Setting the optimal price for products has been a challenge for retailers that lack formal, centralised software solutions for many years, but the current crisis has brought to the surface just how inadequate a mix of largely manual and departmentally disintegrated processes really are.
With demand patterns significantly altered, as a result of an accelerated move to shopping online and the emergence of new behaviours from consumers battling to cope during the current crisis, our new report comes at the perfect time.
The goal is for retailers to make their prices attractive and profitable by accurately predicting consumer demand. However, although retailers have more data available today than ever, the disruptions of 2020 have made that information more imperfect, confusing, and lacking in actionable insights. As a result, planning teams are now faced with a number of new outliers.
As new channels, retail formats and competitors continue to emerge, it will become harder for teams to read and understand these dynamics as they change and fragment, let alone act on them. In short, it has never been harder for retailers to satisfy demand by category, product, price, customer type and channel at human speed.
Artificial intelligence (AI) is the key to responding to these unusual market and consumer dynamics due to the complexity of the job in hand. Retailers are seeking the right competitive position across the product lifecycle, from everyday pricing through to promotions and into markdowns, that will balance competitive investments with profitability, whilst maintaining a positive price perception with their changing customers.
AI-backed price management tools use customer behaviour in all its variations to determine the optimal price by category, product, retail format and region, and manage those prices dynamically as customer behaviour, market conditions and competitive activity changes.
The retailers that take this approach generally set prices, manage promotions and leverage markdowns most profitably. Revionics’ application of AI and data science has enabled retailers to rebalance their price investments between more and less price sensitive articles in a way that pricing can be self-financing. Volume increases on those products with the highest price elasticity was so significant that it drove higher profit value in total.
Here are the core elements of a holistic pricing strategy
- Understand your customers’ price perception to determine elasticity.
Pricing technologies, by evaluating large volumes of historical transaction data, can determine, very precisely, how price elastic is the demand for a particular product. And this can be applied for both every-day and promotional pricing. These technologies help retailers identify products for which they can increase the price, without consumers reacting. Or indeed, to reduce the price where that drives significantly higher volumes and improves price perception. The key thing is to identify the fine balance: when the price will go from being fair to being offensive that will start damaging the price image of the store or brand.
- Clustering stores according to their demand elasticity and overlaying the geographic zones with the pricing clusters.
Advanced algorithms now enable retailers to dynamically enforce zone and sub-zone pricing based on local demand right down to store level and then allocate and assort accordingly. They can also create special pricing subsets built around, for instance, local products that merit a more sensitive pricing approach that will attract local demand. In addition, retailers can respond quickly to local competitors that start to promote or discount particular products.
- Use AI to respond more quickly to competitor activity.
Once retailers are using data science to understand each item’s price elasticity and a wider range of demand signals, they can start to make price changes weekly, daily or for grocers operating in a very fierce competitive environment, in real or near-real time. Consumers are now used to seeing frequent price changes and price differences, particularly as the number of sales channels rises, as long as they result in a fair and non-arbitrary price for them in the moment of purchase. In fact, in a study with Forrester, Revionics found 78 per cent of shoppers believe changing prices with data science is fair.
- Compete on sustainability.
Sustainable products tend to be less price elastic and hence can command a higher profit margin. By identifying your sustainable products, demand can be better gauged by monitoring current and past sales data. By increasing the number of sustainable brands compared with less sustainable brands, retailers are better positioned to compete on sustainability with other retailers because they have the tools to evaluate demand and ensure they are priced more competitively.
A similar approach can be taken with promotions. By tracking the affinity and the movements between a more organic, sustainable or locally produced product, as opposed to the more well-known national, but less sustainable product or brand, the financial impact can be assessed to see if a trade-off is worth making.
Download our report, Pricing strategies to create loyalty and profitability to discover more about using dedicated price management solutions backed by AI to set prices for maximum returns.
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