Van Westendorp pricing (the Price Sensitivity Meter)
This is a follow up to classes I taught that included a short section on pricing research methodologies. I promised some more details on the Van Westendorp approach, in part because information available online may be confusing, or worse. This article is intended to be a practitioner’s guide for those conducting their own research.
First, a refresher. Van Westendorp’s Price Sensitivity Meter is one of a number of direct techniques to research pricing. Direct techniques assume that people have some understanding of what a product or service is worth, and therefore that it makes sense to ask explicitly about price. By contrast, indirect techniques, typically using conjoint or discrete choice analysis, combine the price with other attributes, ask questions about the total package, and then extract feelings about price from the results.
I prefer direct pricing techniques in most situations for several reasons:
- I believe people can usually give realistic answers about price.
- Indirect techniques are generally more expensive because of setup and analysis.
- It is harder to explain the results of conjoint or discrete choice to managers or other stakeholders.
- Direct techniques can be incorporated into qualitative studies in addition to their usual use in a survey.
Remember that all pricing research makes the assumption that people understand enough about the landscape to make valid comments. If someone doesn’t really have any idea about what they might be buying, the response won’t mean much regardless of whether the question is direct or the price is buried. Lack of knowledge presents challenges for radically new products. This aspect is one reason why pricing research should be treated as providing an input into pricing decisions, not a complete or absolute answer.
Other than Van Westendorp, the main direct pricing research methods are these:
- Direct open-ended questioning (“How much would you pay for this”). This is generally a bad way to ask, but you might get away with it at the end of a in-depth (qualitative) interview.
- Monadic (“Would you be willing to buy at $10”). This method has some merits, including being able to create a demand curve with a large enough sample and multiple price points. But there are some problems, chief being the difficulty of choosing price points, particularly when the prospective purchaser’s view of value is wildly different from the vendor’s. Running a pilot might help, but you run the risk of having to throw away results from the pilot. But if you include open-ended questions for comments, and people tell you the suggested price is ridiculous, at least you’ll know why nobody wants to buy at the price you set in the pilot. Monadic questioning is pretty simple, but it is generally easy to do better without much extra work.
- Laddering (“would you buy at $10”, then “would you buy at $8” or “would you still buy at $12″). Don’t even think about using this approach, as the results won’t tell you anything. The respondent will treat the series of questions as a negotiation rather than research. If you wanted to ask
about different configurations the problem is even worse. - Van Westendorp’s Price Sensitivity Meter uses open-ended questions combining price and quality. Since there is an inherent assumption that price is a reflection of value or quality, the technique is not useful for a true luxury good (that is, when sales volume increases at higher prices). Peter Van Westendorp introduced the Price Sensitivity Meter in 1976 and it has been widely used since then throughout the market research industry.
How to set up and analyze using Van Westendorp questions
The actual text typically varies with the product or service being tested, but usually the questions are worded like this:
- At what price would you begin to think product is too expensive to consider?
- At what price would you begin to think product is so inexpensive that you would question the quality and not consider it?
- At what price would you begin to think product is getting expensive, but you still might consider it?
- At what price would you think product is a bargain – a great buy for the money
There is debate over the order of questions, so you should probably just choose the order that feels right to you.
The questions can be asked in-person, by telephone, on paper or (most frequently these days) online questionnaire. In the absence of a human administrator who can assure comprehension and valid results, online or paper surveys require well-written instructions. You may want to emphasize that the questions are different and highlight the differences. Some researchers use validation to force the respondent to create the expected relationships between the various values, but if done incorrectly this can backfire (see my earlier post). If you can’t validate in real-time (some survey tools won’t support the necessary programming), then you’ll need to clean the data (eliminate inconsistent responses) before analyzing. Whether you validate or not, remember that the questions use open-ended numeric responses. Don’t make the mistake of imposing your view of the world by offering ranges.
Excel formulae make it easy to do the checking, but to simplify things for an eyeball check, make sure the questions are ordered in your spreadsheet as you would expect prices to be ranked, that is Too Cheap, Bargain, Getting Expensive, Too Expensive.
Ensure that the values are numeric (you did set up your survey tool to store values rather than text didn’t you? – if not another Excel manipulation is needed), and then create your formula like this:
IF(AND(TooCheap<=Bargain,Bargain<=GettingExpensive, GettingExpensive<=TooExpensive), OK, FAIL)
You should end up with something like this extract:
|
ID |
Too Cheap |
Bargain |
GettingExpensive |
TooExpensive |
Valid |
|
1 |
40 |
100 |
500 |
500 |
OK |
|
2 |
1 |
99 |
100 |
500 |
OK |
|
3 |
10 |
2000 |
70000 |
100 |
FAIL |
|
4 |
0 |
30 |
100 |
150 |
OK |
|
5 |
0 |
500 |
1000 |
1000 |
OK |
Perhaps respondent 3 didn’t understand the wording of the questions, or perhaps (s)he didn’t want to give a useful response. Either way, the results can’t be used. If the survey had used validation, the problem would have been avoided, but we would also have run the risk of annoying someone and causing them to terminate, potentially losing other useful data. Not an easy call.
