Accelerating Feedback, Justifying Development, and Anticipating Questions with AI

Customer support is about expectations, and the expectations that customers have today were shaped by their previous experiences. No customer experience exists in a vacuum either. Expectations for all service providers are influenced by all service providers in every industry. Consumer expectations are changing all the time. As businesses scramble to get a competitive advantage over others with ever improving customer service, customer expectation raised across industries. In the taxi industry for example, all that was expected in the past was to get from point A to point B. Today, driven by Uber’s driver rating system, it would be strange not to be offered mints, bottled water, and your choice in music.

mike-wilson-96168-unsplashHow can your support team keep up with these ever-increasing customer service expectations? Thankfully, technology in support is also advancing at a rapid pace. We now have a range of add-on solutions like chatbots, automating the support process; unified communications, enabling true omnichannel support; and artificial intelligence (AI), offering deeper insight into customer problems.

What is AI? It’s become a lofty buzzword you hear about in the news describing a future of computers that think like humans, drive our cars, and take our jobs! AI isn’t as far off into the future as many might think, last year the Boston Consulting Group found that 85% of executives believed AI will allow their companies to obtain competitive advantage. It’s already available to help your support team provide the best possible customer experience you can and give you a step above the rest.

I’ll cut to the chase: here’s how AI can help your team provide better support and draw insights from customers’ issues.

  1.     Shortening the product feedback loop

Your development team has just released a new version of your product. They have spent weeks trying to come up with better features, they have brainstormed, gathered customer inputs and beta tested the early versions. Release day comes and all the hard work that has been done is shown to the world in all its glory. At the end of the day though, it’s only once the product is in the hands of the masses that you really start to get feedback. Before that, it’s just an educated guess.

Once the version is released, your product team will be eager to understand what customers are saying about it, and what they are struggling with. But the ticket tags your agents currently use don’t cover any of the new issues that arise from this brand-new release, so any of this vital feedback will be lost under the mountain of tickets your team deals with every day. It’s a pity, you have the data and the feedback there, it’s just not easy to get it.

Equipped with AI, your support team will be able to detect and report this early user feedback by automatically categorizing all your tickets on a wide range of issues, applying multiple tags to each ticket.  You will be able to track early issues associated to this new version, with very low volumes carrying-casual-cheerful-1162964at launch date but with the potential to grow much bigger if not fixed. Now, you can see that the new login page has a bug and no longer believes that France is a country or the thank you page just keeps popping up in Portuguese. You can monitor and report on this early feedback from customers helping you and your team to quickly iterate and make improvements to the product based on real customer feedback.

  1.     Justifying improvements and fixes

You work with customers every day. You know your customers’ biggest issues and what they are complaining about and you can see that there are changes that need to be made to help improve their experience. You go to your development team, website team or marketing team and explain that customers are really unhappy about your “add to cart” button or all the emails they are getting after sale. They already have so much on their plate, why should they listen to your hunch and make the fix when they’re already up to their eyes? You spend your weekend reading through your support tickets trying to find examples of the issue cropping up and hours exporting data to excel trying to get a sense of how big the problem really

With an AI solution, all of your tickets are automatically analysed and categorised so you can see what customer issues just keep reoccurring. Not only that, you can also see the trend of those customer issues over time allowing you to see what problems your customers are having that are on the rise and need a quick fix fast. Now with all of this information on the number of customers complaining about this issue, what part of the business it relates to and how it has been trending over time, you can easily escalate the issue to the team in charge and show them why you need the fix and how it will help.

  1.     Creating useful FAQ contentanswer-business-career-221164

Customers want to figure out their issues for themselves if they can do so without much hassle and consequence. This is a win-win for your support team as it means you spend less time answering your customers’ most common questions, so you have more time for the meaty stuff. Knowledge bases and FAQs are a great way to help your customers help themselves. But how do you know what are your customers’ biggest issues and what are they getting hung up on without reading through piles of tickets and relying on agent ticket tagging? By using an AI reporting tool within your Helpdesk you can automatically see what are your customers’ top reasons for contacting you so you can create content to provide them with an easy fix. You get data on how these customer issues are trending over time and how they affect satisfaction scores, so you can spot issues on the rise or causing significant upset and create content to alleviate these problem.  You don’t need to make shots in the dark, hoping to solve the biggest issues, now you can have the data on your customers top reasons to contact and build your content around it.


AI isn’t just a futuristic concept for mathematicians or reserved for tech giants, it can be implemented into your Helpdesk in just a few clicks and help you to report on your support team’s activity. You no longer need to dig through mountains of tickets to find your customers’ reasons to call your team. With natural language processing and machine learning techniques you can have your Helpdesk tickets automatically analysed and tagged, saving you time and helping you and the rest of your business to make your customers experience with you a happy one.

Ben Morrisroe

Ben Morrisroe is a guest contributor to the Tin Cans & String blog. Ben is the Marketing Manager for a support analytics platform, Cx MOMENTS, out of Dublin, Ireland. You can follow Ben and Cx MOMENTS on LinkedInTwitter or at

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