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Mythbusters: Do We Need More Customer Data?

By Feather Hickox

With our ongoing series of mythbusters, inspired by the Discovery Channel’s television show MythBusters, we’ve been tackling hotly debated beliefs related to Big Data, analytics, customer engagement and mobile technology to determine whether the belief can be confirmed, is plausible or is busted (not true). Today's myth that will be put under the microscope: Do we need more customer data?

The “do we need more customer data” debate has been making the rounds for the last year. All Things D ran a series last year asking the question “More Data or Better Algorithms?” It ended with the punch line that better data wins. This goes in stark contrast to the conclusion of Kenneth Cukier and Viktor Mayer-Schönberger.  In their book Big Data: A Revolution That Will Transform How We Live, Work, and Think, they describe Big Data as unbounded and unstructured; imprecise but predictive; and not causal, but correlative. Big Data by its nature is messy data, it doesn’t fit in neat rows and columns, and by definition it begins when current data collection and analytical paradigms fail to fit your needs. Their premise is that more messy data, not better data, wins.

Our take is that we DO need more customer data—but that voluminous data alone won’t lead to success. What will is the ability to pull more meaningful insights both from the data you have, as well as from new data sources.

In the era of Big Data, you need to be able to add new data sources and analytics to your decision processes with ease and without having to queue up for IT time. Business rules management systems (BRMS) and applications that incorporate them enable nontechnical users to inject these powerful new elements into existing processes. Very quickly, decisions get smarter.

Many companies have piles of data they’ve collected but are not yet leveraging for better decisions. Unstructured data, for instance, from customer service chats and phone conversations, as well as online customer product reviews, are full of potentially valuable insights. Analytic techniques, such as FICO’s Semantic Scorecard technology, can extract the most predictive features from this data, and combine it with traditional structured data analytics to improve the accuracy of customer behavioral predictions.

In other cases, where data is already being analyzed, there may still be potential to draw out more value by looking at it in a new way. Many companies have collected transaction log (TLOG) data, for example. But few are currently analyzing it with time-to-event models in order to predict with precision when a customer behavior is most likely to occur (this week? next week?).

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Figure 1: Time-to-event models pinpoint the best time to make an offer

There are also analytic techniques today that can go beyond finding the usual correlative data relationships (when A occurs B also occurs). They can scour existing data to find more powerful, previously undiscovered causal relationships (A affects B in this specific way). With these and other advanced techniques, companies can answer complex questions such as: “Which customers will buy only with this particular coupon, which will buy with any one of three possible coupons, and which will buy whether or not they receive a discount?”

Using mathematical optimization, we can find the best decision strategy, given all business objectives and constraints, for maximizing a particular goal, such as profit. We can also simulate the impact on key performance indicators of making adjustments to one or more constraints.

FICO has helped a large US retailer use techniques like these to achieve response rates of as high as 30% from its loyalty club members. We’ve also helped a leading Canadian bank adopt the analytic techniques to develop an “optimization culture” for driving performance improvements ever higher. In credit line management, as shown in Figure 2, this bank’s latest optimized strategy (purple V3 line) has already lifted incremental profit by $5 per account at the 4-month mark, set to outpace its previous 2 successful optimized strategies (blue V2 and green V1 lines).

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Figure 2: Using optimization to drive incremental profit gains

The reality is that more data sits on a continuum between where it can offer real insight and where it will be a costly distraction. The better question to ask is: What value can you get from more customer data? Collecting data is easy and relatively inexpensive; making the data useful is what’s challenging – whether you’re dealing with more data or not.

Did we convince you? For more mythbusting, check out our Insights paper titled: Marketing Mythbusting—Six Maxims Get put to the Test (No. 73; log-in required)

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