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.

In a world where value is defined relative to the bottom line, it’s easy for call centers to come under scrutiny when it comes time for budget allocation. Part of that is just the nature of the business, with primary use cases revolving around KPIs weighed down by negative connotations—metrics like problem resolution rates, customer effort scores, and churn. 

In that light, goal-setting often becomes an exercise in mitigation: reducing call volumes, minimizing customer effort, and curbing churn. At times, even the insights that surfaced from customer-agent interactions can feel burdensome—an endless inbox of problems to solve. The downstream effects of this mindset can be quite limiting to the potential functions an effective call center should serve in your broader organizational strategy. 

The truth is, customers calling your call center don’t just have an issue they want you to resolve—they have a relationship with your brand that matters enough to them to raise that issue. When you listen to those concerns—truly listen—you won’t just salvage the relationship; you’ll discover opportunities to grow it in ways that directly impact the bottom line. That’s when those budget discussions become less about managing costs and more about tracking investments. 

Shifting the Cost Center Mindset

To understand the perception of call centers as cost centers, let’s start with the cold, hard math: a typical inbound customer service call costs an average of $2 per interaction. Multiply that across thousands or millions of calls a year, and it’s easy to see why companies want to save money. 

But here’s the problem: when you’re laser-focused on cutting costs, it’s easy to miss out on revenue growth opportunities. 

Imagine a scenario in which a customer calls complaining about the size of a shirt purchased online because she is dissatisfied with the fit. The agent understands the issue and resolves it by offering an exchange for a different size. Still, in discussing the logistics of the exchange, a nugget of information emerges amid the customer’s relief at having the issue resolved promptly—a comment about wanting to look professional for an upcoming work event. 

Instead of closing the conversation, the agent seizes the moment by offering a complementary product, a free styling session, to create a set of work outfits incorporating the shirt. The customer, already engaged and receptive, books the session, which results in them purchasing additional products. Now, that inbound call that would have cost you $2 has just become the foundation for an expanded relationship. For that customer, your brand isn’t just another apparel warehouse—you’re their fashion consultant, style authority, and default choice for wardrobe-building. 

For most companies, this scenario remains unrealized potential. Call centers focus on handling issues as quickly as possible, often at the expense of the bigger picture. But what if a contact center could become a powerful sales engine, transforming routine support calls into avenues for growth?

The Current State of Customer Calls: Costs and Missed Opportunities

When each call has an associated cost, it’s easy to land on North Star metrics like call volume and average handle time. And to be clear, managing those variables is mission-critical for achieving, calculating, and proving ROI. After all, an effective call center plays a pivotal role in identifying root causes to eliminate recurring issues, finding ways to redirect calls to more cost-effective channels when possible, and ultimately working to help create a customer experience that doesn’t result in a call center interaction at all.

What’s often missed, though, is that contact centers aren’t just problem hubs—they’re a rich source of customer insights that should radiate out into other parts of the business, especially when working within a fully integrated CX approach. Combining mountains of contact center interactions with feedback from other channels like surveys, chat, email, and social media gives you a fuller understanding of customer needs, behaviors, and preferences. This omnichannel approach provides a more complete VoC picture, helping you iron out the wrinkles in your customer journey to create seamless experiences across your brand. 

Now, imagine your agents, equipped with this comprehensive data, are trained not only to resolve issues but also to identify key moments when customers express interest or hint at future needs. Your call center managers will be notified when these opportunities are missed so they can identify coachable moments and refine strategies. 

With real-time access to this data, marketing and operations teams can act immediately—sending targeted offers, automating follow-up emails, or launching personalized ads based on these insights. Every conversation becomes a strategic touchpoint, driving additional sales while delivering personalized experiences that meet customers’ needs at precisely the right time.

Turning Customer Calls into Sales Opportunities: The Power of Active Listening and Suggestion

The key to transforming a contact center from a cost center to a profit center lies in how you train and empower your agents with the right tools and active listening strategies to help them see every customer interaction as a potential opportunity. 

An important note: it’s not about pushing products but understanding the customer’s needs and intentions. The agent can recommend an upgrade when a customer mentions that they’re looking for more storage space. When they express frustration with a service limitation, the agent can offer a premium tier. These are the moments where customer experience and sales intersect, and where the call center can start delivering serious returns on investment.

After all, the same math that multiplies per-call costs at scale can be applied to the calls that surface opportunities. Take, for example, a large athletic retailer that implemented InMoment’s Conversational Intelligence solution to analyze 4.4 million voice transcripts, 574,000 web chat transcripts, and 100,000 survey responses. 

Previously focused on cost reduction, their contact center strategy shifted after empowering agents with real-time customer data. Agents began listening for customer cues and started upselling, cross-selling, and suggesting relevant products, driving greater value. Even if just 2% of interactions reveal an opportunity to cross-sell or up-sell, that’s more than 100,000 opportunities to grow those customer relationships.

And it doesn’t stop there. AI-Driven Text Analytics and Conversational Analytics offer businesses a way to surface deeper insights from customer interactions. These tools unlock unstructured data, detecting feedback themes and anomalies. 

