How Conversational Analytics Works And How You Can Implement it
As businesses become more digital, conversations now happen across chatbots, social media, emails and messaging apps. This creates a ton of data with potential value for the business. Conversational analytics unlocks the value in this data by using AI, such as natural language processing (NLP) and machine learning, to make it easy to find business insights.
Here, we'll explain how conversational analytics works, the benefits and how to implement it in your business so you can make informed decisions to improve customer experiences and products.
Before we get into the how-to part of the post we need to cover some basics so let’s get started.
What is Conversational Analytics?
Conversational analytics is the process of analyzing customer conversations to extract insights about customer behavior, preferences and overall sentiment. Conversational analytics uses Artificial Intelligence (AI) methods, such as Machine Learning, Natural Language Processing (NLP) and various algorithms to transcribe and analyze thousands of voice calls, chat interactions, social media engagements and more.
By analyzing these interactions at scale, businesses can find patterns in customer behavior, gauge sentiment and ultimately improve customer experiences.
Do not confuse Conversational Analytics with Conversational AI. Conversational AI refers to various chatbots that help a business efficiently serve customers and prospects. However, the output from Conversational Analytics can be useful to build and fine-tune Conversational AI.
What's the difference between traditional analytics and conversational analytics?
The traditional contact center relies on voice as the only or main customer service channel. They mainly generate KPI contact center data, such as average call times, wait times, and first call resolution from sales conversations. Typically, companies use statistical analysis and text analytics to assess operational efficiency and to glean the customer service experience. Since the data is gathered in a somewhat piecemeal fashion and analyzed in silos, the traditional analysis approach to analyzing conversations limits the quality and scope of insight.
With the rise of AI and new technologies, companies can now use software built specifically to analyze all the conversations channels in modern contact centers. With the tools and AI to organize, analyze and present the data from calls, chatbots, email, and text message channels, it's now possible to get much more comprehensive insights.
Conversational analytics differs from traditional analytics in its depth and timeliness. Traditional analytics uses text analytics methods to get insights from historical sales conversations, delivering a high-level and broad view of the call center. This can be useful for understanding long term trends and overall contact center dynamics. But, since the data is gathered in a piecemeal fashion from historical data, the insights can quickly become outdated.
Conversational analytics delivers in-depth business insights by using AI to transcribe & analyze customer-agent interactions. The data can be analyzed in real-time giving managers timely oversight of agent performance, call volume trends, customer satisfaction and more. Insights from Conversational analytics can be extremely effective for guiding agents to make better decisions in live interactions, building a deeper understanding of the customer and reducing operational costs through data-driven problem-solving. Getting this quality of insights is something you can't get from traditional contact center analytics.
Feature/Aspect | Conversational Analytics | Traditional Analytics |
Focus | All customer-agent interactions and CRM data | Phone conversations and customer profiles |
Data Sources | Recent conversations across calls, chat, text message, emails (e.g., customer-agent interactions) | Historical records (e.g., sales, customer profiles) |
Timing | Recent | Retrospective |
Insights Provided | Deep understanding of customer pain points and needs, identify agent training requirements and inefficiencies, identify emerging issues, and more | High level contact center insights, over time |
Use Case | Improving customer satisfaction during interactions, improving agent behavior, reducing operational costs | Understanding long-term trends and business dynamics |
Immediacy | High - analyzes very recent data | Low - insights developed over longer periods |
What's the difference between speech analytics and conversational analytics?
Speech analytics and conversational analytics both extract insights from customer conversations, but with a slightly different focus.
Speech analytics assesses the acoustic elements of speech – tone, pitch, speed – as well as the words spoken during customer service calls. Its main use is to measure call quality and agent performance.
Conversational analytics goes beyond voice to include chats, texts and other forms of communication. The analysis includes the context of the interaction, the sentiment and intent behind conversations, as well as the acoustic properties.
Historically, speech analytics methods were simplistic, analyzing the text for keywords. With the rise of AI, conversational analytics provides a more complete view of interactions, to support the contact center in achieving it's goals.
How conversational analytics fits into the Voice of the Customer (VoC)
Contact center managers and quality assurance (QA) specialists are moving from speech analytics to conversational analytics. But the new methods also unlock value for Customer Experience and Research teams responsible for the Voice of the Customer (VoC).
