Pricing is one of the most important but least understood marketing decisions. Learn and practice concepts, techniques, and get to grips with the latest thinking on assessing and formulating pricing strategies. Analyze how firms attempt to capture value, as well as profits, in the revenues they earn. After this course, you will be able to contribute to the process of formulating pricing strategies for your own products and services, or those of your firm. This course has an additional focus on pricing dynamics and the reaction to and by competitors, taking a highly pragmatic approach and one that is directly applicable to your day-to-day professional life. Martin Boehm - IE Professor and Dean of the Business School with over 10 years experience - will guide you through four modules. We begin with the importance of pricing and how it ultimately can affect the bottom line. Then, we move on to methods to help you decide what is the optimal price for your product; and then price discrimination - should all your products follow the same pricing strategy or should you differentiate depending on the customer segment? Finally, we look at pricing psychology and how you can manipulate customers in order to drive the highest possible price for your product.
Advanced (OPTIONAL): Conjoint Analysis
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Del curso dictado por IE Business School
Pricing Strategy
441 calificación
De la lección
Determining Willingness to Pay
After finishing this module you will be able to take advantage of four different methods to determine the willingness to pay from customers. Whether using Market Data or Experiments (Observation based) or Expert or Customer Surveys (Survey based), you'll be able to use this information in order to identify the optimal price.
Conoce a los instructores
Martin BoehmProfessor of Marketing
Dean of IE Business School
[MUSIC]
Most decisions in the real world involve trade offs.
Should I travel business class or should I travel economy class?
Is maybe design more important for me, or do I care more about customer service?
What is more relevant, having the lowest price possible,
or having the best quality product?
As you can see, many decisions in the real world involve these kind of trade offs
that consumers make on a daily basis.
For businesses,
it's absolutely crucial understanding how consumers make these different trade offs.
What preferences they might have for
the different alternatives that are presented to them out there in the market.
What conjoint analysis does is exactly providing answers to these kind
of questions.
What it essentially does, it tries to build a mathematical model that
represents the preferences of consumers for certain alternatives.
But also the preference for certain characteristics, features, or
attributes of products and services.
That obviously allows managers and
businesses in order to make predictions about customers.
How they would behave,
how they would choose out of the different alternatives that are presented to them.
Let me give you an example.
Let’s, for instance, apply the idea of a conjoint analysis, or
the basic principle of a conjoint analysis, to the computer market.
You could easily describe any computer as a bundle of different product
attributes or product features.
There might be, for instance, the processor speed,
which obviously represents, to some extent, performance.
You could say as well there is maybe the design.
There is a third factor, which might be, obviously the price, and
the fourth factor might be, for instance, the size of the hard drive.
These are actually different product attributes.
What we typically see as well, for any product attribute,
there might as well different product attribute levels.
So let's take, again, the idea of the hard drive.
I might have a PC with a hard drive of 100 gig, 200 gig, 300, 400 and so forth.
So what we can see is we have different levels for
these different product attributes.
In conjoint analysis, we assume that every level of a product
attribute has a certain utility of value to the consumers.
Sometimes this is actually called as well, the part worth, why?
Because, it basically describes a certain part
of a product that might have a certain value or worth to the consumers.
So the basic idea behind this is once we've figured out what is the part worth
for the different attributes.
And the different levels of an attribute to consumers,
we can actually construct products as bundles.
And as a consequence, take the total sum of this part worth,
which is representing the overall value for the consumers.
Doing this for multiple products or different product combinations.
What we can determine as well is what is the overall value of the different
alternatives we can send to our consumers.
And that, in the end, will allow us to make the predictions about how customers
choose between different alternatives.
Or make even predictions about market shares out there in the market.
Let's actually look into a very simple example.
Let's assume that BMW is planning to launch a new product into the market.
In order to do that, or in order to ensure success,
they actually would like to understand much more in detail how customers value
the different product attributes.
And at the same time how they value the different levels within these different
product attributes.
Let's now see how we could use our conjoined analysis in order to help BMW to
answer some of the pressing questions they might have.
Obviously we're going to look at BMW, so we're looking at a car.
So we first have to understand what the main product attributes that we
should be considering.
Typically, some of the main product attributes are, for instance, the brand,
such as BMW or Mercedes.
But it could also include, for instance, the body style,
such as whether it is a sedan or an SUV.
We could consider as well the engine type.
And finally, something that is obviously important for us is,
what price to charge for the product.
Once we have done this, we are able to advance to the second stage.
What we're now trying to do is trying to understand what are the relevant product
attribute levels for
each of the different product attributes that we have identified.
