With how the trends have been behaving lately, a lot of the spotlight was directed toward artificial intelligence and its capability to converse with humans. From AI-based chatbots and AI content generators to others, we have seen technology use available data to interact with others. But, it’s one thing to use data available through databases or some devices. What happens when you need actual human data derived from their interaction with other people? This is where conversational analytics comes into play. It uses technology to analyze speech and conversations among people or agents.
Conversational analytics is the process of using technology, artificial intelligence (AI), Natural Language Processing (NLP), and machine learning techniques to convert natural language conversations into a machine-readable format. But, let’s not limit it only to conversations. It also gathers data from posts, comments, and similar. It draws insights from any interaction people make, in real life or online.
How does it work?
Conversational analytics is used to draw insights about customers, their behavior, and how they interact with a certain product or service. Its main purpose is to fully understand customer motivations and wants. Most businesses use it to get vital information based on which they can take instant actions in reaction to issues or previously unidentified customer needs. It depends on the usage of AI to convert natural language conversations into formats applicable for further analysis and machine learning methods. The analysis finds patterns, trends, or anomalies among consumers and even identifies root causes for dissatisfaction or customer churn.
Some components of conversational analytics include:
Text analytics
Through natural language processing (NLP), conversational analytics can analyze text from chat, email, or social media. It analyzes common denominators or certain patterns to collect insights and present them through visualizations. It allows businesses to delve deeper into customer opinions and views on products or services.
Speech analytics
This analytics recognizes and turns speech into text format, to be analyzed through the same methods as text analytics. Why is this important? Well, if you have call centers or you want to collect data from videos or recordings of sorts, you need methods to analyze that data without having to manually transcribe it.
Voice analytics
This type of analysis explores how was something said and not what was said. It discovers the changes in someone’s voice, for example, if the speed of talking changes and if the voice gets louder or not. It serves as a great way to determine if perhaps customers get agitated or frustrated. The methods used here are different from speech analysis, but it still uses AI to detect those voice changes.
Sentiment analytics
One can recognize specific keywords or patterns in conversations through sentiment analytics. Each word used can mean a different emotion or sentiment towards a product or service. It uses similar methods as in text and speech analytics to discover certain words and phrases which describe an emotion or opinions.
Benefits and possibilities of conversational analytics
With the ability to convert conversations into actionable insights, we can clearly see the multiple benefits conversational analytics can bring to the game. Imagine all the possibilities for marketing and sales if they can act on that information instantaneously. Previously, the only available data was from static text and databases, but now with speech recognition, those insights convey a broader meaning and value. Data can be collected from chats, social media comments, phone calls, emails, tweets, shopping and business reviews, and many others. This generates so many points of data entry, that it can provide more detailed information about customer journeys and their interactions with the company or brand.
Getting the whole story
Some customer insights are limited and do not convey the whole message. Some people are freer to articulate their wants, needs, or problem by talking, rather than writing them down. By including speech, text, voice, and sentiment analysis all in one, it provides a much broader picture of customers and their behavior.
Time-saving
If there is a tool for conversational analytics, there won’t be a need for manual intervention and manual transcribing of calls or other voice records. This saves time, not only in data preparation but also in terms of creating valuable insight or metrics much-needed in real-time. This can make a difference in reacting timely to customer pain points or issues.
Upselling and creating great leads and conversions
If we can monitor what customers say or want at the exact moment, this can create an opportunity for upselling or a bigger conversion rate. For example, if a person contacts customer service asking questions about a product, they can target that person with an email about that same product and a call-to-action that leads to a direct purchase. This way, that caller can go from the first point of contact or inquiry to purchase in just one click.
Increasing customer satisfaction and creating a better customer experience
If companies can identify customer needs and wants accurately, the products and services they provided can be optimized and adapted to meet those exact needs. Even, being more efficient in answering customers’ questions leads to bigger satisfaction and a much better customer experience.
Reducing operational costs
By eliminating the need for manual conversation or speech transcribing, companies can reduce the cost of labor and time spent on those tasks. But also, by reacting on time to customer demands or dissatisfaction, one can reduce costs and possible losses.
Quicker and more effective damage control
If a customer expresses their issue or pain point, through customer service or even through social media or business reviews, conversational analytics allows companies to instantly react to those issues and minimize the negative influence. This way they identify the pain point and can solve it in no time, which leads to lower costs in the long run. Imagine if a customer complains about a certain product attribute or difficulty on social media. Companies can react to that, take notice, and try to resolve it, so they won’t have any future complaints or even lose customers.
Predicting future behavior
With such broad customer analysis and data availability, it’s safe to assume that making predictions will be more accurate. When the basic customer data is complemented with data derived from conversational analytics, it shows a more precise picture of consumer behavior. With more data, ML models and predictions become more correct and in line with reality.
Product or service testing and trials
This is a great way to test products or services if comprehensive and detailed market research is expansive. Companies can give their product to a certain number of people and track what they write and converse with others about it across different channels.
What are some limitations?
Even though conversational analytics brings multiple versatile benefits, it has some major limitations that influence its effectiveness and accuracy. Each person has their own way of communicating and talking. From various languages to different dialects, not every word or phrase will have the same pronunciation or meaning. So, it’s hard for machines to make distinctions between those.
Based on that, let’s dive into some main limitations and difficulties in creating a perfect conversational analytics software:
Speech and phonetic pronunciation
Each person, by default, has a different way of talking. They all come from different backgrounds, countries, or even cities. Some might drawl, some might speak quickly, and some might word things differently. So it’s difficult for AI to recognize those differences and treat them as the same values or meanings.
Dialects
Certain dialects have completely different ways of pronunciation. Or they have different words for one subject or meaning. A person from one city will talk so differently from a person front another city. If the model is trained to search for one word, pronounced exactly by the rules, then it will miss all the others that have the same meaning.
Slang
If there is anything that diversifies a language more, it’s slang. We all have witnessed the creation of completely new words. Or words that usually mean one thing, now represent something completely different. Even within different age groups, we find contrasting variations of words.
Lack of human context
Even when models are trained right, they lack the ability to pinpoint context. For example, if a person is sarcastic, the software will not recognize the text as such. It will take all the words for their exact meaning, without the inclusion of a possible figure of speech. There is a human element to understanding such phrases and machines can not comprehend them.
Connection issues
When we talk about chatbots, call centers, and such, connectivity issues are what pose a problem in understanding someone’s talk or communication. If a person calls up an agent, if the connection is bad, there is no way of correctly conveying the message.
Conclusion
No matter where, no matter what industry, or product and service, conversational analytics are becoming a hot new trend that makes customer insights more accurate and engageable. A rather complex tool, that is not easy to implement and develop, but it has advantages that have turned heads. We believe that conversational analytics, not to be confused with conversation analytics, will become something that will garner more attention. It can definitely be expected that it will become more reliable and more accurate as time progresses and as AI develops. Recognizing and utilizing all data consumers generate, will lead to more precise and efficient, not only marketing and sales but also company strategies and actions.
