Pricing Strategy
Episode 04
Strategy: Episode 04 Transcript
Hi, I’m Beth Horn, SVP of Advanced Analytics at Decision Analyst, and today we’re going to talk about pricing strategy and methods use to assess them.
The most common pricing strategy is to maximize profit-line revenue or profit. Sometimes market share can be maximized, particularly in early stage rollout of a new product. The second strategy is to set prices to maximize customer perceived value. In other words, set higher prices for products that have more value and lower prices for products that have less value. A third strategy is to customize price based on customer segment. The idea is to charge more for customers who are willing to pay more. However, this strategy should be used with caution. There must be legal and compelling reasons to offer the same product to different consumers at different prices. The fourth pricing strategy is to set pricing differently based on purchase channel. For example, mass merchandisers will sell an organic product at considerably less [price], oftentimes, than a specialty organic store would. Finally, optimizing price for a new product can be a challenging proposition; often, consumers don’t have really good competitive benchmarks with which to assess the new product.
Now let’s turn to some research methods to assess these pricing strategies and I’ll talk about three of them.
The first is econometric demand modeling. With this particular method, an analyst uses historical data and models a price elasticity curve. This method is very robust and reliable, yields good measures of price elasticity. However, it is historical data, so if there is a price that a company wishes to charge that is outside that range, this econometric demand modeling is not going to be as good in extrapolating reactions or price elasticity for that higher price. Another drawback is that markets are rapidly changing; the online commerce or e-commerce phenomenon continues to change our retail landscape, so using historical data may often render models simply out of date.
The second strategy is choice modeling, and this relies on primary research. Several screens of current and future market scenarios are served up to respondents, usually in an online environment, and respondents select one product or several products based on prices, and features; and econometrics demand model is actually estimated from this data. The difference from the prior technique, though, is that choice modeling looks forward, so, toward the future, and is better at predicting consumer reactions to realities that don’t exist yet but can be tested in that online environment.
Our third method is really a suite of methods–stated preference techniques–and I’ll talk about one: this is the Gabor Granger technique. This is very straightforward and simple; serve up questions, purchase intent questions for particular price points that have been predefined. A consumer rates how likely he or she would be to buy the product at those price points, and the methods vary widely: you can serve up first the middle price point of the range that you want to test get a reaction; and then, serve a higher or lower price point to consumers depending upon how they responded to the question. Other analysts use a random presentation of the price points, but either way,a demand curve can be drawn from this data, and price elasticity estimated. This is a very simple, low-cost straightforward approach and can be used when budget is a factor and timing could be a factor as well.
Well, regardless of what pricing strategy you adopt for your company in your products, be sure to select the correct method to assess consumers’ reaction, make sure that you solicit help from a reliable research partner that will help you sift through the various methods and find the right match for your pricing strategy. Well, that’s all for now, thank you for watching.
Presenter
Elizabeth Horn
Senior VP, Advanced Analytics
Beth has provided expertise and high-end analytics for Decision Analyst for over 25 years. She is responsible for design, analyses, and insights derived from discrete choice models; MaxDiff analysis; volumetric forecasting; predictive modeling; GIS analysis; and market segmentation. She regularly consults with clients regarding best practices in research methodology. Beth earned a Ph.D. and a Master of Science in Experimental Psychology with emphasis on psychological principles, research methods, and statistics from Texas Christian University in Fort Worth, TX.
