This is the fifth in a series of posts based on a white paper titled “The Top 8 Myths of Pricing Optimization,” which is downloadable from the PROS website.

Myth #5: Price Optimization Requires Loss Data

Companies have struggled for years, burdened by the belief they must collect sales-loss data to understand a product’s market price.  As conventional wisdom goes, to understand winning price points in the market, companies must collect and analyze both win and loss data. Otherwise, how could they possibly know the market price if they don’t know which price points turn from wins to losses?

First of all, in my ten years in the pricing space, do you know how many companies I have come across that actually have reliable sales-loss collection mechanisms? Exactly two. Want to know how they did it? Both companies had less than 500 annual transactions, and every deal was scrutinized up and down the management chain. When they won, it was clearly visible; when they lost, it was equally as visible. Both companies’ environments operated where winning each and every deal had a material impact on revenues, margins and market share. They had to know why they won or lost every deal because the continuity of their business depended upon the information.

For many high-transaction environments, it’s simply not possible to analyze this level of detail. In fact, many companies don’t even agree what constitutes a win and a loss. For instance, if they quote a deal and the customer accepts their pricing but they’re the secondary supplier, does that constitute a win? If they quote a deal and the customer never makes a decision, is that a loss? Will the sales organization take the time to enter this information when it provides no value to them?

The truth is you don’t need loss information to determine market pricing.  Let me give you an example. When my wife and I purchased our current house, and we were considering an offer to the seller, how did we determine a “good” offer? We had our real estate agent compile all housing sales data in our new neighborhood for the last six months, focusing on houses of like-quality and lots of similar size. Using this data, we calculated the range and average price per-square-foot to gauge the prices likely to result in an accepted offer. As you would expect, the seller’s offer price was at the high end of the price per-square-foot range, and we made an offer at the lower end. After some back and forth – which was during the time of the mortgage crisis – we negotiated an offer much closer to the low end of the range, versus the high.

Using just “win” data from recent home sales, we developed the “pricing envelope” – the range of prices per-square-foot — based on the “market segmentation” – the neighborhood, building quality and lot size. At the low end of the price per square foot, we had a lower probability of closing on the house; at the high end, a higher probability. We had “optimized” our offer price based solely on “win” data.

All companies have this “win” data today – it’s their transaction history. To apply the principles, they must segment and calculate the market range of winning prices. Using this data gives salespeople guidance as to their probability of winning a bid at a quoted price point. Based on their confidence level – are they in a strong or weak negotiating position – they can quote a price that either has a strong probability of winning, or a higher-priced, lower-probability quote that leverages their strong negotiating position?

What would it mean to your business if you were calculating these today?

Patrick