Now you need to analyze the valid data. Van Westendorp results are displayed graphically for analysis, using plots of cumulative percentages. I use Excel’s Histogram tool to generate the values for the plots. You’ll need to set up the buckets,so it might be worth rank ordering the responses to get a good idea of the right approach. Or you might have an idea of price increments that make sense.
Create your own buckets, otherwise the Excel Histogram tool will make its own from the data, but they won’t be helpful.
Just to make the process even more complicated, you will need to plot inverse cumulative distributions (1 minus the number from the Histogram tool) for two of the questions – Too Cheap and Bargain. Warning: if you search online you may find that plots vary, particularly in which questions are flipped. What I’m telling you here is my approach which seems to be the most common, and is also consistent with the Wikipedia article, but the final cross check is the vocalizing test, which we’ll get to shortly.
Van Westendorp Example
Before we get to interpretation, let’s apply the vocalization test. Read some of the results from the plots to see if everything makes sense intuitively.
“At $10, only 12% think the product is NOT a bargain, and at $26, 90% think it is NOT a bargain.”
“44% think it is too cheap at $5, but at $19 only 5% think it is too cheap.”
“At $30, 62% think it is too expensive, while 31% think it is NOT expensive – meaning 69% think it is getting expensve” (Remember these are cumulative – the 69% includes the 62%). Maybe this last one isn’t a good example of the vocalization check as you have to revert back to the non flipped version. But it is still a good check; more people will perceive something as getting expensive than too expensive.
Interpretation
Much has been written on interpreting the different intersections and the relationships between intersections of Van Westendorp plots. Personally, I think the most useful result is the Range of Acceptable Prices. The lower bound is the intersection of Too Cheap and Expensive (sometimes called the point of marginal cheapness). The upper bound is the intersection of Too Expensive and Cheap (the point of marginal expensiveness). In the chart above, this range is from $50 to $100. As you can see, there is a very significant perception shift either side of the $50 and $100 price points.
Some people think there is so-called optimal price (the intersection of Too Expensive and Too Cheap) is useful, but I think there is a danger of trying to create static perfection in a dynamic world, especially since pricing research is generally only one input to a pricing decision. For more on the overall discipline of pricing, Thomas Nagle’s book is a great source.
Going beyond Van Westendorp’s original questions
As originally proposed, the Van Westendorp questions provide no information about willingness to purchase, and thus nothing about expected revenue or margin.
To provide more insight into demand and profit, we can add one or two more questions.
The simple approach is to add a single question along the following lines:
At a price between the price you identified as ‘a bargain’ and the price you said was ‘getting expensive’, how likely would you be to purchase?
With a single question, we’d generally use a Likert scale response (Very unlikely, Unlikely, Unsure, Likely, Very Likely) and apply a model to generate an expected purchase likelihood at each point. The model will probably vary by product and situation, but let’s say 60% of Very Likely + 25% of Likely as a starting point. It is generally better to be conservative and assume that fewer will actually buy than tell you they will, but there is no harm in using what-ifs to plan in case of a runaway success, especially if there is a manufacturing impact.
A more comprehensive approach is to ask separate questions for the ‘bargain’ and ‘getting expensive’ prices, in this case using percentage responses. The resulting data can be turned into demand/revenue curves, again based on modeled assumptions or what-ifs for the specific situation.
Conclusion
Van Westendorp pricing questions offer a simple, yet powerful way to incorporate price perceptions into pricing decisions. In addition to their use in large scale surveys described here, I’ve used these questions for in-depth interviews and focus groups (individual responses followed by group discussion).
Idiosyncratically,
Mike Pritchard
References
Wikipedia article: http://en.wikipedia.org/wiki/Van_Westendorp’s_Price_Sensitivity_Meter
The Strategy and Tactics of Pricing, Thomas Nagle,
Van-Westendorp PH,(1976), NSS Price Sensitivity Meter – a new approach to the study of consumer perception of price. Proceedings of the 29th Congress, Venice ESOMAR
Vetri Vellore
May 28th, 2009 at 4:54 pm #
Very helpful writeup – thanks Mike!
