Addressing AI Hallucinations for Improved Business Performance

AI hallucinations occur when AI models generate incorrect or made-up responses. These hallucinations create challenges across industries relying on AI, causing customer dissatisfaction and reputational harm. Addressing this issue is key to improving business performance and customer experiences.
Graphic of AI and a user on a laptop.

Think about the last time you asked ChatGPT a fairly simple question but got an unexpected response. Perhaps it provided a factually incorrect statement or just misunderstood your prompt. The result is described as a “hallucination”, a growing concern for businesses using AI systems.

What is an AI hallucination?

An AI hallucination occurs when an AI system produces false or misleading results as facts. A popular example is a large language model (LLM) giving a fabricated answer to a prompt it fails to understand.

Humans hallucinate when they see something that isn’t there. While AI models don’t “see” anything, the concept works well to describe their output when it’s inconsistent with reality. These hallucinations are mainly the result of issues with the training data. If the model is trained on insufficient or biased data, it’s likely to generate incorrect outputs.

An AI system is only as good as the data you feed it. It doesn’t “know” anything beyond its training data and has no concept of fact or fiction. An AI model like ChatGPT has one goal: predict the most appropriate response to a prompt. The problem is that its prediction can sometimes be well off the mark!

Types of AI hallucinations

There are various types of hallucinations, based on what a model contradicts:

  • Prompt contradiction is when an LLM’s output is inconsistent with the information requested in the prompt. An example would be responding with an anniversary message to a prompt asking for a birthday card.
  • Factual contradiction is when an LLM produces an incorrect answer as fact. For example, responding with “New York” to a question about the French capital.
  • Random hallucination occurs when the model’s output has no connection with the prompt. If you ask for a chocolate cake recipe and receive a phrase like “Owls are nocturnal birds” in response, that would be a random hallucination.
  • Sentence contradiction is when an LLM generates a sentence that contradicts its previous sentence. An example would be saying “Roses are red” only to say “Roses are purple” later in the output.

AI Hallucination Examples

  1. Stating obvious errors or false information as fact.
  2. Making up information and references. 
  3. Misunderstanding the prompt.
  4. Providing incomplete information or context.

Generative AI has made impressive progress in content generation. However, it’s still capable of generating incorrect or misleading information. These hallucinations are a concern for AI in customer experience, affecting individuals and businesses alike. Here are some common examples of AI hallucinations in real-world systems.

Stating obvious errors or false information as fact

AI models sometimes generate text that is inconsistent with factual information. A famous example of this hallucination is Gemini’s incorrect response in a promotional video. The chatbot, formerly Bard, was asked, “What new discoveries from the James Webb Space Telescope can I tell my 9-year-old about?” 

Gemini claimed that the JWST took the first image of a planet outside our solar system. This information is false since it was the European Southern Observatory’s Very Large Telescope (VLT) that took the first photos of an exoplanet back in 2004!

Making up information and references

AI models may invent details or references that don’t exist. For example, Google’s AI Overview generated this response to a prompt asking how long one should stare at the sun for best health:

According to WebMD, scientists say that staring at the sun for 5-15 minutes, or up to 30 minutes if you have darker skin, is generally safe and provides the most health benefits.

AI Overview states incorrect information here and wrongly attributes it to WebMD.

Similarly, speech-to-text AI tools that transcribe audio recordings are prone to hallucinations. For example, transcription tools tend to insert random phrases from their training data when they encounter a pause in the audio. 

A concerning fact is that these phrases can be inaccurate and misleading, or even worse offensive and potentially harmful such as incorrect treatments in the case of medical transcriptions. Therefore, the inability of traditional AI tools to handle breaks in audio can have negative consequences for organizations.

Misunderstanding the prompt

A generative AI system may respond appropriately but still misunderstand your prompt. An example of this hallucination is asking ChatGPT to solve a Wordle puzzle. 

While the system generates a coherent response, its solutions tend to be well off the mark. For instance, it may suggest a word that doesn’t match the pattern of letters you provide as input.

Providing incomplete information or context

Sometimes, AI models fail to respond comprehensively, leading to dangerous results. Once again, Google’s AI Overview provides an example of this occurrence. It generated largely correct information when asked which wild mushrooms are safe to eat.

However, it failed to specify how to identify fatal mushrooms. It suggested that mushrooms with “solid white flesh” are safe to eat, but it didn’t mention that some dangerous variants have the same feature.

What Problems Does AI Hallucination Cause?

AI hallucinations create challenges across various industries. Its inaccurate predictions and information hurt the customer experience, impacting the business’s reputation. Here are some of the problems these hallucinations cause in key sectors:

Healthcare

AI has become a significant part of healthcare workflows. Its ability to summarize patient information and even help with diagnoses is impactful. One of its most notable applications is transcribing medical visits. AI-powered transcriptions help doctors record and review patient interactions to make informed decisions.

It is vital to maintain accuracy and completeness in these transcriptions. A hallucination in the text would make it difficult to provide effective treatment and diagnoses. 

For example, OpenAI’s Whisper, an AI-powered transcription tool, raised concerns by inventing phrases during moments of silence in medical conversations. Researchers found that Whisper was hallucinating in 1.4% of its transcriptions. This is a significant figure given that the tool had been used to transcribe around 7 million patient visits.

Some hallucinations were in the form of irrelevant text like “Thank you for watching!” during a conversation break in the transcription. Other instances were far more concerning, including fake medication like “hyperactivated antibiotics” and racial remarks. These hallucinations can have harmful consequences as they misinterpret the patient’s intent, leading to misdiagnoses and irrelevant treatments.

Contact Centers

In customer service, contact center AI hallucinations can damage brand credibility. Customers won’t be able to trust a business after getting an inappropriate response to their queries. 

For example, a chatbot might give incorrect information about a product, policy, or support steps. Similarly, transcription tools often hallucinate phrases during pauses in agent-customer conversations. These hallucinations can provide an inaccurate view of the customer’s experience, resulting in poor analysis that fails to solve actual pain points.

Therefore, your CX program will suffer if it’s relying on inaccurate call center transcriptions. Despite your best intentions, a hallucination could be enough to cause customer dissatisfaction.

Unlike traditional tools, InMoment’s advanced AI-powered solution addresses this specific problem to ensure your CX team accurately records customer interactions. As a result, you can be ensured you’re taking the right steps towards improving the customer experience.

Legal

AI enables legal professionals to save time on research and brief generation. Generative AI models can help produce drafts and summarize key points. However, due to hallucinations, relying on these models for crucial information like legal references can be tricky.

A law firm was fined $5,000 after its lawyers submitted fake citations hallucinated by ChatGPT in a court filing. The model invented six cases, which the lawyers used to support their arguments without verifying their accuracy. These cases were either not real, misidentified judges, or featured non-existent airlines.

Finance

In the financial sector, where precision is crucial, AI hallucinations can be costly. While AI systems can help crunch numbers, they can also hurt financial services reputation management efforts. Inaccurate financial reporting can affect investment decisions and stakeholder trust.

A popular instance is Microsoft’s first public demo of Bing AI. The model wrongly summarized a Q3 financial report for Gap, incorrectly reporting the gross and operating margins. 

For example, the report stated a gross margin of 37.4 percent and an adjusted gross margin of 38.7% (excluding an impairment charge). However, Bing incorrectly reported the 37.4% margin as inclusive of adjustments and impairments.

Media and Journalism

Journalism suffers from AI hallucinations, such as fabricated quotes and inaccurate facts. While generative AI can help draft news stories and articles, it should combine human editing and verification to ensure accuracy. Otherwise, a single misstep like a misattributed quote can cause public backlash and reputational harm.

Education

The education sector has benefited from AI for research purposes. For instance, AI models are reasonably good at summarizing articles, generating ideas, and writing whole sections. Just like legal professionals, though, students and researchers must be extra careful with references.

For example, a librarian at the University of Southern California was asked to produce articles based on a list of 35 references provided by a professor. Despite her vast experience, the librarian couldn’t locate a single article. The professor eventually revealed that since ChatGPT invented these references, the articles simply didn’t exist!

This example highlights a common challenge for AI models. The National Institute of Health found that up to 47% of ChatGPT references are fabricated. Human oversight is essential to prevent incorrect citations and loss of trust.

Why Does AI Hallucinate?

  1. Low-Quality Training Data
  2. Overfitting
  3. Lack of Real-World Grounding
  4. Inability to Fact-Check

AI hallucinations are a by-product of how we design and train these systems. Common causes include:

Low-Quality Training Data 

An AI model is only as good as the data you provide. Biased, outdated, and insufficient datasets will cause AI to generate inappropriate results. Even if it doesn’t understand your prompt, AI will craft a response based on its data, resulting in factual contradictions.

Overfitting

Even with the best training data, AI models will suffer if they can’t generalize to new data. An excellent accuracy score in the training phase sounds good in theory. But, what if the model is simply memorizing inputs and outputs? It won’t be able to produce accurate predictions or information when presented with inputs it hasn’t seen before. It’s important to prevent overfitting the model to ensure reliability in real-world systems.

Lack of Real-World Grounding

Many AI models are trained without real-world situational grounding. Think about the examples in which AI invents legal and academic references. These fabrications occur because AI struggles to understand real-world facts and physical properties. As a result, it produces outputs that look coherent but are inconsistent with reality. 

Inability to Fact-Check

AI systems aren’t designed to fact-check information. They can only rely on patterns in the training data, even if they are incorrect or outdated. The lack of real-world understanding and fact-checking highlights the importance of human oversight for verification.

How to Prevent AI Hallucinations?

  1. Create restraints to limit outcomes
  2. High-quality training data
  3. Use data templates
  4. Combine with human oversight
  5. Provide clear, specific prompts

Preventing AI hallucinations requires specific prompting and improvements in training. Effective approaches include:

Create restraints to limit outcomes

AI models are trained to respond to prompts, even with little to no relevant information. This is how issues like inappropriate responses regarding dangerous mushrooms arise. 

Therefore, it’s important to set restraints limiting the possible outcomes AI can generate. This occurs during the training phase, where you can provide examples and formats that encourage the AI to respond in a certain way. This prevents extreme outcomes and reduces the likelihood of hallucinations.

High-quality training data

The training data sets the foundation for generative AI results. High-quality training data is specific, complete, and free of biases. Using relevant data for a specific use case will enable the AI to produce consistently helpful outputs.

Use data templates

A template is helpful because it guides the AI model toward complete and accurate outputs. For example, if your model skips the introduction section in its articles, a template can encourage it to produce better responses. Data templates ensure consistency and reduce the likelihood of incorrect outcomes.

Combine with human oversight

Human oversight is valuable for ensuring AI accuracy. The models’ inability to fact-check their sources and ground their outputs in the real world can make them unreliable. 

Regularly monitoring and reviewing AI outputs helps humans adjust AI performance for consistency and reliability. Human review also ensures the AI remains up-to-date with current trends and information. This prevents misinformation and improves model performance over time.

Provide clear, specific prompts

Clear prompts guide the AI toward the correct response. Specific and relevant inputs reduce the likelihood of inaccurate outputs. Vague prompts can lead to misinterpretation, resulting in hallucinations. Specific and targeted prompts help AI understand the context and expectations, improving response quality and relevance.

Can AI hallucinations be fixed?

You can prevent hallucinations by improving the training process and investing in good generative AI solutions.

For example, InMoment’s CX-trained LLMs are specifically designed to address customer queries. It leverages sentiment analysis to understand customer intent and generate meaningful responses. As a result, your CX teams save time and effort that they can invest in building deeper customer relationships.

InMoment AI is particularly useful for preventing hallucinations in transcribed conversations. Traditional AI systems hallucinate when they encounter pauses in conversations. Since they aren’t trained to handle moments of silence, they respond with random phrases from their training data. Think about how Whisper would include statements like “Thank you for watching!” in its medical visit transcriptions!

InMoment’s solution works around this issue by detecting and removing all pauses in the audio file. As a result, it avoids hallucinating and processes all the words exchanged in an interaction to provide a complete and accurate transcription. This is helpful for healthcare and contact centers, enabling them to understand their clients and respond correctly.

Will AI hallucinations go away?

According to experts like Meta’s Yann LeCun, AI hallucinations are unsolvable. However, advancements in training and prompt engineering will reduce these occurrences over time. Combining human oversight and good model design practices can help you address hallucinations before they impact your business.

InMoment’s Awarding Advanced AI 

AI hallucinations can impact business performance by providing inappropriate responses to customers. The good news is that the right generative AI solution can help prevent these hallucinations.

