Marrying art and science to improve lifecycle pricing

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To help manage pricing complexity, most large retailers use traditional big data analytics to make better decisions but a few of the most innovative companies are using a more holistic approach to make demonstrably superior pricing decisions.

Retail prices look simpler than ever to consumers–and more complex than ever to retailers. Every day, tens of millions of shoppers around the world go online to get reliable price comparisons in seconds. This phenomenon, the “Amazon effect,” will only get stronger.

To drive profitable growth, retailers from soft lines to consumer electronics are learning to harness enormous troves of data and anticipate and account for promotions, couponing, markdowns, “buzz”, and even weather to set the right out-the-door price every day for each product in each marketplace–and determine exactly what else they should offer to persuade some consumers to look beyond price.

To make progress, they must overcome substantial challenges. Setting the first price in a retail setting is like making the first move in a three-dimensional chess game in a hailstorm: it’s only the beginning, conditions are shifting by the hour, and each move changes competitors’ responses. Multiply that complexity by thousands of SKUs per store, hundreds of stores, multiple channels from digital and catalogs to bricks & mortar, disjointed promotional and markdown decisions, and shoppers whose preferences and behaviors vary by region and demographics, and the game becomes far too complex for even the most experienced human to master.

To help manage that complexity, most large retailers now use traditional big data analytics to make better pricing decisions. But a few of the most innovative companies are now using a more holistic approach to make demonstrably superior pricing decisions at every stage of the sales life cycle, from regular to promotional and markdown prices in each region and season.

The most effective holistic approaches we’re seeing don’t rely only on big data analytics: they also incorporate the judgment of human experts. Computers are great at sifting through mountains of data gathered last week but don’t have intuition about competitors’ moves or sudden changes in consumers’ tastes next week. The pricing process seems to work best when humans with appropriate experience in the business understand and oversee it, even if it is largely automated.

That’s why the most sophisticated retailers are taking a fresh look not just at pricing tools but at how humans use those tools, including decision-making processes and organizational structures. Retailers who strike the right balance between human judgment and analytics are expanding sales and margins simultaneously by 1-3% overall and up to 10% in some categories. Some are generating hundreds of millions of dollars in bottom-line impact each year. Here we outline some of the ways they’re finding this balance.