So looking at this particular example,
we now see the BMW has chosen three product attribute levels for
all the four attributes that we have identified.
Obviously, as you can imagine, it is not necessary to always have the same
number of levels for each product attribute.
So we could have, for instance, for one of the product attributes,
only two product attribute levels.
And maybe for another one, three or four.
The recommendation, however, is typically that we should not exceed to have
more than four or five product attribute levels.
Because otherwise it might be a little bit challenging for the participants to answer
the questions you're going to pose to them later on in the process.
As you can see, as well, looking at the different product attributes and
their respective levels.
We see that we can distinguish two different types of product attributes.
One product attribute might be more structured in a categorical way.
One good example in this particular example is, for instance,
the brand, we can either have a BMW or a Mercedes.
The other type of product attribute would be continuous product attributes.
One good example in this example we're looking at is, for instance, price, so
it could be actually a continuous number.
Nevertheless, we have actually chosen to categorize as well this
continuous variable.
The third step of conducting a conjoined analysis is to create so
called product profiles.
We could also say that product profiles are fictitious alternatives or
fictitious products that we're going to launch to the market.
So what we essentially do is we're going to take the different
product attributes that we have identified before.
And we assemble them in a way that we come up with a multitude of different
alternatives that potentially could be available out there in the market.
We now see on the screen some of the alternatives that we actually thought of.
The next step is now to actually collect the data.
So we have to poll our customers about how they think or
how they feel about the different product profiles that we have just created.
Typically we can distinguish between two different types of asking customers to
respond to different profiles that we have created.
One alternative is called the choice-based conjoined analysis and
you now see one example on the screen.
It essentially replicates very much decision making processes that we would
go through as consumers if you walk into a store or if you walk into a supermarket.
Typically we are confronted with different alternatives and
we have to choose one of these alternatives that are offered to us.
Or we basically say,
we're not interested in any of the alternatives that are given to us.
Another alternative would be to go for a rating based conjoint analysis, and
we see an example of that now on the screen.
It is maybe less realistic, but
typically I've seen that it actually produces really good results as well.
In step five of the conjoined analysis, we're now going to leverage and
use the data we have just collected and try to make sense of it.
As you can imagine, there are a multitude of different statistical tools available
in order to analyze the data.
Essentially what we're now trying to do is we're trying to
put a numerical value to the utility or the value perceived by the consumers for
the different product attributes.
And within each product attribute to each product attribute level.
That is actually going to allow us to then make predictions about the preferences
of our consumers.
And again, as we said before, to construct fictitious products.
And make predictions about how customers would choose one product over the other.
Let's now come back to our BMW example.
Let's assume that the BMW managers use a statistical model called
regression analysis in order to make sense out of the data.
Essentially what we have to do is we first have to establish something
like a baseline product, let's just pick something.
Let's say the baseline product for us is in terms of the brand,
being Audi, the baseline brand in terms of the body style, the sedan.
And in terms of the engine type, let's say a gas run car is essentially our, or
gasoline run car is the baseline as well.
In terms of price, let's assume as well that $20,000 would be the baseline.
Here we see at the end, once we analyze the data,
that essentially the value that we would get for that particular product is zero.
We would also see that anything that shows up positive is
clearly superior to this baseline product.
And hence would be preferred by consumers, or would be chosen by consumers.
Every product profile that comes up that is negative, or
is lower than the baseline that we have actually identified.
Obviously will be perceived as well as inferior,
and would be less likely consumers.
And as a consequence, if they would be offered a choice between a negative
product and a baseline product, they would clearly go for the baseline product.
Let's now go to one specific customer, and
to the analysis that we have drawn up for this customer.
What we see here is that apparently this customer favors a sports car over a sedan,
because the customer has a positive util of 2.21.
And remember we established the sedan as the baseline, which is essentially zero.
At the same time, we actually see that he or
she likes the sedan much more than the SUV.
Because the util showing up for SUV is a negative 2.5.
So in summary, we could say just looking at the body style that apparently this
particular respondent.
This particular customer prefers the most a sports type car,
second, a sedan type car, and least prefers actually an SUV.
Looking at the regression report,
we see here as well something called the intercept.
The intercept actually represents the value or
the utility that customers perceive when choosing and when going for
the baseline model that we identified before.
In this particular case, it's a 4.12.
What we can now do is we can actually construct different product profiles.
And we can choose, out of the different product attributes,
different product levels.
And use the information that is given in this particular regression report.
So let's look at one particular example.
Looking at this particular product profile, we see that the resulting utility
or value of received value by the consumers, it says 0.91.