With the help of InMoment Advanced AI, you can quickly generate complete and meaningful responses to customer feedback. It combines sentiment analysis, predictive modeling, and real-time insights to help you drive customer satisfaction and loyalty.

Call Center Metrics: How To Track & Improve for Better Customer Service

The call center is often the first point of contact between customers and the business. By tracking and improving key call center metrics, you can resolve customer queries effectively and foster long-term loyalty.
Support, training and coaching, a call center manager is happy to help her team.

Your call center plays a huge role in your brand reputation. A single negative experience with one of your agents can be enough to drive a customer to your competitor. 

Despite the availability of digital channels, many customers pick up the phone to complain or seek support. As a result, it’s important to deliver a positive call center experience that meets customer expectations. The best way to get started is by tracking and monitoring call center metrics.

What Are Important Call Center Metrics to Measure?

Call center metrics provide insight into the customer experience and quantify agent productivity. They remove the guesswork for companies and help pinpoint areas for improvement.

With an overwhelming number of key performance indicators (KPIs) available, it’s crucial to focus on the most impactful ones. 

Here are 30 important metrics you can track to ensure your call center achieves its goals. These metrics are categorized by call center performance, operations, and customer experience:

  1. Average Handle Time (AHT)
  2. Average Speed of Answer
  3. Agent Utilization Rate
  4. Agent Effort Score
  5. Call Availability
  6. Average First Response Time
  7. Average Hold Time
  8. Service Level Rate
  9. Active Waiting Calls
  10. Average Talk Time
  11. Average Time in Queue
  12. Wrap-Up Time
  13. Average Call Abandonment Rate
  14. Total Resolution Time
  15. Transfer Rate
  16. Adherence to Schedule
  17. Calls Answered per Hour
  18. Calls Handled
  19. Types of Calls Handled
  20. Cost Per Call (CPC)
  21. Call Arrival Rate
  22. Peak-Hour Traffic
  23. Average Age of Query
  24. Repeat Call Rate
  25. Percentage of Calls Blocked
  26. First Call Resolution (FCR)
  27. Customer Satisfaction Score (CSAT)
  28. Quality assurance (QA)
  29. Net Promoter Score (NPS)
  30. Customer Effort Score (CES)

Call Center Performance Metrics

To achieve effective contact center optimization, start by gathering and analyzing call center performance metrics. This approach helps identify improvement opportunities that can swiftly boost customer satisfaction. To show you can further improve the performance of your contact center, fill out the calculator below to discover your business’s ROI using InMoment’s conversational intelligence tools:

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Average Handle Time (AHT)

Average Handle Time (AHT) measures the average time taken by an agent to complete a single call. Lower AHT reflects efficient service. However, to ensure customer satisfaction, it’s important to balance speed with high-quality support.

For example, an agent who consistently records low AHT might not be resolving all the customer’s issues. On the other hand, a high AHT implies that the agent is not being productive.

You can improve AHT by providing comprehensive training to agents. Another good practice is to prepare effective scripts that agents can follow for issue resolution. Consider including self-service options like chatbots for customers who don’t want to spend time with an agent.

InMoment’s contact center solution can reduce AHT by up to 33% with one-click conversation summaries that improve contact center capacity and overall experience. 

AI generated conversation summary that highlights customer insights.

Average Speed of Answer (ASA)

This metric measures the time it takes for an agent to answer an incoming call. In the call center industry, the standard time to answer is 20 seconds or less. 

A lower ASA improves the contact center experience by reducing wait times. A high ASA suggests that your agents either struggle to answer calls quickly or the volume of calls is overwhelming for them.

Hiring more agents and investing in training programs can help you improve the average speed to answer.

Agent Utilization Rate

This metric measures the time agents spend actively handling calls relative to their total available time. For example, if an agent spends 6 of 8 hours on calls, their utilization rate is 75%. High utilization shows efficient agent deployment but requires balanced workloads to prevent burnout.

Agent Effort Score (AES)

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.

Improving AES is key to agent satisfaction, which in turn has a positive impact on customer experiences. In fact, call center managers believe that improving agent satisfaction can boost customer satisfaction scores by 62%!

You can improve AES by leveraging call center management software like InMoment. With its ability to integrate with CRM systems and organize feedback in a central place, it simplifies the process of gathering and analyzing customer data.

Call Availability

Time management is a crucial skill for call center agents. A productive agent who manages their time effectively can be more available for customers throughout the day. Call availability is a metric that looks at the total time an agent is ready to receive a call. 

Low availability suggests that the agent might be struggling to manage their time. It can also highlight peak hours for the call center. Businesses can use this information to train agents and adjust their schedules to ensure availability at all times.

Average First Response Time

This metric measures how quickly an agent initially responds to a customer inquiry. A fast response time improves customer satisfaction. You can improve the metric with a priority system to handle inquiries based on urgency. Consider assigning simpler queries to chatbots to reduce wait times for initial responses.

Average Hold Time

No customer likes to be kept on hold, especially when they require urgent resolution. The Average Hold Time metric calculates how long customers wait on hold during a call. Train your agents to embrace smart workflows and software for quick access to customer data. Invest in self-service options to enable customers to find answers faster if they are experiencing a basic issue.

Service Level Rate

This KPI measures the percentage of calls answered within a specified timeframe. Optimizing this rate depends on your service level standards. For example, answering 80% of calls within 20 seconds could be a standard you encourage agents to meet. 

If your staff struggles to fulfill this goal, it could suggest that your scheduling is not optimal. Emphasize the importance of adhering to a schedule and hiring more agents if necessary. Offer multiple interaction channels to customers so they don’t have to rely on calls alone.

Active Waiting Calls

Addressing a single call successfully is one thing, but how do your agents handle larger volumes? The active waiting calls metric looks at the proportion of active calls that are on hold. A high rate means many customers have to wait before agents get back to them, which has a negative effect on their experience.

You can improve this metric by focusing on smarter workflows that reduce wait times. For example, automating simple tasks and effective scripts for agents can speed up resolutions. Consider hiring more agents if you’re struggling to distribute call volume among your current staff.

Average Talk Time (ATT)

ATT tracks the duration of conversations between agents and customers. It differs from AHT as it doesn’t account for hold time or follow-up actions after the initial call. 

Just like with AHT, though, a low score doesn’t necessarily indicate good performance. It may suggest efficiency, but it’s important to deliver quality solutions, too. Providing agents with resources and scripts can help manage talk time effectively.

Average Time in Queue (ATQ)

ATQ measures the average wait time customers experience before connecting with an agent. Reducing queue times involves efficient staffing and optimized call routing to ensure minimal delays for customers.

Wrap-Up Time

Wrap-Up Time measures the time agents spend finalizing a call after the customer has hung up. Post-call actions can include updating records, sending follow-up emails, or escalating the ticket. While these steps are necessary for complete customer satisfaction, they contribute to wait times for other customers. 

Companies using AI-powered automation are cutting repetitive tasks by 40%, so it makes sense to invest in this technology. Leverage automated workflows for activities like updating records to save time that agents can utilize for other calls.

Average Call Abandonment Rate

If customers have to wait longer than expected, they will likely hang up out of frustration. The average call abandonment rate is the proportion of received calls that your agents didn’t handle. Tracking this KPI will provide insights into how frequently customers have given up on waiting.

You can lower this rate by letting customers request a callback. This allows callers to keep their place in the queue without staying on hold. As a result, they don’t have to waste their valuable time since the agent can call them back when it’s their turn.

Another good practice is to use customer data from the abandoned call. Even though the customer had a bad experience, the agent can call them again to see if they can provide any support.

Total Resolution Time

This KPI tracks the average duration of resolving a customer ticket. It’s a marker of agent productivity as it indicates their effectiveness at addressing and resolving caller concerns. 

A high total resolution time suggests that your agents might be struggling to access relevant customer data. For example, if the caller initially complained via email before picking up the phone, they will expect the agent to have a record of that initial communication. 

This is where the omnichannel contact center solution provided by InMoment can assist your agents. By integrating customer data from various channels into a unified dashboard, the software saves agents valuable time and effort that they can put towards resolving the issue.

Overview of contact center channel interactions in InMoment's XI Platform.

Transfer Rate

Transfer Rate tracks the percentage of calls that agents transfer to other departments. For example, if a billing inquiry is transferred to the finance team, it counts toward the transfer rate.

Lower rates suggest that agents are well-equipped to resolve issues directly. Meanwhile, a high transfer rate suggests that customers might be reaching the wrong agent on their first attempt.

Therefore, one way to reduce the rate is to improve your internal routing system. Simplify your interactive voice response (IVR) menu by making the options user-friendly. Collect feedback on the IVR system’s ease of use at the end of a call and adjust accordingly.

Adherence to Schedule

This KPI reflects how closely agents follow their assigned schedules. For example, if an agent starts on time and sticks to breaks, they have high adherence. Improving the adherence to schedule ensures adequate coverage and reduces wait times during peak hours.

Calls Answered per Hour

This metric counts the number of calls an agent completes within an hour. High calls per hour indicate efficiency. However, balancing quality with quantity is key for customer satisfaction. Effective call center scripts and software help streamline CX workflows without compromising on quality.

Types of Calls Handled

Agents have to address and resolve various types of customer concerns. Common types of calls include:

  • Queries
  • Technical support
  • Refunds or claims
  • Complaints
  • Order placement and tracking

Analyzing the most common call types will help you identify trends and prioritize resource allocation.

Call Center Operations Metrics

Tracking call center operations metrics is essential to making sure you are running a sustainable and effective call center. The following metrics help provide a clear view of daily performance and resource allocation. By monitoring these, managers can identify areas for improvement, optimize processes, and ultimately deliver a higher standard of service.

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Calls Handled

This simple metric counts the total number of calls handled by the call center within a given period. Monitoring the number of calls handled helps in understanding workload distribution and identifying peak hours. Ensure you have an adequate agent count to evenly distribute calls and balance the workload.

Cost Per Call (CPC)

CPC measures the average cost of handling each call. You can calculate this metric by adding up all associated costs, like labor, technology, and overhead, and dividing it by the total number of calls. A lower CPC indicates efficient usage of resources to address and resolve customer queries.

Reduce CPC by leveraging self-service options for basic queries and automating repetitive tasks. This approach frees up agents to handle more complex issues, optimizing resource allocation.

Call Arrival Rate

Call Arrival Rate tracks the number of incoming calls within a specific period. This metric is especially useful in preparing for seasonal or promotional spikes. Use historical data to forecast call volume and adjust staffing schedules accordingly.

Peak-Hour Traffic

This metric helps you identify peak hours, which is when your agents receive the highest volume of calls. Understanding your busiest hours can help you schedule and allocate resources accordingly. Increasing staff availability and training your agents to handle peak-hour scenarios can help.

Average Age of Query

The average age of query metric determines how long unresolved customer tickets stay open. It reflects the efficiency of query management and response processes. A high figure suggests that agents are struggling to resolve certain queries. You can lower this metric by intelligently routing queries to agents who have the right skill set to resolve them.

Repeat Call Rate

This contact center metric tracks the percentage of repeat calls received by a business. Repeat calls occur when the issue isn’t resolved on the first attempt. As a result, a high rate is indicative of sub-optimal first contact resolution.

Identifying and analyzing recurring issues can help enable effective resolution. InMoment’s contact center AI can help by providing insight into repeat call customer profiles. It leverages analytics and intent recognition to highlight common issues and the information sought by these customers.

Smart summary of customer feedback within InMoment's platform that simplifies customer insights.

Percentage of Calls Blocked

This KPI tracks the proportion of calls that fail to connect because the call center’s capacity is full. High rates indicate that customers are unable to reach support, which can dent their perception of your business. Invest in a good IVR system to handle customers if they can’t reach your agents.

Customer Experience Metrics

​​Call center metrics are essential to a holistic CX strategy. They serve as vital indicators for your customer experience KPIs, enabling you to track and enhance success across touchpoints.

First Call Resolution (FCR)

This metric evaluates the percentage of calls an agent resolves during the initial interaction without any follow-ups. These follow-up actions could include transferring, escalating, or returning the call later. High FCR indicates effective problem-solving during the first attempt, as it reduces repeat calls for customers.

By training your agents to handle tickets effectively, you can improve your FCR score. The training could include educational resources and role-playing exercises. Leveraging self-service channels can also help address customer concerns without multiple calls.

InMoment’s conversational analytics software also helps improve your FCR score by allowing you to efficiently analyze speaker data for insights and opportunities to better understand your customers and improve your customer service. 

InMoment's contact center solution that shows individual speaker insights to help improve customer service.

