Over the past year, customer behavior has evolved as customers have become more digitally inclined and reliant on online brands to meet their needs. With many brands offering exceptional customer service, customer expectations have risen, and addressing changing customer demands and behaviors has become a top priority for businesses.
A recent CCW Digital research has found that 65% of companies now place more importance on the customer experience (CX) than they did prior to the COVID-19 pandemic. Organizations must now enable engagement on their customers’ preferred channels and form an in-depth understanding of their preferences, intentions, and end-to-end interaction with the brand.
The adoption of conversational AI and self-service channels like AI-chatbots, voice bots, and virtual assistants has grown exponentially as customers look for faster and more flexible ways of finding solutions and receiving support. Conversational AI chatbots leverage machine learning and natural language processing to hold human-like conversations with customers. As per Gartner, the conversational AI market size is expected to grow from AUD 6 billion in 2019 to AUD 22.6 billion by 2024, at a CAGR of 30.2%, during 2019-2024.
Modern customers seek proactive and personalized brand engagements while upholding their desire for instant interactions. Traditionally, companies have relied on data, such as click-through rates, browsing session length, page views, etc., to get insights into customer behavior.
With the rapid growth in the use of chatbots, customers are signaling their preferences, perspectives, expectations, and intentions through their interactions with brands. Hence, many organizations are increasingly adopting conversational analytics to measure and personalize the customer experience in line with this trend.
Let’s explore further.
With the onset of the digital era, customer expectations have transformed significantly, and nothing short of exceptional customer service shall suffice. Consequently, customers are levitating towards brands that can respond effectively to their omnichannel needs, tailor experiences, and predict what they might ask or inquire, even as customer needs become more dynamic and hard to fathom.
As customers interact with brands through chatbots and other self-service channels, real-time conversational analytics has emerged as the new paradigm in the CX world.
What is real-time conversational analytics? It is the ability to capture, analyze, and evaluate customer conversations that take place with the brand while they happen.
Word, phrase, and tonality data, coupled with sentiment analysis, generate real-time insights into customer preferences and perspectives so that organizations can better personalize customer experiences. Conversational analytics leverages AI and machine learning to generate actionable customer intelligence that measures and improves customer satisfaction, prevents customer churn, and enhances revenue.
Customers are emotional beings. The ability of an organization to understand customer sentiment and respond empathetically helps improve the overall customer experience.
As chatbot usage has grown in the CX industry, the need to be sensitive to customer sentiment has become more pronounced. In the process of augmenting traditional customer interactions with AI, brands don’t want to let go of the human touch.
Powered by natural language processing and machine learning technologies, sentiment analysis enables chatbots to comprehend customer mood from words and utterances that indicate a particular sentiment.
Consequently, sentiment analysis renders bots emotional intelligence by helping them measure the polarity and intensity of customer emotions and respond suitably.
For example, a customer utterance (such as delighted, thrilled, satisfied, etc.) indicates happiness and is an opportunity for the bot to upsell. On the other hand, words like annoyed, dissatisfied, unhappy, etc., show anger/sadness and may require the bot to escalate the chat to a human agent.
Conversational analytics helps organizations understand trends in user utterances and how different customers pose the same query, which can help make needed investments and upgrades in AI and automation.
Metrics, such as customer satisfaction with the product or service (measured through survey responses, customer ratings, etc.) and the percentage of chatbot interactions that escalate to a human agent, help organizations improve CX on various fronts.
Conversational analytics provides valuable customer data that can be leveraged to innovate and design better products, personalize marketing campaigns, and improve customer service.
Traditional web analytics show customer reactions to what is presented to them; they do not provide information to help companies build better products and services. However, insights delivered by conversational analytics show what product features work for customers, which don’t, and how products and services can better align to their expectations.
While customers ask similar, repetitive questions that chatbots can automatically handle, real-time analytics has the power to adjust responses to a person’s unique question and disposition (tone, emotion, etc.), hyper-personalizing customer journeys to connect with them on an individual basis.
Moreover, customer data analysis reveals if a particular customer is likely to react positively to an upsell/cross-sell attempt and helps the bot recommend suitable products to meet that need.
Real-time conversational analytics can turn a negative interaction into a positive one by providing intelligent, AI-driven recommendations to agents in real-time, advising them on the next best course of action to take while handling customer queries.
Real-time targeted alerts help agents fix mistakes committed at the moment and immediately improve performance, reducing the risk of further complaints.
Such smart suggestions help agents deliver exceptional interactions, personalize solutions, avoid churn, and build loyal customers.
Moreover, real-time sentiment analysis fathoms customer sentiment, intent, and tone and helps agents respond according to the customer’s disposition. For example, if a customer expresses joy on his latest purchase, the system may indicate the agent to use it as an opportunity to upsell and collect more data.
Customer effort score is a CX metric that indicates the amount of effort a customer has to put in to purchase a product or get an issue resolved, a request fulfilled, or a query answered.
Advanced real-time conversational analytics helps improve the customer effort score by learning from customer interactions to collect valuable customer insights, improve the knowledge base, predict customer behavior, and shorten in-call and post-call interactions.
At Acuvate, we help clients build AI-enabled chatbots with our enterprise bot-building platform called BotCore.
With minimalistic coding requirements and a visual design interface, our chatbots can be built and deployed across different channels within a few weeks, with channel-specific API. Additionally, BotCore’s chatbots support multiple languages (French, German, English, Spanish, Italian, etc.) to help you reach out to a global consumer base.
BotCore also offers a conversational analytics module to provide a deeper understanding of customer needs and intent, turn insights into action, and personalize the entire customer journey from end to end.
To know more, please feel free to schedule a personalized consultation with our experts.