Customer Experience Tools and Trends

Good point about tool use.  As long as the few tools you use cover what you need.   Good to have a mix of generalists available that can help you choose,  and then focus in on common methods.  Document the results and repeat as often as possible.  Automate as much of the process as possible, especially initial investigations.   Make sure your goals are repeatable.   Schedule re-tests as data changes.    Use learning from experience with industry peers.    Make sure that you also learn about availability of both internal and external data.

Customer Experience Tools and Trends 2018

When it comes to using tools that improve the experience, it pays to go all in with a few rather than dabble with many.   By Gerard du Toit, Andreas Dullweber, Richard Hatherall and Martha Moreau

Among the 20 tools assessed in our global research, the top 3 in adoption are predictive analytics, sensors in products and operations, and personalized experience.

New tools with the lowest current adoption have high satisfaction rates: delivery drones, episode management and privacy management. Early adopters get excited about the benefits and possible competitive advantage.

The greatest benefits typically result from major efforts and investment in a handful of tools, not from limited efforts in a broad range.

Executives are most bullish about three trends: a substantial drop in cash transactions, automated in-store checkout and automatic shipping of products when customers run out.

DiDi Chuxing, the Chinese ride-sharing firm, has more than 450 million users and handles 25 million rides each day. The terabytes of data generated by all those transactions gives DiDi a huge information advantage. Matching the data it collects on every aspect of millions of rides with end-of-ride ratings from customers allows the company to create predictive models: What sorts of experiences typically produce promoters among its customers? Which ones produce detractors? As a result, DiDi doesn’t need to ask all its riders for Net Promoter® feedback; instead, its computer models generate a rating score for almost every ride. Those predictive scores match up very reliably—more than 80% and improving—with what customers say in traditional Net Promoter feedback.

This gives DiDi two advantages: First, it provides almost instantaneous modeled feedback to its drivers. Second, it instantly identifies situations where there’s some need for relationship or service recovery, triggering an intervention. If DiDi’s algorithms identify a pickup that went awry or a ride that took longer than it should have to reach the destination, the company can issue an apology or a credit before the customer even exits the vehicle. If things went especially well, then DiDi’s app can prompt the customer with ways to tell friends about the ride-sharing service’s benefits. .... "