How To Analyze and Draw Insights From Conversational Analytics (at scale)

Understanding customer sentiment is critical for any business looking to improve its products and services. But what if you could go beyond traditional surveys and actually listen in on real customer conversations?

This is where conversational analytics comes in. Conversational analytics is the process of analyzing natural language conversations between customers and businesses to gain insights into customer sentiment, opinion, and preferences. It helps businesses understand their customers better and improve their marketing campaigns and products accordingly.

It is used by companies across all industries - from ecommerce websites to media platforms, from software companies to F&B brands. And as a business owner using machine learning to optimize your business, it's important for you to understand how to derive insights from conversational analytics.

In this article, we will explore how to both analyze conversational data and draw insights from conversational analytics to help you better understand your customers.

What is conversational analytics and why should you use it?

In a rapidly digitizing world, businesses must find ways to keep pace with their customers' ever-changing preferences and needs. To stay ahead of the curve, organizations are turning to conversational analytics—the process of collecting data from all channels where you interact with customers and analyzing that data with machine learning.

To get started with conversational analytics, you'll need a data collection strategy. Collecting data from customer interactions across all channels—such as phone calls, contact center chat sessions, emails, social media, and even real-time data—will give you the richest source of information. This data can be accessed by exporting it from your customer relationship management (CRM) system, analyzing conversations, using APIs to integrate with customer support software, or transcribing it from recordings.

Once you have your data, you'll need to clean and prepare it for analysis. This may involve removing any sensitive information, such as names and addresses, and standardizing the format of the data so that it can be easily ingested by an artificial intelligence platform.

Once your data is ready, you can begin analyzing it with machine learning. There are a number of different ways to do this, but one common approach is to use a technique called topic modeling. This involves training an NLP (natural language processing) model to identify the key topics or themes in your customer conversations.

Product and service innovation

These techniques can be used for a variety of purposes, such as identifying areas for product and service innovation. By understanding the key topics and issues that are important to your customers, you can identify areas for improvement. 

For example, suppose you're a company that sells products online. You might use a conversational analytics tool to understand the key reasons why customers are contacting you. Are they having trouble with the checkout process? Are they looking for specific products that you don't currently carry? By understanding the pain points that your customers are experiencing, you can make changes to your product or website that will improve their experience and reduce customer support costs.

You could also analyze social media conversations to identify new product ideas. For example, suppose you sell clothing. You might use machine learning to analyze social media posts about your clothing to identify new styles or trends that your customers are interested in. You could then add these new styles to your product line, giving your customers what they want and increasing sales.

Market opportunity identification

In addition to product and service innovation, conversational analytics can also be used for market opportunity identification. By understanding the needs and wants of your customers, you can identify new markets to enter or new products to develop that will address these needs. 

For example, suppose you sell car parts. You might use machine learning to analyze customer conversations to understand the most common problems that they're having with their cars. You could then develop new car parts or accessories that solve these problems, giving you a competitive advantage in the marketplace.

Another common use case for conversational analytics is identifying feature requests from customers. By understanding the features that your customers are asking for, you can prioritize development efforts and ensure that you're building the products and features that your customers actually want. 

Personalization is another important use case for conversational analytics. By understanding the needs and preferences of individual customers, you can provide them with a more personalized experience that meets their specific needs. 

For example, suppose you run an online store. You might use machine learning to analyze customer conversations to understand their purchase history and preferences. You could then use this information to recommend other products that they might be interested in or provide them with discounts on items that they frequently purchase. This would provide a more personalized experience for your customers and increase loyalty and sales.

Better customer support

Have you ever tried to contact customer support only to be met with a long wait time, automated responses, or unhelpful agents? If so, you're not alone. In today's world, customers expect more from customer support than ever before. They want their problems to be solved quickly and efficiently, and they're not afraid to take their business elsewhere if they're not happy with the service they're receiving. 

Conversational analytics can be used to improve customer support in a number of ways. For example, by analyzing customer conversations, you can identify areas where customers are having difficulty or getting frustrated. You can then use this information to improve your self-service options or make changes to your customer support processes that will reduce frustration and speed up resolutions. 

In addition, by understanding the most common issues that customers are having, you can develop new solutions that prevent these problems from occurring in the first place. For example, if you find that a lot of customers are having problems with a particular product, you could develop new training materials or redesign the product to make it easier to use. Through preventative actions like these, you can reduce customer support costs while also making your customers happier.

Proactive legal protection

In today's world, businesses must be proactive in their legal protection. One way to do this is to use conversational analytics to identify potential legal problems before they occur. 

For example, suppose you're a company that sells products online. You might use machine learning to analyze customer conversations to identify cases where customers are unhappy with the product they received or feel that they were misled about what they were buying. By taking action to resolve these issues before they turn into legal problems, you can save your company time and money. 

