A Crawl, Walk, Run Approach to Optimized Pricing
Optimized Pricing Made Easy
It’s no secret that today’s retail environment has morphed into a fast-paced and competitive market. And in today’s omnichannel world, retailers are dealing with demanding consumers who expect complete price transparency and are not brand loyal.
Price remains king with consumers rating price as a top priority for their purchases in certain retail channels—89% for web, 67% for mobile, and 62% for in-store. And today’s shoppers are extremely empowered with 90% of shoppers stating they would leave a store to buy someplace else due to price.
With price as a primary purchase driver and a major contributor to margin leakage, it’s not surprising many retailers place it at the top of their challenges to tackle. Fortunately, price optimization can be implemented in a crawl, walk, run paradigm for easy implementation and adoption.
With the ROI from each stage more than funding the next, retailers can also reinvest in other areas of innovation.
In the crawl stage, retailers gain a solid understanding of both shoppers and competitors and a clear-eyed view of how effective their pricing and promotions really are—what worked, what didn’t and why. With this information, retailers can build solid pricing and promotional strategies—driving margin, growing basket, generating traffic, enhancing price image—and know which products can help achieve them.
Good analytics typically reveal counter-intuitive insights. For example, did you know for a typical retailer that 90% of its promotional revenue comes from just 30% of promotions, while 85% of profit comes from just 15% of promotions? And a third of promotional revenue lift comes from the cannibalization or drag effect of the other items while only 5% of promotions drive incremental profit. In other words, 65% of promotions add to profit, which is in turn wiped out by the remaining 35%.
In the walk stage, retailers move on to implement price management (rules-based pricing) and forecasting, allowing them to align their pricing to key policies such as good-better-best relationships, family and promotional groups, and end number rules and MAPs.
The underlying science understands how shoppers react to price and can predict with high accuracy the impact of systematic price changes that are aligned to support category strategies and business rules or policies. Retailers can model different strategies, see them side-by-side and implement new strategies with ease. Additional ROI can be derived in the walking stage.
In the run stage, retailers move to price optimization, applying Artificial Intelligence (AI) science to determine the shoppers’ price sensitivity or elasticity-level for each item and to see how raising or lowering a price will impact demand and overall performance.
The science is always on, monitoring market shifts and learning shopper’s sensitivity or elasticity to price for every item within a retailer’s assortment. This information is used to identify the optimal everyday price, determine the markdown price, and cadence to close discontinued items. The information from machine learning and optimization technology can also recommend targeted, prescriptive promotions including which exact products to promote, which channels to promote them through, the right combination of marketing vehicles, the optimal offer, and price.
Retail is detail and the massive amount of data retail generates can propel store owners from the state of analysis paralysis into the world of predictive and prescriptive pricing. Data science and agile cloud-based technology can remove the need for costly internal infrastructure and resources that were once required to support pricing and promotional practices and quickly even the playing field in a hypercompetitive market.