You’re sitting on a balcony, scanning the news on your mobile. You tap the app of your favorite local coffee house to order your caffeinated beverage of choice. Based on your preferences, the app recommends an exotic coffee sourced from Indonesia, and you decide to try it. Ten minutes later, you hear a distinctive buzz coming towards you. You take your double latte from the drone hovering at your elbow, sip and sigh. Life is good.
Aging inventory and shrinking clearance margins don’t just affect traditional retail stores—factory outlets are equally susceptible. We recently completed an engagement for an international athletic apparel company that demonstrates the significant revenue lift that factory outlets can realize through pricing analytics and markdown optimization.
What makes me an analyst and not just a prognosticator? I verify that my predictions can be trusted—that the number of bananas in my forecast for next month, for example, is a number that can be used to positively influence business decisions. But due to the nature of forecasting, we’re forced to wait for the future to evaluate my present forecasts. So how can we validate the effectiveness of current forecasting methods? It’s a simple matter of traveling back in time.
Lowering the Barriers to Understanding Analytics
on February 24, 2017
| By Nicholas Wegman
|
Demand Analytics
Without question, the worst analytical results are those that are never acted upon. Imagine a company that invests significant capital (both human and financial) into an analytics project. Afterwards, the insights are ignored and never used to drive a decision. Why does waste like this occur regularly?
“You don’t sell a product or service—you sell value.” Salespeople often hear that piece of advice, and for good reason: The value that each customer sees in a product or service is different—and so is his or her willingness to pay for it. Segmentation reveals how much various types of customers (eg, by geography, sales channel, etc.) will pay for your offering.
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