Artificial Intelligence [AI] for customer care has been a trending topic in recent times. AI has spread across all corners of the world that we are living in. It has become ubiquitous in our professional and personal lives. At times, we do not realize the extent of AI penetration, because of seamless integration of automated workflows with AI. One of the most challenging question for all enterprises is how to effectively harness AI for every single facet of operation, and more pertinently in customer engagement. An IBM research report states that by 2020 more than 85% of customer interactions will be handled without any human intervention. Which means most of the customer interactions will be carried out by the AI-driven chatbots.
AI is the ability of a machine to mimic natural responses. We discussed chatbots and their abilities in our previous blog. Virtual Assistant [VA] or chatbot can serve the purpose of a true assistant when integrated with the customer service support. It can automate many repetitive tasks which otherwise consume a lot of time, if done manually. These chatbots or VAs can do a lot more than just giving canned responses to generic questions. According to an article from Forbes, customers are starting to prefer self-service chatbots versus a true customer agent.
This blog delves furthermore in showcasing how AI can possibly help in customer support. According to a study conducted by Zendesk, it is evident that more than 42% of customers purchased more after having a good customer service. It is also true that 55% of customers stopped interactions with the brand after experiencing a bad customer service. There are a lot of mix-ups and confusions associated with AI in customer service. AI supplemented customer service needs two main technologies to provide better customer interactions.
- Machine learning [ML]
- Natural language processing [NLP]
Facebook messenger, automatic product recommendations in Amazon, friends suggestions in Facebook are few examples of machine learning. VAs like Google Assistant, Siri, Alexa and Cortana are some of the examples of NLP which can replicate and construe the language of humans. In order to provide good customer support, a chatbot needs both ML and NLP to process and understand the humans at the other end.
Let us see what are the benefits that organizations will derive by employing chatbot based customer service support.
Increased customer engagement: From last decade there is a whopping 90% increase in the usage of messaging applications. Customers not only use these applications to chat with friends and families but also use them to connect with their brands to give their feedback or for various other reasons. What brands should get out of this fact is that, having a real-time bot which answers the queries of customers will allow customers to build trust with the brand. This allows business to retain the existing customers and connect with new customers.
Train them once and let them learn: Training an individual consumes many resources and time. Training a bunch of staff and keeping them up-to-date with the new products released into the market requires staff to undergo training repetitively. This is tedious and a nightmare for organizations. By deploying an automated AI based chatbot, allows organizations to reduce both response time and interaction cost. This is what IBM’s Watson does, an automated chatbot service which requires training only once. When there is a need for change in the process, all it takes is to re-configure the software and they will be good to carry on with the work. This allows organizations to address some of the common and repetitive queries much faster and more efficiently.
Customer service that doesn’t sleep and take leaves: These chatbots are not constrained to any region or holidays. Hence the brands can offer service to their customers 24 hours a day. The customers don’t have to wait for hours and days to get a reply. This process can also be automated for e-mail support. This will tremendously increase customer satisfaction which influences churn rate as well. This shows that the brands are committed to their customers which is valuable for the brand to retain and develop the trust.
Personalized support: It wows us when YouTube app suggests new videos based on our viewing patterns and the subscriptions. This is where it proves that the machine learning is performing its task efficiently. By having chatbots in customer support, brands can provide personalized response and support for individual customers. This, in turn, contribute immensely to customer satisfaction and loyalty.
Reliability in the process: If these AI technology tools are employed correctly, then a robust reliability quotient can be achieved, which is significantly better than the traditional way.
Need of chatbot analytics
At basic level chatbot analytics provides features like heat mapping and click tracking which is leveraged to provide better customer experience. For example, assume that you own an online retail business which sells apparels. You can easily find the sweet spots of your website using the heat mapping software. You can get to know where the most audience click and when visitors decide to exit the website. But what if your customers have decided to exit because they didn’t find the product that they were looking for. In this situation, chatbot will be able to react in real time by asking “have you found the product that you are looking for?” This allows the customers to provide immediate feedback on their experience. This acts as an instantaneous way of engaging a departing visitor, and provide alternate solutions, or re-reach out to them again, when the issue has been resolved.
Below are some of the reasons why chatbot analytics is important to improve customer service:
Demographic purposes: Chatbots are not limited to a single language. A single chatbot can be trained to answer in as many as 7 to 10 languages. This is almost impossible for a human agent. Apart from that this requires only a few additional codes and not days of training. This allows organizations to draft a blueprint of their demographic response needs.
Improved and fast customer service: As we have already discussed, a chatbot can easily handle repetitive tasks efficiently and 24×7. But to know how efficient that is, we need a data-driven tool which will extract the insights of the customer experience in several particular interactions. This allows the organizations to re-orient the chatbots if there is a necessity for the same.
Learning from customer feedback: Customer feedback is always needed for any brands to grow. Feedback allows a brand to differentiate between the areas which customer likes and dislikes. From chatbots it will become very easy for a brand to collect the customer feedback and act on it quickly. This helps in reduction of customer churn and increase the trust with the existing customers.
Bottom line: AI-powered customer support chatbots are on the verge to change the classical way of interacting with the customers. It is much more efficient and can be automated, personalized and immediate. All these features will add to the increased customer satisfaction. There is no doubt that the companies will embrace chatbots in near future. Since these AI-driven chatbots are charting exploratory steps in recent times, we will see further major technological advancements in the future.
We are business analytics at the core. We believe that the sole purpose of analytics is to be able to solve business problems using available and generated data. With machine learning, it becomes easy to discover possibilities out of the realm of human cognizance. This outcome is driven by Frugal Innovation at minimal CAPX investment.
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