Conversational Analytics 101

Conversational Analytics 101

Conversational analytics have allowed researchers and developers to explore further the delicate framework that defines human behavior and interactions. Conversation can intricately detail the seemingly simple building blocks from which all interactions are formed. By dissecting expressions in such an intricate way, researchers developed a more substantial base for computer-aided analytics to sort conversational phrases by sentiment, trends, speech patterns, and more.

Conversational analytics now represent an endless potential for businesses that want to understand their customers better. However, reaping the benefits of this incredible technology means understanding what exactly it is, what it can accomplish, and why it matters for your business and your customers. 

What is Conversational Analytics?

Conversation analytics is the computerized process of studying customer conversations. This consists of customer interaction over the telephone or chat functions through your contact center, social media platforms, third-party reviews, word-of-mouth, and more. 

How does it work?

Conversational Intelligence (sometimes referred to as conversational data) is collected through the use of revolutionary artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and a collection of various algorithms. NLP specifically plays a significant role in conversational analytics due to the combination of linguistics, computer science, and artificial intelligence. 

Unlike speech analytics, which focuses more on spoken communications, conversation analytics focuses more profoundly on the context of interactions, between participants, across a multitude of channels or platforms. This technology is then used to transcribe phone calls and chats from multiple platforms, review customer-made posts or other posts about your business, and more; they are all used to gain further insight into your customers' behavior. It assesses these conversations for customer sentiment and assists in finding patterns that can be beneficial for a deeper understanding of customer behavior. 

Why Do Conversational Analytics Matter?

Conversational analytics are sometimes overlooked and underappreciated by businesses when it comes down to it. While many bot analytics are unlikely to be the "make or break" of the bot's success, they can still provide valuable insights into opportunities for professional growth and improvement areas by allowing developers to see into the minds of consumers. Key Performance Indicators (KPIs) that are used to track various aspects of your business will vary based on the specific use case of the bot and the demographics of the customer or user base. However, some key metrics can provide insight for any bot over a variety of demographics or use cases.

What Kind of Insights Can I Gain From Conversational Analytics?

When companies or brands think about gaining customer feedback, traditionally, surveys have been the first tool used. While they have always been beneficial, they only cover explicit or specific feedback based on the particular questions asked. 

Implicit feedback, however, can cover anything and everything else, and this is where conversational analytics can truly shine. Implicit feedback can provide a closer look at how your customers think, feel, and behave. 

The feedback companies can gain from this technology can be split into four types, with some crossover:

  • Structured-solicited data-This covers feedback collected from CSAT, NPS, CES, and more. This is incredibly useful for generating benchmarks and getting in-depth information about how your company is performing within your current customer base.
  • Structured-unconsolidated data- This covers operational data. Your company is not asking your customer for this information, but it is gathered internally and organically through all customer communications. 
  • Unstructured-solicited data- This includes texts, comments, or other social responses received from your surveys or social media interactions. 
  • Unstructured-unsolicited data- This type of feedback strongly uses conversational analytics and encompasses the rather difficult to collect information like:
    • Social Mentions: When a user mentions your company on social media but is not directly corresponding to your account.
    • Customer phone calls: The words used in customer-agent interactions. Speech analytics can even assess how fast the customer is speaking, if and when they interrupt the agent, the tone of voice, volume of voice, and more, all of which can be used for training purposes in the future as well. 
    •  Text-based conversations with bots and agents: How are your customers interacting with a website or social media bots? How are they interacting through chat functions with a live human agent?
    • Third-party reviewers: When customers discuss your organization, how are you being portrayed?

This conversational data can be very beneficial for better understanding your customers' true feelings and the reasons behind them. It covers the components of your customers' experience and journeys that you may not think to discuss, but ones that weigh on your customers' minds. Unless you consistently gather unstructured, unsolicited data, you might not be getting the whole narrative or picture.

Benefits of Conversational Analytics

Every business strives to deliver the best customer experience possible. Modern organizations worldwide are beginning to fine-tune their development goals to assist their customers' wants and needs better. However, to do so, they must truly understand their customers on a deeper level for this approach to work successfully.

Conversational analytics provide new opportunities for customers to share their wants, needs, and general sentiments to be showcased and acted upon by organizational team members who work in tandem with them and the higher-ups within the company. With more and more customers willing to pay more for a better experience, the use of conversational analytics may no longer be an optional component of a business but a necessary one. Below, we dive into the benefits of conversational analytics and how they can improve your business and customer experience strategies. 

Identifying Emotions

Expressing empathy for customer issues and addressing them based on their emotional state can drastically change the end result the agent will receive from them. Conversational analytics can highlight notable emotions as a conversation continues and a teaching moment in retrospect for agents. When used for training purposes, conversational analytics allow agents to better understand the emotional progression of customers over time.

Training agents to handle a wide range of specific scenarios can become a much easier task when using this technology. Scenarios that may have been troubling for live agents to identify and assist with can be assessed in greater detail to develop the best way of handling a case like this in the future. 

Predicting Customer Behavior

Many organizations have started to focus their efforts on identifying customer behavior trends to assist them better in the future. Conversational analytics help make this possible on a case-by-case basis and yield results that can be used in the broader sense.

This technology can help identify trends by cross-referencing keywords or phrases and the emotional components of customer interactions. This can help craft better agent scripts, save time, optimize customer behavioral profiles, and more!

Live and Retrospective Suggestions

Live training and coaching have proven to be highly effective methods for improving agent performance and productivity compared to other approaches. With highly accurate analyses of customer-agent interactions, companies can actively monitor agents and make relevant suggestions to them seamlessly.

This technology can also make it easier than ever to identify any potential security risks. With modern threats from cyber-criminals on the rise since 2020, the importance of fighting against possible vulnerabilities is crucial. It is possible to minimize any potential lapses in security by utilizing conversational analytics. As a customer-company conversation progresses, agents can automatically be alerted to any suspicious activity. As agents work through customer interactions, risk levels can be assessed, determined, and displayed right on the screen. 

Businesses must attend to quite a few regulations to address and solve customer issues. Live suggestions help avoid any potential missteps on the business side and keep both your customers and business secure. 

Modern companies use conversational analytics to gain traction within their industry and have happier customers. 

Customer conversations are now easier than ever to understand, decipher, and personalize with modern technology and the insights they can provide. With this technology, it is possible to drive user adoption engagement, increase the use of technology on the customer journey, and improve technological literacy across entire organizations, allowing for a better customer and team member experience because people can get connected easier and in real-time.

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