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Analysing Customer Queries to Improve Customer Service

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.

Conversational AI Data and Powerful Customer Insights

By Mandy Reed, Global Head of Marketing

These days it can be easy to feel like we’re in a constant state of information overload. The 24-hour news cycle combined with social media and the ability to Google anything always at our fingertips makes it difficult to escape the relentless barrage of information. This can also make it difficult to discern what information is genuinely important and worthy of our time.

The same is true when it comes to your organisation’s customer experience (CX). There is information floating around everywhere on customer behaviours, preferences, and sentiments. However, it can be a struggle to gather and identify the customer insights that are meaningful and most important for your CX strategy.

In fact, Gartner analyst Augie Ray identified ‘looking in the wrong places for customer insight’ as one of the key mistakes that kill CX programmes in their infancy. He points out that you can’t expect customer-centric results from a strategy that is not based on real customer insight and understanding. Likewise, you won’t be able to measure the impact of your programme if you aren’t gathering and analysing customer feedback, sentiment, and experiences.

Conversational AI is growing in popularity among businesses and their customers because of the positive self-service experience high-quality virtual agents and chatbots are delivering. However, organisations should never overlook the added benefits they gain by analysing the conversational data these tools can collect.

In a chatbot vendor selection guide, conversational AI expert Ridhi Mathur explains:

“In this era of data, a sophisticated virtual agent can play a very significant role for many businesses. The conversational data collected is very rich. It can be analysed and mined to understand your customers’ views, identify developing trends and make informed predictions, which can in turn contribute to areas such as product or service innovations and marketing.”

As you are developing your CX programme and selecting tools, be sure to keep this in mind. You want to implement a conversational AI solution that is not only built based on a deep understanding of your customers but will enable you to gather valuable customer data as well. You also want to ensure that you are properly analysing that data to assist with ongoing improvements to your CX and inform other areas of your business, such as product development and marketing programmes.

The customer insights that can be provided by chatbots and virtual agents are too beneficial to overlook when you are selecting a technology to implement. Consider this as you are reviewing conversational AI pricing options. Purchasing a chatbot without the right tools for data collection and reporting functionality never pays off, no matter how much money you might think you are saving. It is also key to work with a provider that has the expertise to assist you with mining and analysing the conversational data to maximise your benefit.

If you need some help cutting through the information overload when it comes to selecting a conversational AI vendor, check out the ISG Provider Lens™ Intelligent Automation – Solutions & Services. This report provides an independent, market-driven evaluation of the industry and comparison of 19 conversational AI providers.