Retailers openly concede that today the shopper is more in control than ever, with the ability to see all prices across all channels from all competitors with the swipe of a screen. The rise of the discounters and the continuous encroachment of Amazon into more sectors and formats only adds to the ferocious competitive environment. But there is a potent weapon in the savvy retailers’ arsenal that enables them to deliver finely crafted and targeted prices and promotions that appeal to their target shoppers – while at the same time preserving valuable margins and protecting profits for business sustainability.
Science-Based Analytics and Optimization
The good news is that retailers can tap into affordable, scalable science-based price and promotion capabilities to get unprecedented insights into what items their shoppers are price-sensitive on, where competitive elasticity is most critical – and where retailers can safely recover margins to preserve the bottom line. But don’t pundits frequently tell us that shoppers are suspicious of algorithm-based retail pricing? More good news: two Revionics-commissioned global shopper studies revealed some myth-busting insights into what shoppers truly want in various aspects of retail prices and promotionsi , and a key finding is that 78% of shoppers indicated they think it is fair for retailers to use data science to update prices as long as the pricing is fair – in other words, in line with what the shopper is willing to pay.
Another very interesting finding, as my colleague Cheryl Sullivan (Revionics’ Chief Marketing and Strategy Officer) recently wrote in Retail TouchPointsii, is that “a whopping 52% of the weekly or monthly promotions retailers offer go to customers who would happily have paid full price.” The obvious – and shocking – implication is that retailers are leaving huge sums of money on the table by offering promotions on items to customers who don’t want them. Want to take quick action to stop that bleeding, yet sustain those promotional campaigns that DO resonate with your shoppers? With deep-dive analytics like Revionics Promotion Performance Analysis, retailers can get clear-eyed insights into historical promotions and learn which promotions work, and just as importantly, which ones are ineffective or even downright damaging because they cannibalize more profitable promotions or items. Going a step further, using Promotion Optimization capabilities, the A.I. machine learning models are able to rapidly learn and model a vast amount of possibilities not humanly possible, and to predict and prescribe the right products to promote as well as the optimal offer and price. It is also able to break down promotional lift by vehicle to pinpoint the optimal promotional vehicle combination. Retailers can even simulate multiple promotional strategies and view various offers/pricing with side-by-side comparisons to predict the impact and understand tradeoffs and select the best offer based on defined strategies and targets. By using AI machine learning science to optimize your vehicle mix and item selection for each promotion, you’ll ensure that you maximize the return on your investments.
Another area of the studies that caused us to sit up and take notice is the finding that only 6% of shoppers said they don’t think it is fair at all for prices to change dynamically. Cheryl wrote about this, among other points, recently in RIS Newsiii, and in that article she gives a clear definition of dynamic pricing: “Dynamic pricing has three key elements: it harnesses science to provide pricing that is both targeted and smart, as we just described; it has flexible frequency to enable price changes at a cadence that makes sense for that retailer’s business, be it weekly, daily or even intraday; and it supports rapid, automated processes with self-learning algorithms and automated workflows to enable hands-off updates within a retailer’s prescribed parameters.” As the Forrester study demonstrates, shoppers are ahead of where many industry observers assumed they were in terms of embracing science-based price updates. And today’s AI machine-learning capabilities enable retailers to do deep-dive analytics that factor in competitive elasticity, shopper price sensitivity and demand signal shifts to deliver price recommendations down to the item level as frequently as the retailer’s business demands, be it weekly, daily or hourly.
Altogether this gives retailers a huge opportunity to act now and meet their shoppers in solid win-win territory: leveraging science to give their shoppers fair pricing, particularly on the items they care most about, while making informed decisions to selectively recover margins and sustain a healthy business.
i “Understanding Retail Customers’ Pricing Expectations and Tolerances” and “Demystifying Price and Promotion,” commissioned studies conducted by Forrester Consulting on behalf of Revionics, May and November 2017.
ii “Retail Price Myths Broken Down: The Shoppers Speak On Their Views Around Pricing And Promotions,” Retail TouchPoints, January 18, 2018.
iii “Does Your Pricing Strategy Match What Shoppers Want?” RIS News, January 30, 2018.