Critical Metrics for Pricing Execution - Insights from Dick Braun
By Steven Forth
The Professional Pricing Society had its Fall Conference last week in Las Vegas. One of the keynote speakers was Dick Braun, Vice President of Strategic Pricing at Parker Hannifin. Dick is one of the most effective pricing executives around, and over the past 15 years he has built a high impact pricing function at his company. His talk was on “Building and Running a Company’s Pricing Program,” and it was chock full of excellent advice.
He introduced a key metric and a key tool in his talk. The tool was SePVA (pronounced ‘sep-vah’) or Segmented Price Variance Analysis. He sees this as one of the core ways he gets his teams thinking about the strategic impact of pricing and helping them target the critical opportunities for pricing actions. Dick has kindly offered this approach for broader use. The metric is the Sales Price Index or SPI, which measures the impact of pricing changes on revenues.
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Before looking at at these tools and metrics, it is useful to know that the Parker Hannifin pricing team is distributed, the pricing experts are located in the businesses and are close to the P/L owner. They work closely with sales, product development and the business leaders, and are the glue that holds the different business function together (those are my words, not Dick’s, but I have watched this team in action).
One of the core metrics, that these embedded pricing experts are expected to track, is the Selling Price Index (SPI). This is calculated as follows:
Average Price This Year – Average Price Last Year x Volume for the Quarter
The average price is the average for wherever you are focussing your pricing lens. It could be for a product line, a geography, a segment or even a specific customer (looking at the SPI for a specific customer is important for your big customers). As revenue is just price x units, the SPI measures the impact of price changes on sales. It is not always positive. Knowing your SPI is a duty of the pricing expert. Knowing the reasons for changes and the trends is critical to taking effective pricing actions.
So what is SePVA or Segmented Price Variance Analysis? It is a chart of revenues per client (or segment or offer or geography – whatever you are analysing, plotted against price. At Ibbaka we also refer to this as a price dispersion. Before you try this, sketch what you think the shape of this line should be. Will it slope down from left to right as your largest customers are getting better prices? Or perhaps the slope goes the other way, as the largest customers get the most value and are willing to pay more. Will the curve be linear, concave or convex? What is the logic behind the curve you expect?
Once you have the data, do some curve fitting and see what kind of curve best fits the data and how much of the variance (or r-squared) the line accounts for. At Ibbaka we generally want r-squared to be greater than 0.7, though in companies with poor pricing discipline it can be much lower.
Of course some accounts will fall above the line, they are relatively overpriced, and others will fall under the line. Some variance is normal, what you are looking for is outliers or even better groups of outliers.
A typical SePVA or Price Dispersion Graph
Are the groups of outliers actually signalling that they belong in different segments?
In some cases, one just gets clusters and there is no meaningful curve that can be fit. In this case, you are almost certainly dealing with customers that belong in different market segments. Sometimes there will be clear clusters, but in other cases the dispersion will look random.
When this happens, you need to explore other dimensions. What else could you use for the X-Axis? Is there some other variable that would snap the data into order, so that you could fit a line, or at least find some meaningful clusters. Some things to look at include:
Usage data. Is there some pattern of use that is correlated with price? If there is, that would be a very good thing as it would suggest a new pricing metric based on use.
Industry data. Are companies in one industry getting a different price than companies in another industry? If so, why? Are there packages that can be designed for each industry?
Geographical data. Are some geographies getting a better price? If so, why? Are there grey markets where people are buying in one market for use in another?
The most interesting thing to do is to map price against value metrics and value models. If you have data on the value drivers that matter to each customer, you can see if the customers cluster around value drivers (Ibbaka has software that helps to do this). If you have been able to develop value models for each customer (an emerging best practice), check and see how value and price are correlated. Wouldn’t it be nice if customers getting the most value are paying the highest price. Of course, you may want to correct for volume when you do this. If company A and Company B are getting a similar unit value, are they also getting a similar unit price?
One can go a step farther and create a SePVA for the SPI (Sales Price Index). Map the SPI for each company on the Y-Axis and try other values on the Y-Axis. Find out which values find order in the data.
The first step in good pricing strategy is to find the order in the data. If there is no order, you will struggle to execute on pricing strategy.
