Last weekend there was an article in the Wall Street Journal, How Not to Be Mislead by Data. The point of the article was people will use a number as evidence of whatever point they are trying to make, and use it to end an argument. After all, if the number supports your story, what is left to debate? The problem is, the number cited is often one which sounds dramatic but is not really representative of a complete picture, is an isolated case, or is meaningless. The first example in the article was New York’s Governor Andrew Cuomo bragging that unemployment in New York had decreased, and the state was “on the move”, even though New York’s rate was moving at the same speed as the whole country. He is not alone; many politicians like to cite unemployment figures as evidence of their success. However, they fail to mention that the percentage of the population actually working is lower than it has been in 10 years. Which number is more important? It is not just politicians who will mislead their audience with numbers. Dubious statements occur in many (if not most) companies when discussing pricing. It is important for all of us to be aware of the potential for misleading statistics and be thorough in our analyses.
One potentially misleading statement we hear frequently is, “We raised the price on that product and volume decreased. We should lower the price.” However that single statistic rarely provides real insight, because we usually find similar products with similar price increases where volume increased. And we also find that customers with higher prices or higher price increases performed equally as well as customers with low prices. So, perhaps something else is driving the volume change. Your analytics need to help you determine that before you adjust your pricing strategy.
One client with whom we had that conversation was certain their price change had cost them volume, and we asked if they had talked to the customers that were buying less to find out why they were buying less. Our client answered that their customers had told them headcounts were being reduced and budgets were being slashed, so they just could not afford as much. When we compared our client’s volume decrease to the headcount reductions in the customer industry, we found our client’s market share had actually increased. We also found our client’s prices had increased at similar rates to competitors. So if our client were to lower prices, it was highly likely that competitors would follow suit, and everyone’s margins would decrease. They opted not to lower prices.
Another example occasionally cited is when a company’s win rate decreases, price is often listed as the cause. That is, if the amount of business won in competitive situations decreases as a percentage of the total opportunities, it must be due to high prices – right? Well one of the simplest comparisons we make is the win rate on existing customers versus the win rate on potential new business. As long as customers are happy with their service, they do not typically like to switch providers for modest price savings. That means it is harder to take prospective customers away from competitors if those customers are happy. When we do our analysis, we often find that their high win rate on existing customers has not changed. The potential new business has just become a larger proportion of the total opportunities, driving down the overall win rate. Lowering prices in an attempt to take away more business from competitors often just leads to price wars and lower margins.
Sadly we have also found a company’s own pricing team imitating Andrew Cuomo and overstating their own impact. In one case, a pricing team looked at all customer/product transactions that sold below average price in year one, but were above average price in year two, plus all new business that sold above average. They added up the differences between average prices and the higher sale prices, and claimed ownership of that profit improvement. Unfortunately they did not account for the fact that the new business was predominately small customers and was being sold at the average price of similar small customers. They also did not account for the fact that average prices decreased because a few very large accounts received price reductions, and the price changes on existing business were comparable to increases in cost of goods sold. When we went through these details, it was apparent the pricing team had overstated their own impact.
The point of all this is people can use numbers and statistics to confirm existing points of view, to provide support for actions they want to take, or to make themselves look good. It is worth the effort to be rigorous in your analytics to make sure you are getting the complete picture. Don’t make pricing decisions on half-baked numbers.
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