The Real Reason your Customer is Calling – Doing Root Cause Analysis
Your team read and answer customer issues every day. You are flooded with these tickets! And you know better than anyone else in your company why your customers are calling and what they really want. Unless you commit yourself to read every single ticket that ever comes into your Helpdesk, much like Dave Grow (COO of Lucid software). But then 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. How do you keep from falling into backlog? Let alone finding the underlying root cause of these customer issues.
The only way 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? Can this larger issue be pinpointed, measured and fixed?
The Treasure Trove of Your Support Data
Today, this level of investigation and depth is not possible with the support data you have. You have a treasure trove of information in 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 try to do will be based on reading through a portion of the tickets and making estimated guesses. Either that or relying on agent tagging of tickets based on what category your agent thought the customer issues belonged to.
From our own data, we have found that the average support team routinely uses just 8 tags in their Helpdesk. That means your ability to analyse your support data is reduced to just 8 categories of customer issues. Which is mad! How can you make any decisions based on actionable data to improve your customers experience and support? Especially 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. Artificial Intelligence can also find the patterns that your agents don’t know to look for. Each agent is only seeing a small slice of the total number of customer conversations. So it’s impossible for them to know if the questions they are answering are a one-off or part 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 find 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 causes were found when running root cause analysis. They had a large number of late delivery complaints coming in and it was causing high customer dissatisfaction. Each customer issue had to be individually dealt with, and they could all see that these complaints were taking up a large chunk 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 below the surface and run root cause analysis. When viewing the support tickets related to customers complaining about late deliveries, these tickets were closely linked to delivery partners. When customers complained about late deliveries they also mentioned shipping partners. Which is expected of course. After all, when they are complaining about their order of printed photos or business cards being late, they are going to mention the company 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? However, when looking at the number of tickets by shipping partner this is not the case. We can see that they had lower delivery volumes than other partners.
Now their support team had actionable data showing that the delivery issues were directly caused by one shipping partner. And they had all of the related tickets as evidence. The support team was able to present this issue to the management and logistics teams and solve their customers biggest issue.
Without AI that can analyse your support data and find patterns and correlations within your customer issues, it can be difficult to find the root cause of your customers issues. Finding these can give your support team a secret weapon to really start improving your customer’s experience.
You no longer need to fight fires and answer individual customer issues. Now you can look at the data on a broader level and 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.