# Real-Time Pricing Algorithms – For or Against Us?

December 13, 2012
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In 2012, Cyber Monday sales climbed 30% over the previous year’s results. Indeed, Cyber Monday benefits both online retailers as they gain massive Christmas spend in one day, and consumers can shop at work or home and thus skip holiday crowds.

In 2012, Cyber Monday sales climbed 30% over the previous year’s results. Indeed, Cyber Monday benefits both online retailers as they gain massive Christmas spend in one day, and consumers can shop at work or home and thus skip holiday crowds.

And yet, underneath the bustle of ringing “cyber cash registers”, a battle brews as retailers now can easily change prices, even by the second, using sophisticated algorithms to out-sell competitors. Consumers aren’t standing still though. They also have algorithmic tools available to help them determine the best prices.

Let’s say you are thinking about buying a big screen television from a major online retailer.  The price at 12 noon is \$546.40, but you decide to go get some lunch to think about it. An hour later, you check back on that same item and now it’s priced at \$547.50.  What gives?  Depending on your perspective, you’ll either end up being the beneficiary of algorithmic pricing models or the victim.

A Financial Times article notes the price of an Apple TV device sold by three major online retailers changed anywhere from 5-10% daily (both up and down) in late November. Some HDTVs changed prices by the hour.

These up to the minute changes are made possible by real time pricing algorithms that collect data from competitor websites and customer interactions on their own sites, and then make pricing adjustments based on inventory, margins, and competitive strategies.

An algorithm is really just a recipe if you will, codified into steps and executed at blinding speed by computers.  Thus, a pricing algorithm may be using inputs from competitor websites and other data sources, and then based on pre-defined logic, churn out a “price” that is then posted on a website. Typically this process is executed in seconds.

Thus, it is increasingly common –depending on the specific item, day, hour, or even minute—that prices of online items change in a moment’s notice. If keeping up with rapidly rising and falling prices seems like a shopper’s nightmare, you’re right. However, consumers also have tools to fight back.

The same FT article points out that some consumers are using websites such as Decide.com to determine the best if not the most “fair” price points. Using either Decide.com, or Decide’s convenient smartphone app, for an annual fee of \$30, a consumer can access pricing predictions of items based on Decide’s predictive pricing algorithms.  Simply look up an item, and Decide.com gives its best prediction of when to buy an item and where.

Today, we take for granted that grocery store prices generally don’t change within the hour, and that prices at the gas pump (while sometimes changing intra-day) generally don’t change by the minute. As data collection processes move from overnight batch to near real time, expect more aggressive algorithmic pricing, coming to a grocer, gas pump—or theatre near you!