When shoppers perceive that an item’s price is unfair or arbitrary, a majority (59%) will refuse to buy the product. However, they will accept price increases or decreases that remain within a “fair” range, particularly if the changes are based on data science, according to a Forrester Consulting study that examined consumer attitudes toward pricing and promotions.
An original survey in May 2017 queried 1,250 shoppers in the U.S., UK, France, Germany and Brazil and Forrester followed up with additional research in April 2018. Key findings included:
• 52% of weekly or monthly retail promotions go to customers who would have happily paid full price for the promoted items;
• Among consumers receiving promotions for items they would have paid full price for, 17% said it made them want to shop the store or brand less often, and 11% were annoyed;
• 65% of shoppers appreciate personalized prices, but 47% said they would be angry if someone else received a better price; and
• 53% of consumers will wait as long as it takes for an item they want to drop to a price they are willing to pay.
In addition to shrinking their margins by indiscriminately lowering prices, retailers may be spending too much on promotional vehicles themselves. For example, careful analysis might reveal that retailers gain no incremental “bump” by featuring a promoted item on an in-store end-cap. Placement in a targeted email or the weekly flyer may provide all the promotional lift that’s needed. Armed with this information, retailers can use the valuable real estate within the store to feature another product that will perform better with greater exposure.
It’s All In The Timing
Retailers also can maximize their promotional budgets by better synchronizing offers with purchase frequency. “More frequently purchased items, such as groceries, household essentials, personal care products and convenience products, are the most likely to benefit from daily or weekly offers,” said Sullivan in an interview with Retail TouchPoints.
When the TechStyle Group sends retargeting emails to customers that have not purchased for a while, the retailer times the promotional offers to match individual customers’ purchasing cadences. The parent company of Fabletics, JustFab and ShoeDazzle uses algorithms to monitor purchase frequency in order to maximize each email’s impact. So rather than waiting a standard six months to send a “we miss you” email, TechStyle varies the time frame based on an individual’s purchasing patterns.
“We have some people that shop us every single day, while there are others that are ‘wardrobers’ who shop twice a year,” said Traci Inglis, TechStyle Brand President, during a presentation at the 2018 Retail Innovation Conference. She noted that if the algorithm indicates a customer shops every three months, waiting six months to send an email wastes that additional three months’ time. “On the other hand, if she typically only shops every six months, that means you’ve wasted a promotional offer, since she was likely to return to make a purchase in any case,” said Inglis. By using this purchase frequency algorithm, TechStyle achieved a 22% lift for its lapsed customer campaign compared to a previous campaign.
Retailers shouldn’t be timid about changing prices when the data supports the decision, but they do need to “tread lightly,” said Revionics’ Sullivan. She noted that 47% of consumers get angry when someone else’s personalized prices are lower than theirs, so retailers need to “remain focused on getting their customer segments right. For example, you should not be delivering cat food offers to someone who only owns dogs,” she said. “It should be delivered in their preferred channel and meet the criteria of being perceived as fair, non-arbitrary and relevant.”
AI-Powered Algorithms Bring Science To Promotions And Pricing
As retailers try to maximize margins while avoiding “arbitrary” pricing, they have a range of next-generation tools to navigate their course. “Today’s analytics and algorithms using artificial intelligence (AI) can do fine-grained segmenting of shoppers, to sort them by purchase frequency on various demographic characteristics and also provide competitive elasticity analysis,” said Sullivan. “For example, a pet supply company may find that dog owners have very different shopping cadences than cat owners or fish owners, or that urban shopper patterns are different from suburban, exurban and rural shoppers.”
The machine learning-powered algorithms continuously learn shoppers’ purchase behavior patterns and sensitivity to everyday prices as well as promotional discounts, the impact of vendor funds, and the methods/promotional vehicles to reach them, down to a very granular level, according to Sullivan. “The science has learned to detect changes in the signals to anticipate trends before human analysis can uncover them, allowing rapid response to shopper and competitive shifts,” she noted.
“AI-based price and promotional solutions can be a retailer’s best friend and offer a competitive advantage,” Sullivan added. “In fact, I assert that it’s table stakes for any retailer to have any hope of surviving in today’s retail environment.”