Customer Satisfaction Score (CSAT)

Businesses calculate this metric with the help of a customer satisfaction survey featuring a set of questions. These questions ask customers to rate how satisfied they are with the support provided by the contact center. Higher scores indicate that customers are largely happy with the service and are likely to call again.

Since the CSAT is a quantitative metric, it provides limited context. A customer may have an 8/10 experience, but you’ll have no idea what they liked or disliked about your agent’s performance. After all, the rating suggests that while the overall experience was good, there is slight room for improvement.

Therefore, a good practice is to include text fields at the end of surveys. This option encourages customers to provide relevant details that will help you make better decisions.

Quality Assurance (QA)

Call centers use quality assurance (QA) to monitor their customer service quality. A QA score is generated based on a scorecard after reviewing call recordings and interactions. The scores are used to determine if agents are offering the efficient services expected from them.

For example, are they hesitant when offering solutions? How do they behave in front of a disgruntled customer? Do they balance the quality of the solution with their speed of service?

You can improve QA scores by emphasizing the importance of quality service to your agents. Consider setting objectives for them and giving them recognition when they meet their targets. This can motivate agents to deliver the best possible experiences to customers.

Net Promoter Score (NPS)

If a customer is loyal to your brand, they have likely had a positive experience with your call center, too. The Net Promoter Score (NPS) metric measures loyalty by asking customers how likely they are to recommend your business to others.

Responses are measured on a scale from 0 to 10, classifying customers as promoters, passives, or detractors. Your goal is to understand what experiences contribute to each category. For example, if you find that detractors are disappointed by long wait times, you can potentially convert them into promoters by making your workflows more efficient.

Customer Effort Score (CES)

The Customer Effort Score (CES) for call centers highlights how difficult it is for customers to resolve their issues with your agents. It is usually calculated on a 5- or 7-point scale. Higher scores indicate that customers agree that it was easy to interact with the call center.

You can improve this metric by simplifying the contact center journey. Provide multiple interaction channels, like email and live chat, for contacting agents.  Leverage self-service options like chatbots to help customers resolve simpler issues on their own.

Call Center Metrics Examples

Businesses in various industries rely on call center metrics to better serve their customers. Here are two examples from the retail and hospitality sectors that demonstrate the impact of tracking these KPIs.

Retail Call Center

Jane, a customer at a fashion retailer, has a complaint regarding her latest purchase. The boots she ordered online are the wrong size, so she’s hoping to get a replacement. She picks up the phone and is eventually routed to a call center agent.

Despite her negative experience, the business can still make a good impression on Jane by focusing on the following call center metrics:

  • Average Speed of Answer: Responding to Jane within 15 seconds will prevent a lengthy wait for her and avoid further frustration.
  • Average Handle Time: By following a script and leveraging CRM software to fetch Jane’s details, the agent can quickly understand that she’s looking for a replacement.
  • First Call Resolution: Jane isn’t interested in talking to multiple agents. She simply wants to lodge a complaint and get the boots she ordered in the right size. If the agent can process her request and send a replacement by the time the first call ends, Jane is likely to feel better about the brand.
  • Repeat Call Rate: It turns out that Jane received the wrong order because of an oversight from the staff. By addressing this specific act of negligence, the retailer can prevent repeat calls related to this issue from Jane and other customers.

Therefore, by tracking a few impactful metrics, the retailer can succeed in resolving Jane’s concern and retaining her as a customer.

Hospitality Call Center

Mark is looking to stay at a hotel for the weekend during his business trip. After a quick Google search, he finds a hotel to his liking. However, he wants to know more about the location and the amenities he can expect. Before picking up the phone, he decides to visit the hotel website.

By including an AI-powered chatbot on its website, the hotel contact center can improve the following metrics:

  • Average Time in Queue: Mark asks the chatbot a few questions and receives satisfactory answers. As a result, it’s very likely that he doesn’t need to call the hotel anymore. He doesn’t have to wait in a queue, and things are off to a great start for him!
  • Cost Per Call: Since Mark’s query is resolved on the website, he doesn’t have to contact an agent. It saves the business time and labor costs that can be invested into improving its operations.
  • Peak-Hour Traffic: By encouraging Mark and other customers to utilize its chatbot, the hotel can prevent calls for basic to moderate queries. As a result, it’s less likely to receive an overwhelming volume of calls, which will help it manage peak-hour traffic.

Therefore, investing in self-service technology helps the hotel’s call center agents by saving them time and effort.

How to Improve Call Center Metrics?

  1. Invest in agent training and coaching.
  2. Implement self-service options.
  3. Automate routine tasks.
  4. Use QA scores to monitor and improve performance.
  5. Emphasize First Contact Resolution.
  6. Track call center progress over time.
  7. Set goals for your agents based on metrics.
  8. Create effective call center scripts.
  9. Collect and act on customer feedback.
  10. Leverage contact center software.

Tracking call center metrics highlights key strengths and weaknesses. For example, a high cost per call might indicate efficient use of resources and workforce. However, a low call availability could suggest that you need to invest in more agents to avoid overwhelming your current staff.

With this information, you can make adjustments to optimize call center performance. Here are some strategies you can implement to improve call center metrics for customer satisfaction.

1. Invest in Agent Training and Coaching

Agent performance metrics help you identify issues holding your agents back. Maybe they struggle to work under pressure in peak hours. Perhaps they don’t have easy access to customer data. 

By training your agents to handle a range of scenarios, you can ensure they are better prepared to meet customer expectations at all times. Consider surveying them and tracking their QA scores to create targeted coaching programs for them.

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. 

Smart action plans for the most effective employee training.

2. Implement Self-Service Options

Self-service options like chatbots, IVRs, and online FAQs encourage customers to find quick answers. As a result, customers with basic queries don’t have to wait in queues or be put on hold. It also frees up time and effort for human agents that they can put towards resolving complex issues.

3. Automate Routine Tasks

A great way to make the most of your agents’ time is to automate repetitive tasks. For example, processes like data entry and follow-up emails don’t require human intervention. Automating these tasks can save agents time that they can put toward more impactful customer experience tasks.

4. Use QA Scores to Monitor and Improve Performance

QA scores evaluate agents’ interactions to ensure high standards. Regularly monitoring these scores highlights areas for coaching and skill improvement. This helps agents deliver consistently high-quality service.

5. Emphasize First Contact Resolution

Emphasize the importance of effective issue resolution to your agents. Solving problems within the first attempt reduces the repeat call rate and customer frustration. Training and equipping agents with CRM software can help enhance first contact resolution.

6. Create Effective Call Center Scripts

A good call center script provides a template that agents can refer to for quick issue resolution. It also helps prevent inaccurate responses to customer inquiries. However, a script alone isn’t enough. Your agents need to understand the value of improvisation to address customer needs. 

Train your staff with role-playing scenarios so that they can practice how to use their scripts in various scenarios. Encourage them to come up with quick solutions in situations where scripts don’t provide the relevant information.

7. Track Call Center Progress Over Time

Monitoring call center metrics over time helps identify trends and areas that need attention. Consistent tracking enables data-driven decisions. It also allows managers to adjust strategies to meet performance goals effectively.

8. Set Goals Based on Metrics

Setting metric-based goals gives your agents something to work towards. For example, you can set a goal of answering calls, on average, in less than 20 seconds. Giving your agents recognition for achieving these goals will motivate them to be even more productive in the future.

9. Collect and Act on Customer Feedback

A customer-centric brand understands the value of feedback for all aspects of its business. Ask customers to rate their experience after each call with a single digit. Collect more in-depth data through a customer feedback questionnaire every month. Acting on this feedback shows your commitment to customer experience. It also highlights the necessary changes you need to make to improve call center performance.

10. Leverage Contact Center Software

Contact center software helps streamline data tracking, call routing, and analytics. For example, InMoment’s tools offer insights into performance metrics, which enables informed decisions for process improvements. These tools support consistent, efficient service so that your agents can deliver positive experiences.

How to Report Call Center Metrics and KPIs

The right call center technology can help you track and report key metrics. Reporting is crucial as it transforms raw figures into actionable reports for call center management. It provides insight into call center performance and what aspects require improvement.

Using a Call Center Metric Dashboard

Dashboards are powerful tools for reporting and visualizing call center metrics. They can help you improve performance by providing real-time visibility into KPIs and operational data.

Agents can leverage dashboards to track important metrics like call volume, average talk time, and satisfaction scores. This transparency helps them identify wins and areas for improvement in their performance. As a result, they have the information and motivation to meet their targets.

Call center managers can leverage dashboards to monitor their department’s performance. For example, they get a comprehensive view of metrics like adherence to schedule and service level rate. This enables them to make data-driven decisions, allocate resources effectively, and identify which agents to train.

Dashboards for a contact center agent and contact center manager.

Enhancing Call Center Analytics with InMoment

A call center can be a valuable asset to your business. By providing instant and efficient support to customers, your agents can help encourage customer loyalty. With the help of InMoment’s contact center software, you can report key metrics and gain analytical insight into both customer and agent experiences. See what InMoment’s platform can do for you by taking a product tour today!

References 

Invoca. 39 Call Centre Statistics You Need to Know in 2024 (https://www.invoca.com/uk/blog/statistics-call-center-managers). Accessed 30/10/2024.

InMoment. InMoment Market Pulse (https://www.linkedin.com/posts/weareinmoment_b2b-customersuccess-ai-activity-7251989745914818560-haGe?utm_source=share&utm_medium=member_desktop). Accessed 10/30/2024.

Natural Language Processing: Transforming Large Data into Strategic Business Insights

Natural Language Processing (NLP) enables computers to understand and respond to human language. By analyzing unstructured data like emails, social media posts, and customer reviews, NLP helps businesses gain valuable insights and make informed decisions that drive growth and efficiency.
Two hands typing on a laptop

Natural Language Processing (NLP) is a complex, yet fascinating and rapidly evolving field. It combines the specialties of computer science, artificial intelligence, and linguistics. Merging all three disciplines, it focuses on the interaction between computers and humans through natural language. This enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful. 

This technology supports a wide array of applications, from voice-activated assistants and chatbots to sophisticated text analysis tools and language translation services. By leveraging complex algorithms, machine learning techniques, and vast amounts of linguistic data, NLP aims to bridge the gap between human communication and computer understanding, transforming how we interact with technology in our daily lives. As the capabilities of NLP continue to expand, it further revolutionizes various industries, enhances user experiences, and opens new avenues for research and innovation.

What is Natural Language Processing?

Natural Language Processing, or NLP, is a part of computer science that helps computers understand, interpret, and use human language. Basically, it’s like teaching a computer how to talk and write like a human.

Imagine you’re talking to your friend, you can understand each other easily, right? But if you try to talk to a computer in the same way, it might get confused because it doesn’t understand things like jokes, sarcasm, or even some common phrases. Natural language processing tries to solve this problem. It’s like building a bridge between human language and computer language.

Natural Language Processing in AI

Natural language processing is a critical area of artificial intelligence. It allows computers to understand, interpret, and generate human languages. Therefore, enhancing the interactions between computers and humans to be useful and meaningful is crucial. 

One of the most beneficial aspects of this interaction is how NLP can rapidly process and analyze vast amounts of data, far quicker than a person could. This accelerates data analysis, allowing us to concentrate on more important tasks. It filters out unnecessary and irrelevant information, enabling businesses to focus on what truly matters. For instance, product teams can extract data specific to their products, while service teams can focus on improving customer service.

Why is Natural Language Processing Important?

Natural language processing has recently become an integral part of our everyday lives. However, for businesses—especially those in finance, healthcare, and legal sectors—NLP has long been essential for processing large amounts of unstructured data. Without NLP, businesses could not efficiently and effectively analyze data that would play a critical role in informed decision-making. 

