Today’s fast-changing retail landscape is, depending whom you ask, frightening or thrilling, full of menace or ripe with opportunity. Competition has proliferated from the big box store down the street to eTail giants, specialty retailers and discounters not just locally but in countries halfway around the globe. Shoppers surf prices across all items and all channels, 24/7, and demand total convenience in fulfillment, shipping, and returns. Traditional retail bedrock assumptions are thrown out the window — with one notable exception: price is still king. In a recent Revionics-commissioned global shopper study conducted by Forrester Consulting, that came through loud and clear, with price being cited as the #1 factor when consumers are deciding where to shop, across every retail sector.i
With shoppers in complete control, retailers that fail to innovate in pricing and promotions are at risk of losing relevance — forever. Evidence shows that to succeed, retailers must completely rethink their approach to pricing. Instead of focusing on knee-jerk price-matching for each SKU, they need to understand what prices and offers are relevant on which items to which of their shoppers, and in which channels.
Fortunately, the current generation of price and promotion optimization tools utilize sophisticated artificial intelligence (AI), models that ingest huge quantities of data and apply algorithms that describe, predict and ultimately prescribe prices and promotions to bring a retailer’s price strategy to life, engage shoppers meaningfully and craft a resonant price image — all while ensuring healthy overall margins for a sustainable business.
Frequently retailers approach price and promotion optimization as a multi-step journey, delivering ROI at each phase. For example, an initial implementation can deliver ROI within a few weeks simply by grabbing the low-hanging fruit of science-based key-value item, store zone cluster and competitive elasticity analyses. As retailers progress with the implementation of price optimization, they can boost that ROI by taking price recommendations in certain categories, expanding to the full assortment over time.
Similarly, they may begin their promotion optimization journey with science-based promotion performance analysis, which systematically assesses past promotions, including cannibalization and affinity analysis across channels and promotional vehicles, to show which promotions, channels and vehicles really helped them achieve their business goals and which missed the mark — and leaked precious margins in the process.
With 52% of shoppers saying they receive weekly or monthly promotions on items for which they would have been content to pay full price,ii retailers can see immediate, powerful bottom-line impact just by stopping ineffective promotions. These promotions not only needlessly cost money and fail to achieve their business objectives, but they may even alienate the very shoppers they are trying to attract: studies show that 37% of shoppers feel annoyed, shop less often or are indifferent when they receive these misguided promotions for items on which they would be content to pay full price. ii
But at this phase, retailers are still highly focused on item-driven pricing and promotions. As they move up the value chain and begin to unlock the power of more strategic price and promotion optimization that takes ML and AI science to another level, they can do what-if scenario analysis on finely sliced shopper segments to know exactly how different shoppers will respond to various prices and promotions and in which channels. Pet supply companies may find that dog owners respond to different types of offers than do cat owners, while grocers may come to see that shoppers in certain zip codes respond more readily to mobile offers versus promotions mailed in a flyer.
This is where the retailer begins the all-important evolution from item-centric price and promotions to shopper-centric price and promotions, giving shoppers the personalized experience they crave. And retailers may be surprised to learn how comfortable shoppers are with retailers taking this AI-driven approach: an overwhelming 78% of shoppers think it is fair to use data science to increase and decrease prices, as long as they are presented with prices they’re willing to pay.iii
Adding to the virtuous cycle is the fact that the science-based tools, which continue to evolve and learn as shopper behaviors and preferences shift, know far better than an unassisted human what prices the shoppers consider fair and non-arbitrary. At a time when retailers fight for the attention and loyalty of every single customer, this is indeed critical: 59% of shoppers report that they are angry when they encounter prices that they perceive as arbitrary and don't make sense.ii
In an increasingly fast-moving and complex landscape, the sense of fairness between a shopper and a retailer can mean the difference between thriving and surviving — or even outright failure. With a shopper-centric AI-based price and promotions approach, retailers can achieve pricing nirvana: giving shoppers prices and offers that are most meaningful to them while at the same time knowing exactly where they can recover margin, invest in the long-term health of the business and remain relevant.
[i] Demystifying Price and Promotion, a Forrester Consulting study commissioned by Revionics, November 2017
[ii] Indiscriminate Promotions Cost Retailers, a Forrester Consulting study commissioned by Revionics, May 2018
[iii] Understanding Retail Customers' Pricing Expectations and Tolerances, a Forrester Consulting study commissioned by Revionics, June 2017
Jeff Smith is Founder and EVP Corporate Strategy and Development for Revionics. He has over 23 years of experience in the software industry, with over 11 years working in retail price optimization. He was a founding member of Khimetrics, where he helped create the retail price optimization industry in his sales, marketing and product development roles. Throughout his career, Smith has managed software development teams, worked closely with clients during beta and installation phases, led worldwide product marketing teams, performed software development tasks, and has many years of sales and pre-sales experience.