Ever heard of the term price elasticity? Unless you’ve been an economics student or specialise in retail pricing, the phrase might raise quite a few eyebrows. But it’s one of the most important phrases in the retail and merchandising world that is largely unknown.
Retailers, especially in post-holiday season, can end up with heaps of unsold, surplus stock. To move this stock, they may slash their prices – but knowing how much to reduce prices in order to move stock and protect margins, or how to best drive revenue if margins can’t be made, depends on setting the optimal price in line with demand.
Understanding price elasticity enables you to choose the price that is most likely to shift the amount of stock you have in the timeframe you need to do it in. Yet without the right insights, retailers could reduce prices too much and therefore miss out on profits, or not reduce them enough and end up with money going down the drain in housing stock or wasting it.
So, how does it work?
Getting to grips with price elasticity
Price elasticity is the relationship between how changing the price of a product will impact demand. How easily it changes demand equates to how elastic the product is. So, if it’s very elastic, you only need to drop the price slightly and the rate of sale will dramatically increase. If it’s not elastic, knocking a fiver off, for example, won’t impact the demand at all.
A retailer could end up with products that are really inelastic, such as holding a warehouse of leftover winter coats in February. They might need a 50, 60, 70 per cent discount to be moved on. But instead the retailer keeps the price too high or pays the costs of storing them until autumn (when they might be out of fashion anyway).
Conversely, you might have a highly elastic product like some in-demand trainers that only require a small decrease in price to produce high demand. But when sale periods approach, the retailer ends up slashing the prices too much. They might mark them down from £100 to £60 and they fly off the shelves in three weeks instead of the anticipated timeline of 12 weeks.
With hindsight, they could have priced them at £85. But because they didn’t have the data to show them how in demand they were, they couldn’t find the right price. And that’s where AI is needed.
Getting your systems up to speed
Nowadays, the market and trends move so quickly that without AI it’s incredibly hard to accurately work out the optimal price for products and to make them as elastic as possible. AI works best when it comes to objectively analysing a range of data points and then making recommendations from that data – and that’s exactly what calculating price elasticity needs.
Using AI models, retailers can analyse and model hundreds of thousands of stock and price combinations, accessing individual price elasticity models for each product to visualise the impact on demand with and without various promotions and for different time periods.
But how do retailers integrate AI into their operations?
Retailers need to move at pace. Many companies are tied to cloud data management systems which are business critical. Attempting a complex and time-consuming migration, or even trying to replace the system altogether, will just leave them playing catchup and footing a higher bill. We actually spoke with a company who said they should be migrating their SAP within a decade – a decade!? Nobody’s got time for that.
The key is to augment what you already have in place. Retailers should look for agile and adaptable SaaS AI solutions, for example, that can integrate into their core system. This allows them to move at pace while leveraging the benefits of AI.
Getting your tech aligned with strategy
Technology can provide retailers with a whole wealth of data. But what KPIs they use and what data they choose to act on comes from strategy. The key to getting the most out of AI insights is to set them in line with business targets and constraints.
So, when AI is analysing a retailer’s inventory, transaction and pricing data, for example, it also accounts for KPIs when it provides its suggestions for pricing products. This could be a target to sell out-of-season stock over six weeks, in a certain city, and both online and in store. Through this approach, retailers can best preserve margins while also driving their revenue – and that’s generally the goal.
However, sometimes there is also benefit in knowing when to embrace driving revenue instead of margins; knowing when you are in a ‘needs must’ situation and have to clear the decks in the most optimal – and responsible – way.
A trend we increasingly came across towards the second half of last year was retailers looking to find the optimal price to shift their remaining products which had already been priced too low, so they could minimise their losses and start with a clean slate.
A smarter, more elastic way to price products
We’ve talked a lot about how understanding price elasticity can drive moving surplus stock. But price elasticity is just as important to maximising revenue and margins for day-to-day business and stealing a lead on competitors. And if it’s embedded into processes, it can significantly reduce the amount of money tied up in slow-moving stock, as there will be less of it in the first place.
AI underpins an effective price elasticity strategy. It provides the data that allows teams to discover the optimal product price or even if a markdown is necessary at all. And by combining AI with business targets, they know which products to discount, when, and by how much. Retailers can then make fast, informed decisions that best preserve their margins and revenue – or the optimal way to drive sales in ‘needs must’ situations.
The gains are there to be made. If you hadn’t heard of price elasticity before, it’ll certainly be on your radar now.