Natural Language Processing Examples & Use Cases

A good example of NLP is voice assistants like Siri or Alexa. You can ask them questions in normal human language and they can understand and respond to you. That’s because they use NLP to understand what you’re saying. However, natural language processing goes far beyond Siri or Alexa and has many advantages for businesses including:

  • Customer Service Automation: Many companies use NLP to automate customer service through chatbots. These AI bots can understand and answer customer questions. This reduces the need for human help and speeds up response time. 
  • Sentiment Analysis: Businesses use NLP to analyze customer feedback, reviews, and social media conversations to determine customer sentiment toward their products, services, or brands.
  • Market Intelligence: NLP can analyze many news articles, blog posts, and social media posts. This helps provide insights about market trends, competitor activities, and possible business opportunities.
  • Email Filtering: NLP helps filter out spam and sort emails into different folders. This makes managing emails easier.
  • Resume Screening: HR departments and recruitment agencies use NLP to help screen resumes. This technology matches the candidate’s skills and experience with job requirements.
  • Personalized Advertising: NLP can analyze a user’s online behavior and preferences. It does this by identifying specific words, both positive and negative. This enables businesses to optimize personalized ads and product recommendations.
  • Content Creation: Generative AI is part of NLP. It can help automate content creation. This allows news outlets and companies to create simple reports or articles automatically.
  • Search Engine Optimization: NLP can help businesses optimize their content to rank higher in search engine results. It does this by identifying relevant keywords and phrases in your content and comparing it to high-ranking competitors.
  • Contract Analysis: NLP can analyze contracts and legal documents. This helps businesses find important information and possible risks. It can ensure that the language within a contract is present and can look for problematic wording.

Natural Language Processing Applications in Business

Natural Language Processing is transforming how businesses interact with data and customers by enabling machines to understand, analyze, and respond to human language. From automating customer support to extracting insights from large datasets, NLP applications help streamline operations and enhance decision-making. Here are some real-life examples of businesses applying natural language processing to their operations.

Biotechnology

When someone calls the Medical Information Department (MID) at Biogen, they’re routed to operators who search through FAQs, brochures, and product resources to answer questions. If the answer cannot be provided within a minute, the call escalates to an expensive medical director. Biogen wanted to reduce the involvement of these directors. So, they turned to InMoment for a solution to empower, not replace, their human operators using NLP in healthcare

First, we configured our core NLP to identify relevant information within Biogen’s resources. Then, we combined this solution with an open-source search engine and custom user interface. The resulting system understands complex relationships within Biogen’s data. Now, MID operators can type in keywords or questions to get answers in seconds. Early testing by Biogen already shows faster responses and fewer calls sent to medical directors.

“We’ve worked with InMoment for years on programs surrounding Voice of the Patient, Voice of the Key Opinion Leader (KoL), and social media monitoring… They’ve always been a key partner.” — Keith Ho Director of Customer Focus and Medical Digital, Biogen

Sports & Entertainment

Brandtix delivers actionable brand performance insight for the world’s top athletes and teams by gathering data from social media and news platforms. They turned to InMoment for a powerful NLP platform that could analyze and decode the jargon-filled language of professional sports. 

Together, InMoment’s cloud API and Brandtix’s proprietary algorithms now process fan vernacular across 19 languages. As part of this, InMoment analyzes and structures the sentiment of fan conversations as positive or negative, based on context. These capabilities play a crucial role in brand reputation management, enabling franchise owners, player agents, and PR teams to separate meaningful mentions from general chatter and address PR problems before they get out of hand.

“Choosing InMoment over its competitors was easy — thanks to the mix of service, price, ease of use, and language packs. Further, InMoment counts extraction and sentiment analysis as one action. The other solutions we looked at bill extraction and sentiment separately, charging double the volume and double the price.” — Shahar Fogel Vice President of Product

Social Media Monitoring

evolve24 is a data analytics firm that combines myriad data sources to help companies develop strategic direction. To process information and provide market intelligence in real-time, evolve24 can only employ best-in-class toolsets with the lowest possible latency and downtime. 

Social media monitoring that pulls reviews from TripAdvisor, Google, Threads, and Facebook.

InMoment’s core AI-based NLP engine, provides low-latency text mining and analytics that process five or more tweets every second, expediting evolve24’s time-to-value for their customers. Salience’s power and customizability give evolve24 the ability to keep up with increasing volumes while helping them maintain high standards of consistency and measurement across a range of text data sources.

“The text analytics engine is a key tool for us in conjunction with our proprietary emotion metric; this next evolution of functionality promises an even more comprehensive look into the conversations our customers’ customers are having.” — Noah Krusell VP of Product Development, evolve24

Customer Experience Management

VOZIQ offers a suite of Predictive Customer Retention and Customer Experience Management solutions for call centers. Traditional customer churn prediction models rely on transaction histories and demographics data but fail to incorporate consumer-generated input with real customer sentiment. VOZIQ turned to InMoment to fill this gap.

Customer experience management through executive advice

With InMoment, VOZIQ categorizes the text comments and identifies customer sentiment from survey scores and keywords in each call log. Since partnering with InMoment, VOZIQ has retained thousands of customers for their clients, resulting in millions of dollars in additional revenue each year.

Industrial & Aviation Design

Gensler’s Los Angeles Aviation and Transportation Studio partnered with InMoment, leveraging sentiment analysis on customer feedback to make better-informed decisions about the planning and design of airports. The result is a data-driven voice of customer program that can help win contracts and build airports that better serve stakeholders and travelers alike.

“As a global industry leader in airport architecture, we utilize the power of Semantria’s rapid and precise data analysis to create better-informed designs for the airports of tomorrow.” — Andy Huang, AIA LEED Associate Designer, Gensler Aviation and Transportation Studio

Hospitality & Hotel Management

Revinate helps over 30,000 hospitality providers measure online presence, analyze consumer feedback, and reinvent the guest experience. With over 2,700 categories, 100 restaurant topics, 200 hotel topics, and nine languages, Revinate gives their clients the ability to measure consumer sentiment in critical categories, such as rooms, staff, service, and food. InMoment’s customizability lets Revinate’s users create lists of custom topics, follow trending topics as they evolve, and compare sentiment scores across multiple organization-specific metrics.

“The support from the team at InMoment was outstanding; they made a very complex project seem simple. With their partnership, we met our goals on time, delivered the best possible product, and were set up to ensure continued success.” — Matt Zarem, Senior Director of Product, Revinate

Technology & Electronics

A large tech company’s Customer Market Research (CMR) team helps managers across the company make better decisions regarding product and market strategy. Before, the CMR team used to listen to the Voice of the Customer by designing, distributing, and analyzing a wide range of surveys. As the group began working to integrate social media data, they turned to InMoment.

Their team needed to effectively filter social content in order to extract relevant data, reduce survey spend, easily configure flexible one-off analyses, and validate long-term trends. Traditional social listening tools didn’t offer the customizability and scalability that the CMR team needed, so they contacted InMoment to discuss a “semi-custom” solution.

First, the CMR team extracts a subset of social comments from an InMoment-built data warehouse, based on the products and brands they want to know more about. Then they use InMoment’s Spotlight tool to analyze this data and understand what people are saying, how they feel, and why they feel that way. Next, they validate the results and relate the net sentiment score to quantitative Likert scale survey data. This approach allows them to compare and contrast what people say in structured surveys, versus what they say in the unstructured environment of social media.

“InMoment is the only vendor we’ve seen that can offer the flexibility that is required to support our complex product line.” — Csaba Dancshazy Senior Market Research Manager 

Fitness Lifestyle & Events

Tough Mudder Inc. has grown to become a leading active lifestyle brand and endurance event company with more than 2.5 million global participants. The Net Promoter Score (NPS) is an essential measurement for the company. However, the volume and the qualitative format of their post-event surveys make it challenging to garner insight.

Using InMoment’s API for Excel, the Tough Mudder team reduced manual survey coding time by 90%. Working with InMoment staff, they designed custom queries to solve an industry-specific sentiment analysis problem. In total, Tough Mudder uses InMoment to process 2,000 surveys for each of the company’s 78 events per season, some 156,000 surveys total.

“By teaming with InMoment, Tough Mudder is able to report Net Promoter Scores and review participant feedback within a week of every event. The company’s ability to make strategic adjustments based on customer insights is invaluable to providing the ultimate event experience.” — Sydney Friedkin Consumer Insights Analyst, Tough Mudder Inc.

Regulatory Compliance & Financial Services

The Australian government mandates that financial Statements of Advice (SoAs) include disclosures covering conflicts of interest, own product recommendations, and more. Financial services providers doing business in Australia use SoA templates and frequent spot-checks. This helps make sure that financial advisors aren’t modifying or deleting critical disclosures.

An average-sized firm produces hundreds of pages of SoAs each week. Manual review is costly, unreliable, and exposes the firm to high non-compliance risk. One such firm, unable to find an existing contract analysis tool that could solve this exact problem, turned to InMoment for help. 

First, we trained our semi-structured data parser with machine learning to understand the underlying structure of the Statement of Advice document. Then, we built a custom natural language processing configuration to extract and analyze entities and other text elements. Then, we structured and exported the resulting data into a simple spreadsheet.

Now, in mere minutes the firm’s auditors can see whether proper disclosures were made across hundreds of documents. They can even identify where an advisor’s recommendations may go against their client’s stated goals and risk attitude. This substantially lowers the firm’s non-compliance risk even while reducing their disclosure compliance costs.

Natural Language Processing Techniques & Models

Human language is complex and flexible. Many NLP models have been created to process it well for different needs and tasks. Here are a few common types of natural language processing models:

1. Rule-Based Models: This type of NLP model uses specific rules and grammar to understand and interpret natural language. 

2. Statistical Models: These models use statistical methods and algorithms to understand the probability of certain words appearing together to make meaningful sentences. 

3. Machine Learning Models: Machine learning models use algorithms that can learn from data and improve over time. They use features like words, phrases, sentences, etc., to classify, predict, or translate text.

4. Deep Learning Models: These are a type of machine learning models that use neural networks with multiple layers (deep networks) to understand and interpret natural language. 

5. Sequence-to-Sequence Models: This type of model is used for tasks where the input and the output are sequences, like in machine translation or voice recognition.

6. Transformers Models: Introduced by Google, Transformers models are based on an attention mechanism that allows the model to focus on different words in the input sequence while generating the output sequence. Examples include BERT, GPT-3, and T5.

7. Hybrid Models: These models combine several techniques like rule-based, statistical, machine learning, etc., to improve the accuracy and efficiency of natural language processing tasks.

8. Reinforcement Learning Models: These models learn by interacting with their environment, and receiving rewards or penalties based on their actions.

9. Cognitive Language Models: These models use cognitive psychology to better understand human language processing and build models that can mimic human-like language understanding. 

10. Convolutional Neural Network (CNN) Models: These are primarily used for text classification, sentiment analysis, and other NLP tasks.

11. Recurrent Neural Network (RNN) Models: These are especially useful for sequence prediction problems, as they can use their reasoning from previous inputs to inform the current one.

What are the Benefits of Natural Language Processing?

Natural language processing can be highly beneficial for businesses as it enables technology to understand, interpret, and respond to people in a valuable way. NLP enhances communication between people and computers, making interactions more efficient and effective including:

Improved Customer Service

NLP helps create chatbots and virtual assistants. These tools can understand and answer customer questions quickly. This reduces wait times and boosts customer satisfaction.

The review process being improved by NLP software.

Enhanced Decision Making

By analyzing vast amounts of textual data, NLP can help businesses make data-driven decisions. It can identify patterns, sentiments, and trends, providing valuable insights to businesses.

Increased Efficiency

Tasks like data entry, customer service, and report generation can be automated using NLP, freeing up staff to focus on more complex tasks. 

Better Accuracy 

NLP can significantly reduce human error in tasks like transcription services, data extraction, and language translation.

Personalized Marketing

By analyzing customer behavior and preferences, natural language understanding can help businesses create personalized marketing campaigns and product recommendations.

Advanced-Data Analysis 

NLP can process and analyze unstructured data. This includes emails, social media posts, and customer reviews. Traditional data analysis tools cannot handle this type of data.

Improved Accessibility

NLP can be used to develop applications for people with disabilities. For example, speech recognition systems can help people with mobility issues, and text-to-speech systems can assist people with visual impairments.

Enhanced Language Translation

NLP improves the capabilities of language translation software, making it possible for businesses to communicate with customers in different languages more effectively.

Early Risk Detection

It can be used to monitor digital conversations on social media, emails, and forums to identify potential threats or negative sentiments toward a brand or product.

How has Natural Language Processing Evolved?

While natural language processing might seem like a modern concept to the general public, NLP has been around since the 1950s and has evolved rapidly over the years. 

1950s – 1970s

When NLP was first developed, it started as a rule-based system where the rules were manually created by linguists for language processing. Since the system was heavily dependent on manually handcrafted rules, NLP was limited and lacked generalization. 

1980s – 1990s

The introduction of statistical methods would be the next shift of natural language processing which allowed NLP to become popular for simple tasks such as part-of-speech tagging and machine translation. This led to a more robust and scalable solution, compared to the initial rule-based systems. 

1990s – 2000s

Natural language processing (NLP) continues to evolve quickly, especially from the 1990s to the 2000s. During this time, the integration of machine learning algorithms allowed NLP to handle more complex tasks. Significant advancements were made in areas like identifying named entities (like names and places), analyzing sentiments (understanding emotions in text), and parsing syntax (understanding sentence structure). These improvements enhanced both the performance and efficiency of NLP applications.

2010 – Present

This period marks the revolution of natural language processing with the advent of deep learning, especially neural networks. These progressions have greatly improved NLP’s ability to understand and interpret the meanings of words and sentences. The introduction of pre-trained language models, such as GPT and BERT, has further transformed NLP by enabling the handling of vast amounts of text data and performing specific tasks with high accuracy.

Future Trends in Natural Language Processing

With NLP rapidly evolving over the past 70 years, it doesn’t seem to be slowing down anytime soon. As demand continues to rise, this will help set future trends for NLP including:

Advance Language Models

We’re already seeing continued development and refinements of large-scale pre-trained models such as GPT and BERT. These models will only become more accurate, efficient, and capable of understanding context, nuances, and generating human-like text. 

Multimodal NLP

Taking language to the next level by integrating text with other types of data like images, audio, and video. This opens up new possibilities for content generation and improving applications such as virtual assistants and translation services. 

Improving Trust and Transparency

For critical applications, especially within the healthcare, finance, and legal industries there will be a need for enhanced transparency in the NLP models. This will help provide clear explanations and understanding for predictions and decisions.   

Ethical and Fair Processing

As natural language processing becomes more engrained in our lives, ensuring these models are used ethically and do not create harm. 

Integrations

With how popular NLP has become, we can only expect continual integrations with other technologies allowing for a more intuitive voice-controlled interaction from smart devices, vehicles, and other connected technologies. 

With how rapidly NLP is evolving, future trends for natural language processing can change quickly as well. Therefore, it’s crucial to stay ahead of these trends so that your business can leverage the full potential of NLP to make intelligent business decisions.

What to Look for in a Natural Language Processing System

When it comes to analyzing unstructured data, NLP involves these core functionalities including keyword extraction, sentiment analysis, and classification. Other features to consider in natural language processing software include: 

  • Predictable Pricing: Pricing for NLP software can be complicated and cumbersome. CX leaders and teams should know exactly how much they will be charged for a product or service, with no hidden fees or unexpected costs. Predictable pricing should make it easy for you to budget and plan expenses more effectively.
  • Stable and Scalable Architecture: Stable and scalable architecture in NLP software refers to a system design that ensures reliable and efficient operation, is capable of handling increasing workloads. Stability means the system performs consistently without crashing, handles various input types, and is easy to maintain and update. Scalability involves the ability to expand the system’s capacity by adding more machines or enhancing existing ones, effectively distributing workloads to prevent any single server from being overwhelmed. This architecture allows the system to automatically adjust resources based on demand, ensuring optimal performance and reliability even as usage grows.
  • Customization Through Tuning and Configuration Tools: Customization through tuning and configuration tools in NLP is vital for adapting models to specific use cases, improving accuracy, and handling unique language variations like industry-specific jargon. It allows optimization of performance, balancing speed and accuracy, while also reducing biases that may arise from pre-trained data. This ensures that NLP models deliver more precise and context-aware results, aligned with the needs of the task.

In addition to these important functionalities, you should also consider these features:

  • High Support Availability: ​​Dependable support availability in an NLP tool is essential because it ensures timely resolution of issues, minimizing downtime and ensuring smooth operation. Since NLP models can be complex and require frequent tuning or updates, accessible support helps users address challenges quickly, whether it’s troubleshooting, fine-tuning, or integrating the tool into existing systems. This support enhances user confidence, boosts productivity, and ensures the tool’s long-term success in meeting business needs.
  • Private Hosting Environment: A private hosting environment is crucial for NLP tools, especially when handling sensitive or proprietary data, as it ensures enhanced security and privacy. By hosting the tool privately, organizations have full control over data access and compliance with regulations, reducing the risk of data breaches. Additionally, private hosting allows for greater customization, performance optimization, and scalability tailored to specific business needs, all while maintaining a secure infrastructure.
  • Custom ML Models: In NLP tools, custom ML models are important because they allow organizations to tailor the tool to their specific needs, such as adapting to unique industry jargon, data formats, or specialized tasks. By creating or fine-tuning these models, businesses can achieve greater accuracy and relevance in the results, leading to more meaningful insights and better performance. Custom models also provide flexibility, ensuring the NLP tool evolves with the organization’s changing requirements and delivers optimal outcomes.

Stay Ahead and Informed with InMoment

For businesses aiming to revolutionize their customer experience management, analyzing large sets of unstructured data from emails, social media posts, customer feedback, and contact centers can be transformative. InMoment’s NLP model powers our text mining and analytics platforms, enabling top brands to uncover powerful insights that drive significant changes. Learn more about InMoment’s powerful natural language processing AI software, which has won numerous awards as a leader. 

Understand Active Listening and How It’s Revolutionizing Feedback Collection with 2.4x More Actionable Responses

Active Listening utilizes AI to improve the feedback collection experience. This allows you to gain a better understanding of the customer experience and what can be done to improve it.
Customer Satisfaction Survey

Capturing genuine and actionable feedback from customers and employees can be a challenge, especially when traditional surveys feel long, tedious, and uninspiring. Many organizations struggle with vague, short responses that offer little insight, leaving them chasing scores rather than focusing on meaningful improvements. This is where Active Listening comes in—a revolutionary approach to feedback collection that enhances the quality and depth of responses.

What is Active Listening?

Active Listening is a conversational AI agent designed to improve the feedback collection experience by engaging respondents in real-time. It prompts users with context-aware follow-up questions, encouraging them to provide richer, more detailed answers. Whether through simple rule-based interactions or advanced AI, Active Listening transforms survey data from shallow comments into meaningful insights.

AI prompting a user to share more detail in a survey response

Active Listening Agents

  • AI-Powered Active Listening: This agent uses advanced AI to continuously learn and adapt based on user feedback, evolving its language to elicit more detailed responses. It identifies patterns in real-time, helping businesses quickly uncover emerging trends and improve decision-making.
  • Basic Active Listening: For organizations that prefer full control over their feedback collection process, this agent offers customizable rules. It allows you to tailor how Active Listening interacts with respondents, adjusting trigger phrases and follow-up prompts to gather more valuable data on specific topics.

Benefits of Active Listening for Feedback Collection

Why does it matter for your business? The benefits of Active Listening go beyond improving survey response rates; it drives more actionable insights and ultimately enhances your overall customer and employee experiences.

  • Actionable Insights: Captures deeper, more meaningful feedback, enabling businesses to uncover the root causes of issues and identify growth opportunities.
  • Improved Engagement: Respondents feel heard, leading to higher satisfaction rates as surveys become more conversational and personalized.
  • Increased Efficiency: By automating follow-ups with AI or customizable prompts, teams spend less time chasing responses and more time acting on valuable insights.
  • Enhanced Security: With locally hosted AI models, businesses can maintain strict control over their data, ensuring compliance with privacy regulations.
Active listening and rapid resolution demo inmoment

Who Would Benefit

Organizations that prioritize understanding and improving experiences will find great value in advanced feedback collection tools.

  • Experience Leaders: Those responsible for customer and employee experience will benefit from more detailed, actionable feedback that allows them to address pain points and drive loyalty.
  • Customers & Employees: Respondents appreciate the ease of giving feedback and knowing it will be used to make meaningful improvements, increasing their willingness to engage.
Business improvement suggestions from AI Active Listening

How is Active Listening Different from Competitors?

InMoment’s Active Listening stands out in the feedback market by offering both AI-powered and customizable rules-based agents, giving businesses flexibility that competitors lack. 

  • Unlike others, InMoment uses a privately hosted LLM to keep data secure, while real-time follow-up prompts improve feedback quality before submission. 
  • The platform’s Strength Meter gamifies the survey experience, increasing engagement, and its Topics to Avoid feature ensures on-brand prompts. 
  • Designed for both CX and EX, Active Listening keeps surveys concise and impactful, delivering deeper, actionable insights more efficiently than traditional platforms.
An overview of AI Active Listening software

Why Active Listening Matters

For businesses seeking to transform vague, unactionable feedback into insights that can drive real change, Active Listening is the solution for effective feedback collection. It’s a game-changer for brands looking to improve both customer and employee experiences. Companies using Active Listening have seen a 10x increase in survey responses and are resolving issues 62% faster. With the ability to customize every aspect of the feedback process—whether through AI or predefined rules—Active Listening ensures that businesses get the insights they need while maintaining full control over the process.

Elevate Your Feedback Collection with Active Listening

Ready to take your feedback collection to the next level? Active Listening is your key to deeper insights, better engagement, and stronger results.

Speech Analytics: Turning Conversations into Actionable Insights

Speech analytics encompasses the transformation and analysis of audio recordings into text. This analysis provides businesses with key consumer insights, such as emotional tags and trending complaints. It can be used by businesses to understand the customer experience and make business improvements.
InMoment Contact Center intelligence solution for faster action and better insights

Speech analytics is quickly becoming a foundational aspect of successful experience improvement programs. Historically, it has been difficult to quantify metrics from customer calls. However, the rise of speech analytics has given businesses to understand their customers like never before. 

What is Speech Analytics?

Speech analytics is the process of analyzing recorded calls to gather customer information to improve communication and future interaction. It uses advanced technology to transcribe and analyze audio recordings. In doing so, speech analytics gives businesses the ability to uncover insights into customer behavior, sentiment, and preferences. This allows companies to enhance their customer service, marketing strategies, and overall operational efficiency. 

What is Contact Center Speech Analytics?

Contact center speech analytics specifically refers to the application of speech analytics technology in a call center environment. Usually working as part of contact center solutions, it involves analyzing the vast volumes of calls handled by contact centers to extract actionable insights from audio recordings. 

Contact center speech analytics is useful for businesses looking to improve their contact center performance and overall customer experience. The insights from recorded calls help identify common issues and train agents, which helps improve key customer experience metrics. 

By leveraging this technology, contact centers can turn every customer interaction into a valuable data point that drives continuous improvement.

How Does Speech Analytics Work?

Speech analytics combines several advanced technologies to analyze spoken language. The process involves multiple steps:

  • Capturing Audio: The first step is recording conversations between customers and agents. These recordings are stored for analysis.
  • Automatic Speech Recognition (ASR): ASR technology converts spoken words into text. This is a crucial step as it translates the audio data into a format that can be analyzed.
  • Natural Language Processing (NLP): Natural language processing algorithms process the transcribed text to understand the context, sentiment, and meaning behind the words. This involves parsing the text and identifying keywords, phrases, and patterns.
  • Machine Learning: Machine learning models analyze the processed text to extract insights. These models can identify trends, detect anomalies, and predict outcomes based on historical data.
  • Share Insights: The final step is generating reports and dashboards that present the insights in an easily digestible manner. These insights can be shared with managers, executives, or other stakeholders in order to make informed decisions. 

Why is Speech Analytics Important?

Speech analytics is important because it gives you a way of understanding your customers that may not have been previously accessible. A recent study showed that 86% of consumers preferred to talk to a real agent rather than a chatbot or AI-based system regarding customer service inquiries. If you did not have speech analytics software, the only way you would be able to identify common themes and trends is to manually listen to recordings. This is a costly and time-consuming process. 

With speech analytics and speech analytics software, you are still able to provide the consumer with the experience they are looking for, while also getting the analysis and data that is important to your business. 

What are the Benefits of Speech Analytics?

The benefits of speech analytics extend beyond just sentiment analysis. These tools can help you train employees, develop marketing campaigns, and invest in business improvement that will make an immediate impact. Here are some common benefits of utilizing speech analytics:

Improved Agent Performance

Speech analytics can be a tool to help benchmark agent performance. The insights from speech analytics can help identify the strengths and weaknesses of agents. Businesses can identify best practices to be taught in onboarding and training sessions by analyzing interactions with multiple agents.

A dashboard showing a call center agent's performance in the current month comapred to the previous month.

Enhanced Customer Understanding

By utilizing speech analytics software, businesses can gain a deeper understanding of their customers by analyzing the context and content of their conversations. They have the ability to view a conversation in its entirety and identify specific emotional tags that arose throughout the course of the case. Identifying these themes will help companies meet customer expectations, which will lead to increased customer satisfaction and loyalty. 

Real-time Problem Resolution

With real-time speech analytics, businesses can detect and address issues as they occur during customer interactions. This immediate insight allows supervisors to intervene when necessary, preventing escalation and improving the chances of resolving issues on the first call. Real-time analytics also helps in managing high-stress situations and ensuring that customer concerns are addressed promptly and effectively.

