by Paul Hunt – President
When I started working as a pricing consultant more than 20 years ago, I became really excited about the development of “conjoint analysis.”
The word “conjoint,” which is short for “consider jointly,” is a method of measuring price elasticity. While most of us learned about price elasticity in university, it was a purely theoretical concept that we assumed (with some relief ) we would never see again, let alone use!
But in recent years, price elasticity has moved from the classroom to the boardroom in a big way. It is now a source of competitive advantage, and is being used in industries as diverse as consumer goods, financial services, construction, high technology and aerospace. Today’s executives ignore price elasticity at their peril!
Conjoint analysis, in a nutshell, is a method of doing research in which you simulate a buying experience for the customer in which they are put in the position of making tradeoffs. Let’s say, for example, he or she was buying a car. We could identify various attributes the customer is trading off, such as brand, safety, reliability, comfort, etc., and then measure how much he or she is willing to pay for each one.
In the past, pricing researchers would simply ask: “How much would you be willing to pay for this new product/ service?” This approach is tremendously flawed, however, because there is no incentive for respondents to answer honestly.
On the contrary, there is an incentive for them to answer dishonestly, thinking that if they tell the researcher a low price, then the company might price the product or service lower and the customer will get a better deal.
Conjoint analysis does not suffer from this flaw, because it asks customers to trade off quality and price without creating a situation in which the customer can “game” the system.
Over the past 20 years, conjoint analysis has become an important tool for price optimization. Initially, it was used primarily by consumer-goods and pharmaceutical companies, but it is now used in virtually every industry, ranging from manufacturing to services to high technology.
Conjoint analysis enables companies to answer questions such as: Are you leaving money on the table? What is the optimal price for a new product? What price gap can you sustain versus the competition? What is the optimal price gap between various versions of a product within our product line? What if the competition lowers its price in response to the change we implement? What attributes drive the most and least value? What is the correct frequency and depth of discounts?
The deliverable from a conjoint study is an optimization model that enables you to test the impact of making changes to the price and the attributes of your product or service. In fact, you can even ask the model to optimize for market share, volume, and profit, or you can set constraints — for example, maximize profit but don’t let volume decline by more than 5%.
The model will then automatically run up to 30,000 combinations and provide the optimal solutions. The following case studies provide some insight into the power of this methodology.
Case study 1: Chemicals Our client, a chemical company with a plant in North America, was selling at a price virtually identical to that of its Japanese competitor (who did not have a plant in North America). The client wondered if there was value in being located locally and if that translated into a premium price versus the competitor. We conducted a conjoint study with existing and potential customers, interviewing two key decision-makers: purchasing agents and technical buyers.
The research indicated that the purchasing agents placed no value on the product being supplied locally and based their decisions strictly on pricing.
The technical buyers, however, indicated they would pay 15% more for local production, as they could not afford to run out of this product, and so local supply was very important. Armed with this information, the client implemented a guaranteed supply clause in contracts and raised prices by 15% for clients who wanted that certainty. The result: no lost business and a much greater profit.
Case study 2: Consumer packaged goods A leading consumer-goods brand was under attack. The largest retailer in Canada planned to launch a private-label product that was aimed directly at the brand’s leading product.
The private-label product would be priced at a 50% discount and placed on the shelf right beside the branded product. Debates raged internally about what the company should do.
Some staff advocated for price reductions in excess of 30%, while others thought the price should not be lowered by more than 5%, and still others thought the company should pull the product off that retailer’s shelf and sell exclusively in other channels.
Fortunately, the company undertook a conjoint study and tested the impact of the new competitor at different price gaps. The findings were very interesting. While the company had to accept that there would be some share loss, it was much less than feared; consumers really trusted this brand and were not going to switch just because the new product was cheaper. As a result of the conjoint study, the company invested heavily in the brand to reinforce the value.
Meanwhile, another competitor panicked, lowering its price significantly. Both its market share and profit dropped dramatically.
Unfortunately, many executives still do not recognize the potential rewards of measuring price elasticity. I remember one senior manager saying to me: “I don’t believe you can measure price elasticity.”
Ironically, he was an economics major, and yet his educational background was precisely why he felt that way — he had learned the theory but had never seen it applied in practice.
Therefore, I encourage you to open your mind to the idea that measuring price elasticity/ optimization is possible within your business, and get busy figuring out how to do it. Otherwise, you are missing out on a major opportunity for competitive advantage and superior profits.
–Paul Hunt is president of Pricing Solutions Ltd. His pricing column appears monthly in the Financial Post. He can be contacted at email@example.com
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