Mike Pritchard (That Research Guy)
May 31st, 2009 at 4:08 pm #
I’m glad you found the article helpful Vetri. Let me know if there are other survey or research topics you’d like covered.
Mike
January 19th, 2010 at 9:47 pm #
Anna, thanks for your interest.
Van Westendorp is a price optimization technique. The point where the Too Expensive and Too Cheap curves cross is called the point of marginal cheapness. This is where the fewest people will not buy because they consider the product too expensive or too cheap. So the maximum volume of product will be sold at this point. I usually place less emphasis on this point because it looks as if it gives a more precise result than the data generally supports – especially for products that are not all that mature or well-defined. However, it serves to illustrate the reason why the cumulative plots are valuable. To look at it another way, using the vocalization examples, at $300 only 5% think it is too cheap. But at $35, 40% think it is too cheap. The 40% who think $35 is too cheap includes the 5% who thought $300 too cheap.
The vocalization doesn’t cast doubt on the research results, it merely makes sure that you have plotted the curves correctly, and that you are comfortable talking about them in front of management and clients. If the results don’t make sense intuitively, that probably means that the respondents don’t understand the product well enough. Perhaps it is too new for them to appreciate the value proposition, or perhaps the information provided in the survey wasn’t adequate.
I hope this helps.
Mike
Anna
January 19th, 2010 at 8:53 pm #
Mike, couple of follow up questions:
1. Why is cumulative frequency used in this method?
2. What if vocalisation reveals that the results don’t make sense intuitively – do we reject the research outcomes? What flaw does it point to?
Mark
February 13th, 2010 at 11:33 am #
I ran the model two ways. The first way, I broke the prices into increments of $1.50. The second way, I broke the prices into increments of $.25. I got wildly different results, based on how I grouped the prices. I checked and double-checked that I did everything correctly. Have you ever seen this before? It just doesn’t make sense that this would happen.
n = 306 consumers
Mike
March 8th, 2010 at 9:14 pm #
Hi Mark, interesting results. Can you give more details on the ranges of values you got? How did the results vary?
I can imagine that there would be some differences if the product/service should be priced close to the increments. In other words, if the fair price is $2.50 say, you might have spiky results. That would be similar to any situation where there is a threshold and there are big jumps in that area. If there isn’t anything like that, I’m not sure what could be the cause.
Mike
Mark
March 18th, 2010 at 12:57 pm #
I found the problem and it is me. I got wildly different results because I screwed up two of the cumulative distributions in a way that is too complex to explain and would embarrass me even more than I already am. At least the problem is solved and whatever faith in the model we have has been restored.
Mike
March 22nd, 2010 at 4:36 pm #
Don’t be too hard on yourself Mark. Although the concept behind Van Westendorp is simple, it seems that it is also easy to make mistakes with the plots.
I’m glad you got it figured out.
Mike
Mark
June 24th, 2010 at 6:18 am #
Hi Mike,
I was wondering if you could expand a bit on how to analyze/model the “going beyond” questions — if you use the two additional questions on likelihood to purchase — the bargain and getting expensive price points? Do you just use the price point they are most likely to purchase at (it would be the bargain price almost all the time wouldn’t it) or do you average the 2 price points when plotting the demand/revenue curves? Thanks for any guidance you can provide!
Mike
June 25th, 2010 at 4:32 pm #
Hi Mark. Thanks for your interest. There are a couple of different ways to come up with demand/revenue curves. I’ll do an update or a new post shortly.
Mike
Daniel
August 14th, 2010 at 2:52 pm #
Hi Mike,
I just ran the van Westendorp survey over a group of respondents and plotted the cumulative frequencies on a graph. The problem is my “Too Cheap” and “Too Expensive” lines don’t seem to intersect, so I couldn’t get the Optimal Price Point (OPP). They’re really close to intersecting, though. I have the rest of the intersection points IPP, PME and PMC. I tried running the survey over a bigger number of respondents, but there’s still no progress over this matter. What should I do? Should I just extend the lines and guess on an OPP? Something doesn’t feel right doing that though.. or should I just leave it the way it is? But how do I interpret my results then?
Sorry to bother you with all these questions. I’m really stuck on this matter, I hope you can offer me some help. Thanks for your time!
Joe
August 27th, 2010 at 12:25 pm #
Mike: Interesting and useful article, as this is my first experience with VW. Are you aware of any software or Excel template that “automates” the data reduction?
Mike Pritchard
November 10th, 2010 at 10:03 am #
Hi Daniel. I’ve just been checking old comments. I was waiting for a private reply from you so I could do a more complete response, but maybe it went into your spam. I’d like to look at the data to better understand what’s going on.
