How German retailers free themselves from price pressure by individual promotions
Once in a blue moon, a clear and unchallenged winner climbs the rostrum of economic success by revolutionizing an industry – as seen with Apple, Amazon, Google. In academic terms, this is often described as blue ocean strategy. But typically, most companies’ “smartest” moves are repeating those tactics and tools, that have shown some alleged effects in the past. This is also true for today’s promotional fight for a consumer’s attention and share of wallet. If company A spends 100 on discounts, company B will spend 110 to outperform A – and the tools itself, let it be circulars, coupons, FSI and the like, also remain unquestioned. Unfortunately, with this “strategy” all players face severe losses, as their lack of true differentiation leads to red oceans – initiated by a war of imitation (wasting more and more discounts) and basic incremental improvements only (“Why not color the circular red?”). The results are massive and ever-present pressures on prices and margins.
The ever-present sword of Damocles
One out of many industries facing the sword of Damocles of high price pressure and low margins is grocery retail. Causes are manifold:
- Fierce, cutthroat competition by other retailers leading to a devil’s circle of increasing discounts
- New digital models and players (like Amazon) challenging the old incumbents (brick & mortar)
- Use of outdated tools (untargeted circulars, dumb coupon distribution rules)
- High dependency on short term goals and habitual revenue streams (advertising subsidies)
- Lack of creativity and technical capacity to master the digital transformation (like inability to best leverage the big data available)
Maybe the idea of “discount” was not a German invention, but Aldi and Lidl crafted it into a real masterpiece, sending prices on a downhill ride in Germany for many years – and now exporting their winning concept to the US and other global markets.
Disruption is under way
Meanwhile, in Germany, first grocery retailers have understood, that a “keep going” mentality will not free themselves from the price pressure they face. Thus, nearly 500 stores from different, leading retail chains, like Rewe Group or Edeka Group, are either testing or have already fully implemented individual promotions based on Artificial Intelligence powered by So1. These individual promotions disrupt generic mass promotion tools and allow retailers to customize offers by combining the right product, at the right time, at the right discount to the right consumer – fully automated, and aligned with the retailer’s objectives and restrictions. First results are quite impressive: across all retailers, individual promotions proved to boost efficiency and customer satisfaction in parallel, e.g. by showing 5 times higher redemption rates, baskets growing by 13% AND discount spendings being significantly reduced by 44% (delivering a 1:1 improvement for retailer’s margins).
Obviously, individual promotions also beware of price comparisons between retailers, reducing the risk of price wars.
Deeply understanding what consumers want makes individual promotions so powerful
This basic idea is as simple as appealing – but to build such a powerful tool from scratch is complex, time and cost consuming. Fortunately, So1 employed some of the best talents worldwide to create and deliver an AI platform (called “So1 Engine”) which ranks campaigns from a large offer pool to suit individual preferences and individualizes discount levels. It takes retailer and brand level substitution into consideration and can perform forward looking multi-goal optimization with consumer satisfaction, revenue, or profit.
More technically speaking, the Engine integrates with the checkout to gather real-time line-item and (anonymous) loyalty card data – refined by GfK or other household panel information. The Engine extracts from an observed purchase the relevant elements of the consumer’s decision making process by means of machine learning:
Whether or not a consumer will buy a product is a function of his (mostly unobserved)
- (1) preferences for product attributes in general,
- (2) the specific attributes of the product,
- (3) his purchase probability at a given time and
- (4) the resulting willingness-to-pay taking into consideration his alternative actions, plus
- (5) the on-shelf availability of the product.
Based on these insights, the So1 Engine autonomously picks the best products in store (or from a given offer pool) to best fit the consumer’s needs, ranks them according to his or her preferences, personalizes the discount, even eliminates unnecessary ones, and distributes the optimal individual promotions via all devices and channels available to the retailer.
Of course, some consumers will now get higher discounts for a specific product than before, but they will also get fewer discounts on other products. And thus, on average, individual promotions save on retailer’s tight budgets and drive incremental purchases, while making consumers feel understood and taken care of.
Chances are high, that these satisfied customers will come back shortly, and that retailer’s free themselves from the price pressure seen in this fierce market.