In search of the perfect price point

Price optimization software suggests retail pricing based on fluctuating consumer demand

A year ago I rarely heard the term price optimization. Now on any given day my inbox has two or three e-mails announcing software with that capability. In addition, during the last year and a half the big four consulting firms have all added pricing optimization practices to their portfolios.

The reasons for the sudden interest are numerous. In manufacturing, material costs are rising. Manufacturers want to protect their margins without risking sales or customers. On the retail side, savvy consumers have learned to look for marked-down goods. Retailers would like to adjust prices according to how popular products are and how many are left, says John Parkinson, CTO of Capgemini, but without pricing optimization they don't know what the customer will accept.

Price optimization taps into historical data of customer buying patterns in different segments, using any number of variables -- location, time of year, or even day of the week or time of day -- in order to determine how to price products. The software predicts how a unit change in price will affect demand. For example, it might determine that a price change from $2.29 to $2.59 would cause no drop in demand for a box of tissues.

This technology is already in demand in both b-to-b and b-to-c industries. In both cases, IT needs to deliver the technology to make it happen.

On the b-to-b side, I spoke with Pete Eppele, senior director of products at Zilliant, an ISV with a suite of pricing software. Eppele notes that, until recently, price optimization has been driven by the business center or as a departmental buy. Lately it's begun climbing higher on the IT priority list as it enters the mainstream.

Zilliant's software looks at the history of price variations over a period of time, builds a statistical model, and infers effects of pricing. It sees different price actions, determines the response based on cost and inventory position, and makes a price recommendation for each market.

SAS focuses more on the retail industry with its SAS Revenue Optimization suite, which includes Regular Price Optimization, Markdown Optimization, and Promotion Optimization components. Among many other things, SAS solutions can determine how far a consumer will travel to get a better price, and for which products.

Cyndy Renfrow, senior director of global retail market development at SAS, says that in retail the trend toward price optimization is giving CTOs a new career path. "Now they are moving into the EVP level of merchandizing and planning. IT is becoming a necessary part of the business process, which has to be embedded," she told me.

Price optimization is also enabling the "real-time enterprise," Capgemini's Parkinson says. "You can't be batch processing anymore. You need to tell what product sold in the last 30 minutes."

Parkinson says having a persistent connection to the store is essential, and some local processing is also required. This means a more distributed network and more connectivity to worry about. It also signals a greater need for accurate data on promotions and markdowns, obtained by tying the retail operations system to the merchandizing, planning, and allocation systems that set prices.

Most importantly, when gaining insight into the business is the goal, solutions such as pricing optimization unite the tech and operational aspects of the enterprise. "They both need to be collaborating on the solution," Renfrow says.

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