Your team read and answer customer issues every day. Your flooded with these tickets and you know better than anyone else in your company why your customers are calling and what they really want. But unless you commit yourself to read every single ticket that ever comes into your Helpdesk, much like Dave Grow (COO of Lucid software), you only get to see a portion of these customer issues. And with all of these tickets coming in you barely have enough time to answer them all and keep from falling into backlog, never mind finding the underlying root cause of these customer issues.

The only real way, however that you can start improving your customer support, reducing your workload on recurring issues and making your customers happier is to find the root cause of their issues. What is the real reason they are calling, and can this larger issue be pinpointed, measured and fixed?

The Treasure Trove of Your Support Data

Today, this kind of level of investigation and depth is just not possible with the supporting data you have. You have a treasure trove of information within all of the thousands of support tickets you receive every week, all in the form of unstructured text data. Your customers explain to you in detail everything that is wrong with your business! Any root cause analysis you may try to do will be based on either reading through a portion of the tickets and making estimated guesses or relying on agent tagging of tickets based on what category your agent thought the customer issues fell in to.

From our own data, we have found that the average support team routinely uses just 8 tags in their Helpdesk. That means the totality of your ability to analyse your support data is reduced to just 8 categories of customer issues. That’s mad! How can you make any decisions based on actionable data to improve your customers experience and support, when you don’t yet have any data to base it off?

Without an AI solution that can automatically tag all of your customer issues based on why they are calling, you are really in the dark as to what is the root cause of your customers issues and what can be done to improve them.

AI is Faster Than Agents

AI and machine learning can derive quantitative data from the qualitative much faster than agents can. AI can also find the patterns that your agents didn’t even think to look for. Because each agent is only seeing a small slice of the total number of customer conversations, it’s impossible for them to determine if the questions they are answering are one-offs or symptoms of a much bigger issue.

These patterns are what we are looking for when finding a root cause to your customers issues. Artificial intelligence will tag and categorize all of your support tickets based on hundreds of possible issues, with each ticket receiving multiple tags. This means patterns can start to appear with tickets containing more than one issue. The AI will begin to correlate tickets which have similar issues cropping up together at the same time.

Root Cause Findings

So how does this look? With one of our customers, an e-commerce business that sells printed products, some interesting correlations and root causes were found. They had a large number of late delivery complaints coming in and it was causing significant customer dissatisfaction. Each customer issue had to be individually dealt with, and they could all see that these complaints were taking up a significant period of their time but as each agent is busy dealing with them, they were unable to see the underlying problem.

What Cx MOMENTS Found

After connecting their Helpdesk data to Cx MOMENTS, they were quickly able to see a deeper issue lying below the surface. When viewing the support tickets related to customers complaining about late deliveries, these tickets were, as expected closely correlated to delivery partners. This means that when customers complained about late deliveries they also mentioned shipping partners. This is expected of course, when they are complaining about their order of printed photos or business cards being late, they are going to mention the shipper that was meant to deliver them.

Finding The Patterns

The insight came from which shipping partner was seen to have a higher correlation to the issue. This was how they were able to get to the root cause of the shipping issue. When customers mentioned a late delivery, they mentioned DHL twice as often as any other shipping partner. But maybe DHL just handles more deliveries and so would appear more? But when looking at the number of tickets by shipping partner, we can see that they had lower delivery volumes than a number of other partners.

Now their support team had actionable data showing that their delivery issues were directly caused by one shipping partner, with all of the tickets related as evidence. The support team was able to present this issue to the management and logistics teams and solve their customers biggest issue.

Conclusion

Without AI that can analyse your support data and find patterns and correlations within your customer issues, It can be very difficult to find the root cause of your customers issues. Finding these though can give your support team a secret weapon to really start improving your customer’s experience.

You are no longer there to fight fires and answer individual customer issues. You can look at the data at a broader level to see where customer problems are occurring and what can be done to solve them. With your support data unleashed you have the power to instigate real positive change for your customers within your company.