Back in the day, the process of retaining loyal customers and attracting new ones was more art than science. Business managers and their sales forces relied on their own observations and gut instincts to market to customers. But not anymore.
As the science of market research practices continues to prove its value, companies are swarming to business tools that help them refine these homegrown approaches. But powerful computers and the ease of tracking large amounts of data means research is becoming more complex and specialized.
Enter “Business Intelligence”, a catch-all term to describe the reams of information that churns through a company’s databases used to make informed business decisions. Companies can track sales by zip code, bar code, and determine the effectiveness of sales fliers versus e-mail coupons. More specifically, companies might be able to see a size-12 dress in paisley prints sold out in days in Indiana, but lingered for months on the discount racks of Brooklyn clothing boutiques.
It’s dependable data, for the most part, assuming consumer behavior remains the same.
But more recently, as companies contemplate global competition with the click of a mouse, the hazards of just-in-time delivery, fluctuating consumer demand and razor-thin profit margins, they’re not content to rest on past information. They want to predict the future, as accurately and quickly as possible. Thus the rise of predictive analytics.
The power of predictive analytics
Predictive analytics allow companies to make refined decisions or plans, using the same sophisticated analysis. For example, companies can notice that not only are paisley dresses a hit in the Midwest, but customers over 40 in Indiana love coupons in the Sunday paper. The same customer in the same demographic in Ohio only wants the dress she bought online shipped for free. Twenty-somethings from Brooklyn who shunned the same dresses at full price will snap them up at a twenty percent discount, provided that they are stocked next to the cowboy boots and leather jackets for ironic layering.
But the same tools that make predictive analytics so powerful – and precise – are also the reason that some corporations may shy away. These tools can require dedicated programs and even databases, specialized programming skills to input complex formulas and specific knowledge of advanced mathematics and statistics to input and interpret the correct data, barriers that can seem insurmountable to businesses that need to use their resources in the broadest and most efficient ways.
For a long time, the added costs of such analysis outweighed its benefits. But these days, powerful new software allows even small and lean businesses to cash in on the untapped gold mine of information at their fingertips. They take the math out of the mathematical analysis, providing ways to integrate predictive analysis into multipurpose business tools that offer point-and-click formulas and allow broad data mining that draws from a company’s existing databases. Which means that even small and mid-sized corporations can take look to the future, rather than just reflect on the past, all without breaking their quarterly budgets.
So when the nephew from Brooklyn shows up at the barbecue with his new girlfriend who interns for that social media start-up in SoHo and she’s wearing a paisley dress and hipster boots, you’ll not only know why – but also that she’s probably dying for a kale martini.
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