When it comes to automated pricing, retailers have several different strategies to choose from.
Two of the most popular, however, are dynamic and personalized pricing strategies. Both capture the power of big data to provide competitive pricing on key products, but the difference between the two might be unclear to some retailers.
In this blog post, we’ll highlight the key things you need to know about each of these methods, as well as the pros and cons of each.
What is personalized pricing?
Personalized pricing is the latest automated pricing model available to retailers. In essence, it uses automation to target each individual website visitor with a price that matches their personal buying threshold.
A great example of personalized pricing is used by the hotel website Orbitz. The company uses data such as zip code, type of browser, and even type of device to determine the spending threshold of a website visitor. Then they display prices for each user depending on the data. For example, Mac users can expect to see higher prices for hotels on Orbitz than their PC-using counterparts.
Personalized pricing is a growing trend in retail. By pricing for the individual and not for a broader demographic group, companies can earn extra sales that they might otherwise lose. Personalizing the shopping experience can also increase customer loyalty and happiness, and companies can reward returning customers with lower prices and other incentives.
However, personalized pricing does have some downsides. This model is complicated to implement because it needs to use data from each individual shopper as well as the broader market.
What is dynamic pricing?
Dynamic pricing, on the other hand, looks at the broader market rather than the individual customer.
With dynamic pricing, the changes in price are not dependent on the individual customer at all. Instead, prices change because of outside variables, such as the weather, time of day, or available stock. McKinsey reports that retailers who use dynamic pricing report sales growth of 2%-5%, as well as margin increases from 5%-10%. These retailers also report higher levels levels of customer satisfaction.
A fantastic example of dynamic pricing that you’ve likely interacted with is Uber’s “Surge Pricing.” During peak hours when the demand for a cab is higher, the company automatically creates a “surge rate” that everyone in a given city must pay. This surge rate applies to every user, regardless of whether they are a loyal Uber customer or a first-time rider.
Dynamic pricing has numerous benefits, mostly derived from the fact that it synthesizes internal product and sales data with external market and consumer data. Retailers can choose how they want to price themselves, such as whether they want to match their competition’s pricing model or vary pricing based on the customers’ perceived value of the product. In fact, it’s even possible to combine multiple pricing methods into one algorithm to ensure that your pricing remains competitive at all times.
However, there is one main drawback of dynamic pricing: retailers can’t use data to incentivize individual customers into action. Personalized pricing allows you to look at individual behaviors and characteristics and pinpoint promotions for that specific person.
Both personalized and dynamic pricing use data to give online products a competitive edge, but personalized pricing methods are still in their early adoption stages.
At Omnia, we use dynamic pricing to help retailers offer the most competitive and attractive prices in the marketplace. By using automation, the process of increasing profitability in a constantly-shifting marketplace becomes significantly easier. And with a clear 5-step implementation process, Omnia makes it easy for retailers to get started.