Companies can then analyze threaded conversations between agents and customers to uncover patterns that reveal unmet needs, potential sales, or growth opportunities. With these insights, agents can proactively offer solutions or products that align with the customer’s unspoken desires, turning every call into a chance to build revenue.

Implementing a Customer-Centric Approach: Empowering Agents and Leveraging Technology

The foundation of transforming your call center lies in a customer-centric approach that puts the customer’s needs and desires at the heart of every interaction. But to make this vision a reality, you must arm your agents with the right tools and training.

One of the main reasons call center agents struggle with active listening and suggestive selling is that they’re simply too busy doing other things—categorizing calls, searching knowledge bases, and following scripts. With the right tools, those sorts of rote manual tasks can be automated, enabling agents to focus on truly listening to customers and uncovering their needs. 

InMoment’s Conversational Intelligence solution features automated categorization, allowing agents to stay focused on listening to the customer.

Imagine a company that has implemented this approach. Instead of treating each call as transactional, their agents are trained to listen for opportunities. Empowered agents offer real solutions that align with the customer’s needs, leading to higher satisfaction rates and increased sales.

Measuring Success: Metrics that Matter

Once you’ve implemented the right technologies and strategies, how can you tell if it’s working? It’s not enough to simply count the number of calls resolved—success needs to be measured by the impact of those interactions. Metrics like conversion rates, revenue per call, and customer satisfaction scores (CSAT) provide a holistic view of how well your contact center solution performs as both a support function and a revenue driver.

Consider the case of a global retail giant that recently implemented Conversational Intelligence to enhance customer interactions. By integrating post-call and post-visit survey data with sentiment analysis and real-time reporting, they can now track the revenue impact of each customer touchpoint. The solution ingests approximately 300,000 calls annually and utilizes AI-driven conversational summaries to provide timely insights into customer needs and preferences. This approach has allowed the company to break down internal data silos, analyze trends across all channels, and deliver a more seamless, frictionless customer experience.

The takeaway is clear: the success of your contact center is measured by more than how quickly agents can resolve issues. Instead, it’s about effectively identifying opportunities to create value during customer interactions. The right metrics allow you to continually refine your approach, ensuring long-term success.

In addition to traditional performance measures, Agent and Coach Scorecards help identify individual strengths and areas for improvement. With in-platform Quality Assurance features, businesses can pinpoint knowledge gaps and implement targeted training to ensure consistent performance. These scorecards, combined with Impact Prediction capabilities, allow contact centers to explore critical factors influencing their metrics. Understanding these drivers provides a roadmap to continuous improvement, whether it’s agent behavior, product knowledge, or customer sentiment.

Unlock the Untapped Potential of Your Contact Center

In an era where brands must differentiate on experiences to remain competitive, viewing your contact center as a cost center can lead to a massive insight blindspot. With the right tools and mindset, you can enhance every interaction and unlock the revenue potential of every customer call. By equipping your agents with the ability to actively listen and suggest relevant solutions, you can turn each interaction into a growth opportunity, driving customer loyalty and boosting your bottom line.

With omnichannel data, advanced AI, and deep analytics, InMoment’s Conversational Intelligence solution offers a full view of every customer interaction—across voice, chat, email, social media, and other feedback signals. This comprehensive view allows businesses to understand customer needs from every angle, empowering agents to deliver personalized solutions that drive both satisfaction and sales.

If you’re ready to shift your organization’s cost-centered mindset and start viewing customer interactions as opportunities to drive revenue, check out InMoment’s recent webinar co-hosted with CXPA—or schedule your demo today.

In the fast-paced world of customer service, call center management is pivotal for organizations working to maintain efficient operations, improve customer satisfaction, and improve customer experience. 

Understanding the ins and outs of call center management is crucial for seasoned professionals and newcomers to the field. When successful call center management is achieved, it can affect the entire organization, from a reduced number of customer complaints to increased revenue. 

What Is Call Center Management?

At its core, call center management is the art of overseeing the day-to-day operations of a call center, ensuring that it runs smoothly and efficiently. Call centers serve as hubs for customer interactions, making them a vital customer support element.

The role of call center management extends beyond the daily operational aspects. It also encompasses strategic planning, workforce management, and technology integration. This multifaceted approach is essential for meeting today’s customers’ ever-evolving needs and expectations. 

With the advent of omnichannel customer experience programs and increasing customer demands, effective call center management has become more challenging and pivotal than ever before. It requires a harmonious blend of leadership, technology, and a customer-centric mindset to succeed in this dynamic landscape.

How Do Call Centers Work?

Call centers are the central point for customer inquiries, issues, and support. They employ skilled agents who communicate with customers to address their concerns, answer questions, or provide assistance. Call centers bridge the gap between a business and its customers.

Successful call centers have evolved their operations with conversation analytics software, embracing a more comprehensive approach to customer engagement. Modern call centers not only handle inbound customer inquiries but also proactively reach out to customers through outbound communications. 

