There’s a “perfect storm” of issues causing an increase in costs: rising wholesale prices, disrupted supply chains, increasing wages, rising rents, etc. Small businesses must raise prices, but the big question is how to do it most effectively.
The good news is consumers expect price increases right now, so it’s a good time to reassess the pricing of your products. The bad news is that most small business owners just increase prices to match their increased costs – not realizing consumers are more price-sensitive for some products than others. If your costs rise by 10%, you might be able to raise some prices by 20%, some by 10%, and some not at all. For this last group of products, you might need to discontinue or rework them to reduce your costs.
A data-driven approach analyzes sales and profitability trends before and after each price increase. The customers’ response to price increases provides great insight into how price-sensitive they are and what their tolerance might be for future increases.
Let’s walk through three examples for a traditional bakery to get a sense of how analytics can give you a unique competitive advantage in this area.
Measuring the Impact of Price Increases: Bakery Examples
When looking at trend graphs, it’s best to start with the Sales metric. Or more specifically, the product sales as a percentage of total sales. There are many reasons why Sales could go up or down that are unrelated to the price increase (such as seasonality), so using % of Total Sales for a specific product (rather than Sales) excludes many of those factors.
The other metrics we’ll use in these examples are % of Total Orders, Order Size, Items Per Order and Profit Margin.
Example #1: Muffins
In March 2022, this retailer increased the price of muffins from $3.50 to $4.00 (+14.3%). The last price was $0.50 (+16.6%) in January 2021.
The first thing to look for is a sudden drop in sales immediately after the change. This would indicate a serious issue that might require reverting to the previous price (or a smaller increase). In this example, sales are down the first week but within the range of normal variability. Looking over the longer term (about three months, in this case), we can see sales were essentially flat before and after the change – as shown by the horizontal trend line.
If this price increase had no impact on customer behavior, sales would have increased by 14.3% because that is how much the price increased. However, since sales were flat, it indicates muffin orders decreased as a result of this price increase – which is exactly what is shown in the graph on the right.
Let’s look at two more metrics before drawing conclusions about this price increase. The first one shows the average order size for orders that included one or more muffins. It’s increasing at what appears to be close to the 14.3% rate. The second graph shows the number of muffins per order, which remains flat at about 1.4 muffins per order.
So was this price increase a net positive or negative for the business?
Customers were clearly sensitive to the price increase, but not so much that it hurt sales. So from that perspective, this increase was fine but not ideal. (If there was no price sensitivity, orders would have remained flat and sales would have increased by 14.3%.)
The win for this small business is the improved profitability of this product. If a muffin costs $2.00 to produce, the profit margin at the original $3.50 price was 42.8%. With this change, the profit margin increased to 50%.
This is exactly what is shown in the graph below. The bakery’s profit margin had been eroding due to increased costs (ingredients and/or wages), so this price increase successfully returned the profit margin back to the original target of 50% without having a negative effect on sales.
The key takeaway is that this price increase was successful at improving the profit margin without negatively affecting revenue.
Example #2: Jumbo Cookies
In this next example, the price of jumbo cookies increased from $3.00 to $3.75 (+25.0%). The last price increase was at least three years ago.
This was a large price increase, so the risk of a drop-off in sales was fairly high. Waiting three years between price increases would likely mitigate this risk a bit.
Here are the same four graphs showing the impact of the price increase for jumbo cookies.
In this case, the percentage of orders that included a jumbo cookie declined (upper right graph) and the number of cookies per order declined very slightly (lower right graph), but sales were up despite these declines.
These results show there is some price sensitivity for this product, but not nearly as much as in the previous example. This retailer might have been able to increase the price by $0.25 to $0.50 without any negative impact on quantity sold, but the reduction in quantity sold was more than offset by the increase in sales and profits.
Example #3: Brownies
In this final example, the price of brownies increased from $4.25 to 4.50 (+5.9%). The last price increase was $0.25 (+6.3%) in September 2021.
This is the smallest increase of these three examples, but it had the most negative impact. Both sales and orders were down noticeably. Sales were declining before the price increase, so it was bad timing and may have accelerated this product’s decline.
In addition, the price had been increased most recently of these three examples (7 months vs. 14 months vs. 3+ years), which may have been a factor in the customer response to this price increase.
Next Steps
As these three examples show, it’s not easy to predict what impact a price increase will have on product performance. The smallest price increase had disastrous results, while the biggest one was positive in all respects. Every situation is different and presents an interesting opportunity to learn more about your customers.
By using analytics to measure the impact of each pricing change, you can start to see which products/categories have the most price sensitivity. You’ll start to get a better feel for how often you can increase prices and by how much. You’ll also have the opportunity to reverse a change if you see an immediate catastrophic drop in sales as a result of a poorly received price increase.
