Tuesday, June 9, 2015

“Small Data” and Dynamic Scripting – Carlisle Research Scrapbook

“Big Data” is a big thing in aftersales service support – it will make our lives better in inventory management, sales forecasting, parts and service pricing, … and, ultimately, in better serving the needs of service customers. But, it’s going to take awhile.


Sorting through blizzards of data, big data is a valuable predictive tool. But, when the sightlines are fairly clear, we don’t have to put a lot of time and effort into fancy models and monster analyses. Effective service processes and simple surveys can capture the data directly from the customer - sample size of one - and be 100% accurate.


We might start with some recently available “small data.” We are about to release the syndicated 2015 Consumer Sentiment Survey – Dealer Customer Wave. Let’s take a peek.


#1. Some people will wait a long time, others won’t wait at all, yet others (more than a third) will wait an hour. Knowing the customer’s urgency is important information when prioritizing service, offering substitute transportation, and dispatching jobs to the service bays.


#2. Some people want to be greeted at their vehicle; others prefer to be greeted at a more traditional service advisor’s desk. Knowing this is important in designing dealer service lane processes – but again, “small data” shows us that even within a single dealership; there is no one size fits all.


#3. Not every service customer has the same preferences regarding how they are contacted for status updates. Less than 40% prefer “modern” methods – about the same as those who prefer phone calls. Again, different people are different.


These simple insights come from “Small Data” – a few thousand surveys of individual brand-service customers. The insights here show us that there isn’t one right answer that fits all – the data steers us into looking at customers within discrete segments, each segment having discrete preferences. We all know this is true.


So, why do we treat each service customer the same way when they schedule/arrive for a service appointment? It makes no sense.


Bottom Line: The technologists who are designing the tablets for Service Advisors should rely on some “small data” to accommodate service customer segment preferences. The service experience should be dynamically scripted to maximize accommodation of consumer preferences, which should then maximize consumer satisfaction.


To be perfectly clear, if I know Mary-Joe’s preferences are that: (1) she will wait up to 1.5 hours, (2) likes to be greeted at her car, and (3) wants texts to keep her up to date with her service, then I can…
  1. Prioritize my workload to make sure her car is finished inside 1.5 hours and not have to give her a loaner,
  2. Give her a priority customer card and shift her to a desk-less service lane, and
  3. Set up a texting schedule that keeps her apprised of progress.
We can accomplish results more readily than with big data by doing simple things like capturing customer profiles at first appointment, at service handoff, at scheduling, or at the owner portal. These can be done up front and occasionally refreshed. This removes the margin of error of big data, because we know we are right. And it’s a lot cheaper.


And, most importantly, we can deliver to Mary-Joe a flawless, repeatable service experience.


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