How to find the right price for your product or service, using Van Westendorp’s Price Sensitivity Meter
In this post:
- I’ll explain how to create a survey which will enable you to measure customer’s perceived value of your product or service
- How to assess the results in order to choose the right price to charge
- I’ll also share a Google Sheet template which contains several analytical tools to make the process easier
Why use this model?
We’ve recently gone through a process of reviewing the price of our subscriptions at the startup that I work for called Genuine Impact.
The idea of trying to choose the right price without any data felt like trying to shoot a moving target in the dark. So I was surprised that it was difficult to find guides which shared models that could be used to work out what price you should charge for your service.
But fortunately I happened to listen to Rahul Vohra’s interview on the Acquired podcast and he casually mentioned that he’d used Van Westendorp’s Price Sensitivity Meter to decide to charge the unconventionally high price of $30 per month for his email app called Superhuman.
We decided to use the same model to try to find the right price to charge for our subscriptions and I was happy to find that it worked incredibly well.
The main benefits of using the model that I’ve found are:
- The results of the survey give you a price range which you can choose from, not just one ‘right price’.
- You may decide to choose a price from the lower end of the range, in order to gain as many paying customers as possible or you could choose a price at the higher end of the range, if you need higher margins to fund paid growth, for example.
- When considering different prices, you’ll be able to see what % of your current customers perceive that price as too low, a bargain, pricey and too expensive
The survey
Van Westendorp’s Price Sensitivity Meter is based on 4 simple questions which you ask your customers:
- At what price would you consider the product to be so expensive that you would not consider buying it? (I’ll refer to this as ‘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 to be starting to get expensive, so that it is not out of the question, but you would have to give some thought to buying it? (‘Pricey’)
- At what price would you consider the product to be a bargain — a great buy for the money? (‘A Bargain’)
I sent this survey without modifying the questions or their order. I don’t know whether there’s an underlying theory behind either of those variables but “if ain’t broke, then don’t try to fix it”.
The email
The email was straightforward, I made the subject:
How much do you want to pay for Genuine Impact?
and wrote a brief intro and explained that:
All we need you to tell us is 4 prices. That’ll show us how you — our anonymous customer — perceive the value of our service and what the price of the Premium subscription should be.
We sent this to all of our customers who had used the app enough to discover the benefits of our service.
We had a higher click through and response rate than average for this survey, presumably customers appreciated being able to have a say in the price that they would pay for our service.
Assessing the results
To save you time, I’ve created a template Google Sheet which contains dummy data (not the actual responses we received), and all of the analytical tools that I’ll explain in this section of the post.
You’ll need to sign into your Google account before you can copy the document.
You’ll end up with 4 columns containing the responses.
The first thing that I did was to calculate the median answer for each question. The difference between the median ‘bargain’ and ‘pricey’ prices gives you a rough ‘acceptable price range’. You should choose a price that’s somewhere within this range.
But there’s two more tools which I used to give us a better sense of which price to choose.
Plotting the responses on a graph enables you to see the acceptable price range more clearly and to identify some extra data points, which will give you a better sense of where you could set your price.
I’ve included a more detailed explanation of the calculations that I used to create this graph at the end of the post.
There’s 4 key points on this graph:
1 & 2. The point where the line for the ‘Too Cheap’ series crosses the line for the ‘Pricey’ series. Along with the point where the ‘Bargain’ series crosses the ‘Too Expensive’ series. These points are marked by black crosses on the graph.
The prices between those two points are your acceptable price range. We found that these prices were slightly different than the median prices that I mentioned earlier in the post.
3. The point where the ‘Bargain’ series crosses the ‘Pricey’ series is the Indifference Price Point. This point is marked by the magenta diamond on the graph.
In theory most of your customers shouldn’t mind paying this price.
4. Lastly the point where ‘Too Expensive’ and ‘Too Cheap’ cross, is an alternative Sweet Spot. This point is marked by the turquoise square on the graph.
In our case both the Indifference Price Point and Sweet Spot landed on the same price, which kept the analysis simple.
Finally you can check what % of your customers might not want to pay the price that you’re considering.
To do that you can create a couple of simple calculations, which use the same formula that you’ll use to calculate the % of responses that were below a certain price for the graph. Only this time you’ll make the formula reference a cell which contains the price that you’re considering, to see what % of people consider that price too expensive or over their ‘pricey’ price.
In our case, if we increased the price that was in the Indifference Price Point / Sweet Spot by £1, then the % of people who would consider it both ‘Too Expensive’ and ‘Pricey’ doubled so it was pretty clear that, that was the right threshold.
Wrapping up
We haven’t yet announced this new price for our customers so time will tell how well received our new price will be. But the clarity of the results from this survey look extremely promising and (now that I know the name of the model), a quick Google search shows that this method’s been pretty popular among tech companies. Ultimately if we find that the price isn’t quite right, we’ll still know the boundaries that we should stay within in order to avoid an undesired outcome which is very reassuring.
I hope you found this post useful! If you’re interested in helping everyday investors pick great investments from the stock market, then email us at hello@genuineimpact.io to find out more about the roles that we’re hiring for. You can also follow me on twitter here.
Here’s my more detailed guide to configuring the graph.
Graph calculations
I plotted the complete price range from all of the responses in a separate sheet, then calculated the % of responses which were above or below that price.
In order to create a graph which shows you where most customer’s perception crosses from cheap to expensive (when the lines cross over), you need to ‘invert’ either the cheap responses or expensive responses. It’s more common to invert the former. So the calculation looks like this:
Too cheap / a bargain series
The formula for this is =countif('Form responses 1'!B$3:B$100,">="&$B3)/counta('Form responses 1'!A$3:A$100).
Where the range B3:B100 contains the responses to a particular question, B3 is the price that you're comparing the responses to and column A contains the timestamps for the responses.
The countif() function counts the number of responses that match the criteria ">="&$B3. Then the counta() function counts the total number of responses. Finally you divide the former by the latter to calculate the % of responses that meet the criteria.
Pricey / too expensive series
The formula for this simply changes to =countif('Form responses 1'!C$3:C$100,"<="&$B3)/counta('Form responses 1'!A$3:A$100).
Formatting the graph
You can highlight each of the 5 key points on the graph by changing the format of individual data points on the graph.
Here’s a more in-depth guide which explains how to do that.
Update — This model was recently used by OpenAI when planning the launch of ChatGPT Pro