But in general, your example is a good one to support my point that the most useful result from Van Westendorp analysis is the range of acceptable prices. Without knowing more about your study, I hesitate to speculate too much on the reasons, but here’s one scenario that might generate the results you describe. Imagine that the questions were being asked about a car. A car in general, not a specific car. The upper boundary for ‘Too Cheap’ could easily be quite low because people might be imagine a whole range of cars that would be available, perhaps including used cars. The lower point for ‘Too Expensive’ could be higher if people aren’t thinking about the same car for the two questions.
Does that help? If you want to send me some more information (ideally the data) privately I’ll be glad to take a look.
Mike
Mike Pritchard
November 10th, 2010 at 10:06 am #
Hi Joe. Sorry about the delayed response. I’m just about to release something that should do what you want, so watch this space….
Mike
Zack Apkarian
December 11th, 2010 at 5:56 am #
Mike,
Thanks for the article — very helpful and informative. I do have one question: If price ranges were used in the questions instead of leaving them open ended, how would you handle that from an interpretive standpoint?
Thanks in advance for the response.
Zack Apkarian
Director, Retail Insights
Pfizer Consumer Healthcare
Mat
January 10th, 2011 at 3:43 pm #
Hi Mike,
Thanks for this post. I’ve been learning how to do these analyses and your site was the most helpful from my extensive searching online.
I had two follow-up questions:
First, I’m not confident that I’m plotting my histogram properly. My prices range from 1-100, and so when I create my bins in the Histogram, I use 0-99 as my manual ‘bins’. Then, when I plot the data I use $1 for the ’0′ bin and $11 for the ’10′ bin, and so forth. This is critical for me because obviously people will give numbers that break on regular intervals ($10, $20, etc.). In your interpretation above you wrote, “At $50, about 55% think it is a bargain.” Yet, depending on this binning issues and where the stairstep function occurs, I would have read your chart as “At $50, about 67% think it is a bargain”. Does that make sense?
My other question is the follow-up interpretation. I ask a single follow-up using the average of their “Bargain” and “GettingExpensive” prices about likelihood to purchase. To apply the model you casually referenced, do we just multiply the weightings by the respondent percentages? So, (60% multiplied by the “Probably buy” percentage), summed with (25% multiplied by the “Definitely buy” percentage)? I know there are many models, but is that what you meant by your one example? Do we factor at all the actual average price they used, before considering their answer to the likelihood issue? Does that get weighted in at all?
Thanks!
Andrew
January 24th, 2011 at 1:22 pm #
Hello Mike-
Thanks for the helpful post! I’m tracing your process with my own set of data, and I’m not clear on how the final histogram was built. Did you create one histogram from your four buckets of values, or four separate histograms? If they were separate, when you built one master chart, how did you set the x-axis to include all the data points and how they differ per bucket? Thanks!
Mike
January 31st, 2011 at 8:58 pm #
Zack, thanks for the kind words, and sorry for the delayed response.
I’ve been reviewing the literature (in between project work and overseeing a website overhaul). But I haven’t been able to come up with a reasonable way to use price ranges instead of open-ends. In fact, letting prospective purchasers choose their own price points is one of the main points in Van Westendorp’s Price Sensitivity Meter. From my experience with many studies, people really do give valid and useful information. Of course, they need to know enough about the product or service. I’ve certainly seen situations where the client didn’t like the results, and in one case they would probably have preferred a different style of question with fixed points, because the new product had already been pre-sold to management at a higher price. But the survey results proved more predictive – the product was released at too high a price and taken off the market within six months.
Back to your question. There are always some outliers that need to be eliminated. Some think that a price of zero is not too cheap, some are unrealistic and very high prices are stated. These get flushed out in the graphical analysis. But if you are absolutely convinced that your product should be within a certain range, and that despite giving all the information you can, people may still give the wrong answer without help, you could load the deck. That is, you could say something like “we are planning on a price of between X and Y“. Maybe something about “we are planning to introduce a product, exact features to be decided, at a price between X and Y“. This still doesn’t make a lot of sense to me.
The other thing you could do, is specify fixed prices in the follow up purchase likelihood question, instead of using their inputs. I’ve done this when I worked with another consultant who couldn’t convince the client to use Van Westendorp alone. The result was rather odd. Someone who stated that $80 was too high for them to consider would be asked how likely they would be to buy at $100.
But maybe this isn’t along the lines of your question. In any case, I’d love to talk more about it. Feel free to give me a call.
Mike
Mike
January 31st, 2011 at 9:23 pm #
Mat, I’m glad you found the post helpful.
