big data the exchangeHardly a day goes by for me without seeing an article, blog or webinar on Big Data and its potential impact on sales and marketing for financial services organizations. Recent examples have covered banking, insurance and asset management, to name a few.

With all these big ideas about Big Data, the new ways that customer-facing organizations can potentially add value seem limitless. (And there is almost certainly no end to how consulting, software and technology firms stand to benefit, including a firm near and dear to my heart.)

But with all the excitement around Big Data, it’s important to remember an observation that likely holds for most firms: There is often more upside in avoiding mistakes than there is in moving to the "optimal" solution. Phil Birnbaum of the Society for American Baseball Research (SABR) made this point in a blunt and entertaining manner in his blog entry about winning through analytics in baseball (it’s a good read, even if you are not interested in the sport).

Case in point: We recently worked with a company to help improve the effectiveness of its outbound call center. Much of that work centered on data that was truly "big"—hundreds of thousands of outbound calls, dozens of customer profile variables and some complex interactions to track over time.

Early on, we observed that the call center team attempted many outbound calls before 9 a.m., and a quick tabulation of the sales data showed that calls placed that early were tremendously unsuccessful. This was hardly a Big Data insight—the analysis consisted of 10 rows and one column of data—but it was extremely powerful. By simply minimizing the number of calls placed before 9 a.m., the company was able to boost its sales productivity by nearly 15%.

We went on to find the "optimal" customer contact solution, considering such trade-offs as, say, calling a prospect at 1 p.m. versus 10 a.m., as well as examining how often to call each prospect, when to call back, how long to wait between calls to the same prospect and so on.

The additional lift from implementing the "best" call algorithm was expected to produce another 5% to 10% improvement—quite a significant amount over a large volume of calls, but still less than what was gained by simply avoiding the "worst" decisions. While Big Data has justifiably gotten a lot of attention from marketing and sales teams, it seems that most of the focus has been on finding the best possible recipe for customer interactions. But perhaps a better focus would be on avoiding bad interactions, which are often easy to eliminate and very likely to translate into significant productivity improvements.

Topics: big data, Jason Brown, asset management, insurance, call planning