Because pricing is one of the Four Ps of Marketing (product, price, promotion and place) it is critically important in understanding consumer behavior.  Pricing is where marketing happens, because a market is the place where buyers meet sellers.

However, how do marketers know what price to charge?  Part of it has to do with what strategy (skimming, penetrating, other) are they pursuing.  But in general, too high a price and they get no sales, too low a price and they get no profit.  Typically the market is the LAST place to experiment with trail and error.

So there are several generally accepted ways to research the “right” price to charge.  A practical decision is whether or not the product exists, real in the market place.  If it is a new product then elasticity modeling cannot really be done.  If it exists then there are four choices: a general survey, a van Westendorp survey, conjoint analysis and elasticity modeling.

This post favors (for an existing product) elasticity modeling because it is real responses to real price changes in an economic environment.  If it is a new product the least favorable choice is a general survey.  Each of these methods will be briefly discussed.



The first and simplest solution is to ask customers what they think.  This requires taking a random sample and asking “Are our prices too high?”

There is a lot of thought put behind this, to seek granularity, but generally marketers want to know if customers think their prices are too high.  The probing can be /should be aimed at different segments (high volume or low volume users, new or established customers, a particular product, geographically disbursed, etc.)

But the overwhelming answers customers give to the question “Are our prices too high” is “Yes!  Your prices are too high.”  It is self- reported and self-serving.  Money was wasted on a survey.  So what usually happens in a large corporation is that many creative people will try to slice-and-dice until they find some answers they want, some “segments” or cohorts, etc., where prices are reported as NOT too high.  That is, if you look at say customers who have been on the database longer than three years that have bought more than $450 of product X who reside in the northeast reported that, “No, your prices are competitive”!”  This is just window dressing and not analytic.

Thus, for an existing product, a general survey among current customers offers no real insights.

This post advocates NOT using a survey for an existing product.  The only choice is to use a survey for a new product.  This too has pitfalls.  Remember Chrysler used marketing research and asked potential customers how likely they would be to buy a minivan, a very new concept.  Thee customers had no experience with it and indicated lackluster demand.  Iacocca ignored the research and built the minivan anyway, saving the company.  So in short, a general marketing research survey has little value except as gee whiz info.



A second common option is the van Westendorp survey.  (Those who use it do not call it a survey but a Price Sensitivity Analysis (PSA)).  But it really is a survey.  It takes a random sample of customers and asks them questions.  It is usually a “tracking” study, so as to gauge price sensitivity movement over time.  In general the point is to find out what prices are considered too high or too low (again, self-reported).  These results are graphed onto a “Price Map”.

Customers are asked four questions:

  • At what price would you consider the product/service to be priced so low that you feel that the quality can’t be very good?
  • At what price would you consider this product/service to be a bargain—a great buy for the money?
  • At what price would you say this product/service is starting to get expensive—it’s not out of the question, but you’d have to give some thought to buying it?
  • At what price would you consider the product/service to be so expensive that you would not consider buying it?


Usually question 1 (too cheap) and question 4 (too expensive) are the primary graphs.  The intersection of these two are meant to reveal the optimal price, in the below case about $21.


too cheap too expensive price
100% 0% $5
100% 0% $6
100% 0% $7
100% 0% $8
100% 0% $9
100% 6% $10
100% 6% $11
100% 6% $12
100% 13% $13
100% 19% $14
94% 44% $15
88% 50% $16
81% 56% $18
69% 63% $19
56% 69% $20
44% 75% $21
38% 81% $22
25% 88% $25
19% 94% $30
13% 100% $35



The “optimal” price is a bit debatable.  The idea though is that about 65% think the price of $21 is too cheap and 65% think the price of $21 is too expensive.  That is, to extract maximum value from customers, $21 is seen as simultaneously too cheap and too expensive, hence the “optimal” price.

Conjoint (considered jointly) is a powerful technique favored primarily by marketing researchers.  There are dozens of books detailing all the cool types and techniques of conjoint.

To elaborate the last point, conjoint serves an important purpose, especially in marketing research, especially in product design (before the product is introduced).  My main problem with surveys overall is that they are self-reported and artificial.  Conjoint sets up a contrived situation for each respondent (customer) and asks them to make choices.  The customer makes choices and these choices are typically in terms of purchasing a product.  You know I’m an econ guy and these customers are not really purchasing.  They are not weighing real choices.  They are not using their own money.  They are not buying products in a real economic arena.  The artificialness is why I do not advocate conjoint for much else other then new product design.  That is, if you have real data use it, if you need (potential) customer’s input in designing a new product use conjoint for that.  Also, please recognize that conjoint analysis is not actually an “analysis” (like regression, etc.) but a framework for parsing out simultaneous choices.  Conjoint means “considered jointly”.

The general process of conjoint is to design choices, depending on what is being studied.  Marketing researchers are trying to understand what attributes (independent variables) are more / less important in terms of customers purchasing a product.  So a collection of experiments is designed to ask customers how they’d rate (how likely they would be to purchase) given varying product attributes.

In terms of say PC manufacturing, choice 1 might be: $800 PC, 17 inch monitor, 1 Gig hard drive, 1 Gig RAM, etc.  Choice 2 might be: $850 PC, 19 inch monitor, 1 Gig hard drive, 1 Gig RAM, etc.  There are enough choices designed to show each customer in order to calculate “part-worths” that show how much they value different product attributes.  This is supposed to give marketers and product designers an indication of market size and optimal design for the new product.

