Target's war on suppliers could be averted through more creative IT

Better analytics and mobile crowdsourcing could deliver better pricing

target checkout lines
Credit: Kevin Lamarque/Reuters

When Reuters last week reported that Target "is asking many suppliers to take on up to an extra 3-5 percent of the cost of promotions and price cuts after slow sales so far this year," it revealed some serious weaknesses within Target as well as a huge opportunity to prove the monumental ROI of better analytics and crowdsourcing.

First, let me be explicit that Target is trying to get away with bullying tactics that no supplier should have to deal with. In its story, Reuters quoted one supplier making this point: "We have budgets to stick to. We cannot just keep giving them discounts on (product) volumes they are not able to sell — we have to make money when doing business with Target."

It's the retailer's job to intimately know its customers and to understand what products they would like to buy and at what price. If the retailer doesn't guess/project those answers properly, it can't demand that its suppliers take the hit as long as those suppliers did everything they were supposed to. (There were some references to late deliveries causing Target to fine some suppliers, which is very different and is absolutely appropriate.)

Some of this is societal. In Japan, layoffs are an embarrassment and mean humiliation for the responsible executives, since it indicates that they made poor decisions that resulted in having too many employees. In the U.S., Wall Street more often than not cheers companies that announce layoffs.

This pressure has been placed on suppliers because Target dropped the ball and made improper purchasing and/or pricing decisions. Target purchased a specific amount of product for a specific price because it believed it could sell that number at that price. If Target's management made poor calls on those points, the suppliers should pay for it?

The matter on technology, though, is whether analytics — and the underlying data for those analytics — is being handled properly. With today's mobile shoppers, there is even the potential for leveraging more crowdsourcing.

I was particularly impressed recently with a crowdsourcing example from mobile-traffic-tracker Waze, out of Maryland. The story involves a group of consumers who, in an attempt to divert traffic away from their neighborhood, tried to trick Waze into sending drivers somewhere else. The interesting part is not that the trick worked — it did, albeit very briefly — but how quickly the software outsmarted the con.

When a neighborhood became a popular shortcut and its residents didn't care for the increased traffic, several gamed the app. "Every rush hour, he went on the Google-owned social-media app and posted false reports of a wreck, speed trap or other blockage on his street, hoping to deflect some of the flow," said a Washington Post story about the incident.

The story noted, though, that the software quickly detected and blocked the bogus posters and resumed routing people to the shortcut. “The nature of crowdsourcing is that if you put in a fake accident, the next 10 people are going to report that it’s not there,” the story quoted Julie Mossler, Waze’s head of communications, as saying. "The company will suspend users they suspect of 'tampering with the map,' she said."

Let's consider a reasonably sophisticated approach for the Target inventory problem that would leverage comments and preferences uttered by Target mobile users and layer them atop routine transaction data and digital observations (aisle-based cameras) of what shoppers pause to look at and what they ignore.

As for price sensitivity, experiments with mobile pricing show that there is a better way. A few years back, one of the warehouse clubs did an experiment with Microsoft where key products featured QR codes. When logged-in mobile shoppers scanned the code, it would show them a customized price, based on their purchase history and sometimes demographics. The software would repeatedly adjust the price displayed and map it against when the shopper purchased.

Within a few weeks of testing, pricing became far more accurate. Well, accurate in the sense that an ideal price is the one that delivers the best revenue. In other words, it is the highest it can be to still deliver the maximum in sales.

My point is simply that Target — no slouch when it comes to experimentation — should push for more IT creativity before reflexively cracking the whip on suppliers. Why? Because retail wars can never be won by cutting prices. They are won by offering the best merchandise. And a chain that is repeatedly angering key suppliers will eventually find its merchandise options shrink.

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