Conversational analytics and everything you need to know about it
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