired with an end-to-end customer experience (CX) platform, conversation intelligence can even enable adjustments in real time based on live data from specific areas. Sudden weather events could trigger adjusted messaging, steering customers toward indoor experiences or reminding them of weather-related items. 

Brands can use real-time personalization to adjust messaging based on local events as they are happening or immediately afterward as well. The possibilities here are wide-ranging, depending on what a business offers and how real-time events might affect sales.

5. Tracking Regional Sentiment and Trends

CI analyzes sentiment on multiple levels, including at a regional level. Organizations can use this level of granularity to identify emerging trends that may not extend across the entire customer base but are still significant at the regional level.

There are all sorts of reasons (from regional preferences to slang to sensibilities) that a particular campaign could perform well in most markets but poorly in a specific region. Rather than scrapping a campaign entirely, businesses can use regional sentiment tracking to identify where to keep the campaign and where to adjust.

How To Get Started with Conversational Intelligence for Local Campaigns

Conversational intelligence can deliver powerful insights across all types of location-based campaigns. But first, you’ll need to integrate the tools and platforms you’re using for both CI and location-specific data.

The simplest way to do this is to use InMoment’s integrated Experience Improvement (XI) platform, where the integration is done for you automatically. But, whether you use InMoment’s comprehensive tool or another option, you can follow these five steps to get started with CI for local campaigns:

• Pair data sources: Ensure you can access your conversational data sources and location data in the same place (and that those data sets can communicate with one another).
• Select the right tools: Not every analytics tool is capable of collecting or working with CI data, so ensure you choose one with dedicated CI features.
• Identify key location-based metrics: Some metrics have highly localized value, while others aren’t as useful at the location level. This may also vary depending on what you’re selling and who your customers are.
• Train staff to analyze localized insights: Make sure those who work with your analytics tools know how to interpret localized insights and when to prioritize them.
• Optimize region-specific marketing efforts: Use the insights you’ve gained to begin optimizing marketing campaigns for specific regions where applicable.

Elevate Your Location-Based Campaigns With Conversational Intelligence from InMoment

Conversational intelligence is a true paradigm shift for businesses that need to understand customer sentiment at scale. CI is increasingly playing a role beyond customer support, expanding into marketing and location-based marketing efforts.

InMoment is the Experience Intelligence platform built for large businesses that need a modern approach to scaling customer experience efforts and understanding customer sentiment. It’s the ideal solution for data-minded organizations looking to step up their marketing efforts with conversational insights.

See how your business can leverage InMoment’s conversational intelligence tools to deliver hyper-personalized marketing at scale.