Pricing strategy is basically what makes money for your business. It can decide if you are losing, or if you are profitable and how much. For that reason, it is necessary to have the pricing well optimized to make sure you are not losing money as there might be pressure on shelf prices by your competitors, suppliers, and even by the customers. That’s why Price Elasticity is so important; when you look at any Economics 101 textbook, there will be that understanding Price Elasticity is the key to an optimal pricing strategy. However, understanding Price Elasticity becomes very tricky, very quickly and using it in the pricing strategy makes it even more so.
In this article, we will show you several perspectives on Price Elasticity and a way to better utilize it in your strategies.
What will we cover?
How and by which factors is the Price Elasticity limited. Then we will look into what information is needed for optimal pricing and in the end, we talk about how to utilize Price Elasticity in the pricing strategy.
The Case Against Price Elasticity
There is likely more than one product that you’re selling, therefore cross-elasticities come into place. Promoting or just lowering the price of one product can attract more customers to purchase it, but it can also cannibalize the sales of its substitutes.
Furthermore, there also exist support effects between products, where inducing higher demand for one product will positively affect demand for other ones. For example, when promoting burgers in a grocery store, the sales of buns will increase without any change in their prices; the same occurs with gin and tonic, etc.
It goes without saying that there are more factors influencing customer purchasing behavior than just price. For example, promotional signs or prominent placement within stores typically have an even greater impact.
Considering you have competitors in the market, there surely will be a difference in sales. In the online world, when the prices are above or below your competitors, it might have an immediate effect on sales. In physical stores, it might rather be a gradual change in sales quantities. Such a gradual change may be impossible to detect among all the other factors that are influencing sales, such as various trends, seasonalities, weather changes, or greater market shifts.
There are simply too many different factors affecting sales of individual articles that it would be impossible to reliably quantify them all. Not only would it be extremely difficult to distinguish them, but there could never be enough data to do such thing.
Also, don’t forget that time plays a role as well. The behavior of customers changes over time, even the product portfolio changes, so it might be relatively safe to look for an example from a year back. Within that period, only a limited amount of different scenarios occurred. Some articles possibly never changed their prices at all. In light of this, it is no wonder that even large retailers choose to build their pricing strategies without greater insight from their data.
The Case for Price Elasticity
No retailer should ever lose focus from their customers. This is even more important when it comes to such a key aspect of their business as the pricing strategy. But how to include customers into the pricing? Well, you can obviously ask the customers directly, surveying their opinions about store personnel, offered product, and of course the prices.
However, the results of such surveys can never be detailed enough for building a pricing strategy for all articles individually. Moreover, conclusions from these kinds of surveys are notoriously skewed by what kind of customers even take part and what actually sticks in their minds. Fortunately, there is a much less biased and more complete dataset of customers, answering the question “Would you buy this product for such price?”, and that is the history of all transactions. Customers answer this question with their wallet, the most reliable source you can get.
But how can we get around the problems from the previous paragraphs, namely the fact that there are just too many interacting factors? Fortunately, mathematicians have been developing tools for dealing with this problem for over a century in the field of statistics, specifically statistical modeling. Thanks to the computational power available today, we can now even work with datasets as large as those from chain retailers.
However, it is still not possible to incorporate all the factors together into one model and fully describe something as complex as human decision behavior. When this becomes possible, we will be living in a vastly different world.
On the other hand, even today, we can achieve great results by cleverly combining multiple complex models that take into consideration all the important factors affecting your customers. That allows us, with various degrees of accuracy, to estimate the scale of those factors, including price elasticities and cross-elasticities. Let’s take advantage of that!
The Best of Both Worlds
Now let’s say you’re creating a pricing strategy based on following the prices of your competitors. Being an average player in the field you don’t want to be too expensive or too cheap. Therefore, you decide to price the products somewhere between the cheapest and the most expensive offer among your competitors. How do you decide which type of prices will work best? Will you use the average of their prices or the median, or will you aggregate them in a different way? In any case, nobody can really tell what is the best option of all the choices. There must be a better way, right?
Maybe some of the products are rather inelastic; customers don’t really remember their prices and will buy them regardless of the price being either lower or higher. Increasing margin on those products is most likely a right decision, but you don’t want to get too far with it. Ideally doing so only within some reasonable boundaries, let’s say not to exceed the range of prices among your competitors too much. The same can happen the other way around when more of your customers do care about the price of a product and will buy it only when the price is lower.
What to do with it?
Clearly, the best solution would be, if someone, or something, could consider all factors that influence pricing to find the optimal set of prices to achieve your goals. It would have to take into account factors like price elasticities, cross-elasticities, and their varying degree of accuracy while operating within reasonable price boundaries to ensure that you are following a set strategy. Then it doesn’t matter if the strategy is based on following competitors, your margins, or something else.
Customers’ past purchasing behavior is the fundamental learning material that you can use for pricing. After changing prices, the customers will have different purchasing behavior. With this captured data you can utilize for further price adjustments and for learning more about your customers.
That is exactly what we, at Yieldigo, provide to our grocery clients. We have developed an AI pricing system that learns from past purchasing behavior of customers, understands elasticity, and is able to combine with all the other factors that influence purchasing behavior (seasonality, weather, competitors, cannibalization, promotions, etc.). Due to that, we can create optimal prices that will unprecedently increase the profit or the revenue.
In addition, our calculation of the expected sales impact of the price changes allows you to refine set price boundaries, test various alternatives, and even steer each product category individually towards different optimization targets.
If you are interested in how Yieldigo works in more detail, you will find it here.