Speech Analytics Uses Cases & Examples

The applications of speech analytics are not limited to one industry or use case. This form of analytics has a wide range of capabilities that can be used from healthcare to e-commerce.  The following examples highlight different speech analytics use cases

Speech Analytics in Healthcare

Consider a large healthcare contact center that handles patient inquiries, appointment scheduling, prescription refills, and other services. The nature of this contact center means that they handle thousands of calls daily, and manually listening to the recordings would be an ineffective use of time. 

By implementing speech analytics software, all incoming and outgoing calls can be recorded and stored securely in compliance with HIPAA regulations. These calls are transcribed into text, analyzed, and categorized by common tags such as calls related to appointments, billing, or prescription refills. 

After the calls are categorized, the trends can be analyzed. Perhaps this call center notices that over the last 90 days, there has been an increase in calls related to long wait times. These insights are shared with hospital management, which may result in an increase in staffing during certain hours to decrease wait times. By utilizing speech analytics, this healthcare organization was able to increase patient satisfaction. 

Speech Analytics in E-Commerce

Imagine an e-commerce company that operates an omnichannel contact center. This contact center handles customer inquiries across multiple channels such as phone, email, online chat, and even review management. This contact center may already have a text analytics solution in place to tag and categorize customer feedback in the form of text data. However, being unable to effectively tag customer calls, these are left out of the data. This leads to an incomplete picture of the e-commerce customer experience. 

By implementing speech analytics, customer calls can be recorded in a way that allows them to be added to the rest of the contact center customer data. This provides the business with a complete view of its customer experience and helps identify areas of improvement. 

What is Next For Speech Analytics?

While speech analytics is not necessarily in its infancy, it has been no stranger to increased growth. The future of speech analytics is closely tied to advancements in AI and machine learning. These AI algorithms will enable better detection of emotions and sentiments. You can expect speech analytics to improve in two major ways in the coming future: 

AI Speech Analytics

AI speech analytics can be used as another way to describe the current capabilities of speech analytics because of the use of machine learning. However, AI speech analytics will continue to grow by going beyond just categorizing audio recordings. After identifying different emotional tags and trends, AI speech analytics will be able to quickly summarize the findings and offer immediate areas for improvement. This, as opposed to presenting the data and requiring human input, will make the end-to-end process of speech analytics quicker. 

Of course, as these capabilities grow, there will be errors, and all important data and decisions should be human-reviewed. That being said, the continued growth of speech analytics will most likely result in the further optimization and improvement of businesses. 

Holistic Analytics

Holistic analytics represents the combination of speech analytics, text analytics, and video analytics. Putting these solutions together will make it easier for organizations to understand their customers, regardless of the channel they choose to communicate with them from. Also, as a packaged deal, it will be more cost-effective and most likely provide a better return on investment. 

What to Look For in Speech Analytics Software

Not all speech analytics software comes with the same features. The features that are the most important will depend on the specific goals of your business. However, there are certain features that should be must-haves when looking for a speech analytics solution. 

Diverse Analysis Capabilities

While some businesses may settle for a speech analytics solution that is limited to only analyzing the customer during the call. Advanced speech analytics solutions will have a wide range of analysis capabilities that are able to analyze customer emotions as well as how the agent is responding to the customer and how the two parties interact. By analyzing the conversation in its entirety, you gain access to a more complete analysis. 

A conversation between a contact center agent and a customer, where AI speech analytics has highlighted customer sentiment.

Integration Capabilities 

It is important to choose a solution that supports a wide range of integrations, such as integrating into your contact center analytics software. Being able to implement speech analytics software that works alongside your current systems will increase the adoption rate within the organization and get the most out of the software as soon as possible. 

Support

It is important to choose speech analytics software that comes with a dedicated support team. This is important because having expert support ensures that any issues or challenges are promptly addressed. 

Best Practices for Implementing Speech Analytics Software

In order to complete a smooth implementation of speech analytics software, it is best to have a plan. Having an implementation plan will make sure the organization can succeed with the software, and get all appropriate staff involved in the right order. 

1. Define Objectives

By defining your objectives, you clearly outline what you are aiming to achieve. This will help you stay focused and avoid any distractions that come up in the early stages of adoption. For example, you may be tempted to set up a feature you did not fully understand in the demo. But, doing so wouldn’t allow your team to use the core functionalities they need. By having a goal, you can make sure your team has what they need as soon as possible. 

2. Integrate with Existing Systems

During the implementation process, it is vital to ensure that the software works well within your current technology stack. If there is an issue, contact your support team as soon as possible to try and get the issue resolved. 

3. Train Employees

As soon as the software is accessible to all employees, provide comprehensive training on how to use the software and interpret its insights. This step is crucial to the adoption across the company. Avoiding this will cause employees to make mistakes, which may take more time to resolve. 

4. Monitor

After the initial implementation push is over, monitoring the chosen software is important to its continued success. As it becomes a more integral part of your business processes, certain discrepancies may arise that need to be addressed. Conversely, all early successes with the software need to be highlighted so they can be recreated in the future. 

Choose InMoment as Your Speech Analytics Solution

InMoment’s conversation analytics software allows your business to have access to state-of-the-art speech analytics software, as well as other capabilities such as AI summarization, agent and coach scorecards, and more! See what InMoment can do for you by scheduling a demo today. 

References 

CGS. CGS Survey Reveals Consumers Prefer a Hybrid AI/Human Approach to Customer Service. Is there Chatbot Fatigue? (https://www.cgsinc.com/en/resources/2019-cgs-customer-service-chatbots-channels-survey). Accessed 6/27/2024.

REPORT

InMoment Named a Leader in The Forrester Wave™: Text Mining And Analytics Platforms, Q2 2024

Learn how InMoment is pioneering innovative solutions for businesses to extract insights and drive meaningful change from their unstructured text data.

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Predictive Analytics: Unveiling the Future with Data

Predictive analytics analyzes data to predict the likelihood of certain events happening in the future. Through predictive analytics software, businesses across all industries can understand their customers better and make more informed business decisions.
What Is Predictive Analytics

Organizations should take a closer look at predictive analytics to discover the myriad of ways that data and artificial intelligence (AI) can power more personalized customer experiences and enhance brand loyalty and customer retention. From a cost and ROI perspective, the impact and benefits of predictive analytics in customer experience management cannot be ignored. 

It’s an opportunity that your company can capitalize on today. According to Forrester, fewer than 10% of enterprises are advanced in their insights-driven capabilities. By equipping your organization with predictive analytics tools, you can gain rich insights into customer behavior, make data-driven decisions, and optimize business operations.

What is Predictive Analytics?

Predictive analytics is a category of data analytics and the process of using data, statistical algorithms, AI, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Put simply: it involves analyzing current and historical data to make predictions about future events or trends.

Advancements in computing power, storage, and algorithms, along with the rise of AI, have made predictive analytics more feasible and accessible to businesses of all sizes. Machine learning algorithms can analyze large datasets quickly and efficiently, enabling businesses to derive insights in real time.

For example, predictive analytics can examine text reviews from customers and predict what steps they are likely to take. Predictive models trained on large datasets of similar text inputs can learn to recognize such patterns and predict future behavior, such as making a purchase or churning.

A conversation between a customer and a representative from the company. Predictive analytics predicts the customer wants to buy

Predictive Analytics vs Prescriptive Analytics

It can be easy to confuse predictive analytics and prescriptive analytics. While they sound similar, they also go hand in hand with each other in practice. These two types of analytics are both designed to provide a comprehensive approach to data-driven decision-making. 

As mentioned earlier, predictive analytics is focused on forecasting future events, trends, or behaviors based on historical data. Conversely, prescriptive analytics goes a step further by not only predicting future outcomes but also recommending actions to achieve desired results. 

Prescriptive analytics combines predictive models with optimization algorithms and business rules, employing techniques such as simulation, optimization models, and decision analysis. These methods evaluate various possible actions and their outcomes to suggest the best course of action. 

Why is Predictive Analytics Important?

Predictive analytics is important because it empowers businesses to make informed decisions that enhance strategic planning and operational efficiency. By analyzing historical data to identify patterns and predict future outcomes, predictive analytics helps organizations anticipate trends, behaviors, and potential risks. This foresight enables businesses to proactively address issues before they become problems, optimize resource allocation, and improve overall performance.

For example, predictive analytics in healthcare enhances patient care by anticipating readmissions and improving diagnostic accuracy. This allows healthcare organizations to proactively manage patient outcomes, allocate resources more efficiently, and implement targeted interventions that reduce hospital stays and associated costs. By identifying at-risk patients early and providing personalized treatment plans, healthcare providers can improve overall patient health and satisfaction, ultimately leading to better clinical outcomes and a more sustainable healthcare system.

Benefits of Predictive Analytics in CX

Predictive analytics is also making an impact on the way companies manage the customer experience. By leveraging data-driven insights from predictive analytics, your company can foster meaningful connections with customers and achieve differentiation in today’s competitive marketplace. The wide-ranging benefits of predictive analytics applications in customer experience management include:

  • Enhanced customer loyalty and satisfaction. By predicting what customers want before they even ask for it, your company can provide a proactive and personalized experience that increases satisfaction and fosters loyalty. 
  • Improve customer lifetime value. Predictive analytics helps identify the most valuable customers and understand their behavior, allowing you to implement strategies that maximize the value these customers bring over their lifetime. 
  • Reduce customer churn. By identifying patterns that indicate a customer is at risk of leaving, you can take proactive measures to retain them, thereby reducing customer churn. 
  • Enhance cross-selling and up-selling opportunities. With predictive analytics, marketers can identify which customers are most likely to be interested in additional products or services, creating more opportunities for successful cross-selling and up-selling. 
  • Accelerate operational improvement. By enhancing the customer experience and making operations more efficient, predictive analytics contributes to accelerated business growth and increased profitability.

What is a Downside of Predictive Analytics?

While predictive analytics can be a powerful tool, organizations need to be aware of the potential downsides and take the proper steps to mitigate or eliminate them. Some of the possible  downsides of predictive analytics include:

  • Incorrect predictions: Predictive analytics relies heavily on the quality and completeness of the data. Inaccurate, outdated, or incomplete data can lead to wrong predictions, which may result in misguided decisions. 
  • Ethical and privacy concerns: Using personal data for predictive analytics raises significant ethical and privacy issues. Misuse or mishandling of sensitive information can lead to privacy violations and loss of customer trust. 
  • False positives and negatives: Predictive models are not perfect and can produce false positives (incorrectly predicting an event will happen) and false negatives (failing to predict an event that does happen). These inaccuracies can lead to inappropriate actions, such as unnecessary interventions or missed opportunities.

These downsides can often be handled and resolved through proper planning, implementation, and maintenance of predictive models. While organizations should be aware of these happenings, they should not deter them from utilizing predictive analytics in their operations. 

Examples of Predictive Analytics

Several predictive analytics examples show how the process is being applied by companies looking to better understand their customers, anticipate their needs, and deliver personalized and proactive experiences that drive satisfaction, loyalty, and ultimately, business success.

Predict Behavior and CLV

More and more retail brands are deploying predictive analytics software to forecast customer behavior and monitor market trends. 

Retailers can personalize the retail customer experience and increase sales by analyzing information such as past purchase history, browsing behavior, and demographic data. Brands can also leverage predictive analytics algorithms to analyze historical data and market trends, helping predict the optimal price points for products in order to maximize revenue while remaining competitive.

By recommending relevant products, delivering personalized content, and identifying cross-selling and up-selling opportunities based on individual customer profiles and purchase history, brands can create highly personalized retail experiences that drive customer lifetime value (CLV).

A review of a product where the words "renewal" and "impressed" are highlighted.

The key is to connect customer experience data from every touchpoint and channel for a complete view of the customer journey. Jim Katzman, Principal of CX Strategy & Enablement for InMoment, suggests that companies should “expand the data sources that you use to understand what your customers are saying and how they perceive you. While surveys will continue to be important, they only give you part of the picture. Expanding your data repertoire to such sources as purchasing data, location-tracking data, web searches, social media, and online reviews is a must.”

The next step is to take a long view when looking at customer relationships. Adds Katzman, “You’ll be surprised at how many brands get caught up in the lure of ‘What can I sell you today?’ without considering what seeds to plant for even more success tomorrow.”

“Equally important is to understand how your competitors view this dynamic and what, if anything, they’re also doing to be proactive when it comes to building lifetime value.