Conversational analytics is gradually becoming a key part of Voice of the Customer (VoC) programs which aim to capture customer expectations, preferences and aversions. Both overall, and for specific products, services, across locations, business unites, and customer touch points.
Traditionally, Voice of the Customer (VoC) programs collected data through surveys and interviews. But conversational analytics gives this team a more direct and comprehensive view of customer needs by looking at their actual words and interactions.
As a result, conversational analytics fills in the gaps left by other solutions and helps break down the silos within the organization.
What is Conversational Analytics Software?
Conversational analytics software is a solution that analyzes customer interactions across multiple communication channels – phone calls, chat sessions, social media, emails.
Conversational analytics software uses AI, NLP, sentiment analysis and machine learning to extract valuable information from customer interactions. It helps businesses understand customer feelings, spot important trends and make smart decisions to improve customer satisfaction and efficiency.
This software can be part of a larger solution, a services package, or a pointed solution to solve this problem. These are some examples of large solutions offering Conversational Analytics as an additional lens / feature:
- Call center software (Verint, Genesys, Talkdesk)
- Support software (Zendesk, Intercom)
- Feedback management software (Thematic, Qualtrics)
Business Process Optimization companies (BPOs) often provide in-house or sourced solution to deliver conversational insights to your agents and customers.
Pointed conversational analytics solutions include software such as:
- Observe.AI,
- Tethr, or
- CallMiner.
When choosing a solution we recommend to review what's most important to you:
- Having a single vendor for simplicity --> Go with a call center software or a BPO
- Unifying all feedback for a comprehensive view of customer needs --> Go with a feedback management solution
- Having a best-in-class solution for agent training --> Go for a pointed solution
Make sure to always run a test on your data to evaluate the lift of having to setup such a solution and train your staff to use it. Look out for ease (beautiful UI), automation (AI) and scalability of engineering.
Thematic
AI-powered software to transform conversational data at scale into useful business insights.
How it Works & How to Do it Step-by-Step
1. Choose your Conversational Analytics Tool
First and foremost to get the most out of conversational analytics you need to choose the right tool. This step will affect the speed and accuracy of your conversational analysis.
In this step-by-step approach, we will demonstrate how to set up and use the analysis of conversations - from importing data to getting results - with Thematic's conversational analytics software.
Thematic is an analytics platform known for its advanced AI capabilities which automatically categorizes customer interactions to uncover hidden patterns and trends in customer conversations.
2. Data Import
Data import is the step where the conversational analytics tool collects the raw data from all sources, call recordings, chat transcripts, social media etc. This stage ensures you have a complete dataset to dive deeper into. In Thematic integrations pull your data from all your channels and funnel your customer interactions into the platform for analysis.
3. Data Preprocessing
Once the data is imported it’s preprocessed to clean and make it usable. Preprocessing tasks include removing irrelevant data, correcting errors and standardizing formats. This stage ensures data quality and gets it ready for more advanced operations. This also includes removing Personally Identifiable Information (PII), so that users of the system do not see names, emails and credit cards.
4. Artificial Intelligence (AI)
AI is applied to understand and interpret the human language in the data. This includes tasks such as language detection, speech-to-text, sentiment analysis and entity recognition. By doing so the software can process and make sense of the subtleties in customer conversations. Thematic automatically summarizes the conversation to extract the root cause. It attaches categories such as "Issue", "Request" or "Question" to customer conversations. Thematic also deduces various scores / metadata from the conversation:
- Resolved/Unresolved - Was the support case resolved?
- CSAT - How satisfied was the customer?
- NPS - How likely are they to recommend the service?
- CES - What was the effort they put into the resolving the issue?
These are necessary for the following stage, the analysis.
5. Analysis
Thematic has 3 ways of providing analysis of the data:
- Visualizations. We call them "Analysis tools". Each one answers a specific question, e.g. Why is customer effort score on the rise? and lets you filter and pivot data by various dimensions.
- Dashboards summarize key metrics and their drivers for stakeholders for quick assessment.
- Thematic Answers works like ChatGPT, where you type your question in natural language. The software can search within a single dataset of support conversations or across many different datasets.
