What you need to know about conversational analytics

Any conversation between an organization and its customers or clients generates large volumes of raw data. Turning this data into valuable insights requires conversational analytics.

Conversational analytics is part of customer experience and support tactics that drive retention and turn customers into brand ambassadors. Analyzing conversational data can power effective decision-making and help your company become an industry leader.

Let's take a closer look at how conversational analytics works and how it can help your organization succeed.

Conversational analytics is an AI-driven process of analyzing data from interactions between the organization and its clients or customers. 

These interactions can come in a variety of forms, including:

• Phone conversations

• Online feedback

• Surveys

• Chatbot conversations

• Video calls

The conversational analytics process leverages artificial intelligence to convert conversations into machine-ready data. It uses statistical algorithms to evaluate this data, find trends, identify patterns, and discover inconsistencies. 

The insight provided by conversational analytics can drive decisions in: 

• Marketing

• Sales

• Customer service

• Compliance

• Fraud detection,

• Product development

The popularity of conversational analytics is growing rapidly. 

Between 2023 and 2033, the conversational AI market is expected to grow by 17.3% CAGR (compound annual growth rate), from $9.6 billion to $47.6 billion in revenue.

Conversational analytics relies heavily on artificial intelligence’s natural language processing (NLP). The program receives a raw conversation and uses NLP to convert it into data. 

Next, the software leverages different algorithms to analyze the data in multiple ways.

Text analysis

The AI-driven program receives chat, email, and social media conversations. It uses NLP to convert this text into a machine-readable form before analyzing it to discover patterns.

Speech analytics 

The software evaluates voice interactions, creates a transcript, and analyzes it like text-based interactions.

Voice analytics

The software analyzes changes in modulations and conversation speed to determine whether the speaker's words have a positive or negative sentiment. 

Algorithms for analyzing voice interactions differ from those for speech and text. However, they are still AI-powered.

For example, a customer uses the chatbot feature on the website to find out why their order from a week ago still hasn't arrived. Then another customer calls the customer service rep to find out how long delivery usually takes.

Over time, you can collect a large number of interactions. After converting them into data and feeding them into the software, you can identify trends, such as:

• Delivery delays

• Time of day when customers usually use chatbots

• How happy customers are with the chatbot feature and your services

The AI-driven program "reads through" chatbot and phone conversations and provides insight that can help you improve your delivery and customer service. 

Meanwhile, AI can use this data to predict future customer behavior. The more customer data it receives, the more it learns and the more meaningful insight it can generate.  

Conversational analytics can provide strategic information to help you: 

• Adjust marketing strategies

• Streamline sales tactics

• Make data-driven business decisions

Since customers are your key revenue-generating assets, working on their experience can significantly impact the company's bottom line. 

Here’s how conversational analytics can help:

Get high-quality feedback

While surveys, questionnaires, and reviews can provide excellent insight into customers' needs and feelings, they are usually limited to a few words or a short multiple-choice answer.

The customer is much more likely to provide detailed feedback, complaints, or suggestions during a conversation, even if it's just a chat with a bot.

Accordingly, conversational analytics tools give you access to a much wider range of data. You’ll find more detailed insights and predictions when you pair them with survey data.  

Gain valuable customer experience insights

Customer experience (CX) is quickly becoming a major operational focus for businesses. More companies are creating CX teams that analyze customer behavior, preferences, and sentiment. This ensures decision-makers can design perfect, frictionless customer experiences.

According to a PWC survey, 73% of consumers name customer experience as a key factor in their purchasing decisions. And only 49% of customers are satisfied with their experience.

Conversational intelligence provides the much-needed data that helps you understand your customers while they are on a journey down the sales funnel. 

AI identifies patterns you wouldn't otherwise notice, such as the quality of customer interaction. It offers predictions that would take your team months to make.  

You can leverage this information to adjust and improve your current sales, marketing, and retention tactics.

