AI-driven contact centres can ease the workload
3 min read 10 May 2022, 11:11 PM ISTExcessive use of automation can leave customers frustrated in emergency situations
PremiumWhat is the most elementary business proverb? The straightforward response is: ‘Customer is king’. Hence, businesses invest significant energies towards designing their products or solutions that please the king. But unfortunately, the royal experience often gets broken as soon as customers reach out to the contact centre of their favourite brand for any query or troubleshooting. This glitch is due to many reasons, including overburdened contact centre agents, lack of contextual data about previous customer engagements, or simply too long waiting in the queue before the call gets a response.
Using an optimistic pair of glasses, the challenge is also a significant opportunity to convert customers, aka kings, to actual brand ambassadors. All a brand needs to do is fix the broken parts of the contact centre experience. Thankfully, technology provides an easy way to do this. As the first step, it is essential to identify appropriate AI-driven solutions that automate the iterative parts of the contact centre experience. An experienced technology partner can help in this process by analysing previous customer voice, email, and chatbot interactions. The recordings or transcriptions of these interactions are run through natural language processing-capable analytics engines to identify the areas where the engagement falls flat. The exercise provides an understanding about which parts of customer engagements should be automated for better output. The result is an optimally automated contact centre that provides fast, accurate, and emphatic experience to the customers.
While automation eases out the workload for contact centre agents, it is not the endgame. After all, customer service is all about the warmth, rationale, and empathy that only a human can offer. When customers reach the contact centre, they seek reassurance that the problem can be fixed in a ‘X’ timeframe and that someone is equally concerned about their situation. Therefore, the next important step is to restructure and reskill human agents to focus on critical incidents which require human intervention. These agents will be aided by more contextual data about a customer or problems through the automated system that remembers and analyses every past interaction. Therefore, if a customer calls the contact centre for following up on her complaint, she does not need to repeat her entire case to a new agent. Based on deep data analytics, the system collects more knowledge about the customer’s engagement with the business. By leveraging this knowledge base, human agents continue to become more effective for problem-solving. The bottom line is—Do not treat a customer as a complaint ticket number. She is a human first and is treated like one. Humans need data, information, and solutions, but they need empathy from fellow humans.
Based on the above use case, businesses need to identify the right balance of technology and connect to provide customers with a distinctive contact centre experience. Excessive use of automation, for example, merely providing an automated chatbot or an autoreply email ID, can leave customers frustrated and disgruntled in emergency situations. Think of your favourite cab aggregator service that only provides an automated chat option when several drivers cancel your trip request post-midnight. On the other hand, too much reliance on human agents makes the system inefficient and leaves the scope for error or other unwanted breaches of service level agreements. Both extreme situations are harmful to the brand and must be avoided. A better way is to critically analyse every aspect of contact centre experience through deep data analytics and gradual investment in automating those parts where there is an engagement gap. The continuous analysis keeps the customer experience intact and also identifies areas where technology can help drive better efficiency. Subsequently, it is important to continuously train the agents to focus only on critical parts of the conversation and expand the use of technology to peripheral parts of the business based on the initial experience. Identifying an experienced and capable technology partner makes this journey very rewarding for businesses.
Rashi Gupta is chief data scientist and co-founder, Rezo.ai.