These centers are equipped with advanced technologies, including customer relationship management (CRM) software, predictive dialers, and analytics tools. This technology allows them to provide a more personalized and efficient service. Whether it’s resolving issues, processing orders, conducting market research, or offering technical support, the modern call center is a versatile hub for customer interactions, adapting to the diverse needs of businesses and their clientele.

Benefits of Having Call Center Management

Effective call center management offers many advantages for businesses, propelling them towards enhanced customer satisfaction, operational efficiency, and, ultimately, improved profitability. 

Enhanced Customer Experience

Implemented call center management can significantly enhance the overall customer experience. Well-managed call centers ensure that customers receive prompt, accurate, and helpful support, resulting in higher levels of customer satisfaction. This boost in customer satisfaction, in turn, can lead to increased customer loyalty and long-term relationships.

Increased Operational Efficiency

By efficiently handling a high volume of customer inquiries, well-managed call centers minimize customer wait times and ensure that issues are resolved swiftly. This not only improves customer satisfaction but also optimizes the utilization of resources and reduces operational costs, leading to significant cost savings for the organization.

Improved Data-Driven Insights

Call center management also provides invaluable data-driven insights. By collecting and analyzing customer data, businesses gain a better understanding of customer needs, preferences, and pain points. This data, in turn, informs strategic decisions, helping businesses refine their products, services, and customer support processes.

Call Center Management Best Practices

Creating and maintaining an effective call center can be a difficult task. But, with the right tools and procedures in place, you can build a call center that contributes to the success of your business. Here are some of the best call center management practices to follow to ensure your team stays on the right track: 

1. Set Clear Objectives
2. Hire the Right Team
3. Train & Coach Agents
4. Implement Self-Service Functionality
5. Automate Mundane Tasks
6. Focus on First Contact Resolution
7. Leverage Call Center Scripts
8. Use Prediitve Analytics
9. Prioritize Omnichannel Communication
10. Implement A Call Center Dashboard
11. Invest in Leadership Development
12. Develop A Crisis Management Plan
13. Create A Knowledge Base
14. Maintain Compliance
15. Promote A Healthy Work-Life Balance
16. Utilize QA Scores to Monitor Performance
17. Recognize Employees
18. Optimize Scheduling
19. Gather Customer Feedback
20. Monitor Metrics Over Time

1. Set Clear Objectives

Before you develop training programs, offer incentives to employees, or purchase a contact center platform to improve your operations, you need to have a clear vision of what you want out of your contact center. 

Take the time to define goals for your contact center, such as improving overall customer satisfaction or identifying the most frequent customer complaints so that other teams can fix those problems. 

2. Hire the Right Team

You need the best employees handling customer inquiries to set your contact center up for success. As part of your contact center management process, recruit employees with the right skills and experience, such as strong communication skills, empathy, problem-solving skills, and past experience working in customer service. 

3. Train & Coach Agents

Your agents are the lifeblood of your contact center and should be treated as such. Implement agent performance metrics and programs that help your agents perform their best. 

When sufficiently trained, agents are prepared to deal with a wide range of customer inquiries. 

InMoment’s contact center solution gives managers the power to create action plans for employees based on smart recommendations from past interactions. With these customized action plans, managers can effectively improve employees’ performance. 

4. Implement Self-Service Functionality 

Did you know that over a quarter of all consumers will give up solving a problem if they can’t find the answer themselves? In order to retain these customers, your call center must have self-service options. 

Self-service options such as chatbots, IVRs, and online FAQs help reduce the workload of your call center employees while also improving the overall customer experience. 

5. Automate Mundane Tasks

In another effort to ensure your agents are being as efficient as possible, make it a priority to automate repetitive tasks. Tasks such as data entry or follow-up communications can be easily automated using the right technology. By automating these tasks, your agents spend more time on the highest-priority customer inquiries. 

InMoment’s contact center automation platform leverages generative AI to provide quick, on-brand responses to customers. When your agents can respond to customer inquiries within 24-48 hours, your business can realize a boost in retention of over 8%. 

6. Focus on First Contact Resolution 

First contact resolution is a great way to measure the effectiveness of your call center. When your agents are able to solve inquiries on the first call, it reduces repeat call rate and customer churn. 

7. Leverage Call Center Scripts

A good call center script can create consistency in the customer experience. Consider making a standardized script that agents can use in your call center. This ensures that customer interactions stay on track and that they head to a swift resolution. 

 8. Use Predictive Analytics 

Incorporate predictive analytics to anticipate customer needs, forecast call volumes, and optimize resource allocation. 

InMoment’s predictive customer analytics solution helps you analyze current conversational data to understand complex data and give you insights to improve future performance. 

9. Prioritize Omnichannel Communication 

The majority of consumers want to be able to have consistent interactions with customer service teams, regardless of the channels those interactions start and end on. In order to do this, it is important that your call center can operate as an omnichannel contact center that supports interactions from channels such as call, web chat, email, SMS, etc. 

With InMoment’s omnichannel contact center software, you can house all your customer data in one place, allowing your agents to quickly come up to speed on customer interactions.

