Last week I discussed two research techniques which can be used when determining the optimum pricing strategy – Gabor-Granger and Brand Price Trade Off (BPTO). These techniques are beautifully simple, but this simplicity has a flip side.
- The choice may be over-simplified and no longer reflect real-life decision scenarios which can be complex and based on a series of conscious and unconscious trade-offs
- With the linear raising or lowering of prices, respondents can easily guess what the researcher is doing and may be tempted to ‘game’ the result
- Prompting respondents with an initial price point will frame their subsequent responses and thus under- or over-estimate the true price they’d be willing to pay
- Only allowing a binary ‘would buy/wouldn’t buy’ response doesn’t capture important shades in between where people start to become more or less comfortable with a particular price point
This week, I’ll look at two pricing research techniques which can overcome these issues – the Van Westendorp Price Sensitivity Meter and Conjoint Analysis.
The Van Westendorp Price Sensitivity Meter
The Van Westendorp Price Sensitivity Meter overcomes some of these issues by allowing respondents to choose their own price points and share more detail on their reactions. To do so, respondents are shown the product and asked to indicate:
- The price at which it would be too cheap to be of credible quality
- The price where it represents a bargain
- The price where it becomes expensive, but not prohibitively so
- The price where it becomes too expensive to consider
At each price point mentioned, the cumulative percentage of respondents placing the product into each of the four categories above is calculated. For example, in a study to identify the optimum pricing strategy for a Business School’s one day courses for professionals we found the following pattern:
This chart can be interpreted as follows:
- The price for the product should be set somewhere in a range where the greatest proportion of customers are likely to consider buying it. The lower end of this range is set by finding the intersection of ‘too cheap’ and ‘expensive’ (known as the ‘point of marginal cheapness’) and the upper end is set at the intersection of ‘too expensive’ and ‘a bargain’ (‘point of marginal expensiveness’)
- The ‘optimum price point’ is one where the lowest proportion of customers are put off by the price because they consider it too high or too low. This optimum point can be found at the intersection of ‘too expensive’ and ‘too cheap’
So in this example the Business School was advised to ideally set the price at £700 or failing that, somewhere between £650 and £800.
Van Westendorp is a simple and useful technique but has four drawbacks:
- There is no competitive context
- By focussing solely on price it can make respondents artificially price sensitive
- It requires no price/benefit trade-offs as would be made in real-life scenarios
- It doesn’t allow the creation of tiered offerings where customers can choose the ‘standard’ product or upgrade to an enhanced version for a price premium
These issues can be overcome by using one of the most advanced pricing techniques – Conjoint Analysis.
Real-life purchase decisions involve a complex series of conscious and unconscious trade-offs between price and product features. Conjoint Analysis seeks to mimic this. To do so, different components of the offering (product features and price) and the different ‘levels’ at which these components might be realistically ‘set’ are identified, e.g. the feature of product delivery could be offered as same day, next day or 48 hours.
Using this information, the conjoint algorithm creates a series of hypothetical products each with slightly different attributes and prices. These hypothetical offerings are then grouped into sets of 3 or 4 and respondents asked which one, if any, they are most likely to buy.
This is repeated several times until a large number of potential combinations have been compared across all of those people surveyed. In making these choices respondents are indicating, without knowing it, the relative value (called a Utility Value) they attach to different product features and their price sensitivity. This means that, following statistical analysis to identify patterns in the data, Conjoint Analysis reveals:
- The relative importance of different product attributes and prices in driving demand
- The optimum product proposition and price to sell this at to maximise revenue
- How to tier products (good, better, best) and price them relative to each other
It also allows ‘what if?’ scenarios to be explored where the impact on demand of different actions can be estimated, e.g. How would demand change if the price was raised by 10%? What impact would removing feature X have on demand? What would happen to demand if feature X was kept but at a lower performance level?
Learn more about Circle’s approach to business-to-business (B2B) pricing strategy research here.