Everyone’s had bad online experiences — endless registration sequences, timeouts, and poorly designed process flow. Frustrating for the user, who at worst abandons the process and moves on.
“Anyone who’s shopped online or who’s managed their finances online knows on occasion the experience drives you nuts,” UserReplay CEO John Thompson said.
Expensive for the shunned company, who can lose as much as 20 percent of possible revenue due to poor user experiences, Mr. Thompson said.
Those poor experiences are what UserReplay helps companies avoid by providing digital customer experience management software that can replay individual customer sessions to see the obstacles those customers encounter. Analytical tools direct the user to the most relevant customer sessions.
People getting frustrated cause lost revenue but they also create additional expense by tying up human resources who have to personally address those issues, Mr. Thompson added.
“We felt there were technologies in the space that did this but were hard to deploy. Technology has grown to the point where we are able to offer a 2.0 solution.”
UserReplay’s goal is twofold, Mr. Thompson explained. Identify where people struggle and then pinpoint untapped revenue opportunities.
In order to do that UserReplay had to solve a pair of problems. They had to record every customer experience which Mr. Thompson conceded creates a large store of data but which technology makes affordable. Creating clear value for customers was the second.
That is where machine learning comes in, Mr. Thompson said. It can distinguish between a common issue worth devoting resources to and clear outliers.
“There’s an infinite number of combinations of issues,” Mr. Thompson said. “Patterns of struggling customers leave signatures in the data.”
Those signatures may be pauses or departures at common points in the user experience that can be identified and addressed. The reverse is the identification of possible fraud, which could be anything from repeated use of an individual email address, or a single user contradicting themselves over a series of transactions.
The beauty of machine learning is it becomes more accurate as the amount of data it has access to grows. Those results can be compared with the findings of skilled personnel to maximize protection.
“Some are so subtle and complex a human cannot find them,” Mr. Thompson said.
As technology continues to permeate society user expectations continue to grow and their expectations for functionality increase. The move to web, then mobile and then single page apps put pressure on companies to provide an optimal user experience in order to retain customers and maximize revenue.
That gives UserReplay an anticipatory role, which they address by regularly meeting with customers to understand their road maps, Mr. Thompson said.
Financial services companies have been late to the game in developing technology which can facilitate a user experience customers have come to expect. Legacy technology and lack of competition (at one point anyway) have been cited as common factors but those excuses are coming to an end as competitors are clearly present and lost revenue opportunities are quantifiable. The technology is there and the financial institutions are availing themselves of it.
“A trend we believe in is financial services will catch up with the retailer customer experience,” Mr. Thompson said.
Case Study: Pizza Hut
Ordering a pizza should be simpler than applying for a mortgage as one imagines fewer touch point and a quick process. It is, and if Pizza Hut in the United Kingdom can find £4.7 million in annual savings through resolvable technical issues, imagine what a bank can uncover.
Pizza Hut had two issues to resolve. The first was to match incoming calls with their specific online journey so issues could be managed and resolved sooner. The second was validating and tracking orders from confirmation to payment to bake, a process more complex than it looks due to third-party merchants, online suppliers and independent franchises. That process usually unfolds in second so the technology had to quickly identify issues.
The technology allowed Pizza Hut to improve its third party coordination. Individual calls were also replayed to identify specific issues which traced back to customer error.