How Conversational Artificial Intelligence Can Drive Better Customer Experience

Customer Experience, from the day the term surfaced in leadership talks, has been a fox chase. Everybody is courting the concept – technology companies sell tools to improve it, product companies consume them, and analytics companies analyze why ROI is amiss. While CX has become a primary goal (or a pain point), stakeholders seldom get a chance to take a step back and assess if the tools they are using are connecting to the end-user. Or are their strategies fit enough to gauge the perception, reaction, and emotions of a hyper-connected customer? Isn’t it surprising, given the products and services are built and designed to cater to the user’s demands?

Furthermore, in a globalized and growing digitized world, it’s essential to note that the said end-user is being ‘nurtured’ by numerous businesses. Every product or service they buy triggers a unique customer journey with mind-boggling metrics that track what products they browsed, where they abandoned the cart, whether they opened the feedback mail, and so on. These metrics set off an omnichannel blast of communication prodding to take the following action. While this strategy catches the definitive events, it loses on a wide range. There is a lot to address- customers who chose a cheaper product after deciding on an expensive one, the user who removed a product or two because a coupon did not work, or someone who could not find the exact product or service that fits their requirements.

Understanding customers is the first step toward delivering an exceptional customer experience. And conventional metric-based strategies and tools are not enough to understand customers and map their journey in totality. By leveraging Artificial Intelligence, organizations can understand their customers better – faster, in real-time, and at scale. How? Let’s find out!

We all know how Artificial Intelligence has emerged as a catalyst in moving the needle for otherwise stagnated customer experience strategies. Every time you log in to a new website (a prominent service-based site), you’re greeted with pleasantries by a chatbot that can also solve your queries. Now don’t get misled by the prefix ‘artificial.’ AI is becoming increasingly human-like. Its complex machine learning algorithms are helping businesses gauge a broader range of customer actions – interacting naturally, learning intuitively, predicting intelligently, and measuring correctly. AI development services have found their most significant and universally applicable use case – Enhancing Customer Experience with AI. The new benchmark is nurturing customers on their terms via preferred channels, capturing all events they undertake.

AI-powered tools are an upgrade over conventional ones as they try to decipher the intent behind the customer actions, essentially seeing the qualitative aspect and reading between the numbers. Some of the tools and techniques that have featured in the must-have list of businesses and have a shorter payback period are:

Virtual Agents

Connect deeply with customers when they come to you

Virtual Agents are probably the most common AI tool irrespective of industry and business size. Bots, chatbots, or digital assistants – as they are called, range from simple scripted programs to state-of-the-art futuristic Natural Language Processing (NLP) and Understanding (NLU) algorithms as in Cortana, Siri, or Google Now. They have massively diverse applications, such as:

Virtual Agents are the most successful AI-use case as they are customer-led, i.e, the customer approaches the bot with a clear intent of connecting with the brand. Many businesses leverage this intent by orchestrating bots and human agents to work in tandem. To understand and cater to customers’ needs, several AI development companies are helping organizations adopt conversational AI and get the first step right when customers approach them.

#FACT:

40% of online adults prefer self-service rather than speaking to a live person on the phone for customer service. There is a huge opportunity to reduce customer care costs through AI and conserve agent productivity for complex queries. – Forrester Research.

Sentiment Analysis

Connect customers personally when you reach them

What if customers don’t have the intent or time to connect with brands? Firstly, the firms need to reach them and build conversations around their products or services. Then gauge the sentiments of the leads and customers to analyze and develop timely conversations. The Sentiment analysis powered by machine learning development reveals how customers feel about their purchases. The insights are pooled from a plethora of data every brand has, such as emails, social media posts, chat and call logs, survey responses, etc. Hence, brands operating in feeling opacity struggle to reach their customer loyalty potential.

Sentiment analysis or opinion mining is not a breakthrough; instead, it has been used for decades. AI-driven development of sophisticated algorithms has allowed firms to translate subtle nuances in the existing database into accurate insights at speed and scale. For example, IBM Watson’s Tone Analyzer efficiently passes online customer feedback to determine feelings, needs, and desires. When businesses have a deeper understanding of their customers, they can turn casual visitors into customers, customers into brand loyal, and advocates.

Continuous Improvement

Conversational AI can play a pivotal role in improving customer experiences by collecting valuable insights on customer interactions, including pain points, preferences, and feedback. This data can be analyzed to identify patterns, trends, and areas for improvement, enabling enterprises to improve their products and services to better meet changing customer needs. Furthermore, Machine Learning algorithms can be leveraged to continuously train and improve the conversational abilities of chatbots, ensuring they become more effective over time. In short, businesses can deliver highly personalized and improved customer experiences by leveraging data-driven insights.

Proactive Engagement

Conversational AI can not only anticipate customer needs but also initiate conversations with customers based on predefined triggers or events, offering proactive assistance and guidance to improve the customer experience. For example, an AI-powered chatbot can send personalized recommendations to customers based on their purchasing history or browsing behavior. This proactive engagement helps businesses stay connected with their customers throughout the entire customer journey, improving engagement and driving conversion rates.

How to integrate AI in Customer Experience

Businesses need to understand that AI supports human strategy and is not a strategy itself. Poor data, under-challenged algorithms, unskilled employees, and half-baked AI implementation can’t push up the bottom line; worse, it ruins the customer experience.

If your chatbot replies ‘I will route your query to an agent after the customer types a long question, AI is making an unsatisfied customer infuriated. Isn’t it? Have you ever experienced the same? Then what’s the point in integrating AI in customer experience?

Artificial Intelligence development should close the loop, instead of creating more exits for customers. If the chatbot replies, ‘Sorry, Jonathan, I could not get you in the customers mentioned above. Do you have a minute to help me understand your question? The customer will not leave vexed.

Businesses need to find the most efficient mix of humans and AI to make the interactions meaningful, rightly timed, and lead to positive actions. The opportunities for AI in customer experience are endless – tapped only when the implementation is backed by a sound data-driven strategy and a robust solution.