Test and refine your strategy

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Some products are better suited to frequent price changes than others. For example, in the apparel industry, prices for fashion items may change from week to week, but prices for basic items (like basic T-shirts or underwear) should generally remain more stable. Customers who have been buying white socks in your stores for years should not experience price shock when they return for another pair. Carefully consider the length of the purchase cycle, as well as consumer expectations for each set of products. Prices for big-ticket items that are typically heavily researched by consumers, like televisions or sofas, should remain relatively stable, as frequent price changes may upset a potential buyer who has been researching for months.

In our view, all dynamic pricing algorithms should be luxembourg telegram number database reviewed by retailers, and most price changes recommended by algorithms should be approved by the retailer before they are implemented. This way, retailers can avoid the consumer backlash that comes with raising prices. For example, last year, retailers who raised prices on cleaning and disinfecting products were seen as taking advantage of the COVID-19 pandemic and thus lost customer trust and loyalty.

Dynamic pricing is both an art and a science, meaning a test-and-learn approach is critical to getting it right. To manage risk, consult with your CFO and agree on the direction of price changes during the initial testing phases. Start with pilots in just one product category or region. Assuming the first few price changes aren’t successful, develop an approach to track progress, measure impact, and make quick adjustments. Spend time with your salespeople during the initial tests and work with them to formulate next steps before moving forward with automated price changes.
For example, at a high-end accessories store, pricing analysts worked with salespeople to embed the logic of their pricing strategy into algorithms. The retailer then conducted market testing to get two important inputs. The first was the limitations of substitutions between very similar items at different price points—for example, the retailer found that most customers interested in a $350 item switched to a similar item priced at $399, but not when the more expensive item was priced at