The roots of business intelligence relate to customer connection. From the early days of BI, organizations have tried to understand the attitudes and intentions of consumers so they could improve products and services, price them appropriately and plan operations and finances accurately. From sales forecasting to customer retention, demand planning to trade promotion management, critical BI data revolves around the consumer. True, supply chain analytics, budgeting and other areas are relatively independent of consumer data, but best-practice companies covet accurate sources of data about their current and potential customers. For a long time, we have used post-sale data to discern what's going on with the customer. ACNielsen, IRI and other data providers built large businesses based on data collected at the scanner belt in the supermarket and other retail outlets. We've always known that, while interesting and informative, this post-sale data tells us nothing about the consumers who did not buy the product or service, nor does it tell us anything about the thought process that actually led up to the consumer's decision.

I remember working with a consumer packaged goods customer that made two versions of their product package with two different UPC codes. Because the product was vendor stocked, they could ensure equal placement of each of the two packages on the store shelf. Later, they used scanner data to discover which package their customers preferred. Needless to say, we all felt very sophisticated about such uses of experimentation and data at the time!

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