Editor’s Note: This blog is the second in a series.  The first – titled Competitive Pressures Strain Service Parts Manufacturers – is available at this link.

As promised in my first post, I want to delve further into the details of market-based pricing strategies and its drawbacks, as well as cover more accurate methods for price optimization. In an analysis of price data for 1,235 services parts, results indicate that more than 75% were sub-optimally priced when using a market-based pricing strategy.  It’s pretty straightforward to know, then, that companies are leaving money on the table in negotiations, which calls for a new approach to competitive or market-based pricing strategies.

A market-based pricing strategy uses competitors’ prices, coupled with a percentage differential, to generate a price.  As noted in my previous post, one of the difficulties, in addition to mining competitive data and discount practices, is applying strategy to the appropriate competitive situation.

Paris is lovely and the weather is great …

The weather in Paris is lovely, and it hardly ever rains there. By contrast, it nearly always rains in London.  Paris has more dry days each year than London – a lot more. But by checking my international weather database, I have found some interesting figures. The average annual rainfall in Paris is almost three times as high as in London.

So by visiting London, you actually escape the rain, even though it seems like it rains there every day. It sounds like a paradox:  Paris has almost three times as much rain as London, but London is much rainier than Paris. The answer is simple. In Paris when it rains, it pours – unlike the constant drizzle that falls in London.  The same contrast exists when calculating a market-based price. Oh, how averages can be misleading.

There are two terms for describing competitive situations for service parts competition: tight vs. loose. Tight competition suggests that when plotting prices for similar parts there is a “tight” range, or narrow histogram. ”Loose” competition occurs when prices are spread over a very broad distribution or “loose” range, creating a wide histogram. In loose competitive situations, a market-based price is not a reliable indicator of the optimal price.

Is that to say market-based strategies can calculate optimal prices in tightly competitive markets?  Theoretically, that’s true, but it is critical to understand the competitive situation for the entire service parts portfolio, not just a single part. The analysis cited above found that 77% offered competitive prices that were too broad and experienced “loose” competitive situations. They were, therefore, sub-optimally priced. With the skepticism outlined in my first blog – and considering only a small percentage of the service parts portfolio may exist in a tight competitive situation – I am confident a different approach is needed.

Stay tuned!

Sean Duclaux