Is It Too Radical To Rethink Pricing Optimization Strategy? Hint: If The Answer Wasn’t ‘Yes,’ Would We Have Even Bothered To Ask The Question?Written by Evan Schuman
Pricing optimization today has evolved into software from one chain watching software-generated pricing from 20 other chains. The programmed engines then instantly undercut other programmed engines, sometimes resulting in hysterically priced products such as multi-billion-dollar outdated Windows CD-ROMs. But now consumers can see these pricing patterns in real time, courtesy of various data-scraping Web sites.
This new pricing transparency could severely undermine the purchase behaviors these optimized prices were supposed to cause. Will shoppers wait out fluctuations, knowing that prices will eventually drop sharply again? Will the dizzying speed of the changes make them ignore the price wars entirely and default to the chain and products they’re most comfortable with? Should this new shopper pricing transparency change how retailers use pricing optimization?
It’s well known that mobile is having a big impact on in-store pricing. But the impact on Web pricing—let alone the intersection between online and in-store pricing—is less clear. This gets even more complex. Chains today are soon going to have three buckets of pricing optimization options to crunch: internal, such as inventory level, close-outs, slow-moving SKUs, manufacturer incentives, etc.; external, such as competitor price monitoring or what rivals are running out of; and customer/CRM, which is individualized pricing based on a shopper’s purchase history, cart assortment and demographics—for both online and, ultimately, in-store pricing.
(Related story: “Window Shopping Felonies “)
The issue of pricing transparency was given a jolt through some recent mainstream media stories, most notably a story from November 30 in the New York Times that compared pricing at Amazon, Walmart and Target through price-scrubbing (and other techniques) done by pricing vendor Dynamite Data.
Dynamite Data also shared similar Thanksgiving timeframe data from the same three retailers with StorefrontBacktalk, and the numbers showed the expected price tag ping-pong.
On one product—video game Assassin’s Creed III—the vendor looked at pricing from November 18–27 (surrounding Black Friday, November 23). Target was positively stoic, consistently charging $59.99. Walmart was almost as consistent, charging three cents less than Target for every day except November 24, when it dropped the price to $38.96 for that one day, before returning to its regular $59.96.
Amazon, however, was positively manic. It started about $10 less than both Walmart and Target and stayed there for November 18–19. No longer feeling the need to be that generous, Amazon increased its price to $56.57 (just about $3.50 less than Target and Walmart) on November 20–21. This also enabled the E-tailer to visibly drop its pricing way down to $34.99 and to stay there for Black Friday—and the day before Black Friday and the day after Black Friday (so November 22–24). Amazon then slowly hiked its price ($49.99 on November 25, $56.02 on November 26 and $57.04 on November 27) to be almost $3 less than Target and Walmart.
Dynamite Data CEO Diana Schulz said her firm grabs data directly from retailer sites, in addition to examining circulars and tracking lightning deals. To be strictly legal and technical, it’s not clear that these techniques—especially the Web data collection—are permitted. But it’s also unlikely that any chain (which benefits from this data collection and analysis more than it’s harmed by it) would take any action. (StorefrontBacktalk Legal Columnist—and former federal prosecutor—Mark Rasch argues that vendors using such techniques, and the retailers who use the resultant information, are taking a chance.)