The main idea here is that Conversational Analytics software provides you with various ways to discover patterns, trends and anomalies. This can include frequency analysis, correlation studies and causality detection so businesses can understand the overall customer sentiment and behavior trends. But these tools also need to suit users of various skill levels.
At Thematic, we believe that every decision maker needs access to insights from customers in their own voice.
6. Prescriptive Analytics
Machine learning algorithms and AI can also be applied to learn from the data in the moment. For example, Intercom automatically suggests best actions to the agent responding to an inquiry. They can pre-draft a response or review a potential response to a customer.
Generative AI is instrumental in this, but the key is to retain the human in the driver seat. One company reported a solution, where a large language model was trained on the past 500 prior conversation to automatically draft a response to a customer. A support agent had a change to review and edit the response before sending, but they found that in 50% of cases, the responses were untouched by the agents.
Not only does the customer gets a faster and consistent report, the company can now reduce their team size and run their support team more efficiently.
Similarly, prescriptive analytics can help analyzing conversations in bulk and provide suggestions to decision makers on how to resolve the most common issues. In Thematic, we use Generative AI where you can ask the software any question about your data and it will give you not just the answer, but also recommendations. For example, in the above case where support agents barely touch the response, the recommendation could be to provide a self-help area for customers to instantly get answers to frequent questions without having to contact support/
7. Actionable Insights
From the visualized data businesses extract actionable insights. These insights inform strategic decisions such as product improvements, customer service enhancements and marketing strategies. This stage is where you apply the data driven insights to real world actions that will improve customer engagement and satisfaction.
8. Closing the loop
And finally, closing the loop, is a process when the results of the actions are measured and monitored. This information helps the business improve over time and adjust to new trends and changes in business.
Companies have been closing the loop with customers individually, but with the new AI advances, they can also close the loop with customers "in bulk" by messaging them about how they resolved the issues relevant to them long-term.
How to do Conversational Analytics in your business (Summary)
1. Choose the right tools & platforms for Conversational Analytics
Choosing the right tools and platforms is key to implementing conversational analytics. You need to choose solutions that align with your business goals and integrate with your existing tech stack.
Look for platforms that have robust analytics, real time monitoring, sentiment analysis and reporting. And consider scalability so it can handle more data as your business grows.
2. Integrate with existing systems & data sources (CRM, CMS, etc.)
Integrating conversational analytics with existing systems like Customer Relationship Management (CRM) and Content Management Systems (CMS) will give you data synergy and operational efficiency.
Remember to look for integrations with your social media channels and chatbots. This will add more data to the pool.
Consolidating data across platforms will give you a better view of customer interactions and behaviors.
Make sure the conversational analytics tool you choose can connect with these systems easily, preferably with built-in connectors or APIs that will make data flow and sync smoothly.
3. Upskill your teams for conversational analytics
To get the most out of conversational analytics you need to upskill your teams. Provide training sessions that cover not only the technical aspects of the tool but also the analytical skills to interpret the data.
Encourage a data driven culture and employees will use conversational analytics to improve customer engagement and business processes.
4. Data privacy and compliance
Conversational analytics must be done with strict adherence to data privacy laws and regulations like GDPR in Europe or HIPAA in the US.
Make sure the tools and platforms are compliant with these regulations. Implement data anonymization and secure data storage to protect customer information and build trust.
Key Metrics and KPIs for Conversational Analytics
Sentiment Score
Sentiment Score is a key metric in conversational analytics, it gives you insights into the emotional tone behind customer interactions. This metric scores whether the sentiment is positive, negative or neutral based on the words used and context.
Monitoring sentiment scores will help you measure customer satisfaction and detect issues before they become big problems.
Customer Effort Score
Customer Effort Score (CES) measures the ease of interaction from the customer’s point of view. A low effort score means customers can get their issues resolved easily and that equals higher satisfaction.
Tracking CES will help you identify pain points in the customer journey so you can target improvement.
Net Promoter Score
Net Promoter Score (NPS) measures customer loyalty by asking how likely are they to recommend your products or services. NPS is a good indicator of customer satisfaction and can predict future business growth.
Tracking NPS through conversational analytics will tell you how effective your customer retention strategies are.