Note that it is important to design the types and number of levels of each attribute so that the independent variables are orthogonal (not correlated) to each other.  These choice design characteristics are critical to the process.  At the end an ordinary regression is used to optimally calculate the value of part-worths.  It is this estimated value that makes conjoint strategically useful.

Note that the idea is to present to responders choices (in such a way that they are random and orthogonal) and the responders rank these choices.  The choice rankings are a responder’s judgment about the “value” (economists call it utility) of the product or service evaluated.  It is assumed that this total value is broken down into the attributes that make up the choices.  These attributes are the independent variables and these are the part-worths of the model.  That is:

where Ui = total worth for product / service and

X11 = part-worth estimate for level 1 of attribute 1

X12  = part-worth estimate for level 1 of attribute 2

X21 = part-worth estimate for level 2 of attribute 1

X22 = part-worth estimate for level 2 of attribute 2

Xmn = part-worth estimate for level m of attribute n.


Conjoint is not appropriate in the way usually used, especially in terms of pricing, except, as mentioned, in a new product–a product where there is no real data.  For an existing product, it is possible to design a conjoint analysis and put price levels in as choice variables.  Marketing researchers tell me that this price variable derives an elasticity function.  I disagree for the following reasons: 1) those estimates are NOT real economic data.  They are contrived and artificial.  2) The size of the sample that is derived from is too small to make real corporate strategic choices.  3) The data is self-reported.  Those respondents are not responding with their own money in a real economic area purchasing real products.  4) Using real data is far superior to using conjoint data.  Have I said this enough yet?  Ok, the rant will now stop.



Let’s go back to microeconomics 101: price elasticity is the metric that measures the change in an output variable (typically units) from a change, in this case price, from an input variable.  This change is usually calculated as a “pure number” without dimensions.  It is a marginal function over and average function, that is:

mathematically dQ/dP * P / Q or statistically β * P / Q

where P / Q are average price over average units.

If the change is > |1.00|, that demand is called elastic.  If it is < |1.00|, that demand is called inelastic.  These are unfortunate terms, as they nearly hide the real meaning.  The clear concept is one of sensitivity.  That is, how sensitive are customers who purchase units to a change in price?  If there is a 10% increase in price and customers respond by purchasing < 10 % units, they are clearly insensitive to price.  If there is a 10 % increase in price and customers respond by purchasing > 10 % units, they are sensitive to price.

But this is not the key point, at least in terms of marketing strategy.  The law of demand is that price and units are inversely correlated (remember the downward sloping demand curve?)  Units will always go the opposite direction of a price change.  But the real issue is what happens to revenue.  Since revenue is price * units, if demand is inelastic, revenue will follow the price direction.  If demand is elastic revenue will follow unit direction.  Thus, to increase revenue in an inelastic demand curve, price should increase.  To increase revenue in an elastic demand curve, price should decrease.



INELAST 0.075 increase price by 10.0%
p1 $10.00 p2 $11.00 10.0%
u1 1,000 u2 993 -0.75%
tr1 $10,000 tr2 $10,918 9.2%
ELAST 1.250 increase price by 10.0%
p1 $10.00 p2 $11.00 10.0%
u1 1,000 u2 875 -12.50%
tr1 $10,000 tr2 $9,625 -3.8%


See the table above.  There are two kinds of demand: inelastic (0.075) and elastic (1.250).  In the inelastic portion, we increase price (p1)  10% from $10.00 to $11.00 (p2).  Units decrease (because of the law of demand) from u1 at 1,000 units to 993 (note a .75% decrease.)  Now see total revenue tr1 goes from $10,000 ($10.00 * 1,000) to tr2 $10,918 ($11.00 * 993).    This was an inelastic demand curve and price increased and note that while units decreased total revenue increased.

Now the  opposite happens for an elastic demand curve.  p1 = $10.00 and p2 = $11.00 but while u1 starts at 1,000 units the 12.5% decrease sends u2 to 875.  Now see that tr1 goes from $10,000 to a decrease of $9,625.  This means that in order to raise TR prices must be increased in an inelastic demand curve but decreased in an elastic demand curve.  This means that a marketer does not know which way to move price unless they do elasticity modeling.  See?  Wasn’t that fun?

A quick note on a mathematically correct but practically incorrect concept: modeling elasticity in logs.  While it’s true that if the natural log is taken both of the demand and price, there is no calculation at the means, the beta coefficient is the elasticity.  However, and this is important, running a model in natural logs also implies a very wrong assumption: constant elasticity.  This means there is the same impact at a small price change as at a large price change and no marketer believes that.  Thus, modeling in natural logs is never recommended.  No other analytic technique gives these insights except elasticity modeling.



A couple of obvious points: I would clearly recommend real data used on real customers responding to real price changes.  This is the operating economic environment.  That is, for an existing product, use the database of customer’s behavior in purchasing products.  The strategic insights this generates will help save margin and increase total revenue.  For a new product a general survey is the worst choice, incorporate either conjoint or van Westerdorp survey.

Price sensitivity is a key concept in economics and marketing.  Elasticity modeling is hardly ever done, but it should be investigated more often.  The strategic insights gathered from elasticity modeling are worth that investigation.


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