10. Implement A Call Center Dashboard

A call center dashboard provides real-time insights into the performance of employees and the call center as a whole. They can be customized to meet your specific needs in order to help you make the most informed decisions regarding call center operations. 

11. Invest in Leadership Development

Train call center supervisors and customer experience managers in leadership skills to ensure that they can effectively support the teams under them. Strong leadership fosters a positive work environment, improves employee morale, and improves service delivery. 

12. Develop a Crisis Management Plan

During unexpected events, your call center may receive a large volume of specific calls. To successfully handle these situations, it is important to have a contingency plan in place that helps maintain service continuity. 

These plans include disaster recovery challenges, communication protocols, and other solutions to adapt to different challenges. 

13. Create A Knowledge Base

Develop a centralized knowledge base for agents and customers that includes FAQs, troubleshooting guides, and other information that can reduce call handling times and call volume, as well as give agents the most up-to-date information for handling customer inquiries.  

14. Maintain Compliance

Stay up-to-date with industry regulations and ensure that your call center management practices are compliant with current legal and ethical standards. This includes adherence to data privacy laws, industry certifications, and fair labor standards. 

15. Promote A Healthy Work-Life Balance

It is no secret that call center employees have a high turnover rate. By promoting a healthy work-life balance, you can prevent burnout among your employees. You can promote a healthy work-life balance by providing flexible scheduling, wellness programs, and training other management staff to be supportive.  

16. Utilize QA Scores to Monitor Performance

Regularly assess agent performance through quality assurance (QA) scores. Use this data to identify strengths and areas for improvement, provide targeted coaching, and maintain high service standards.

17.  Recognize Employees

Acknowledging and rewarding employees for their hard work boosts morale and motivation. Celebrate individual and team achievements through incentives, public recognition, or personalized thank-you notes. Regular recognition fosters a culture of appreciation, increasing employee satisfaction and retention.

18. Optimize Scheduling

Workforce management tools can help you create efficient schedules that align staffing levels with call volumes to minimize understaffing or overstaffing. For example, a retail call center will want to have more employees working during the holiday season to keep up with the increased number of customer service requests. 

19. Continuously Gather Customer Feedback

The goal of most call centers is to resolve customer complaints. That being said, collecting customer feedback can show your commitment to improving the customer experience and help you proactively solve future customer complaints. 

Consider asking customers to rate their experience after each call. You can collect more in-depth feedback during quarterly or annual customer feedback questionnaires to help you make the necessary changes to improve call center performance. 

20. Monitor Main Metrics Over Time

For successful call center management, you will need to continuously monitor and track the performance of your main metrics over time. 

Organizational Structure of a Call Center

The organizational structure of a call center is a critical component that ensures efficient operations and a seamless customer experience. It typically comprises several key roles and layers of management to function effectively, including:

• Managers
• Supervisors and team leaders
• Agents
• Support staff

What Is a Call Center Manager?

At the top of the hierarchy, you’ll find the call center manager. This individual oversees the entire call center operations, sets strategic objectives, and makes crucial decisions. They are pivotal in aligning the call center’s goals with the broader organizational objectives. The call center manager’s leadership is essential in maintaining a high level of service quality and ensuring that the team meets performance targets.

Supervisors and Team Leaders

Reporting to the call center manager, there are often several supervisors or team leaders. These roles involve more direct oversight of agents and day-to-day operations. Supervisors provide guidance, monitor agent performance, and act as a bridge between the front-line agents and upper management. They play a vital role in maintaining order, supporting agents, and ensuring that the call center meets its targets.

Call Center Agents

The backbone of any call center is its agents. These individuals directly engage with customers, addressing their inquiries, resolving issues, and delivering the quality of service that the organization strives for. Agents are typically divided into teams or departments, each specializing in a particular area of customer support.

Support Staff

In addition to the core roles mentioned above, call centers may also have support staff responsible for tasks like quality assurance, training and development, data analysis, and IT support. These support functions are integral to the call center’s overall effectiveness. Quality assurance ensures that customer interactions meet the desired standards, training and development keeps agents updated and skilled, data analysts gather valuable insights, and IT support maintains the technology infrastructure.

The organizational structure of a call center is designed to create a clear chain of command, establish accountability, and ensure that each component of the operation contributes to the overall success of the center. Effective communication and coordination among these roles are essential for providing exceptional customer service while meeting performance objectives and maintaining a positive work environment for all involved.

How to Successfully Manage a Call Center

Managing a call center operation that consistently delivers exceptional service and meets its objectives is no small feat. It requires a combination of strategic planning, effective management, technology integration, and a dedicated focus on continuous improvement. These effective call center management strategies ensure that your contact center can realize continuous success:

Clear Objectives and Strategy

Success begins with setting clear objectives for your call center. Whether it’s achieving a specific customer satisfaction rating, reducing response times, or increasing first-call resolution rates, having well-defined goals is essential. 

These objectives should align with your organization’s broader business objectives and customer expectations. A well-thought-out strategy, supported by a comprehensive business plan, will guide your call center toward achieving these objectives.

