By Maria Ward, Account Manager & Knowledgebase Engineer
I’ve worked in the chatbot field for over 15 years helping companies deliver better customer service, experiencing the technology both as a client and a provider. One of my favourite parts of my job is being able to use all my years of experience to steer my clients through the minefield of options to deliver a conversational AI solution that is both effective and efficient.
One of the reasons Creative Virtual has so many long-term customers is that we really get to know them and their products and services. This approach allows me to collaborate closely with my clients to identify opportunities that will specifically help them improve their chatbot experience.
Earlier this year I was working with one of my clients on expanding their customer-facing chatbot to provide self-service on more topics. One area we agreed would provide significant benefit to customers was addressing error codes they might encounter. However, due to the wide range of different machines and models customers might be using, the list of possible error messages they could see was very, very long!
Given the knowledge I had about their business and my experience with developing chatbot content, I knew right away that attempting to address every single error message would be wasted effort. I quickly steered them away from that pitfall and instead suggested our first step should be to go directly to the source: their own customer queries.
I analysed the previous year’s data collected by their chatbot to look for trends. What were the most common error codes users were asking about during that period? The goal of this analysis was to better understand which error messages customers were actually needing help with and how they were asking about them.
Once I identified the trends, I liaised with the client to select the error messages that should be added to the chatbot first. We looked at which ones customers were most frequently asking the chatbot about and which ones the client knew were likely to be the most common. We also looked at which errors could be explained and resolved best with a self-service approach. The error messages on this list were the ones that would deliver the biggest impact on their customer service.
Once we narrowed down the list of error messages, the next step was to identify whether each needed a new answer added to the chatbot or if there was existing content that was relevant. I also looked at how conversation flows could be used to guide customers to the specific information they needed to deal with their error code.
In the world of customer service, surveys are common tools for getting customer insights. However, you should never underestimate the value in analysing customer queries. The data collected by a self-service tool like a chatbot provides an honest, unfiltered look into your customers’ needs and what they are really asking.
My tip this Customer Service Week for improving your organisation’s customer service is to analyse your customer queries. Whether that’s transcripts from your chatbot, live chat, or contact centre, you can gain priceless insights that allow you to align your customer service updates with the actual needs of your customers.