Author: Tim J. Smith, PhD

In this article, the author explains why he recommends Exchange Value to Customer calculations according to economic factors, rather than weighted average benefits, when pricing in industrial markets. This industrial markets specific example provides valuable pricing questions and insights that can be utilized by pricers in multiple industries. Tim J. Smith, PhD is the founder and CEO of Wiglaf Pricing, adjunct professor at DePaul University, and Academic Advisor for the Certified Pricing Professional designation. His most recent book is Pricing Done Right:  The Pricing Framework Proven Successful by the World’s Most Profitable Companies (Bloomberg Financial, 2016). He can be reached at

The Pricing Advisor, September 2017

I was recently asked the following question: “Why calculate exchange value to the customer by economic factors when we can do a weighted average benefit analysis?”  And I knew I was being pulled into a best-practice discussion regarding an established standard operation procedure within that particular industrial goods firm. I had a contrasting opinion.  So why do I (and many others) like Exchange Value to Customer calculations according to economic factors, rather than weighted average benefits, when pricing in industrial markets?

What follows is my experience.  Am I crazy or uninformed? Do you have a better argument?  Valuing Benefits: Weighted Average or Economic Impact?

Weighted-Average Analysis

The type of weighted average benefit analysis under consideration used a grid. The top horizontal axis listed the benefits and their weighted importance where the weights added up to 100%. The vertical left axis listed the competitors. Each competitor was scored on a 1 to 10 rating scale according to how well they delivered on individual benefits. Then, the scores were multiplied by the weights to calculate the weighted average benefits of the competing products along with the focal product.

All seems logical enough.  But there were challenges.

First: who chooses the benefits, the weights, and the scores?  While one could imagine a voice of customer exercise followed by a survey to define these factors, in practice they used an internal management consensus.  While imperfect, this approach should be directionally correct.

Second: how sensitive is the resulting metric of benefits to the input parameters?  While the overall measurement of benefits was directionally sound, the specific relative measurements were highly sensitive to input parameters and weightings.  Thus, the benefit differential, which a key issue in pricing and price positioning, was suspect.

While these challenges could be addressed and perhaps reduced through customer surveys, one should back up and ask the following: Why conduct a survey for pricing based on a weighted-average when academic research and industry best practices both point to conjoint analysis as a far superior survey-based tool for measuring perceived differences between products? Along with the relative importance of these differences and the price tradeoffs people are willing to make for those differences?

We know we have a superior customer survey based approach. The attractiveness of the above weighted average approach was that it could be done internally with managerial insight, thus quickly and at a low cost. And while not specifically perfect, it is directionally decent.

So why do the academic literature and best practices both suggest calculating the exchange value to customer when a simple weighted-average approach might work?

Economic Impact

Exchange Value to Customer is calculated by:

  1. Identifying the nearest competing alternative and the differential benefits.
  2. Calculating the value of the differential benefits from the customer’s perspective.
  3. Pricing near but slightly below that of price of the competing alternative after adjusting for the value of the differential benefits.

This too seems logical enough, but has its challenges.

Like the above weighted average approach, this exchange value approach too is improved through direct customer research but is usually chosen as an approach that, with reasonable management insight alone, can produce a relatively accurate price.  And, like the weighted-average approach, the financial impact of a benefit is highly dependent on the input parameters to that model.

Then, it has an additional challenge:  How do you calculate benefit differentials?  Some benefits can be modeled financially, such as labor savings, time savings, risk reduction, disposal cost, output improvements, other benefits cannot.  For instance, the benefit of “brand trustworthiness” might have a large value but it is very difficult to model.  Plug values often get used for psychological factors.  And plugs are little more than guesses, to be honest.

Given the large challenge of actually modeling the financial impact of benefits and the fact that calculating exchange value to customers internally suffers from the same challenges as using a weighted average benefit metric for price positioning, why bother with the extra work?

A Sample Comparison

The answer came from examining the results of the two approaches on the same product in the same market, and seeing the nature of the difference in worldviews and resulting usefulness.

The weighted average benefit metric indicated that the product should be priced high and would be nearly identical in benefits with a product that had a very large market share. When I asked if the two products were really similar, and if so, why are they projecting such a small market share, the product management team quickly stated that the two products did very different things. Yes, they are both used by the same customers and were in the same product category, but the two products solved different problems for those customers and weren’t really comparable.  Thus, the weighted average approach led to a distorted picture of the pricing opportunity.

The weighted-average approach examines all products at once.  Hence, it creates a generalized picture of the market.  Not bad for getting an overall picture, but it can be misleading and is insufficient for pricing.

The exchange value approach indicated that the product should be priced low for a large segment and very high for another small segment. It clarified that some customers would highly value the offering in certain situations, while those same and some other customers would have little value for the offering in other, more common, situations.  The original product that was considered similar in position according to a weighted-average benefit metric was clearly identified as not a real competitor. Rather, a different, inferior product was found to be the nearest competing alternative.  I say inferior yet in some situations the two products were relatively comparable, while in others the inferiority was blatantly obvious.

Because the exchange value approach examines the focal product against its next nearest competitor from the viewpoint of a specific market segment, it creates a focused picture of how an offering is likely to be evaluated by that specific segment. If more segments and competitors are to be considered, more models of the Exchange Value to Customer are needed. This leads to better and more accurate pricing on a segment-by-segment basis.


Recall the adage: markets don’t buy products, customers do. Value-based pricing drives the firm to approach the right customers with the right value proposition to drive their purchases profitably. The more price segmentation a company can do, the more profitable it generally is. Exchange Value to Customer clarifies which segment of the market is likely to purchase at a given price, thus enables the company to pick the segment to serve when management decides on a price. By providing greater clarity than a weighted-average approach, exchange value to customer enables better pricing and decision-making.

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