Technology Integration

Embrace technology to enhance your call center’s efficiency and customer experience. Implement advanced call center technologies, such as customer relationship management systems, automated call routing, and workforce management software. These tools streamline processes, reduce errors, and provide agents with the information they need to serve customers effectively. 

Integrating multi-channel support capabilities, including phone, email, live chat, and social media, is crucial in today’s omnichannel customer service landscape. The right technology empowers your agents to deliver timely and accurate support while also providing valuable data for performance monitoring and decision-making.

Data Analysis and Continuous Improvement

Analyzing customer data is a cornerstone of call center success. Regularly review key performance metrics, such as average response time, first-call resolution, and customer satisfaction ratings. This data provides insights into your operation’s effectiveness and helps identify areas that require improvement. Implement a continuous improvement culture by gathering customer and staff feedback and using these insights to refine your processes. This iterative approach ensures that your call center stays aligned with changing customer needs and market dynamics, fostering long-term success. 

In summary, running a call center successfully requires a combination of well-defined objectives, technology integration, and a commitment to continuous improvement. By focusing on these key aspects, your call center can provide outstanding customer service, optimize its operations, and adapt to the ever-evolving demands of the modern customer service landscape.

Key Success Metrics for Effective Call Center Management 

To measure and improve the performance of your call center operations, it’s important to identify, align on, and track the most important call center metrics associated with the outcomes you’re looking to achieve—for your customers, your agents, your call center, and the business as a whole. 

A comprehensive call center management program should incorporate every measurable element of the call center experience—both from the agent’s perspective and the customer’s—but an effective program will highlight the key drivers of the experience and prioritize improvement efforts where they’ll have the most impact. 

InMoment key driver analysis can help your business understand what is having the greatest impact on your contact center and customer experience and give you the insights to make informed business improvements. 

To make sure those key drivers point you in the right direction, there are three primary sources you should look to for capturing insights on an ongoing basis: 

1. Operational data: in most cases, this can be extracted from the systems and technologies used to handle calls 
2. Customer experience data: captured via post-call surveys and unstructured datasets (call transcripts, chats, email threads, etc.)
3. Agent experience data: captured by surveying agents and used to ensure teams are trained and equipped to excel in their roles

Operational Metrics for Call Centers

Some metrics are inherently embedded within call center operations—things like Call Availability, Average Hold Time, First Contact Resolution, and others. Because these sorts of operational metrics are universal to all call centers, an effective call center management program provides benchmarking analyses to:

• Put performance in perspective by comparing it against industry standards
• Identify areas where you’re falling short of customer expectations
• Prioritize improvement strategies according to potential impact 

Customer Experience Metrics for Call Centers

To add further context and ensure the call center experience measures up to your standards, it’s important to go a click deeper. Many call centers leverage post-call customer satisfaction surveys to surface insights related to service standards by going straight to the source and asking customers about the following:

• Details of the experience: What type of issue they were calling with, how it was handled, and whether it was resolved
• Perceptions of the experience: Agent Knowledge, Problem Solving Ability, Courtesy/Professionalism, Ease of Interaction, etc.
• Satisfaction with the experience: Typically asked in the form of a CSAT (Customer Satisfaction), NPS (Net Promoter Score), or CES (Customer Effort Score) depending on which North Star customer experience metrics the company uses across other feedback channels 

To surface insights beyond the questions they think to ask, brands capture qualitative feedback in the form of open-ended questions. Advanced call center management programs take this unstructured feedback to a new level by leveraging advanced technologies like Conversational Intelligence, which apply natural language processing (NLP), machine learning, and artificial intelligence to gain insights from recorded or written conversations. 

Agent-Specific Metrics for Call Centers

As you can see in the operational and customer experience metrics used thus far, effective call center management aims to understand how easy it is for customers to interact with a business and where to focus improvement strategies. Agents are the ones tasked with facilitating those interactions, so it’s mission-critical to ask them about their experiences and whether they’re adequately trained and equipped to succeed. 

Agent Effort Score (AES) is a unique metric that provides insight into agent performance from their perspective. It measures how easy it is for agents to address and resolve callers’ issues. A low score indicates obstacles or sub-optimal structures that make it difficult for agents to achieve their goals.

You can measure AES by surveying agents on how much effort they have to put into customer interactions. The feedback will highlight the issues preventing agents from being their most productive selves. For example, they might not have easy access to customer data, making it difficult to resolve issues quickly.

Determining the Right Metrics for Your Call Center Management Program

Ultimately, the metrics you use to track performance will be used to prioritize improvements and celebrate successes, so you want to focus on measuring and monitoring what truly matters. While there are many call center metrics to choose from, the main objective is to capture insights on what matters for your brand specifically.

Whether the company is focused on NPS, CSAT, or CES in your post-transaction surveys, make sure that’s reflected in your call center management program as well. Aligning on as many metrics as possible across the business will make it easier to gain organizational buy-in, socialize insights, and celebrate wins.  

