But whatever strategy you use, there’s one thing that will always affect your pricing strategy.
And it’s something almost no one accounts for…
How Volition Affects Consumer Pricing Research
Any information you get from your customers is better than none, of course. In fact, so much formal research occurs in this arena that marketers have traditionally used four distinct techniques.
My premise here, though, is that given the multidimensional world in which we leave, the existing research is simply not enough.
Fact is, even though various pricing models and methodologies exist, and can be helpful, there is one boondoggle that can indirectly or directly impact all of the methods: volition.
Volition is the act of making a choice or decision. Be it buying an item at a certain price point or pulling the lever in the voting booth, volition always lies there lurking, like some deadly toxin in the weeds to complicate research that tries to predict behavior.
If you agree with psychologists who have long described attitudes as “implicit behaviors”, then every form of pricing research, as sophisticated as they may be, can be susceptible to to failing to isolate and acknowledge volition.
So consumers have told us they favor a certain price for a particular product. But the question is, what absolutely true indicators do we have that they’ll actually buy it? And therein lies the rub.
A Voting Research example
Before we delve into pricing research, let’s consider the related field of polling and voting research.
The best political pollsters attempt to cope with the issue of volition by first always utilizing samples of people who have stated that they are likely voters. A further qualifier specifies that they actually plan to vote in that upcoming election. The samples themselves are almost always drawn from lists of registered voters.
While these techniques reduce the possibility of error, there is no way to predict with absolute certainty that those being sampled will actually cast a ballot and that their actual vote will mimic the response they gave while being polled. (Hence the familiar +/- 5% error factor that most polls acknowledge).
Ironically, exit polling of people who have just cast a ballot can be the most accurate because volition has been certified. Alas, national exit polls are usually relegated to media post-election analysis. Exit polls taken after primaries however can be much more useful in crafting strategies for the election to come.
There is, however, a critical difference between polling research and pricing research.
A vote is a value-driven, ego-involved decision for the voter. It is something that they are likely to be much more serious about.
For the consumer, paying $.45 more for a loaf of bread or an extra $.99 for breakfast cereal is not. That makes pricing research all the more difficult. And the lower the price point, the more difficult to predict which one truly reflects what consumers will accept.
Conventional Pricing Research
The number of research techniques that price the total product are succinctly described in a white paper by Curt Stenger of Ipsos Marketing – some of these date back into the 1960’s.
One of the earliest and most widely used is the Gabor Granger Technique.
1. The Gabor Granger Technique
This basically asks the consumer, “would you buy Item A at price B.”
The questions continue depending on the initial response until the highest or lowest acceptable price thresholds are reached. While the “would you buy” phrase attempts to approximate actual behavior or volition, the extent to which it does or does not is arguable.
Thus, if the highest acceptable price is $4.95 for a new deodorant, the only ultimate proof of that acceptability can be measured by actual sales data. In many cases, by then it’s too late to make a correction if the product is not moving at that price.
The Gabor Granger method, ironically, is considered the most primitive of the pricing research strategies. Frankly, despite the accelerating level of statistical sophistication in all of techniques below, their failure to provide a measure for actually identifying specific pricing reactions and subsequent buying behavior makes them all relatively primitive.
2. Price Sensitivity Measurement Approach
Another technique is the Price Sensitivity Measurement approach. Respondents are asked four questions:
- “At what price would you consider the product to be so expensive that you would not consider buying it? [too expensive]
- “At what price would you consider the product to be priced so low that you would feel the quality couldn’t be very good.” [too cheap]
- “At what price would you consider the product starting to get expensive, so that it’s not out of the question, but you have to give some thought before buying it.” [expensive]
- “At what price would you consider the product to be a bargain-a great buy for the money.” [cheap]
With this system, the optimal price point for a product is the intersection between those consumers who thought it to be too expensive and those who thought it to be too cheap.
While the PSM is still widely used, it is again, almost purely theoretical and demands that the respondent has a good deal of product knowledge.
