Over the years we have had clients tell us their pricing analytics were strong and did not need help. They thought they could use more help in setting pricing strategies to protect against declines in Average Selling Price (ASP), which was one of their metrics. Our first reaction is always to look at the analytical work the client is doing and how they turn it into insight. What we often find is that the client is measuring something relatively broad that does not lead to any real insight. The goal for all of the clients ultimately becomes analyzing the right things that can enhance their decision making.
One client showed us charts measuring the monthly ASP for some of their high-volume product categories. There were increases and dips along the way, but it was a generally upward sloping trend line. The client thought they were doing a good job of increasing prices. We worked with the client to drill down deeper to each customer and product, and we found they were not increasing prices at all. In fact, they were decreasing prices to smaller customers, but they had a greater mix of small customers and higher-priced products within a given category. So the mix of customers and products caused their measured ASP to increase. When the client saw the results, the client’s reaction was “That’s good, right? By lowering prices on the smaller accounts, we sold more products.”
Unfortunately, it was not that simple. The client’s overall margin dollars were not much higher, but their cost to serve the smaller accounts was higher, leading to overall lower profitability. In addition, those lower prices to small customers had begun to put pressure on prices to their larger customers. Once they learned to measure in the right level of detail, the real opportunity for the client became doing a better job of identifying the value to the smaller customers, beginning to correct those low prices and creating an offering for the most price-sensitive customers that did not destroy the client’s value.
A different client was growing modestly and told us their customer retention was excellent. They showed us charts with average daily sales during the past 3 years, and the charts had an upward trend. However, that merely confirmed they were growing. It did not say anything about customer retention. We analyzed their transaction history and found that their largest customers in most segments were loyal and growing slightly, but there was a substantial number of small and medium accounts that quietly stopped buying each year. These lost accounts were being offset by new accounts, providing the modest overall growth. That led to another analysis to determine if prices were the cause of the customer defections, and we found that the higher-priced customers actually had better results than the lower priced customers, although both groups were negative.
After all of our analysis, we learned that:
- There were a number of products customers just did not purchase year to year, and these had large declines
- The customers who received the least amount of attention from the sales team declined the most
- The products at the top of each customer’s purchase history had the greatest sensitivity to price, and those at the highest end of the price range decreased a little more than those at the bottom end of the price range.
The bottom line for these clients and for all companies is the analytics have to be focused on the right things and the right level of detail to provide any real insight. Measuring the wrong things or just measuring averages can lead to counter-productive decisions and inappropriate changes in pricing strategy.
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