ADDING ATTITUDES AND WALLET SHARE TO EVERY CUSTOMER. FINALLY.
(A 2 Minute Video)
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ENHANCING THE DATAMART WITH THE ATTITUDES THAT DRIVE BEHAVIORS
THE LIMITATIONS OF CONVENTIONAL APPROACHES
DATAMART ENHANCEMENT USING DECISION SUPPORT ENABLEMENT (DSE)
EXAMPLE: ADDING ATTITUDES TO THE DATAMART
CONTACT / QUESTIONS
THE LIMITATIONS OF CONVENTIONAL APPROACHES TO DATAMART ENHANCEMENT
Everyone know that BigData (Predictive Analytics of PA) completely depends on the quality, depth and breath of the data that it operates upon. After all, "Garbage In, Garbage Out." What is lesser known is that PA is undergoing a data revolution because of the following limitations:
REINVENTING DATAMART ENHANCEMENT USING DECISION SUPPORT ENABLEMENT (DSE)
What if you could leap-frog the limitations listed above by:
And... What if the consulting firm that provides this capability:
Decision Support-Enabled Datamart Enhancement achieves all these outcomes and more. The result? Far higher predictive lifts at far lower cost. Delivered you way. That's enablement.
DATAMART ENHANCEMENT EXAMPLE: ADDING ATTITUDES TO THE DATAMART
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KEY BENEFITS (Place your pointer over the benefits to see the short description.)
There are a large number of strategies used to enhance (backfill) datamarts, including 1) field purchase from data vendors, 2) integration with cloud-based data, 3) enhancement via rules based logic, 4) social-media feed capture, etc., etc. Our focus here, however, will be upon one of most powerful but least well-known enhancement strategies: imputation.
To understand imputation it is first necessary to understand that there are many critical types of data that are MIA (Missing in Action) in the retail datamart! Interestingly, almost all these MIA fields can be collected in surveys. But that, then, begs the question, how can we generalize from say, 10,000 surveys to a million customers? The mathematical process of highly accurate generalization is termed imputation.
The ability to add attitudinal data (or any other kind of survey data) is so critical, the Gartner Group called this strategy of datamart enhancement "the second wave of datamart evolution."
As our case study, let's look at imputation in retail banking:
The reason why imputation is so compelling in retail banking? Retail banks generally hold less than 10% of the assets of their own customers! If these institutions can consolidate even ten percent of those assets, it could easily increase their assets under management by billions.
Here is where the numbers become REALLY compelling. By offering each of your consumers their own customized consolidation value proposition, the average top 10 US bank will increase AUM by over 40 billion dollars.
The key to this magnitude of AUM increase is NOT just following the money trail (or more properly predicting who has high hidden balances.) The real key is the consolidation offer; presenting to your customers a compelling (above threshold) "trigger offer" sufficient to cause them to sweep some or all of their assets to your institution. AIM provides that customized offer for each customer in the datamart.
This is NOT theory. Our AIM (Attitudinal business line) leads the industry in imputation accuracy. In this case study, AIM correctly predicted the assets of over 95 percent of a top ten bank's customers to within 12.5 percent of the actual assets.
Given the very precise imputation accuracy just demonstrated, our own client (a top 10 bank) put together a pro-forma that projected that it would yield almost 6 billion in new balances and an incremental 275 million in margin on those assets annually.
Imputation: The Enhancement Powerhouse
Imputation Supplies Critical Missing Data in the Datamart
Benefits According to Gartner
Let's Look at an Example!
Wallet Share Capture
Accurate Prediction of the Trigger Offer for Each Customer in the Datamart
Industry Leading Results in Wallet Share Prediction
Industry Leading Results in ROI