And we actually see that this is perceived as inferior, or less attractive,
as the baseline profile that we have just identified before.
This takes us now to step six, interpreting the part worth.
BMW is now ready to use the information from the regression output in order to
determine the importance of the different product attributes for a respondent.
The basic idea behind this is that looking at each product attributes.
The difference between the highest part worth and
the lowest part worth should represent the importance.
Let's actually look into our example.
And let's calculate the importance for their brand, for the price, for
the body style and obviously as well, for the engine type.
To get a better sense of how the importance of one attribute compares
actually to the importance of another attribute.
Marketing researchers typically look into the relative importance of the different
product attributes.
Actually, it is quite simple as well to calculate them.
We essentially just take the importance of each product attribute, and
divide it by the sum of all the product attributes.
For this particular respondent, we see that body style seems the most
important product attribute, followed by price.
On the other hand, brand and engine type seem a little bit less important.
This now takes us to step seven, predicting choices.
BMW has now gained a full understanding of how this respondent values
the different product attributes.
The importance, he or she attributes to the different product attributes.
And how much the different levels of each product attribute are valued.
As a consequence, what BMW can now do is create different product profiles,
different alternatives, or fictitious products.
And can determine what is the overall value that is perceived by this particular
respondent for the different alternatives that are presented to him or her.
Let's actually look at one example.
Here we have three different alternatives.
Leveraging the information that we have seen before from the regression output,
we can easily determine as well.
What is the resulting utility for alternative A, B, and C?
Now we can try to make predictions as well about how this respondent would choose
between the three different alternatives that have been presented to him or her.
The simplest rule that we can actually use here is called the maximum utility rule.
As the name already implies, it just simply assumes that the customer's going
to choose that alternative that has the highest utility.
In this example, it will be very straightforward,
alternative A seems to be the one that gives the highest level of utility.
And hence, should be the perfect choice for this particular respondent.
We're now coming to the final step in our conjoined analysis process,
and as you can imagine, this is the step we have all been waiting for.
Overall, this is a course on pricing.
So we now have to see how a conjoined analysis can leverage in order to learn
more about prices.
And more specifically, about the willingness to pay of our consumers.
As you might recall, at the beginning, we did set a baseline profile.
And we essentially said that $20,000 is serving as the reference, or
is part of the baseline package.
As a consequence,
the utility that is being assigned to a price of $20,000 is zero.
Let's look at our respondent.
We see that for a price tag of $30,000, the utility is actually negative.
But this doesn't come as a surprise, because we, as consumers,
what we value is overall low prices.
So, we shouldn't see higher utilities for lower prices and lower utilities for
higher prices, and this is exactly what we're actually seeing here.
It also allows us to try to determine some sort of an exchange rate
between the value our customer expects.
For an additional price increase the company might be charging.
In this particular case, we can look first of all at the differential between the two
different utilities at two different price points.
Which is essentially a utility of zero and
the other one at $30,000, which is a negative 1.47.
As we have seen, the two price points we're considering is $20,000 and
$30,000, so essentially, the price differential is $10,000.
Using now these two figures,
we can now calculate this exchange rate I have been talking about.
What we will see also is the resulting exchange rate for the difference between
$20,000 and $30,000 is slightly different to the exchange rate we
could also cold calculate for the difference between $30,000 and $40,000.
So a simple way around that is essentially taking these two exchange rates and
taking the average out of that.
And that's going to give us a good understanding of what is overall for
this respondent.
The exchange rate between money he has to put on the table and
the value he or she expects in return.
This obviously can be leveraged, or it's information can be leveraged,
in order to make a lot of sense of many pricing decisions.
We can now estimate not only what is the willingness to pay for
a certain product profile.
Or in alternative, we're going to launch into the market.
But essentially we can do as well or
break down the willingness to pay of our consumers.
Not only for product attributes such as brands, body style, engine,
but also for each individual level within these product attributes.
So for instance, we can determine what is the willingness to pay for
owning a BMW, or owning a Mercedes, for instance.
This, as you can imagine, opens up so
many different opportunities with respect to pricing.
Let me give you just one example.
By knowing the importance, sorry, by knowing actually willingness to pay for
the different product levels and the different product attributes.
What we now can do is we can contrast the cost of, for instance,
improving the engine, or offering a better engine.
And at the same time, what is the added willingness to
pay customers might have for that improved feature.
And it gives us, some sort of a trade off where we can try to find the perfect
balance between the investment we have to make at BMW.
And the willingness to pay that we should expect from our consumers.
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