September 1, 2023
How to use price optimisation to exit stock at a predictable pace without offering huge discounts and leaving a lot of money on the table.
During the last 5-7 years, retailers and e Commerce pure players have heavily invested in stock management and BI tools to help them mitigate stock discrepancies. However, a simple but efficient tool to impact sales has been not really overlooked but mostly misused. This tool is called price. The way pricing managers have used it has been very straightforward: they applied huge discounts to speed up sales.
Well, surprise: it never actually worked as expected. There are very clear and objective reasons why massive discounts stopped being an efficient tool for managing sales velocity.
Discounts work when you’re the only one using them. When every single seller offers a 50-70% cut (which happens like clockwork at the end of every season), shoppers become overwhelmed with the offers. Their budgets aren’t unlimited, and even though they hunt for deals, they have to prioritise. A new school outfit for the kids or a new vacuum cleaner with a 40% discount in August? A new iPhone or a new couch for the living room?
Think you secured a great deal with a brand that agreed to sponsor your promo discounts? Well, there are always players who can beat you at this game. It doesn’t matter that many of them use illegal methods—like those fake Wilko websites that lured shoppers with ridiculously high discounts. Many still fell into the trap, which is why the real Wilko had to halt all online sales until further notice.
Luckily, there’s a method to use price as an efficient inventory management tool and exit stock at a controlled pace. It’s called price optimization.
Price optimization is a workflow where you optimize pricing dynamically based on your business goals:
Optimise your pricing strategy to capture maximum profit from every sale.
Optimise your prices to exit stock faster at a predictable pace without resorting to clearance and 90% discounts at the season's end.
A type of retail price optimisation where you adjust your price to be#1 for a certain brand, on a specific marketplace, or location.
A price optimiser adjusts your price to maintain the same market position, regardless of how competitors change theirs. This also captures more margin when opportunities arise, such as when competitors run out of stock.
Here's a short video on how to deploy these strategies using AI-driven, machine learning-based price optimization tools. But the beauty of this concept is you can handle price optimisation in Excel if you prefer. Let's dive in and see how you can leverage your internal resources.
But first, let's tackle the fundamental concept that enables price optimization. It's the notion of price elasticity of demand (PED), which means price changes for various products will yield different outcomes. Some products are more price-sensitive than others.
A classic example of a category with low price elasticity is cigarettes. If cigarette prices rise, only a minor portion of smokers would quit. Most will either switch to cheaper brands or make no change.
The opposite is perfectly elastic demand: when the quantity of sold goods isn’t influenced by the price. Here, any price alteration results in a significant change in quantity demanded. But this scenario is more theoretical and rarely seen in real-life markets.
The popular formula to calculate PED isn't that complex:
Q =Quantity Demanded
P = is the initial price (or any given price)
β1 = Coefficient for price, representing PED. Or, in other words, it is a change in quantity demanded for a one-unit change in price.
The important “magic number” in this equation is Coefficient Estimation (β1). This is exactly what will tell you how your sales numbers will change when you change your price by X.
The challenges start to arise on a stage where you’d try to make an estimation for β1. The most common method used for estimating the coefficients is OLS or Ordinary Least Squares Method (it is generally widely used approach for estimating the unknown parameters in linear regressions). You can easily outsource this to Excel as this valuable tool allows you to do linear regression in a couple of clicks. The problem is not the formula. The problem is data!
In order to have a relevant β1 estimation you need whole bunch of data to be collected, normalized and then analysed.
You would need:·
· Historical prices
· Historical Sales (the more your observation period — the more relevant results you’ll get)
· Correlating marketing efforts you had (for example, you’d want to count separately sales that came ducting activeGoogle Ads campaigns and sales when you did nothing to attract additional traffic)
· Historical competitor prices
· External factors like inflation rate or weather data (if you sell seasonal goods)
Data preprocessing includes
· and transforming raw data into a format suitable for analysis.
If you’re not very familiar with this process, you might be surprised by the fact that 90% of data analysts time is actually spent on this pre-processing stage. Why? In simple words, you can’t through together pictures and graphs from ONS, couple of links from weather forecast websites and csv files with your historical sales. They are simply not compatible. So your data analyst has to bring all these different types of data to a single format before analysing.
Data analytics stage includes applying formula or using Excel to build multiple linear regression model. If you have several dozens lines of data it will take Excel several seconds to show you your Coefficient Estimation. If you have thousands of lines (because mind you, all the above mentioned data should be collected and normalised for 1item only!) Excel most probably won’t handle it. Your salvation can be Python or price optimisation software (like Aimondo).
Finally, getting back to our main equation
All the ingredients for optimising your prices based on the predicted change in demand are in place. You can now gain better control over sales based on data-driven precise predictions and not just your “gut feeling” as most probably most of us use.
All price optimisation solutions work based on the logic and principles described above. Their superiority over Excel is that some (like Aimondo) can handle data collection and normalisation on their own. Most can build predictions. And only rare ones can also apply advanced machine learning algorithms such as Random Forest, XGBoost, and Neural Networks that can identify complex relationships within data, allowing for more accurate predictions and offering even more control over your future sales.
If you want to learn more about advanced pricing skills join our free course on pricing
We explain price optimisation there in more detail.
Step-by-step guide on maintaining high profitability in retail.