From Buy-In to All-In: Linking Call Center Metrics to Financial Outcomes 

While improving operational performance, customer satisfaction, and agent retention will undoubtedly generate some enthusiasm around your call center management program, that buzz and level of commitment won’t be sustainable if it doesn’t translate to bottom-line impact. A truly effective program drives financial outcomes by reducing costs, curbing customer churn, and ironing out wrinkles identified by customers throughout their purchase journeys. 

Use the interactive ROI calculator below to determine what success could look like for your brand using InMoment’s conversational intelligence tools. 

Why Call Center Management is Critical for Business Success

The role of call center management cannot be overstated when it comes to ensuring business success. From providing a seamless and satisfying customer experience to optimizing operational efficiency, the benefits of effective management are undeniable. The call center serves as the frontline of customer support, bridging the gap between businesses and their valued customers. By implementing best practices, investing in technology, and nurturing a culture of continuous improvement, businesses not only meet the ever-evolving demands of their customer base but also gain a competitive edge in the market.

To learn more about how InMoment’s conversation intelligence capabilities can take your call center to the next level, schedule a personalized demo today!

References 

Gartner. Top Priorities for Customer Service Leaders in 2024. (https://emt.gartnerweb.com/ngw/globalassets/en/sales-service/documents/trends/customer_service_support_2024_top_priorities.pdf). Accessed 1/2/2025.

Contact centers play a significant role in customer experience management. They provide a central platform for handling customer interactions across various channels. Customers expect quick and seamless support when they pick up the phone or type an email. 71% of customers expect personalized communication, and 76% are frustrated if contact centers can’t meet these expectations. From the agent’s perspective, delivering this type of service can be exhausting, which is where contact center automation can help.

What Is Contact Center Automation?

Contact center automation is the process of using AI-enabled software to automate repetitive tasks. It leverages technology like automatic call distribution (ACD) and real-time transcription to reduce the manual workload for agents. As a result, agents can focus on strengthening customer relationships with a personalized and empathetic approach.

Automation empowers businesses to boost operational efficiency, enhance customer satisfaction, and reduce costs. For example, a call transcription tool prevents the need to listen to lengthy recordings and provides quick insight into customer experiences. These insights ensure agents don’t miss out on valuable information they can use to satisfy and retain clients.

Businesses looking to increase their contact center ROI should invest in automation. It enhances the customer-centric approach without adding excessive strain on agents and managers.

What Are the Benefits of Contact Center Automation?

Contact center automation offers the following benefits to businesses:

• It improves agent productivity and satisfaction. Automation reduces repetitive tasks, allowing agents to focus on complex customer queries. Tools like real-time call transcriptions provide agents with the information they need for quick and effective issue resolution.
• It increases sales and conversions. Automation helps identify upselling and cross-selling opportunities by analyzing customer behavior. Personalized interactions help drive revenue growth by fulfilling customer needs and converting prospects.
• It helps reduce contact center costs. Automating repetitive tasks like call routing and data entry enables call center cost reduction for businesses. It addresses bottlenecks to enable smoother workflows and prevents the need for additional staffing during peak times.
• It boosts customer satisfaction. Automation speeds up resolution times, provides accurate responses, and ensures 24/7 availability for inquiries. These positive results support seamless interactions that satisfy customer needs.

How Does Contact Center Automation Work?

1. Data Collection and Integration
2. Natural Language Processing
3. Response Generation
4. Continuous Improvement

Contact center automation is a structured pipeline integrating AI-powered tools to streamline operations. Here are the key stages of a typical automation workflow:

Data Collection and Integration

The first step is to collect and connect customer data from various channels. Companies leveraging omnichannel engagement retain 89% of their customers. Therefore, it’s essential to enable customer input from every relevant source.

InMoment’s omnichannel contact center solution helps manage interactions beyond traditional phone calls. It ingests feedback from email, social media, and chat and integrates it with customer relationship management (CRM) data.

This approach provides a comprehensive view of the customer experience in one place. As a result, when a customer calls, the system can instantly access details like purchase history to help the agent prepare a personalized response.

Natural Language Processing

The next stage involves analyzing customer input using natural language processing (NLP). The goal is to use machine learning to understand the customer’s intent, emotions, and expectations. 

For example, sentiment analysis is an NLP algorithm that categorizes feedback as positive, neutral, or negative. This categorization provides insights into customer behavior and helps filter out disgruntled profiles for a targeted approach.

InMoment provides award-winning conversation intelligence software to analyze customer sentiment and agent performance. It uses metrics from AI-enabled text analysis to evaluate how well agents respond and handle conversations. Similarly, the insights highlight the extent to which current practices are satisfying customer needs.

Response Generation

Automation tools can also help with response generation once feedback analysis is complete. Chatbots and virtual assistants rely on their knowledge bases to respond to or escalate customer queries. 

Contact center automation platforms like InMoment use generative AI to provide quick and effective responses. Responding to customer feedback within 24-48 hours can boost retention by over 8.5%. As a result, automated responses have greater ROI than manual ones. They also free up valuable time for agents to invest in issue resolution.

Continuous Improvement

AI-enabled customer experience automation learns from interactions to improve over time. It analyzes past conversations, highlighting patterns and areas for improvement. These insights help enhance response frameworks, algorithms, and workflows to boost satisfaction.

