Business Intelligence Is Not an Algorithm
Progressive companies are engaging in sophisticated data collection and data-mining efforts to help uncover hidden insights about their customers' experiences with their products, services, and brands. Realizing that business intelligence is not an algorithm—looking at the same type of data the same way, hoping to see something new—they are adopting tools and techniques that help them gain broader insights, and to do so more quickly.
They are then converting this information into true business intelligence that will accelerate decisive action about product mix, marketing efforts, and service levels.
In large part, however, companies are using quantitative data-mining techniques to uncover trends in customer experience, determine the magnitude of problems or opportunities, and evaluate the relative performance of retail or service outlets.
For example, marketing leaders that want to dig deeper to better understand the real effectiveness of in-store merchandising on their customer experience—and ultimately on financial performance—are typically monitoring in-store merchandising execution through secret shops, tracking unit-level trial and usage statistics, and monitoring customer feedback.
These efforts can tell marketers how well merchandising is set up and identify which units are not seeing the expected sales lift. However, these techniques alone often fail to provide enough intelligence for leaders to know what the right corrective action is.
To enhance these traditional types of quantitative data-mining efforts, marketing innovators are implementing new tools and technologies to add a qualitative dimension to the analysis.
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Ron Halverson is founder and principal of the Halverson Group, Inc. (www.halversongroup.com). Formerly a research psychologist in the US Army, he has over 15 years of expertise in applying advanced analytics in organizational settings.
