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BI and Perceived Customer Value

The “Big 3” American automobile manufacturers are a popular target for scorn these days, especially since they have requested, and received, bailout money from the federal government.  Their challenges are many: a steep decline in consumer spending, restrictive UAW contracts, state laws that make it difficult to weed out weak dealers, healthcare costs for retirees, and so on.  Perhaps their biggest challenge is to make cars that people want to buy.

In a recent post on his Wall Street Journal blog, Gary Hamel traced the problem of uninspiring car design (making cars that people do not want to buy) to a lack of understanding of perceived customer value.  In his words, “the senior executives {in this company} had a hundred ways to parse costs, but were mostly clueless when it came to dissecting perceived customer value.”  What can we learn from the troubles of the domestic automobile industry and apply to the food industry, and, more specifically, to business intelligence (BI) for the food industry?

What Food Companies can Learn About Customer Value

The obvious parallel is that food companies also need to understand the attributes of their products, its packaging, brand positioning, and the shopping experience its customers and consumers value.  The budding consumer insight movement in consumer packaged goods, including food products, is an attempt by manufacturers to understand how consumers perceive value.

But let’s think more broadly.  We who work with business intelligence love data, like to measure things, and endlessly pontificate about a “single version of the truth.”  We are comfortable working with costs, because they are discrete, “hard,” primarily internal to the organization, and easy to either obtain or calculate.  Customer value, on the other hand, is “soft,” external, and often difficult to calculate or even estimate.  Often, we prioritize our BI efforts based primarily on what data exists and is easy to obtain.  Such prioritization is a mistake.  We also must consider what information, insight, and understanding will contribute to the value of the business and work to increase that value, even if doing so will be quite difficult.  If we limit ourselves to BI initiatives that use easy, “hard” data, we risk both our efforts and our enterprises becoming like the Big 3 automakers: irrelevant and uncompetitive.  No enterprise, be it commercial, governmental, or not-for-profit, can cost-cut its way to success.  Ultimately, it must satisfy the needs of and bring value to its customers.  Our highest purpose in BI is to help our enterprise do just that.

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