Improve product design and development

Whether developing a new product or adding features to an existing service, your key goal is to ensure customer satisfaction. Information gathered by your sales and marketing teams may not be sufficient to precisely define customers’ pain points.

With conversational analytics software, you can gain insight into customers’ previous behavior and extract valuable predictions. This can help you quickly change products and services, increase customer loyalty and encourage repeat purchasing.

Conversational intelligence tactics can also help make data-driven decisions about new product development. For example, finding dissatisfaction patterns in customer interactions can uncover what’s hindering the customer's success with your company. 

Increase team productivity

Your sales, marketing, and development teams are always searching for information to make their work more productive.

• Marketers analyze customer behavior to structure effective campaigns. 

• Developers review customer data to determine how the new product or service can solve as many pain points as possible. 

• Sales reps read customer chats for improvement ideas, potential leads, or upselling opportunities: That's why they spend only 28% of their work time selling products.

Searching for high-quality data is time-consuming, but conversational analytics can help your team glean valuable insight within seconds. They can use this information to improve productivity and focus on bringing in revenue.

Make data-driven decisions

Customer interactions generate a sizable amount of data. Organizations often neglect this valuable resource as they don’t have a quality analytics system to trawl through mountains of data. Still, this goldmine can provide the background decision-makers need for optimal business changes.

When you implement conversational AI analytics, ensure no interaction-related data slips through the cracks. The more information you gather, the more insight you can gain. 

These discoveries lead to better quality decisions, enhanced productivity, and higher customer satisfaction rates.

Personalize customer experience

Personalization makes customers feel special—it’s a major factor affecting the quality of the customer experience. Today, customers are willing to pay more for services if they are tailored precisely to their needs.

Conversational analytics allow you to evaluate current customer experience from many perspectives. This data can help you see how different segments of your target audience react negatively, neutrally, or positively to your products or services.

You can use this data to personalize the experience for each customer segment and take a large step toward ensuring customer success with your company. This can encourage customers to become brand ambassadors and drive repeat purchasing.

While conversational analytics tools can be highly beneficial for your customer experience team, data analysis comes with several challenges:

Tone of voice 

When speech and text AI analytics tools make a phone conversation transcript, they miss out on the tone or nuance of the customer's voice. For example, a sarcastic "What a great product!" isn’t the compliment AI might think it is. 

Multiple languages and dialects can also be tricky for AI to understand initially.

Sentiment analytics is currently in the works. Sophisticated conversational AI tools are starting to identify emotions behind words.

Slang or idioms

If the customer is using slang or idioms, AI software may be unable to decipher their true meaning. To start understanding slang or idioms, developers must teach the program what context and meaning they convey. 

It gets more complicated with synonymous slang and regular synonyms. The program can only recognize and translate the sentiment if you teach it to.

Background noise

Many conversational AI tools aren't sophisticated enough to tell background noise from the conversation. 

Accordingly, when the customer is speaking from a public place, the program may be unable to focus on their words. Inaccuracies or misspellings can also create confusion. 

While these challenges seem significant, they’ll probably be better in the future. 

Contextual understanding

If a customer previously had a different experience with your business, AI could struggle to identify this context. 

A customer's history or external factors, such as weather affecting deliveries, can also distort the interpretation of a conversation. 

Volume of data

The sheer volume of customer data means an organization needs robust IT systems to store, manage, and interpret all the information. 

Conversational analytics can streamline your customer experience strategy, help you develop better products, improve internal productivity, and much more. 

Gaining access to new types of data informs effective decisions to work toward obtaining a larger market share.

While conversational AI analytics tools have some limitations, they are already sophisticated enough to streamline your company's operations and contribute to its development.

A conversational analyst leverages conversational AI tools to gain insight into customer behavior, needs, preferences, and complaints.

Three types of conversation analysis are microanalysis, macroanalysis, and ethnomethodological analysis.

Conversational analytics is a wider concept that includes voice, speech, text, and sentiment analytics.