The Optimal, Best Markdown Rules During COVID-19 and Beyond
When my friends talk to me about managing a retail store they all enjoy it apart from one thing. They tell me that seeing the latest trends, working with cool brands and meeting some of the stars promoting the articles is great. It’s just that there is a recurring event that makes long days longer and they are sure can be done better – markdowns.
My friends imagine that a set of retail analysts spend days figuring out the optimal markdown to make sure customers buy the remaining goods at the highest possible price to maximise the possible gross profit.
However, in many cases, there is no group of analysts.
Instead there is a fixed set of well-worn rules, year after year: first a 10% reduction, then a 30% reduction, and finally a 60% reduction until the product clears.
It’s artificial ignorance not artificial intelligence. The retailers have the data, it’s whether they use it – and can make use of it – or not.
As if typical markdown cycles weren’t difficult enough, COVID-19 has turned these processes into a nightmare. Companies are now stuck in a vice trying to clear stock but doing so in a way that loses minimal money and keeps the company afloat.
The old rules are no longer working so companies are experimenting with new permanent discounts – 50%, 70% and 90%! Just whatever it takes to get the stock out the door before it’s too late…
What is needed, now more than ever, is a data driven, machine learning approach to suit each individual business. The best markdown rules use analytics.
Biarri’s research has shown that too many goods are too heavily marked down, even in a time of crisis like this, meaning lost profit margins. In addition, a one-size-fits-all approach doesn’t work – each business needs to take its own unique conditions into account for such markdowns to be successful.
The early conclusion? The best markdown rules
In the current situation, you need an advanced analytics tool (AI, Machine Learning, Optimisation) that takes into account the unique circumstances of your business and can answer questions such as:
- Each store has different stock coverage for an article, so how do we balance a single store’s need with a regional markdown strategy that mandates one markdown for all stores in a region? Or even more difficult, a national markdown strategy?
- If we allow markdowns to vary between stores, then how do we create a markdown strategy that prevents customers shopping between your different stores searching for the biggest discount?
- How do we accurately model price elasticity for goods with only a few sales each month and even fewer historic price points?
- When marking down, how do we pick a smart price point – an “anchor” – that increases demand much more than the surrounding price possibilities?
- How do we accurately predict the demand for each individual article to know what its markdown sticker should show to maximise profit?
As well as much more.
A tool that maximises your gross profit needs to get all the above right for you and your business, not for a generic retailer in a generic retail environment – these disappeared a long time ago.
The way you resolve these challenges is by having an analytics tool which can:
- Group stores together using geographic and sales profiles to provide simplified markdown strategies which staff are prepared to implement.
- Provide a set of optimal markdowns, determined from the historic sales data, which can be applied equally across all stores in a similar area.
- Compute accurate price elasticities. Price elasticity is often like sausages; everyone likes them, but it’s better not to see them being made. Biarri avoids elasticity problems by bootstrapping data to measure how volatile the elasticity estimates are and then weights them accordingly.
- Cleverly choose price points based on known rules of customer pricing behaviour instead of just basing it on the markdown percentage times the price.
- Predict accurately the demand for an article at a given price point by not predicting a number at all – we predict a range.
The last point is key as merely having a “point” forecast is of little value, you need ranges of values so that you can meet your business needs to ensure that you will either:
- Sell all stock with a high certainty at a good price, or
- Ensure the shelves are never empty and you don’t leave customers with a bad impression.
How do I use it?
So now that we’ve identified the key criteria to create the optimal set of markdowns to maximise profit but also clear out the goods, the final step of a successful markdown strategy tailored to your business is building trust in it. This means rolling out the new AI created marks in a smart way. If you roll it out across your entire range and there are any issues, the entire project will be doomed to fail – even if it was immensely successful on some articles.
The way to make this succeed is to do some simple tests in a scientifically correct way. Create a control group where you do what you normally do and test it against a treatment group with the new optimal markdowns. This can be done either on a store by store basis (A/B tests), but if that is not possible then it should be done across similar product categories so we can draw valid conclusions from the experiment to find the best markdown rules.
What are the benefits?
If instead of using intuition and “What we always did” to markdown products, you turn to analytics to do this smarter, the gross profit improvements can be significant. This technique has been the success story of a number of giants such as Walmart and Target Corporation in their retail divisions. Not only has Biarri seen significant benefits from using optimal markdowns, studies have also concluded that it is possible to improve profits by up to 20% by using analytics instead of gut feelings.
COVID-19 is affecting everyone, irrespective of industry, however the retail industry is one of the worst hit. People are scared and not visiting stores leading to a drop in foot traffic of up to 50% and supply chains malfunctioning with over half of retailers impacted by supply chain issues having too much of some stock (sporting jerseys) and not enough of another (running gear).
So something smart needs to be done – and right now. Analytics based markdowns are exactly what are needed to turn the retail industry around. Off the shelf tools don’t cut it for a situation like this as they are not customised to cope with so-called “out of sample” events like the current coronavirus crisis.
So reach out now to find out how you can clear out unwanted stocks effectively and maximise profit at the same time. Biarri’s tools are simple to use, customised to each customer and driven by accurate and correct mathematics to ensure the best result. They provide you with the best markdown rules every time.
Better yet, to simplify things, Biarri is offering its markdown tools as a managed service, so no IT work for you to do! We’ll do the analysis and provide you with the best possible markdowns.
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