The scenario is a familiar one to e-commerce retailers: a supplier increases prices on an item, so a category manager increases the item’s selling price.
NB: This is an article from McKinsey & Co
But this effort to make sales of the item more profitable is promptly undermined by a well-intentioned marketing manager, who lowers the price of the item by 20 percent as part of a promotion.
Such uncoordinated and counterproductive decisions happen much more often than most retailers realize, and they are expensive. Many promotions don’t turn a profit at all, or at least they don’t add nearly as much profitable revenue as retailers expect.
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Addressing this conflict can quickly turn into a game of cat and mouse, in which retailers find themselves constantly chasing the next issue in a highly reactive way. Sometimes they simply avoid the problem by keeping prices low or making small adjustments across the board, in effect creating a permanent discount on their entire assortment.
With better analytics, though, e-commerce retailers can create value by intelligently linking pricing and promotions based on optimal price setting and promotion design. We have observed, for example, several innovative e-commerce retailers increase revenue and profits by three to five percentage points using a highly differentiated analytics process and often achieve improved customer satisfaction and loyalty as well.
Companies that effectively and profitably link pricing and promotions through advanced analytics engage in the following three-step process, which first determines customer price sensitivity, then gauges the likely effectiveness of promotions for every product, and, finally, links the two:
1. Use a wider range of factors to determine price sensitivity
A price-sensitivity score considers the extent to which customers perceive a product’s price and, as a result, react to price changes. If a product has a higher price-sensitivity score, it means that a customer is less likely to accept a price increase. In this case, the price should be kept at a competitive level. (For more on dynamic pricing, see “How retailers can drive profitable growth through dynamic pricing,” on McKinsey.com.)
While most companies consider price sensitivity when they make pricing decisions, the scores often don’t incorporate enough factors and thus aren’t as accurate as they could be. The best price-sensitivity scores are calculated with advanced analytics, using input factors that take customer, competitor, and company considerations into account. For instance, price elasticity is based on different models for each product category, because customer behavior, including purchase frequency and reaction to price changes, differs for each product. By aggregating individual input factors for price sensitivity and promotion affinity, individual scores for each product category can be developed. With price sensitivity identified for all products, items are then grouped into three buckets based on their scores: