How to use price elasticity in brick-and-mortar retail?

The pricing professionals we meet admit that when it comes to putting price elasticity into practice, the technological challenge implied blocks limit their ambitions: they share disappointments due to bad experiences, frustrations with the reliability of the coefficients and their operational execution, or a lack of understanding of the approach proposed by the technologist – which they might consider too opaque. In the end, very few retailers actually implement it today, or when they do, they are forced to limit their ambitions. So, how can we equip ourselves correctly when it comes to elasticity?

Differentiating between web and in-store elasticity

The first step is to assess the relevance regarding the retailer’s business model. A physical retailer does not have the same needs as an online retailer when calculating and using elasticity. Let’s take a closer look at the differences between elasticity in e-commerce and in physical retail:

On the internet, pure players can instantly change a price down and then up in a matter of minutes to generate elasticity tests, the results of which are then used to maximise the margin of certain products depending on the traffic on their web pages. Shops, on the other hand, are constrained by paper labels and price display legislation to avoid misleading consumers between the time they pick up the item on the shelf and the time they check out.
The mechanics of buying online and in physical retail are different. In shop, several behaviours can be observed. To begin with, consumer « A » does not make a comparison once in the shop because he has made the effort to go there. He will potentially buy more so as not to come back and probably has no idea of the competing price or whether the nearest competing shop sells the same reference. In a second case, consumer « B » will compare the web prices on his mobile and the prices in front of him in the shop. He will evaluate the difference between the web prices and the delivery costs and the products in front of him, to judge whether he prefers to wait for his parcel and potentially pay less or pay more and have his product immediately. The retailer does not want to take the risk of shocking the consumer with « insulting » prices that would push him towards the competing retailer or an online purchase. On the other hand, in e-commerce, there is « cheapest price » logic because the Internet user spends more time and uses more sites to compare prices. In addition to the product’s price, they will also be able to compare delivery costs, which have an impact on the overall price image of the online retailer and which can nevertheless be a source of margin for the latter.
This very high comparability of products in e-commerce applies as much to large purchases as to « small » purchases (e.g. a pillow), whereas in physical shops, comparability during a shopping trip or before going to the shop is more likely to be done for « large » purchases (e.g. electronics or household appliances). Thus, it would be a mistake to use web data only and extrapolate it to shops.
Decreases in sales can be explained by events in the shop that are not the result of a consumer reaction: for example, a pallet may have been left in front of a shelf, and a product may have been less visible and therefore less purchased. In addition, consumers are increasingly receiving communication campaigns on social networks from local shops seeking to generate traffic. Consumers can find commercial happenings, tastings, animations linked to annual events in the town (e.g. the 24 hours of Le Mans, the surfing competitions in Biarritz, etc.), or specific events. These events should not bias the analyses, hence the need to gather very large quantities of data to eliminate price-independent factors in the calculation of elasticity.

Physical retailers, therefore, need a physical retail specialist who can offer powerful technology to adapt to the complexities of the shops and processes of the retailer, especially when they have a catalogue of tens or hundreds of thousands of items.

More often than not, the teams we meet regret that they have too few tools (Excel or BI tools) on the subject of price to improve their elasticity performance. Due to a lack of technological resources, they are led to make decisions to change prices « willy-nilly », and sometimes do not risk a change due to a lack of visibility on the possible impacts.

This impact control is tedious because of a time lag in the reporting of till receipts and the time required for reporting, often manually, on Excel. In this configuration, it is impossible for them to identify and react quickly to a loss of sales or cannibalisation between products. In addition, the reliability of the elasticity calculation results relies on massive volumes of data, especially sales history, which only state-of-the-art technology can handle without sequencing or pre-aggregation. Without a dedicated tool, this volumetric nature is an obstacle to the concrete testing of elasticity formulas over time. Finally, if their tool must first overcome this first stage of calculating elasticity, it must also be able to compare, simultaneously, different price scenarios and different volume scenarios over the entire catalogue, in order to become a real decision-making tool.

Price elasticity with Mercio

Mercio offers a unique technology solution to help retailers refine their sales and margin estimates to support pricing decisions. The retailer can not only apply its price testing policy to calibrate the elasticity models to its context but also integrate different price elasticity models into the impact simulations to refine and improve the reliability of the price update. Teams will always retain control over pricing decisions with: upstream of the strategy application, the ability to compare several price and volume scenarios, and downstream, real-time feedback of results to adapt the new strategy if necessary. The strengths of elasticity with Mercio are :

Accessibility: The user logs on and has instant access to the data needed to make projections, taking into account simple elasticities (variation in the demand for a product following its price change) and cross-elasticities (variation in the demand for product X in relation to the variation in price of product Y). This allows him to identify ‘psychological thresholds’, a concept that refers to the consumer’s willingness to pay. How much is the consumer willing to pay for product X according to the value he has of this product, and at what price will he switch to product Y?

Predictive analysis at very fine levels: With Mercio, simulations of the impact of a price variation on demand correspond to different price lists crossed with different possible sales and margin scenarios for each price considered. These simulations are calculated in a matter of seconds, taking into account the large amount of historical data that is essential to the reliability of the result. The user has no constraints on the number of simulations he can run and therefore has all the information at hand to validate the price change that meets his objectives.

Use case

Let’s imagine a product manager in charge of razors working on two strategic products: the Gillette razor and the private label razor. The questions he asks himself before deciding on a price change would be: What do I want to analyse? What decision do I need to make based on my objectives at the product level and the global level?

1) First analysis grid, simple elasticity: analysis of the variation in demand to a change in the price of a product

The product manager wants to change the price of a Gillette razor upwards to increase his margin. He needs to analyse how far he can go in raising Gillette’s price to not lose sales on this product or damage the price index and the overall perception of the brand if the gap with its competitors is too large. Indeed, the consumer can know the prices of razors from memory because :

The disposable razor is a regular purchase.
They may have a particular attachment to the Gillette brand rather than another and therefore know its price. If the price goes up, will the brand lose a loyal customer?
They may have a specific budget in mind for this purchase, this is called the consumer’s « willingness to pay. »

Beyond a price crossing, the consumer can see the price difference and lose confidence in the brand. By making adjustments on the fly, the product manager will be able to determine the psychological price not to be exceeded for the Gillette product according to the objectives he wishes to achieve.

2) Second analysis grid, cross-elasticity: analysis of cannibalisation between products within the offer

The product manager works on the Gillette national brand positioning and its private label, which are the two most strategic brands for the company. He needs to identify:

At what price or price differential between the two brands will consumers switch from one product to the other? The product manager wants to identify the optimal selling price for each product so that each product finds its audience and maximises the margin according to the potential of these products.

What price per product will serve the brand’s strategy? Once a certain threshold is reached, product positioning becomes a real marketing choice. How and on which products does the retailer want to trigger the act of purchase? Which customers does it want to target? Is it prepared to increase a price to avoid customers looking for the first price and attract a new premium customer? Does it want to gain market share in the ND, or does it want to direct customers towards a product with a higher margin potential, such as its private label?

The power of the Mercio simulation engine allows price changes to be simulated as often as necessary in a stand-alone manner. The product manager can compare the calculated impacts on the fly and decide on the optimal prices for each product based on the results of these simulations.

To conclude

The control of price elasticity is within reach of any physical retailer with the right technologies. Their role is to make estimates of the impact on sales volumes and margins more reliable and speed up the decision-making process, but it is about leaving the price experts in control to validate a price change. So beware of tools that promise « total elasticity management through technology » that expose you to the risk of losing control, damaging your price image, and therefore eroding your margins.

Going further with Mercio :

Valentine Dreyfuss, Founder and CEO of Mercio: « Today’s retailers want to put the consumer back at the centre of their pricing strategy. This means going beyond the ‘black box’ solutions of our competitors to integrate their business expertise. How will consumers react if we change prices in relation to the X value axis rather than the Y value axis? Our ambition is to give them the tools to answer that question. »