The system fails to ground the questions in actual buying reality. As noted in the above paragraph, volition is not taken into consideration. Do we know that they actually have any propensity for buying the product at all, at any price? The answer, unfortunately is not really.
3. Monadic Concept Testing
A third system is known as Monadic Concept Testing.
This involves an exercise where, according to Ken Roberts of Roberts/Cooper research, “several cells of the same concept are tested among unique groups of respondents, but for each cell the price is different.”
Again, this approach requires product familiarity and still does not approximate an actual buying situation. But how many companies have made critical mistakes over the years because they accepted the results at face value?
The same can be said for ostensibly the most sophisticated of the pricing studies, generally known as the Discrete Choice Exercise.
4. Discrete Choice Exercise
Conjoint analysis is the statistical tool here and it attempts to approximate the process that consumers go through to make a purchase decision. This approach tackles more than the ideal product price point. Features of the product. the brand, and the price are presented.
While Discrete Choice attempts a multivariate look at the factors in the decision process, and supposedly allows for the construction of a pricing and sales mode that allows for elasticity (I love that word).
Again I find the actual behavioral component to be missing. This does not mean that these tests are without validity. Indeed they have been around for years and have doubtless contributed much information.
But think how much more effective they could have been had they allowed for an environment in which attitudes could truly be traced to purchase behaviors.
A/B/n Testing for Pricing Strategy
The irony here is that, in the apex of the digital age, A/B testing has not been championed by more market researchers who are interested in price testing.
The element that’s missing in all of the above techniques: measuring propensity to actually buy at a certain price, can be fairly simply analyzed with an effective A/B design. The key word here is ‘behavior’ and A/B test designs provide accurate depictions of web traffic and buying behaviors.
Suppose you’ve used one or more of the above methodologies to arrive at a desired price point.
A/B testing allows you to start with the same price and two different sales descriptions or propositions. Then see which one triggers more sales. These sales results, when conducted under the proper testing environments cannot be equivocated. You’ll know which copy descriptions drove sales and which prices were most attractive. The research scenarios here for every other level of the sales and marketing/branding processed are virtually limitless.
You can use A/B testing to approximate the Price Sensitivity Measurement mentioned above by offering similar products at different price points and then tracking the actual sales. For web retailers, the key is actual revenue gained from the optimal price point, and not necessarily conversions.
A/B testing is as real as it gets.
You have demonstrable behaviors that can be easily measured. There’s nothing theoretical here. Volition is readily identifiable by tracking the results of your A and B sites. The beauty of A/B tests is that they also lend themselves to post hoc statistical analysis based not on the presumption of behavior, but on ACTUAL behavior.
In addition to A/B testing, pricing strategies can be configured to actual buying behavior by companies like Reality Mine.
This British research firm focuses on cell phone use and all the web visits and buying behaviors that occur on each phone. A sample of 100 randomly selected users can speak volumes about pricing, brand awareness, and buying behaviors. A/B testing can be utilized with this real world methodology as well.
A.C. Nielsen has also continued to evolve from the company that used to put a “storage instantaneous audimeter” on top of people’s TV’s to measure viewing to a company using sophisticated devices about the size of a small cell phone to measure a multitude of buying behaviors.
All of these techniques had their roots in the early days of single source research which began with the introductions of electronic scanners at grocery stores.
It seems a trifle self-serving that, possessing the digital testing modalities that exist today, we would continue to champion techniques that cannot define that Darwinian moment [if you will] where the missing link between pricing perceptions and actual buying behaviors can be substantively, and finally defined. Well, the future is here and marketers and researchers must embrace it.
All business have to think about pricing strategy – if they want to make money. To make more money, having some form of customer data to optimize pricing is crucial.
While a variety of consumer research methodologies exist, and can be helpful, volition interferes with their accuracy. Unless you can measure actual buying behavior, there will always be a discrepancy between the theory and reality.
Therefore, embrace A/B testing to optimize pricing. This is the realest way to define the optimal price point.