Grocery stores run price promotions all the time. You see them when a particular brand of spaghetti sauce is $1 off or your favorite coffee is buy one get one free. Promotions are used for a variety of reasons from increasing traffic in stores to boosting sales of a particular brand. They are responsible for a lot of revenue, as a 2009 A.C. Nielsen study found that 42.8% of grocery store sales in the U.S. are made during promotions. This raises an important question: How much money does a retailer leave on the table by using current pricing practices as opposed to a more scientific, data-driven approach in order to determine optimal promotional prices?
The promotion planning tools currently available in the industry are mostly manual and based on “what-if” scenarios. In other words, supermarkets tend to use intuition and habit to decide when, how deep, and how often to promote products. Yet promotion pricing is very complicated. Product managers have to solve problems like whether or not to promote an item in a particular week, whether or not to promote two items together, and how to order upcoming discounts ― not to mention incorporating seasonality issues in their decision-making process.
When I get a taxi for the 15-minute ride from my office to the airport, I have two choices. I can hail a cab on the street, and pay a metered fare for the 4.6-mile trip. Or I can walk to the local Marriott and pay a fixed fee of $31.50.
Truthfully, I’m always a lot happier paying the fixed fee. I’m happier even though it probably costs more in the end. (A congestion-free trip on the meter comes out to about $26.) Sitting in a cab watching the meter tick up wrenches my gut: Every eighth of a mile, there goes another 45 cents—tick … tick … tick.
Have you ever been shopping and found a great jacket with a perfect fit? Then you look at the price tag and pause. Should you buy that perfect item now or wait to see if it’s still available during the inevitable end-of-season sale? What if the store told you that it only had a limited number left, or only had two on the rack in your size?
In a recent study I conducted with Prof. Karen Zheng, we found that as consumers have become more strategic about purchases, behavioral motives like regret and availability misperception are significant factors and should play a key role in pricing strategy.
Regret happens when consumers compare the outcome of a chosen action with that of the unchosen one and realize they would have been better off with the latter. In other words, they may regret buying the jacket now at the higher price if it turns out to be available during the sale for 30% off. Similarly, they may regret not buying it now if their size is gone by the time of the sale.
U.S. inflation has been accelerating in recent months, presenting the Federal Reserve with a tricky question as it decides how quickly to remove stimulus from the U.S. economy: Is the rise in prices a precursor of things to come or simply a “catching up” phase as people begin to spend again after a brutal winter?
Recent data from the U.S. Labor Department have led some to suggest that the long run of very low U.S. inflation could be ending. From Dec. 31 through May 31, the consumer price index — not seasonally adjusted — rose a cumulative 2.1 percent. That’s equivalent to an annualized inflation rate of more than 5 percent, far exceeding the Fed’s target of about 2 percent.
If this is more than a temporary phenomenon, the Fed might have to respond by raising interest rates sooner than expected — a move that would restrain economic growth and could trigger sharp declines in stock and bond markets.
Some officials at the Fed, though, reportedly do not believe that the surge in consumer prices represents the beginning of a new inflationary trend. After all, in the period just before the winter, from Sept. 30 to Dec. 31, prices actually fell by a cumulative 0.5 percent. Combine the two periods, one with an increase and one with a small drop, and you get an annualized inflation rate since September of about 2.4 percent.
The business pages are filled with examples of companies that have taken big hits to their brands because they’ve made marketing decisions that ran afoul of customer expectations. Take Netflix, and its aborted scheme to divide its streaming and DVD video offerings. Netflix could have avoided its embarrassing reversal if it had experimented on this decision before publically announcing the change.