For example, a chatbot can update its knowledge base after encountering a new query. This process enables it to better serve customers with similar issues in the future. This stage ensures that the automation pipeline evolves with customer needs. As a result, contact centers succeed in reducing errors and boosting efficiency.

Types of Contact Center Automation

• IVR and IVAs
• Forecasting and scheduling
• Workflow automation
• Live agent assistance
• Auto dialer

Businesses can use various forms of automation to improve the contact center experience. Key types include:

IVR and IVAs

Interactive Voice Response (IVR) is an automated system that replies to incoming calls with a pre-recorded menu. If you’ve ever called a business and heard “Press 1 for Complaints,” you’ve encountered an IVR. It works by greeting the customer with a menu and then accepting their input via keypad or voice before taking the appropriate action. 

IVR frees up time for agents by handling common queries, announcing updates, routing callers to the right agents, and offering basic support.

Intelligent Virtual Agents (IVA) are AI-powered chat assistants that can have context-aware conversations with customers. As a result, they can resolve customer queries without requiring agent intervention. 

Traditional virtual agents are limited to responses from their pre-defined scripts. Intelligent agents, on the other hand, use AI to learn from customer interactions and maintain a natural dialogue with intuitive responses. 

A great example of this technology is InMoment’s Active Listening, which prompts customers with context-aware follow-up questions to capture meaningful feedback. Businesses relying on Active Listening witness a 10x boost in survey responses and resolve issues 62% faster. These results highlight the value of AI-powered agents for smooth contact center operations.

Forecasting and Scheduling

Forecasting and scheduling tools help contact centers predict call volume and optimize staffing accordingly. They analyze historical data, trends, and real-time metrics to forecast customer demand accurately.

For example, a retail contact center could use forecasting to prepare for a high volume of inquiries and complaints during the festive shopping season. This analysis enables it to create schedules for agents who can approve or request modifications.

This automation ensures the right number and type of agents are available at the right time. It contributes to contact center optimization by reducing managers’ workloads and customer wait times.

Workflow Automation

Workflow automation streamlines repetitive tasks to free up agents for more complex interactions. It automates tasks such as call routing, follow-up reminders, and data entry to reduce human error and improve operational efficiency.

For example, when a customer submits a service request, workflow automation routes the ticket to the appropriate department based on the inquiry type. This automatic routing enables faster resolution without requiring unnecessary agent involvement.

Live Agent Assistance

Live agent assistance tools use machine learning to support agents during customer interactions. They provide real-time insights through live call transcripts and sentiment scores. 

This information helps agents provide effective responses by monitoring customer expectations and feelings. It also helps managers capture a comprehensive view of agent performance and customer experiences. For example, they can receive notifications for changes in key call center metrics to make informed decisions.

Auto Dialer

An auto dialer is an automated system that dials customer numbers from a predefined list. It detects who or what is receiving the outgoing call and connects agents only when a human answers it. If the system detects a busy signal, disconnect number, or voicemail, it hangs up or leaves a pre-recorded message. 

As a result, auto dialers eliminate the time agents spend manually dialing numbers or dealing with unanswered calls. Advanced auto dialers can even analyze customer behavior data to prioritize calls for better engagement.

What to Look for in Contact Center Automation Software

1. Text and Speech Analytics
2. Integrations
3. Reporting
4. Customizable Alerts

The right contact center automation software boosts agent productivity and customer satisfaction. It leverages AI, automation workflows, and customer data to optimize contact centers. Key features to look for include:

Text and Speech Analytics

Understanding customer sentiment is essential, but doing so manually is time-consuming. Text and speech analytics use machine learning to provide instant insights into emotions, context, and intent. As a result, agents can quickly identify pain points and opportunities for improvement based on customer feedback.

InMoment provides industry-recognized text analytics that combines machine learning and NLP to extract meaningful insights from unstructured text. The customizable solution leverages over 100 machine learning models and goes beyond sentiment to capture the intent, voiced emotion, and perceived effort in calls. These advanced insights offer a deeper understanding of customer feelings to help reduce churn and close feedback loops.

Integrations

Your automation software should offer integrations with existing systems. For example, it should be able to access data from your help desk system, CRM software, and feedback collection tools. These integrations ensure your agents have everything they need in one place to serve customer needs.

InMoment’s CX integrations allow businesses to access every relevant piece of customer data to make informed decisions. From your internal collaboration tools to call center software, these integrations connect experience data from everywhere to accelerate your product’s time-to-value.

Reporting

The right contact center automation tool should offer customizable reports for tracking key metrics like Average Handle Time (AHT) and First Contact Resolution (FCR). These reports use charts, graphs, and summaries to visualize for stakeholders. Features like filtering and trend analysis provide a better understanding of agent performance and customer behavior.

Customizable Alerts

Alerts and notifications help take immediate action to improve experiences. The right software should provide real-time alerts about customer sentiment and CX metrics. This feature keeps agents and managers in the loop, ensuring they don’t miss out on urgent situations and insights.