First response time and resolution time
First response time is the time it takes for a customer to make contact and the company to respond. Resolution time is the time it takes to resolve the customer’s issue from the first contact.
These metrics are key to measuring customer service teams. Improving these times will usually correlate to higher customer satisfaction.
Real-World Applications and Use-Cases of Conversational Analytics
Customer Support and Call Centers
Conversational analytics is changing the face of customer support and call centers by giving you deep insights into customer conversations. By analyzing voice and text conversations you can identify common issues, measure agent performance and increase customer satisfaction.
This gives you real-time feedback so you can take immediate action and personalise responses which makes customer service operations more efficient.
Sales and Marketing
In sales and marketing conversational analytics is a powerful tool to understand customer preferences and behaviour.
By conversations you can capture insights on product interest, purchase intent and overall customer engagement.
These insights help you to tailor your marketing and sales messages to match customer needs and increase conversion rates and marketing ROI.
Product Development and Feedback
Conversational analytics extends into product development by giving you actionable feedback from customer conversations. This feedback is key to identifying product flaws, understanding user pain points and finding opportunities to improve.
You can use this data to make informed decisions on product development and enhancements so customer needs are met and product usability is maximized.
Sentiment Analysis and Brand Monitoring
Brand monitoring through sentiment analysis is another use case of conversational analytics.
By looking at the tone and emotions in customer conversations you can see overall sentiment towards your brand and spot any emerging issues.
This real-time feedback allows you to respond to negative sentiment and manage your brand.
Conversational Analytics Challenges
1. Data Quality & Accuracy
One of the biggest challenges in conversational analytics is the data quality and accuracy. Poor quality audio, slang and unclear speech can kill speech analytics software and give you inaccurate results.
Fixing this requires robust pre-processing to clean the data before analysis.
2. Unstructured Data
Most of the conversational data is unstructured, managing and analyzing it is tough. Extracting meaningful insights from large volumes of unstructured data requires advanced AI and NLP.
Companies need to invest in the tools and expertise to make the most of conversational data.
3. Unbiased and Ethical AI
Ethical concerns and biases in AI algorithms are a big problem. Companies need to implement conversational analytics solutions that are not only technically good but also bias free so customer insights aren’t skewed.
Regular audits and updates of AI models to maintain fairness and impartiality in automated analysis.
4. Change within the Organisation
Conversational analytics means changing the way the organisation works. Customer service teams used to traditional ways of working will resist the change.
To mitigate this, companies should invest in comprehensive training programs and clearly communicate the benefits of conversational analytics to all stakeholders. This article is a good starting point for that!
Using a Conversational Analytics AI platform
A conversational analytics AI platform like Thematic shows how AI can be used to make sense of customer conversations. By using advanced NLP and AI, Thematic analyzes text and voice conversations to uncover themes and sentiments that drive customer behavior.
It can process and analyze data from multiple sources in real-time so companies can respond to customer needs and market changes.
Companies using Thematic get:
- Theme identification to find recurring topics or issues.
- Sentiment analysis to measure the emotional tone of customer feedback.
- Impact score to quantify the impact of specific themes on overall customer satisfaction.
- Generative AI models so you can ask questions about your data and get intelligent answers
Conversational Analytics AI software reduces the manual effort in data processing and increases the accuracy and relevance of customer insights so you can make better decisions.
The Future of Conversational Analytics & AI
As we move forward in this ever changing tech world, conversational analytics and AI is going to transform both customer interactions and how businesses approach support.
We’ve already seen some amazing strides in AI, NLP and machine learning which has allowed companies to make sense of massive amounts of unstructured conversational data.
Looking forward to these technologies getting even more advanced enabling businesses to:
- provide first-class customer service with more ease and efficiency
- create customer communication loops at scale for building trust and brand equity
- provide instant access to insights to decision makers across all roles and responsibilities
Machine learning algorithms will be able to predict customer needs better and give personalized experiences that will redefine customer satisfaction metrics.
And we may see AI able to do sentiment analysis across multiple languages and dialects in real time, making global customer service more cohesive and intuitive.
These advanced analytics will not only make operations more efficient but also build stronger more meaningful customer relationships.
The future of conversational analytics in AI is where the lines between human and machine in customer service disappear and customer experience is supreme.
So get ahead of the curve!