In addition, by understanding the key topics and concerns that are important to your customers, you can develop new policies and procedures that will prevent future legal problems from occurring. For example, if you find that customers are frequently asking about your return policy, you could develop a more clear and concise policy that would reduce the likelihood of future disputes. 

By taking a proactive approach to legal protection, you can save your company time, money, and aggravation in the long run.

Conversational analytics can also be applied to inbound legal requests, such as by classifying emails or chat messages from customers into categories such as “product liability” or “data deletion request.” This can help you prioritize requests and allocate resources more effectively.

Product Recommendations

If you've ever shopped online, you've probably been presented with product recommendations at some point. These recommendations are based on your past purchase history as well as the purchase history of other customers who have similar buying patterns to you. 

Conversational analytics can be used to create better product recommendations by understanding the needs and preferences of individual customers. For example, suppose you're a customer who frequently buys products from an online store. The store might use machine learning to analyze your conversations with customer service or your purchase history to understand what types of products you're interested in. They could then use this information to recommend other products that you might be interested in or provide you with discounts on items that they think you'll like. 

This would provide a more personalized experience for you as a customer and increase the likelihood that you'll make additional purchases from the store. In addition, it would also save you time by providing recommendations for products that you're likely to be interested in, rather than forcing you to search through the entire catalog yourself.

Appointment No-Show Prediction

Another common use case for conversational analytics is appointment no-show prediction. This helps businesses reduce the number of no-shows by identifying customers who are likely to cancel or not show up for their appointment. 

For example, suppose you run a hair salon. You might use machine learning to analyze customer conversations in order to identify patterns that indicate whether or not a customer is likely to show up for their appointment. For example, if a customer asks a lot of questions about the location or parking situation, they might be more likely to cancel because they're unsure about where they're supposed to go or how long it will take them to find parking. 

By using this information, you can reach out to customers who are at risk of canceling and offer them assistance with finding directions or parking. This would help reduce the number of no-shows and improve customer satisfaction.

Travel Assistants

If you've ever traveled, you know that the planning process can be stressful. There are so many things to think about, such as where you're going to stay, how you're going to get around, and what you're going to do once you're there. This is where travel assistants come in. 

Travel assistants use conversational analytics to understand your needs and preferences in order to make suggestions on things like where to stay, what to do, and how to get around. For example, if you tell a travel assistant that you're interested in exploring different neighborhoods and trying new restaurants, they might recommend a hotel that's located in a trendy area with easy access to public transportation. 

In addition, by understanding your travel plans, the assistant can also provide useful information like weather forecasts and flight delays. This would save you time and make your trip more enjoyable by taking care of the tedious details for you.

How do I implement conversational analytics on my website?

There are a number of ways to implement conversational analytics on your website. The most common approach is to integrate analytics into your help desk or customer support system. This allows you to track and analyze customer conversations as they happen, giving you the ability to quickly identify and resolve problems. 

Another common approach is to use a chatbot. Chatbots are computer programs that can mimic human conversation. By using a chatbot, you can automate customer support tasks, such as answering common questions or providing information about your product. This frees up your customer support team to handle more complex issues and provides a better experience for your customers. 

The customer journey can be highly complex, and when it comes to conversational analytics work, it doesn't help that human speech and linguistics are highly variable. That's why many businesses are now turning to AI providers and apps that enable a new level of speech and conversation analytics solutions.

Get started with Akkio

If you're not sure how to get started, Akkio offers a no-code AI platform that makes it easy to build and deploy predictive models without any coding. Akkio's platform uses a variety of algorithms, including decision trees, random forests, and neural networks, to automatically build models that are tuned for your specific data. 

From customer sentiment analysis and support ticket classification to appointment no-show prediction, you can build and integrate predictive models into your website with Akkio's no-code AI platform.

Simply connect your conversational data, select the KPI you want to predict, and Akkio will automatically optimize for accuracy metrics to generate actionable insights. From better understanding and predict customer behavior to churn prediction and optimizing conversion rates, the value of conversational intelligence is immense.

These models can be deployed anywhere with Akkio's API or no-code integrations, from your call center to augmenting decision making in third party conversational analytics tools.

Conclusion

Conversational analytics is a powerful tool that can be used for a variety of purposes, such as identifying areas for product and service innovation, market opportunity identification, feature requests, personalization, and better customer support.

Akkio's no-code AI platform makes it easy to implement conversational AI in your business and find customer insights. The platform uses various algorithms to understand customer data and extract valuable insights. In addition, the platform is easy to use and doesn't require any coding knowledge. 

If you're looking to improve your customer experience, sign up for a free trial of Akkio today.