Best Practices for Contact Center Automation

You must follow a strategic approach to implement contact center automation for your business. Here are a few best practices to consider:

1. Define Objectives
2. Understand Your Audience
3. Track Key Metrics
4. Always Offer a Live Agent Option
5. Choose the Right Software

Define Objectives

Set clear and measurable goals for your automation efforts. Think about the exact problem you want to solve. For example, if you want to reduce your average call handle time without compromising customer satisfaction, automation can help improve agent efficiency. Real-time call transcription can provide a good understanding of customer expectations without requiring lengthy live calls or recordings.

Understand Your Audience

Knowing your audience is a great way to generate maximum impact with automation. Use demographic data, customer preferences, and insights from previous interactions to train AI tools. For example, understanding your customers’ preferred communication style will ensure more natural and effective dialogue for frictionless experiences.

Track Key Metrics

Monitor critical metrics such as CSAT and call resolution rates to evaluate your automation software’s performance. Leverage predictive analytics to track future trends in key metrics and proactively address issues before they escalate. Test and refine workflows to remove bottlenecks if your metrics start trending downward.

Always Offer a Live Agent Option

The best contact center experience involves a combination of AI and human touch. Always provide customers with the option to connect with a live agent. For example, an IVR system should route customers to a live agent if the pre-recorded answers are insufficient. Allow customers to request a callback if no agents are currently unavailable. This approach to issue resolution is key for preventing customer frustration.

Choose the Right Software

Selecting the right automation platform is crucial for delivering exceptional experiences. Start by understanding your goals, audience, and operational bottlenecks. Then, consider which features would have the greatest impact on your business. 

For instance, a company extracted actionable insights from volumes of customer feedback with InMoment’s Smart Summary solution. As a result, it was able to take immediate and effective action that helped protect over $15M in revenue at risk. 

As you can see, the right software can significantly boost your bottom line. Look for platforms featuring real-time reporting, text analytics, and seamless integrations to optimize workflows.

Fill out the calculator below to see how much ROI your organization could realize from utilizing InMoment’s platform:

Contact Center Automation Challenges

• Customer Trust
• Lack of Personalization
• Compliance

Contact center automation offers several benefits, but it’s not without its challenges. Businesses should be aware of the following obstacles when implementing automation:

Customer Trust

Automation is great for contact center productivity, but it can’t replace the human touch. In fact, it can even leave customers feeling unheard, especially if their queries are complex or emotional. Trust can erode when customers feel they only interact with bots instead of receiving human support. It’s essential to provide readily available live agent support when automation alone can’t meet customer expectations.

Lack of Personalization

Traditional automation tools struggle to deliver personalized experiences. Without machine learning algorithms that learn from customer feedback and data, these tools tend to provide generic responses. 

This lack of personalization can frustrate users and fail to resolve specific needs. Businesses should leverage conversational analytics, generative AI, and human input to tailor responses based on customer history and profiles.

Compliance

Automation tools must comply with industry regulations and data privacy laws. Mishandling sensitive customer data can have legal and financial consequences. Conduct regular audits and invest in secure integrations to ensure your systems stay compliant while protecting customer information.

How Foot Locker Uses Contact Center Automation

AI-enabled automation unlocks opportunities to improve customer experiences for business gains. An excellent example of leveraging automation comes from leading footwear and sportswear retailer Foot Locker. 

The retailer was collecting healthy volumes of feedback through channels such as email, call center logs, and social media. However, it needed help organizing this multi-channel data in one place and extracting meaningful insights.

Its partnership with InMoment enabled it to leverage Spotlight, an award-winning tool for analyzing textual feedback. Developed by Lexalytics, an InMoment company, Spotlight supports Foot Locker by automatically categorizing customer inquiries. It captures key topics, themes, and intent from feedback to highlight pain points and opportunities.

The retailer can customize the text analytics tool to suit its business needs. For example, it modifies the feedback tagging process to focus on the most relevant categories in queries. Spotlight also removes all personal identifiable information (PII) when analyzing feedback to maintain privacy. It enables the CX team to tag the information back to the original ticket to pinpoint the exact customer for personal outreach.

As a result of advanced NLP and text analytics, Foot Locker now has a unified view of feedback for actionable insights. This automation saves valuable time for staff and makes it easier to identify, retain, and satisfy dissatisfied customers.

Utilize Contact Center Automation with InMoment

Contact center automation performs the analytical heavy lifting so your agents can focus on delivering personalized customer service. It helps reduce operational costs while boosting the value of your customer-centric approach. Take a product tour today to see how InMoment can help implement automation to make the most of your contact center!

References 

Invoca. 39 Call Centre Statistics You Need to Know in 2024 (https://www.invoca.com/uk/blog/statistics-call-center-managers). Accessed on 12/18/2024.

InMoment. InMoment Market Pulse. (https://www.linkedin.com/posts/weareinmoment_b2b-customersuccess-ai-activity-7251989745914818560-haGe/?utm_source=share&utm_medium=member_desktop). Accessed 12/19/2024.