Score Leads by Analyzing Customer Data

Another great application example of predictive is lead-scoring marketers leveraging historical data and machine learning algorithms to predict the likelihood of leads converting into customers. Today more than ever, marketers are empowered to make data-driven decisions when scoring and prioritizing leads, resulting in more effective lead management, higher conversion rates, and improved overall sales and marketing performance.

  • Identify Ideal Customer Profiles (ICPs). Predictive customer analytics tools can analyze historical data to identify patterns and characteristics common among high-value customers. By identifying these attributes, marketers can create an ideal customer profile (ICP) that serves as a benchmark for scoring leads based on their similarity to the ICP.
  • Assign predictive lead scores. Marketers are also utilizing statistical algorithms to analyze various data points such as demographics, firmographics, online behaviors, engagement with marketing content, and past purchase history to assign a predictive score to each lead. This score indicates the likelihood of a lead becoming a customer based on similarities to past successful conversions.
  • Prioritize sales efforts. Marketers can use predictive analytics to prioritize leads based on their likelihood to convert. Leads with higher predictive scores can be routed to sales teams for immediate follow-up, while leads with lower scores can be nurtured through targeted marketing campaigns until they demonstrate stronger buying signals.
  • Reduce sales cycle length. Predictive lead scoring enables marketers to identify leads that are further along in the buying process and more likely to make a purchase. By prioritizing these leads for immediate engagement, marketers can accelerate the sales cycle and shorten the time to conversion, leading to faster revenue generation and increased productivity for sales teams.

Harness NLP and Sentiment Analysis to Monitor Brand Reputation

Predictive analytics can also have a significant impact on brand reputation management efforts, helping companies anticipate, monitor, and respond to potential reputation threats more effectively. 

Algorithms, for example, can analyze large volumes of data from various sources such as social media and online reviews to gauge customer sentiment toward the brand. By identifying patterns and trends in sentiment data, teams can proactively address emerging issues or negative perceptions before they escalate into major reputation crises.

These analytical techniques help crystallize information contained in reviews into insights — helping companies achieve a more accurate, complete, and unified view of the customer.

With online reputation management software, companies can also analyze customer feedback and sentiment data to identify areas for improvement and proactively address customer concerns. By identifying recurring themes or issues in customer feedback, brands can take corrective actions to improve products, services, and overall customer experience, which in turn enhances brand reputation. 

InMoment’s approach is based on machine learning, a method of data analysis that allows companies to find patterns and unlock insights as it is exposed to new review and feedback data. This approach is fast, consistent, and programmable, helping teams quickly understand — at a glance and at scale —  exactly what customers are saying. Proprietary relevancy scores for sentiment analysis also provide measurement of positive and negative language, with unparalleled accuracy.

Use AI to Improve Personalization

Predictive analytics empowers companies to better understand their customers, anticipate their needs, and deliver personalized experiences. It’s a particularly powerful tool for curating content based on historical customer data. 

One of the best predictive analytics examples comes from streaming giant Netflix, which has a powerful personalized content recommendation engine. The company analyzes user data, including viewing history, ratings, and browsing behavior, to make predictions about what users might want to watch next. This is all reflected as soon as viewers land on Netflix’s home page, which displays content tailored to individual users, improving user engagement and satisfaction.

With predictive analytics, teams can dynamically customize website content, email marketing campaigns, and other communication channels based on individual customer preferences and behaviors. By delivering content that is relevant and timely, businesses can improve personalization, create more engaging customer experiences, and drive higher conversion rates.

Extract Insights from Reviews and Social Media Data

Online reviews and social media data provide a wealth of insights for a business but can be labor-intensive to read through and digest. There are many ways to try to automate this task. Currently, the leading approaches use deep learning models that extract many different kinds of keywords, predict their sentiment, and classify them into relevant categories. This allows companies to improve operations, make better decisions, and elevate the customer experience with data.

Using AI and advanced machine learning techniques, predictive analytics tools can read through thousands of reviews, comments, and other forms of customer feedback in the time it would take a human to read through just a few. The right technology will provide valuable insights, summaries, trends, and statistics that can be applied to support data-driven decision-making and customer-centric innovations.

Rural King, a family-owned farm supply store with 128 stores across 13 states, is no stranger to leveraging predictive analytics in order to create memorable customer experiences. The company regularly analyzes massive amounts of unsolicited feedback to unlock the potential of all its stores’ review data.

“We are hearing directly from customers about the store experience as well as pricing and product challenges,” says Kirk Waidelich, VP of Marketing for Rural King. “This allows us to narrow in on the stores that are experiencing issues — and to target and understand these issues versus simply guessing.”

What to Look for in Predictive Analytics Software

Predictive analytics software allows users to complete predictive analysis. This software can be used by different professionals across many different industries.  Predictive analytics software will come with different features, and which specific features will work best for you depends on the goal of your business. However, there are a few foundational features that any successful software will have. 

Data Collection and Integration

Data collection and integration is a crucial aspect of predictive analytics software. The feature facilitates the collection of data from various sources, ensuring comprehensive coverage for analysis. It allows users to connect to databases, extract data from APIs, import data from spreadsheets, and integrate data from different systems within the organization.

Data Preprocessing and Cleaning

Another fundamental feature of predictive analytics software is the ability to preprocess and clean data. This allows users to address common data quality issues such as missing values, outliers, duplicate records, and inconsistencies. This feature can also provide automated mechanisms to detect and handle missing values, either by imputing them using statistical techniques or by removing them based on predefined rules. This ensures that the data used for predictive modeling is complete and accurate.

In addition, these features should support outlier detection and treatment. Outliers are data points that deviate significantly from expected patterns. Outlier detection features can identify these outliers and remove them, transform them, or treat them as separate categories based on previously implemented rules or requirements.  

Machine Learning Algorithms 

Effective predictive analytics software incorporates a wide range of machine learning algorithms, which provides users with powerful tools to build accurate and reliable predictive models. These algorithms form the backbone of a software’s capabilities and enable users to leverage the predictive power of their data. 

Model Training and Evaluation 

Predictive analytics software should also provide robust functionalities for model training and evaluation, enabling users to build accurate predictive models and assess their performance effectively.

To ensure optimal model performance, software should have options to fine-tune the model’s parameters and settings. Users can experiment with different configurations and optimize the model to achieve the best possible results. This customization capability allows users to adapt the model to their specific use case, maximizing its predictive accuracy and relevance.

Once the model is trained, the software facilitates a thorough evaluation of its performance. Users can assess how well the model generalizes to unseen data by employing various evaluation techniques, such as cross-validation. Cross-validation involves splitting the data into multiple subsets, training the model on a portion of the data, and evaluating its performance on the remaining subset. This process helps estimate the model’s predictive accuracy and identify any potential overfitting or underfitting issues.

Visualization and Reporting Capabilities

Lastly, predictive analytics software should offer robust visualization and reporting capabilities to help users understand and communicate insights effectively, which helps transform complex data into intuitive visual representations and actionable reports.

Users should be able to easily create visual representations of their data, allowing for quick and comprehensive analysis. Visualization options often include bar charts, line charts, scatter plots, heat maps, and geographic maps, among others. These visualizations enable users to identify patterns, trends, and relationships within the data, facilitating deeper insights and understanding.

Furthermore, predictive analytics software should support interactivity in visualizations, allowing users to explore data from different perspectives and drill down into specific subsets of information. Users can interact with the visualizations, apply filters, and dynamically adjust parameters to gain more detailed insights and make data-driven decisions.

Predictive Analytics Implementation and Best Practices

Implementing predictive analytics involves a structured approach to ensure that the data-driven insights generated are accurate, actionable, and aligned with business goals. Here are some key steps and best practices for successful predictive analytics implementation:

1. Define Clear Objectives

Before embarking on a predictive analytics project, it’s essential to clearly define the objectives. Determine what specific outcomes you want to achieve and how predictive analytics will help you reach these goals. Whether it’s improving customer retention, optimizing inventory management, or reducing operational costs, having a clear objective will guide the entire process.

2. Assemble the Right Team

Successful implementation requires a team with diverse skills, including data scientists, data engineers, domain experts, and IT professionals. Data scientists and engineers are crucial for building and maintaining the predictive models, while domain experts ensure that the insights generated are relevant and actionable. IT professionals play a key role in integrating predictive analytics tools with existing systems.

3. Foster a Data-Driven Culture

For predictive analytics to be truly effective, it must be embraced across the organization. Encourage a data-driven culture by promoting the use of data in decision-making processes. Provide training and resources to employees to help them understand and leverage predictive analytics insights.

Jumpstart Your Predictive Analytics Solution With InMoment

The world’s top brands partner with InMoment AI, the leading predictive customer analytics solution, to facilitate the discovery of real-time insights, drive individual customer recovery, and turn unstructured feedback into a predictable source of business growth. To see how what predictive analytics can do for your business, schedule a demo today!

References 

Forrester. “Data Governance Unlocks The Impact Of Analytics: Data Strategy & Insights 2023” (https://www.forrester.com/blogs/data-governance-unlocks-the-impact-of-analytics-data-strategy-insights-2023/). Access 03/16/2024.

A group of four business people having a discussion around a table

With the increased adoption of AI in business across all industries, there has also been a rise in text mining and analytics. This software, which exists as an extension of AI and natural language processing (NLP), is used to gather insights from unstructured text data in order to make informed business decisions. 

If your business has reached the need to purchase text analysis software, you are more than likely comparing third-party evaluations as part of your research process. Understanding these third-party evaluations is crucial to choosing the right software for your business. Among these evaluation tools are evaluative Analyst reports such as The Forrester Wave, Gartner Magic Quadrant, or IDC MarketScape

The Forrester Wave™ is a valuable resource that evaluates and ranks vendors in a particular market, but understanding how to read and interpret the Wave report can be daunting. By reading this guide, you will understand how to navigate reports like the Forrester Wave and make informed decisions from the reports’ implications. 

What is the Difference Between Gartner and Forrester?

The Forrester Wave™ and the Gartner Magic Quadrant™ are widely recognized and influential market research reports evaluating technology vendors. While both serve to help buyers make informed decisions, they differ in methodology, structure, and focus.

In the Forrester Wave, vendors are ranked based on criteria such as their strategy and current offering, which represent the x and y axes. They are also ranked on market presence, which is represented by the size of the dot on the graphic. For each of these three categories, there are subcategories that vendors are scored on. These scores are taken into account and then vendors are positioned in segments such as Leaders, Strong Performers, Contenders, and Challengers on the Wave graphic. 

Conversely, the Gartner Magic Quadrant™ offers a high-level overview, evaluating vendors based on their Completeness of Vision and Ability to Execute. Vendors are placed in one of four quadrants: Leaders, Challengers, Visionaries, and Niche Players. The Magic Quadrant is useful for quickly comparing vendors and understanding the overall market landscape and strategic positioning.

Ultimately, the Forrester Wave™ is best for buyers seeking a detailed, customizable evaluation, while the Gartner Magic Quadrant™ is suited for those needing a quick, strategic overview of vendor capabilities and market trends. Understanding these differences helps buyers select the right tool for their specific needs.

Understanding the Forrester Wave™ Methodology

The Forrester Wave™ is a comprehensive evaluation of technology providers in a specific market. For text analytics, it assesses vendors based on a detailed set of criteria to provide a comparative analysis. Here’s how it works:

  1. Vendor Selection: Forrester selects the most significant vendors from the preceding Landscape report which acts as a precursor to the evaluative Wave and outlines market dynamics, top business use cases, and provides a list of ‘players’
  2. Criteria and Weightings: Forrester defines a set of criteria that comprise the two categories of strategy and current product offering. Weightings of these criteria (how much each one is worth) are not shared with vendors until post-publication… Each criterion is assigned a weight based on its importance to the overall evaluation.
  3. Data Collection:There are three inputs into a Forrester Wave evaluation: a questionnaire, a strategy and product demo session, and customer references.
  4. Scoring: Each vendor is scored on a scale (0 to 5) for each criterion. These scores are then weighted and combined to produce an overall score for each category.
  5. Wave Graphic: The scores are plotted on a wave graphic, with vendors positioned in different segments: Leaders, Strong Performers, Contenders, and Challengers. The size of the dots are representative of the vendors’ market presence, which is determined by revenue. 

