Last week I read an article in the Wall Street Journal, ‘How to Lie With Statistics’: Teachers Union Edition, in which Alyssia Finley pointed out a number of misleading statistics reported by teachers unions. The statistics she cited included picking the baseline most advantageous to the union’s position, using misleading comparisons with groups that are not comparable, and referencing a stat from a different metric than the one they were actually discussing. These types of BS (Bad Statistics) are not unique to unions. We see them in all types of debates and negotiations. Specifically, BS are often used to justify lowering prices. Don’t be fooled. Use your analytics.
Over the years, several clients have told us that certain groups of customers had decreased from one year to the next. They had recently raised prices, and cited those customer decreases as evidence that their prices were too high. They had simply looked at their customer count and heard their sales team telling them pricing was the cause. The sales team even provided a couple anecdotes in which customers complained about prices. In one example, we worked with the client to go back to a large sample of customers who had completely stopped purchasing from them to ask why they left. The answers included going out of business, discontinuing their use of those products from any supplier, and being placed on credit hold for not paying their bills. None of the responses were related to pricing.
For the same client, we also analyzed volume changes according to the amount of price change the customers had experienced. We found that customers with higher prices or higher price increases performed equally as well as customers with low prices. So, perhaps something else was driving the volume change. Pricing analytics can help you determine the truth, if properly used.
Often when a company’s win rate decreases, price is 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? Two comparisons we like to do in those situations include win rates on existing customers versus the win rate on potential new business, and win/loss rates at different price levels. In general, customers who are happy with their service 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 satisfied. 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 to take away more business from competitors often just leads to price wars and lower margins.
Additionally, looking at win rates by price levels helps clarify the role of price in those bids. We often find no difference in win rates at high prices as compared to low prices. In fact, at one client, we found their win rates were higher when their prices were higher. Obviously, there were more factors at play in the customers’ decisions.
Unfortunately, pricing departments are not immune to the urge to use BS (Bad Statistics). Like any department seeking additional resources or defending their existing headcount, pricing departments can overstate their own impact. In one company, a pricing team looked at all customer/product transactions that sold below average price in year one, but were above the average price in the following year, plus all new business that sold above average. They added up the differences between average prices and the new higher sale prices, and they claimed their pricing strategies had caused that profit improvement. Sadly, their analysis ignored the fact that the new business came primarily from small customers and was being sold at the average price of similar small customers. In other words, small customers as a group tended to have higher prices than larger 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 reviewed 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. But that is BS. It is worth your effort to be rigorous in your analytics and make sure you are getting the complete picture. Don’t make pricing decisions on Bad Statistics.
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