AptEdge started with the belief that the current method of product feedback is broken. I found a disconnect in the workflow while working closely with support teams as a product leader.
At AppDynamics, where Anthony and I met, I worked on the core product and support escalations while Anthony led engineering. We worked together as the company grew from $40 to $200 million in revenue.
One of the problems that caught our attention was the lack of visibility into issues caused by product updates that could lead to escalations.
For example, we had shipped an update that caused the export feature to change on an analytics dashboard, which led to many customer tickets and escalations that were preventable if we had caught the trend sooner.
A reason for the lack of visibility was that our support team did not have a way to capture trends in aggregate ticket data.
Without a view into trends, the number of escalations increased significantly, disrupting product release cycles and expanding the engineering workload.
We realized that when engineering teams continuously ship product updates, support teams get hit with various issues that come from multiple customers about the same problem.
To keep it simple, let’s say a business gets 100 support tickets over a week.
40 are about login issues
30 are about billing
20 are about file upload errors
10 are about a broken dashboard
Now, multiply that by 10 to get the average ticket volume at a fortune 500 enterprise (between 1000-5000 tickets), each with varying levels of complexity. Trends become even more challenging to track as ticket volume increases.
In a 2020 survey of customer experience trends of 45,000 companies, Zendesk found that “Companies that leverage the most customer data — those in the top 25% of managing data relative to other similar companies — see 36% faster resolutions and a 79% reduction in wait times. They also solve four times the number of customer requests.” We’re creating the tools that companies need to leverage their customer support data.
Before AptEdge, there wasn’t an easy way for businesses to quickly determine which tickets are related and if there’s a trend pointing to a particular feature or product update as the root cause.
Without a process to sort and link related tickets, support agents would need to conduct redundant investigations and file duplicate escalations.
The lack of a support workflow led to slower response times, more escalations to product/engineering teams, and ultimately churned customers.
There was no data-driven way to understand the most significant problem support teams saw from a product release or changes in a given week. At AptEdge, we provide support teams the toolkit to assess and respond to tickets faster.
We set out to build a layer of intelligence that sits on top of the ticket data with the ability to analyze tickets in real-time and detect similarities across them.
Agents can now quickly correlate tickets that are about a common problem and see the revenue impact of a particular set of tickets as they come in. This detailed view of estimated revenue impact enhances the prioritization of common issues that affect high-value customers.
By listening to front-line agents about their support workflow, we also learned about the importance of surfacing knowledge articles and providing analytics for all tickets.
After building in silence for over a year, we’ve done much more than just detect ticket trends. Our product also surfaces knowledge articles, customer sentiment, and provides comprehensive analytics for executives to better understand their customers’ voices.
Today, there have been 1000+ Edges created and 1 million+ tickets analyzed in AptEdge. We’re just getting started and are excited to share our journey ahead!
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