Winners vs. losers part II: the optimal price ratio depends on category elasticity

In this thread we are investigating and ultimately uncovering what everybody wants to know: when it comes to pricing, what separates the winners from the losers. Last time we covered price change frequency and why it is vital to timely update your product’s prices if you want to keep up with today’s fast paced world of (online) retail.

Now, hold on to your hats as we dive into the second main element setting the winners apart from the losers: understanding price ratio. 

But before that, we need to take a little step back, perhaps way back to your high school years if you took economics, and talk a little bit about price elasticity. More importantly, how to think about price elasticity within most retail assortments.

For all retailers, elasticity (also known as price sensitivity) is different between products in their assortment. You can say some products have high elasticity, some low, and most in between, forming a normal distribution that looks like the graph below.

Imagine we are running a retail store selling sanitary products. Our branding team is quite creative and came up with the name: Bath’s R Us.

If we were to identify our products on the elasticity distribution above, on the high elastic side we could have a bath tub. It is highly elastic because it’s an orientation product: someone needs a bath tub, finds a model they like, and looks on the internet to get the best offer. It compares the price of your offer, to the offer of the other retailers for the exact same product. The subject price is your price, the reference price is the lowest offer of the main competitors. On the other end we could have a bathtub table, this is an add-on product people mostly buy after they have already chosen to buy the bathtub. Meaning they are partially locked in. You can ask relatively more for the addon product because for the add-on, the reference price is not what other retailers ask for it. Instead, in the mind of the consumer, the reference of the add-on product is a combination of the price of the orientation product (buying a 10,000 euro car will make a 50 euro radio seem cheap), and an intuitive upper limit that the add-on product could be priced at (150 euro for a car radio might seem expensive regardless of the price of the car).

Interestingly, the add-on product sales are mainly influenced by the price of the orientation product. This is called a cross-effect. If the bathtub is reduced in price, becoming cheaper than competitors, their sales will spike. Meanwhile, while the table’s price is unchanged, it’s sales too will spike as a result of the increased bath tub sales.

Understanding the concept price elasticity within retail is the first step to understanding what makes an effective pricing strategy. The second step is understanding the Price ratio.You can see price elasticity as your relative price. For example, product A is 100 euro. This is the absolute price: It doesn't say anything about if it’s expensive or cheap, because there is no reference price. Now let’s say the average price is 120 euro. This would make your price ratio 0.83 (100 / 120). That is a cheap relative price.

It can be very insightful to analyze the price ratio of your whole assortment, especially when seeing how your assortment is distributed among price ratio ‘buckets’. A bucket could be 0.85-0.90 (like product A above), 0.9-0.95, etc.

Since this article is about winners and losers in retail, we wanted to find out how this distribution differs between the two. Interestingly though, at first glance they look almost identical.

Price ratio total assortment

We divided the retailers in our data set in to groups: those performing very well in their category  - winning, and those performing poorly - losing. We then calculated the price ratios (their price / average market price) for all their products, and plotted how much percent of their assortment is in each price ratio 'bucket' (<0.75, 0.75-0.8, etc.) To get an idea of their pricing strategy. As you can see, the distribution is quite equal between the two, with most products having a price ratio around 1 (retailer's price = average market price), and a little skew to the left side (relatively more products below average market price then above).

But this graph only shows the overall and 'high over’ numbers. What we want to see how the distribution looks considering the price sensitivity of the products. We would expect the winners to have properly identified the highly elastic products and priced them accordingly. In practice, this would mean that for example orientation products, the winners will have made their price more competitive, resulting in a lower price ratio.

When we dug into the data some more and made this cross-section, it is exactly what we encountered. Below you again see the number of products per price ratio buckets, for winning and losing retailers, only taking high orientation products into account. In the winning graph you can the distribution is skewed to the low end of the price ratio.

Price ratio high elastic products

Selling more by pricing down is easy, and most retailers that have discovered the effect of competitive pricing do it. But what really sets apart the winners from the losers is knowing when to price UP. We would expect winners to identify which products are less elastic and can benefit from a higher price ratio. Looking at the data, this exactly what we saw. Relative to the losing retailers, the winners far more often had a higher price ratio for their long tail / add-on products.

Price ratio low elastic products

Let us get back into the role of being the CEO of Bath’s R Us. The ‘hot item’, the bath tub, is what all consumers are looking for. Your competitors know this, and everybody is lowering their prices in order to capture the sale. So in the end, the retailer with the lowest purchase price can go the lowest and will win, right? Not quite. 

The margin generated by the bath tub is not the only profit you make for a bath tub sale. We saw before that often, we are able to sell the bath table as an add-on product. By identifying this cross-effect we have established that:

1. If I lower the bath tub price, I will sell more bath tubs AND more bath tables
2. The bath table is likely to have a low price elasticity since it is an add-on product

As a result, with our gained understanding of price elasticity we will want to price the bath tub lower, and partially finance the loss in price by increasing the price of the bath table.

Additionally, with our gained understanding of price ratio, we will apply strategies such as this across our assortment, and make sure we have a winning price ratio distribution rather than a losing one.

In summary: winners recognize the difference in price elasticity in different parts of their assortment and then they adjust the prices to reflect this in their price ratios.

Eager to find out how Omnia can help you determine your optimal price change frequency? Or if you want to discuss how we can advance your pricing strategies with our software - please let us know by contacting us!