Consider consumer expectations

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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 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.

Test and refine your strategy
Dynamic pricing is both an art and a science, meaning a kazakhstan phone number list 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 $400. The second was the response of shoppers to bundle offers. For example, when the retailer bundled two items that would normally be purchased together for the standard price of $499, shoppers only paid attention to the bundle price; they didn’t even notice the price changes for the individual items. The retailer's new pricing strategy increased absolute earnings before interest, taxes, depreciation and amortization (EBITDA) in test categories by more than 50 percent and resulted in an automated pricing system for 500,000 SKUs.

Plan your route
As a basic step, understand your current competitive position in the marketplace and how consumers perceive your brand’s price. Then map out your path to dynamic pricing. Given the starting point of most retailers, reaching the end goal will almost certainly require a phased approach to building and assembling best-in-class data, infrastructure, tools, and people. Don’t expect to get there overnight. Set and manage internal company expectations, demonstrate quick wins, and help move the company forward.

Parsing online store prices: how parsing helps sellers

What not to do:
When implementing dynamic pricing, retailers often make the following mistakes: introducing prices that alienate customers, changing prices too often, and using incorrect data.