Guest Blog By Sudhir Holla
If you’ve ever bought an expensive umbrella from a street vendor on a rainy day, you’ve experienced dynamic pricing. The vendor sets his price by evaluating multiple factors – his inventory levels and pace of sales, the weather, the customer profile (tourist or local), competition levels, the day of the week (holiday or not), and traffic at the location. These days, dynamic pricing is becoming increasingly prevalent, as consumers experience it when buying everything from plane tickets and sporting event tickets to taxi services and even ski tickets.
It makes sense – there are many factors that go into pricing for all types of goods and services. Take skiing for example: weather and snow conditions can change consumers’ perceived value of a day on the slopes. Likewise, it matters to sports fans which opponent their team will face in any given game, and as a result, they’re willing to pay more for some games than for others.
Dynamic pricing has always existed. Whether it is changing prices based on store location or markdowns, retailers have always used price as a key lever to increase margins. What’s changing now is the availability of vast volumes of data related to the digital “footprints” that consumers leave behind as they interact with retailers, and the ability to analyze that data. This data can help retailers anticipate consumer behavior and determine what elements have the biggest impact on price elasticity.
The Impact of Product Images, Videos and Reviews on Consumer Price Perception
There is a strong correlation when it comes to the influence of reviews on product turnover and price elasticity. Even one or two reviews can have a dramatic effect on sales. For example, take Amazon’s top-selling bathroom cleaning product, the spray-before-you-go toilet spray Poo-Pourri . Once the first few positive reviews were posted (and perhaps the product became more socially accepted as a result), the product saw dramatic increases in conversion rates. In Ugam’s research, we’ve found that there is a .25 correlation between the number of reviews and price elasticity, indicating a strong logarithmic relationship (the first 10 reviews have the biggest impact and the next 100 have the same impact).
Images can have a strong impact on price elasticity as well. Some retailers find that by providing a richer consumer experience (with more images, higher-quality images, videos and more useful product descriptions), they reduce price elasticity and can charge higher prices. There is a strong correlation between images and conversion rates supporting this, though not quite as strong as the relationship between reviews and conversion rates.
Price Elasticity and Web Traffic Data
Most retailers capture price elasticity by looking at historic values. They draw a graph comparing how many units sold at different prices, and then come up with a relationship. But historical prices don’t take some things into consideration. If, for example, a sudden surge in demand and similar drop in price sensitivity for a particular dress occurs after a certain celebrity is seen in the dress, historic data can’t capture this information quickly enough, but real-time web analytics can. Retailers can look at the traffic pattern for individual products and use that data on any given day or on a given week to determine the best price to put on the product.
The Science and Art of Dynamic Pricing
Collecting this dynamic pricing data is no easy task. Just the raw product information necessary for product mapping can be enormous. Each variation, color, size, bundle, etc. of a particular product represents a single stock-keeping unit, and online vendors can easily have hundreds of thousands of them. Plus, there’s collecting competitor data and mapping it to align with products, which is not always easy, especially with store brands, differing packages and unit sizes, special offers, etc.
The key to smart dynamic pricing, though, is the analysis of the data and implementing optimum prices quickly – not just based on historic data, anecdotal information about demand or even gut-based decisions. Most electronically controlled pricing today is made primarily by rules engines (i.e., if competitor X has a price lower than mine, lower the price by X), and in many cases, they create a pricing war that no one can win. These simple pricing solutions fail to incorporate the Big Data demand signals.
Only by understanding the demand signals that consumers leave behind as digital footprints can retailers get a step up on their competitors. With the right mix of product intelligence and consumer demand data, retailers have an opportunity to compete that goes beyond just having the lowest prices. They can differentiate themselves by providing a richer consumer experience with less price elasticity, while optimizing prices that help them meet their objectives – from increasing sales to boosting margins and clearing inventory.
Sudhir is a senior vice president at Ugam.