Promotions are part of any typical business model and can have a wide range of applications. Seasonal sales, inventory clearance, and squeeze sales to hit revenue goals are all a huge part of the retail game. Unfortunately, not a lot of eCommerce sellers go back and review what went well and what could have been improved after a promotional push. Most brands see green results in their year-over-year comparisons and decide that’s good enough, avoiding any deeper analysis.
But have you ever brushed aside a finished promotional campaign that was overall profitable and wondered if there was a chance you left money on the table? Do you typically ask yourself how you arrived at your discount rate, or if it should stay the same for the next promotion? Could you have discounted deeper and driven more revenue? Hindsight is 20/20, but looking at past promotion performance under a microscope can help you optimize the outcomes of those decisions in the future.
If you’re curious as to how you can start analyzing promotion performance, we’ll cover the basics right here by talking through one of our clients’ yearly promotions and examining how they could optimize their pricing in the future by comparing the ways that different discount rates will affect revenue.
Let’s take our example client as a case study. This client is a mid-size retailer that is relatively new to the eCommerce world. They have a sale each year where they discount their flagship product. The business produces this product all by themselves, which gives them good control over inventory and usually results in strong margins.
This year, the annual sale took place over an eight-day period in October and had great results. At a 20% discount rate, they drove just over $200,000 in sales for the product–compared to the $26,000 dollars in revenue that product had brought in during the previous eight-day period. An increase of 669% in revenue earned for a 20% price hike absorbed by the business doesn’t sound too bad–but could it have been better?
Let’s start by looking at the price elasticity of demand for the product. Elasticity (equation below) is measured by the change in quantity demanded in relation to the change in price. In other words, if you sell more of a product when the price decreases, demand would be elastic.
The product in this analysis is very much a discretionary purchase. An expensive (but very high-quality product) that most people can live without. Based on the real changes in quantity demanded relative to the price change, we can calculate that this product is quite elastic, with a measure of 4.45.
With this measure of elasticity, we can apply the below equation to project how the quantity sold would change with different discount rates.
Using the elasticity of the 20% price decrease, we can create the demand curve for this product:
As price decreases, quantity demanded increases - what a surprise! But what we really need to do is look at how revenue changes compared to price:
With this graph, we can see that revenue is maximized at a discount rate of 40%. All else equal, we could have driven 12% more revenue with a stronger discount. For a company with a great handle on inventory and strong margins, this may be a possibility, but that is a pretty steep price change.
This company probably won’t want to double the discount for this product, and that is totally fair. It would cut out more profit and could wipe out inventory for the upcoming time period. But in the competitive world of eCommerce, it’s important to always understand your promotional performance. If you have more room for a deeper discount, why leave revenue and potential new customers on the table?
So hopefully now you’ve taken a closer look at your historic promotion performance to better understand what discounts you should offer your customers for the next sale you host. But discounting is just one type of promotion to encourage sales. Even more questions might start to swirl in your head as you do post promotion analysis…
Should different product categories get the same discount or should they be applied on a per-product basis?
How frequently should my store be offering discounts?
What is the best channel to inform customers about promotions that they are more likely to buy through?
Are sales the best promotion my brand can offer, or should we do free gifts, giveaways, bundles, etc?
Do I have a loyalty program offering discounts to returning customers? Should I start one if not?
The list goes on and on. It’s pretty hard to know the answers to these questions without knowing how to access or analyze your customer, campaign, and inventory data all together.
That’s where our software solution Pond and expert data science advisory team can help. If you are looking for the next step in building your data-driven marketing strategy, drop us a line today to see how we can help you make the most of your decisions.