Decoding Forrester Wave™ Classifications

The Forrester Wave graphic visually represents the relative strengths and weaknesses of each vendor. Here’s what the graphic for each Wave looks like as well as what each classification means:

An example of the Forrester Wave graphic showing how vendors are represented.
  • Leaders: These vendors have the highest scores in the evaluation criteria. They exhibit strong current offerings, robust strategies, and a significant market presence. Leaders are generally the safest choice for most buyers.
  • Strong Performers: Vendors in this segment have solid offerings and strategies but may lack in some areas compared to leaders. They are still viable options, especially if they meet specific needs or have unique strengths.
  • Contenders: These vendors may have competitive offerings but are often limited by weaker strategies or lower market presence. They can be suitable for buyers with specific requirements that align with the vendor’s strengths.
  • Challengers: Vendors in this category typically have lower scores across multiple criteria. They may be newer to the market or lack certain features. They are riskier choices but might offer innovative solutions or cost advantages.

Key Components of the Forrester Wave for Text Analytics

The full Forrester Wave report will consist of three main sections: current offering, strategy, and market presence. Each category will cover different aspects of an organization’s presence in the marketplace. 

Current Offering: This category evaluates the product’s features and capabilities. Key criteria might include:

  • AI: ML-based, knowledge-based, or symbolic
  • Generative AI: Pre and post-processing 
  • Deployment options
  • Omnichannel data integration
  • Security and regulatory compliance 

Strategy: This category assesses the vendor’s vision and roadmap. Key criteria might include:

  • Innovation: The vendor’s commitment to innovation and staying ahead of market trends.
  • Product Roadmap: The planned future developments and improvements.
  • Pricing flexibility and transparency

What This Means for Buyers

As a buyer, the Forrester Wave for Text Analytics provides a comprehensive and unbiased assessment of the market. Here’s how you can use it:

  1. Identify Your Needs: Determine what’s most important for your organization. Are you looking for a platform with advanced NLP capabilities? Or is integration with existing systems more critical?
  2. Compare Vendors: Use the Wave graphic to compare vendors at a glance. Focus on the Leaders for well-rounded options, but don’t overlook Strong Performers if they align better with your specific needs.
  3. Dive Deeper: Read the detailed vendor profiles and scores for a deeper understanding of each vendor’s strengths and weaknesses. Pay attention to how vendors perform in areas that matter most to your organization.
  4. Evaluate Market Trends: Consider the market trends and how vendors plan to adapt to them. For example, vendors will no longer differentiate themselves on text mining functionality alone, it is the pre and post-processing processes that will set them apart. 
  5. Consider Future Needs: Look at the strategy scores and product roadmaps to ensure the platform you choose will continue to meet your needs as your organization grows and evolves.

InMoment’s Placement in the Forrester Wave

InMoment was recently recognized as a Leader in the Forrester Text Mining & Analytics Wave ‘24. This achievement highlights the capabilities of the XI Platform such as knowledge-based AI, document-level text mining, natural language understanding, and more!

To learn more about InMoment’s platform, schedule a demo today! 

Text Mining: Everything You Need to Know

Text mining is the process of extracting useful information from large amounts of text using computational techniques. It involves analyzing and transforming unstructured text into structured data for insights.
Two hands typing on a laptop

In today’s data-driven world, businesses generate and accumulate vast amounts of text data from various sources, including customer feedback, social media, emails, and internal documents. However, extracting meaningful insights from this unstructured data can be challenging. 

This is where text mining comes into play. By transforming unstructured text data into valuable information, text mining enables businesses to uncover hidden trends, sentiments, and relationships within the data. This process is crucial for making informed decisions, enhancing the customer experience, and maintaining a competitive edge. 

What is Text Mining?

Text mining, also known as text data mining, is the process of analyzing unstructured text data to extract meaningful patterns and insights. This process involves using techniques from natural language processing (NLP), machine learning, and statistics to transform textual information into a structured format that can be easily analyzed. By doing so, organizations can uncover hidden trends, sentiments, and relationships within the data, which can inform strategic decisions and drive business growth.

Text Mining Examples and Use Cases

Consider a business interested in contact center optimization. They could implement text mining to enhance operations and improve customer satisfaction. The center can identify common customer issues and frequently asked questions by analyzing transcripts of customer service calls, emails, and chat interactions.

From those insights, the contact center can pinpoint areas where agents need additional training and identity processes that require streamlining. For instance, text mining might reveal that a significant number of calls were related to the same few technical issues. This discovery can lead to bug fixes as well as a more comprehensive knowledge base for agents, which can significantly reduce call resolution times. 

Why is Text Mining Important?

In an era where data is considered the new oil, the ability to analyze and derive insights from unstructured text data is invaluable. Text mining is important for several reasons:

1. Extracting Valuable Insights: Text mining enables businesses to sift through large volumes of unstructured text data and extract valuable insights. Whether it’s customer feedback, social media comments, or internal documents, these insights can reveal trends, sentiments, and patterns that are crucial for strategic decision-making.

2. Enhancing Customer Experience: By analyzing customer feedback and sentiment, companies can better understand their customers’ needs, preferences, and pain points. This understanding allows businesses to tailor their products, services, and interactions to meet customer expectations, thereby enhancing overall customer satisfaction and loyalty.

3. Improving Operational Efficiency: Text mining can help identify inefficiencies and areas for improvement within an organization. For example, analyzing support tickets and emails can reveal common issues that need addressing, enabling companies to streamline their operations and improve service quality.

4. Supporting Data-Driven Decision Making: Text mining transforms unstructured data into structured data that can be easily analyzed and visualized. This transformation supports data-driven decision-making processes by providing actionable insights that are grounded in actual data rather than intuition or guesswork.

5. Gaining Competitive Advantage: By leveraging text mining, businesses can stay ahead of the competition by quickly identifying market trends, customer preferences, and emerging issues. This proactive approach allows companies to adapt and innovate faster than their competitors.

6. Enabling Predictive Analytics: Text mining can also be used in conjunction with predictive customer analytics to forecast future trends and behaviors. For instance, sentiment analysis of customer reviews can predict future product success, while topic modeling can identify emerging trends in customer interests.

Difference Between Text Mining and Text Analytics

While text mining and text analytics are often used interchangeably, they have distinct focuses and processes. Understanding the difference between the two can help businesses leverage the right techniques for their specific needs.

Text Mining

Text mining is the process of discovering patterns and extracting useful information from unstructured text data. It involves transforming text into a structured format, which can then be analyzed. The primary goal of text mining is to uncover hidden insights and trends that are not immediately obvious.

Key Components of Text Mining:

  • Data Collection: Gathering text data from various sources such as websites, social media, emails, and internal documents.
  • Preprocessing: Cleaning and preparing the text data by removing noise, normalizing text, and tokenizing.
  • Transformation: Converting text into a structured format using techniques like vectorization.
  • Analysis: Applying NLP, machine learning, and statistical methods to identify patterns and extract insights.

Text Analytics

Text analytics is the application of text mining techniques to solve specific business problems. It involves analyzing the structured data produced by text mining to gain actionable insights and inform decision-making. Text analytics often integrates text mining results with other types of data analysis to provide a comprehensive understanding of the data.

Key Components of Text Analytics:

  • Integration: Combining text data with other data sources to provide a holistic view.
  • Visualization: Presenting the findings in a comprehensible format using graphs, charts, and dashboards.
  • Reporting: Generating reports that highlight key insights and recommendations.
  • Actionable Insights: Using the analyzed data to inform business strategies and decisions.

Consider a company analyzing customer reviews to improve its products. Text mining would involve processing the reviews to identify common themes and sentiments. Text analytics would then take these findings and integrate them with sales data to understand the impact of customer feedback on product performance and make strategic recommendations.

How Text Mining Works

Text mining involves several steps that transform unstructured text data into structured data, which can then be analyzed to extract meaningful insights. Here is a detailed look at the key steps involved in the text mining process:

1. Data Collection: The first step in text mining is gathering text data from various sources. This can include customer feedback, social media posts, emails, online reviews, internal documents, and more. The data collection process may involve web scraping, database extraction, or API integration to aggregate the text data into a single repository.

2. Preprocessing: Once the data is collected, it needs to be cleaned and prepared for analysis. Preprocessing involves several sub-steps:

  • Tokenization: Splitting the text into individual words or tokens.
  • Stop Words Removal: Eliminating common words (e.g., “and”, “the”, “is”) that do not contribute to the analysis.
  • Stemming and Lemmatization: Reducing words to their root form (e.g., “running” to “run”).
  • Normalization: Converting text to a standard format, such as lowercase all words and removing punctuation and special characters.

3. Transformation: After preprocessing, the text needs to be transformed into a structured format. This often involves:

  • Vectorization: Converting text into numerical vectors that represent the frequency or presence of words or phrases. Common techniques include Term Frequency-Inverse Document Frequency (TF-IDF) and word embeddings like Word2Vec.
  • Feature Extraction: Identifying and extracting relevant features from the text that can be used in subsequent analysis.

4. Analysis: With the structured data in hand, various analytical techniques are applied to extract insights:

  • Natural Language Processing (NLP): Techniques such as named entity recognition (NER), part-of-speech tagging, and dependency parsing to understand the structure and meaning of the text.
  • Machine Learning: Applying algorithms to classify, cluster, and predict outcomes based on the text data. Common methods include sentiment analysis, topic modeling, and text classification.
  • Statistical Analysis: Using statistical methods to identify patterns, correlations, and trends within the text data.

5. Visualization: The final step is to present the findings in an easily understandable format. Visualization tools and techniques are used to create graphs, charts, word clouds, and dashboards that highlight key insights and trends. Effective visualization helps stakeholders quickly grasp the results and make informed decisions.

A compilation of images showing a word cloud and analysis produced from text mining

Text Mining Best Practices

Implementing text mining effectively requires adherence to several best practices to ensure accurate, actionable insights and optimal outcomes. By following these best practices, organizations will be set up for success in utilizing text mining effectively.  

1. Define Clear Objectives

Set clear, specific goals for what you want to achieve with text mining. Whether it’s enhancing customer experience, identifying market trends, or detecting fraud, having well-defined objectives will guide your project and measure success.

2. Select the Right Tools

Choose tools and software that align with your project requirements and team expertise. It is important to find text mining software that has all the necessary features to complete the projects you are working on.  

3. Data Quality and Diversity

Ensure that the text data you collect is relevant, high-quality, and diverse, drawing from sources such as customer feedback, social media, emails, and internal documents. Gathering data from multiple sources can decrease the chances of voluntary response bias, or other biases that can damage the integrity of your data. Comprehensive preprocessing is equally important; this includes cleaning the data to remove noise, normalizing text formats, and applying techniques like tokenization, word removal, and stemming/lemmatization to prepare the data for analysis.

4. Effective Data Preprocessing

Preprocess your text data meticulously. Clean the data by removing noise, standardizing text formats, and applying tokenization, stop-word removal, and stemming/lemmatization to prepare the text for analysis.

5. Ethical Considerations

Adhere to ethical standards and data privacy regulations. Anonymize sensitive information, obtain necessary consent, and address biases in your text data and models to ensure fairness and compliance.

Common Use Cases of Text Mining

Text mining has a wide range of applications across various industries. Here are some common use cases where text mining can provide significant value:

1. Customer Feedback Analysis

Businesses receive feedback from customers through various channels such as surveys, reviews, and social media. Text mining helps analyze this feedback to identify common themes, sentiments, and areas for improvement. For example, a company can use text mining to detect recurring complaints about a product feature and take corrective action.

2. Sentiment Analysis

Sentiment analysis involves determining the sentiment behind a piece of text, whether it’s positive, negative, or neutral. This is particularly useful for brands to monitor their reputation online. By analyzing customer reviews, social media posts, and other textual data, businesses can gauge public perception and respond accordingly.

3. Topic Modeling

Topic modeling is a technique used to discover the underlying topics within a large corpus of text. It helps in organizing and summarizing large collections of textual information. For example, a news organization can use topic modeling to automatically categorize articles into topics like politics, sports, and entertainment.

4. Fraud Detection

In sectors like finance and insurance, text mining is used to detect fraudulent activities. Text mining can identify suspicious patterns and flag potential fraud by analyzing claims, transaction records, and customer communications. This proactive approach helps in preventing fraud before it causes significant damage.

5. Market Research

Companies use text mining to analyze consumer opinions and market trends. By examining social media posts, reviews, and forums, businesses can gain insights into consumer preferences and behaviors. This information is valuable for product development, marketing strategies, and competitive analysis.

Implement Text Mining with InMoment

InMoment’s XI Platform has been recognized as one of the premier text-mining software solutions. Having recently been named a Leader in the Forrester Wave™: Text Mining and Analytics, the XI platform was noted as having capabilities that outperform competitors such as Qualtrics, AWS, and Google. To see what our text mining capabilities can do for you, schedule a demo today!