A Snarketing post by Ron Shevlin, Director of Research at Cornerstone Advisors
An American Banker article titled Customer Analytics Growing in Banks, which reported on research it recently conducted, made the following points:
“Most (71%) of the 170 bankers in the weighted survey do not [do customer analytics], but within a year that might not be true. Among those non-users, the plans to buy analytics are not impressive. Only 2% plan to buy customer analytics in the next six months, 4% in the six to 12 months and 14% in more than a year from now. What’s going on here? One problem may be definitional: some of the surveyed banks may well have solutions that conduct customer analytics but go by other names. Cost was the biggest barrier, noted by 36%. About 32% said a focus on other initiatives was the primary obstacle to using customer analytics. 23% [expressed] skepticism about the ability of the software to provide business value or a return on investment.””
My take: These findings contrast the research I’ve done, and how I think about the world of customer analytics.
According to the research I’ve done, more than seven in 10 banks have some sort of predictive models in place to support their marketing efforts. Those models may not be deployed across product lines, but they’re there. AB hints at what may be the issue: “Some banks may well have solutions that conduct customer analytics but go by other names.”
That raises the question: Then what are the banks calling it, if not customer analytics? In my experience, people in the industry are more likely to call what they’re doing customer analytics, regardless of the analytical component, than the other way around.
The future buying plans of non-users of customer analytics was surprising. Among the FIs I’ve surveyed, I’ve found a much higher level of interest among the non-users (who I’ve found to be the minority, not the majority).
The clue to the differences between my view and AB’s findings might just be found in the statements about “buying” customer analytics and the “skepticism about the ability of the software.”
In my view of the customer analytics world, banks don’t “buy software solutions.”
Customer analytics is a business competency or capability, that might certainly utilize various technology capabilities, apps, and solutions. But I’ve never thought of customer analytics as something you can buy off-the-shelf, like an accounts receivable package.
The barriers to deploying (or “buying”) customer analytics surprised me as well.
I’ve found the top two barriers to be skills and data — specifically, the lack of both.
In my research, the banks that don’t have deep analytical capabilities cite the lack of skills as the biggest barrier to developing a customer analytics competency. For banks that do have some degree of customer analytics capability developed, the lack of easy to access data is the barrier they cite to making more investments.
The AB’s survey respondents also had some…let’s call them interesting…views on the benefits of customer analytics. Or I should say customer analytics software. Thirty-seven percent said the software improves the impact of marketing efforts, and 28% said that an increase in wallet share was the top benefit their institution reaped from customer analytics.
First off, when they say it “improves the impact of marketing efforts,” what kind of impact are we talking about, what degree of improvement is it having, and exactly how did they measure that improvement?
Second, regarding the increase in wallet share, my reaction is: OH REALLY? Exactly how did these bankers know what share of wallet they have, or had before deploying customer analytics? Reality: Banks have no clue what wallet share they have, and therefore have no clue what impact customer analytics has on that metric.
Anyway, apologies to American Banker for tearing into its research. It might not sound like it, but my objective is not to tear down the research, but to take the conversation regarding the use of customer analytics in banking to a more detailed, well-defined, and quantitative level.