I need to go back and recheck the vocalizations. I believe the statements were intended to be general, or may have related to a previous chart, but I can see that it would make more sense to have them connect to the specific chart. I won’t attempt that tonight (I don’t usually do Van Westendorp analysis with a glass of wine in hand!). But yes, your comment makes sense. Of course, if you wanted to be more accurate/pedantic you could say that at just 68% state a price of just under $50 to be a bargain, while 55% think it is a bargain at just over $50.
Your second question is more complicated. I’m just getting ready to write a follow up (after the website revision) and I’m getting close to offering a service for the original VW plus the follow up. I usually ask the question in terms of “at a price between [Bargain] and [Getting Expensive]“, but your approach is similar, and has the advantage of offering a specific price that might be less burdensome. Yes, I use a model like yours for actual likelihood, with the percentages varying by the situation (and sometimes with client perceptions). The predicted demand is based on the likelihood model and the price falling within the limits for each person. So if someone’s Bargain price is $25 and their GettingExpensive is $60, their likelihood isn’t counted in demand at a price of $65. I think there are tradeoffs in how many questions to ask for follow up, and at what points. I’ll be exploring those issues in the next post on the subject.
Mike
Mike
January 31st, 2011 at 9:31 pm #
Andrew, I’m glad you liked the post.
I’m actually not creating separate histograms any more, but my tool essentially uses four histograms – they just aren’t visible separately the way we do it. If you prefer to see the histograms you can use the Excel tools to generate histograms based on the data, or specify the buckets. After evaluating the ranges, you’ll find that you can use one set of x-axis values for all four charts. I usually truncate the range (most often at the high end, occasionally at the low end) to eliminate outliers when the rate of change is small.
I can see that I’ll have to revise the main article, not just write the second one!!
Mike
dominic
March 22nd, 2011 at 1:06 pm #
Hi Mike
I want to further explore the price volume relationship after the optimal price is generated. how is this best done? By asking questions like how likely are you to buy at (optimal price and plus/minus 10% from optimal price) and then following up with a question like “out of your next 100 purchases how many would you make at the optimal price and plus/minus 10%?
thanks
Orde
March 24th, 2011 at 6:07 am #
Hi Mike,
I just have a simple question – do you state the type of question inside the question?
For example,
What do you think is the highest price for a Honda Civic 2010 which will make you to never consider buying it? (too expensive)
Thank you
Mike
March 31st, 2011 at 8:09 am #
Hi Orde,
We don’t state the question type along with the question, but try to make the text clear enough so that additional explanation is not necessary. I’d rather frame the question in terms that relate to the respondent and the situation. For your example, I’d suggest a little editing.
What do you think is the highest price for a Honda Civic 2010 which will make you to never consider buying it?
To me, this is a little confusing and seems like a double negative. As a result, it isn’t as connected to the “too expensive” question as it should be. How about this instead?
At what price would a Honda Civic 2010 be too expensive so that you would not consider buying it?
We do emphasize the different words that distinguish between questions.
I hope this helps.
Mike
Mike
March 31st, 2011 at 8:36 am #
Dominic,
I’m sorry that I haven’t yet written the follow up that should make this exploration clearer. The approach we take is to ask a question like this:
At a price between X and Y, how likely are you to buy this product in the next six months?
X and Y are piped from the respondent’s answers to the Bargain and Getting expensive questions (some researchers might use Too Cheap and Too Expensive but I prefer to be more conservative). The future horizon text is product and situation specific (you might say after the product is introduced for something that isn’t in the market. We analyze by using a likelihood model (for example, 80% of the Very Likely, 60% of Somewhat Likely, ignore the others) and then calculating the percentage that would buy for each price. The result is an index of volume at various prices, and we usually also add an index for revenue too. If the volume doesn’t fall off too rapidly, the revenue index will show that a higher price is likely to be better – as long as it’s in the acceptable price window. [As I write this I can see that an article with some graphs will make it clearer].
Your suggested additions might work fine, but we are generally trying to keep to a reasonable time without having too many pricing questions. If you have a larger sample, you might be able to present a random price within the respondent’s window, or perhaps within the broader window of Too Cheap to Too Expensive. But I’ve seen surveys where the follow up question or questions use a price that is unacceptable. A survey taker thinks you are pretty dumb if she told you that the highest price she’d pay is $50 and then you ask “how likely would you be to buy if the price is $100?” Don’t do it.
Does this help?
A side question for you and anyone else reading: Would you be interested in a service that takes your data and turns it into charts so that you have the range of acceptable prices, and can incorporate into your reporting?
Mike