Companies need a way to evaluate how much consumers are willing to pay for a product. If a product price is lowered or raised, how does this impact level of interest? You may assume the interest level declines with higher prices, while interest level increases with lower prices. But pricing a product involves many nuances.
Say, for example, your product has multiple tiers. How do you determine pricing options for a premium tier versus a basic tier? Which feature should be left out and which should be included? Your product may be innovative. How do you price a product that has no peer or familiarity to consumers? Or you may want to understand price floors (so cheap, you would question the quality) and price ceilings (too expensive to even consider) for your product.
In market research, there are four traditional methods for understanding price. The one you choose depends on your research objectives and needs.
- Price Sensitivity Meter (Van Westendorp)
- Monadic Price Testing
- Sequential Monadic
- Conjoint Analysis
Price Sensitivity Meter (Van Westendorp)
For many products, consumers have an expected price range at which they are willing to consider buying an item. The Price Sensitivity Meter is a method used for exploratory pricing purposes and allows a researcher to investigate this expected price range for a product or service. For instance, an Internet company might grant free access to its software in the US.
But should they keep the same price model for other markets like Brazil or Australia? In those markets, what might be an acceptable price range to charge?
The Price Sensitivity Meter (PSM) technique was developed by economist Peter Van Westendorp. It is a four-question design. The researcher has respondents evaluate a product and ask:
- At what price would you say that this product is expensive but worth considering (expensive)?
- At what price would you say that this product is a bargain? (cheap)
- At what price would you say that the product is so expensive you would not consider it? (too expensive)
- At what price would you say that the product is so inexpensive you would question its quality? (too cheap)
Charting the cumulative frequency of the answer to the four questions will allow you to determine a range of acceptable prices (Figure 1). By directly asking consumers, you can understand the price points at which the product is too cheap, a bargain, expensive (but worth considering), or too expensive (completely out of range). Having these data points allows you to assess the price floor as well as a price ceiling for your product.
You’ll notice that the PSM doesn’t address the question of demand or interest in the product. You are not trying to address a person’s willingness to pay or likelihood to buy. Rather the PSM is used to understand pricing expectations. That is: how much would you expect this to cost? It is especially helpful for understanding the marketplace and the price perceptions for the product you wish to offer. Knowing price perceptions for different geographies or different consumer segments can help you develop the appropriate position or pricing strategies as you seek to reach and define your target audience.
Price Sensitivity Meter
Monadic Price Test
A well-established online dating site was considering a new security feature. Members could purchase an option to submit themselves to an extensive background check. By doing so, other members looking at their profile could feel more safe and secure with the person they were connecting with. The dating site wanted to test interest level in this concept as well as a few price points for offering it.
In this case, the dating site had a fairly good understanding of price perception in their marketplace but wanted to more specifically know how the new pricing or a simple add-on would impact that market place. In a monadic price test, respondents are shown a product description and asked their likelihood to buy at a given price point (Figure 2). Here you are directly asking consumers for their propensity to buy something at a given price. It would be wrong to assume that a survey can measure what a person will do in real life (would they actually commit to buying a product if they say so in a survey?). However, direct questioning on price can provide valuable insight into how much interest or appeal your product may have and what differences exist across customer segments.
There are perhaps just a few price points to test and the researcher obtains an understanding of how much value a new feature brings and how demand or interest levels change as the pricing shifts.
In a monadic design, multiple price points of the product are tested using a split cell test. For example, if you wanted to test three price points, you can divide the sample into three ‘cells’, or three different groups. Cell 1 would be shown the $10.99 price point; cell 2, the $11.99 price point; cell 3 the $12.99 price point. Once all of our survey data is collected, you can graph a price sensitivity curve. Figure 2, for example, shows the purchase intent at each of the price points.
When setting up a monadic design, care should be taken to ensure that each cell group consists of a similar demographic mix. You cannot have an age or gender skew for cell 1. That would compromise our ability to compare purchase intent between cell groups. If you randomly assign respondents to a cell as they come through the survey, this will reduce any sample bias between cells.
Monadic Price Test
Spalding is manufacturing a new line of golf balls. These golf balls are designed with the novice user in mind. The unique aerodynamic properties of these new golf balls allow it to veer towards the hole. It will even stop short in mid-air if it detects that you’ve swung too hard.
If a 12-pack of these Spalding golf balls were available for (INSERT PRICE), how likely would you be to buy it?
- Extremely likely
- Very likely
- Somewhat likely
- Neither likely nor unlikely
- Somewhat unlikely
- Very unlikely
- Extremely unlikely
Monadic Price Test
Sequential Monadic Price Test
The sequential monadic price test achieves the same goal as the monadic price test. You are trying to build a price sensitivity curve and gather purchase intent at different price points.
A sequential monadic design differs in that each respondent evaluates each price point, one at a time. The researcher may choose to reveal the prices in ascending or descending order. (Figure 4).
This technique also is called ‘price laddering’. You can limit the number of price points shown by assuming that a person willing to purchase at a high price will also purchase at the lower price and vice versa. If a person is willing to purchase a pack of Spalding golf balls at $12.99, you do not need to ask them if they will purchase it at $10.99. Similarly, if a person is unwilling to purchase the golf balls for $10.99, you do not need to ask them if they will purchase it at $12.99. You can assume that they will not.
A sequential monadic design has the advantage of requiring a smaller sample size (thereby reducing sample costs) than a monadic design. This is because each person evaluates multiple price points. You gather more pricing data from each person this way. But in a monadic design you only gather data for one of the price points from each person, and so you need a bigger sample to make up the difference.
The sequential monadic design isn’t without drawbacks, however. Asking respondents the same question over different price points is a tedious exercise and can tax the respondent. Furthermore, previously shown prices may bias a person’s response to the next price that is shown.
Sequential Monadic Price Test
If a 12-pack of these Spalding golf balls were available for $7.99, how likely would you be to buy it?
Repeat the same question with a higher price until the respondent is unwilling to buy
|Extremely likely||Price 1: $7.99|
|Very likely||Price 2: $8.99|
|Somewhat likely||Price 3: $10.99|
|Neither likely nor unlikely||Price 4: $11.99|
|Somewhat unlikely||Price 5: $12.99|
|Very unlikely||Price 6: $13.99|
Imagine you were a tour agency trying to put together a handful of vacation packages to sell as part of online promotion. With many different destinations, dozens of hotels, meal and other options to choose from how would you configure and price your product? Certain destinations or hotel brands may appeal to your customers, providing a higher value that you can charge. If specific meal options have little interest and greatly impact your bottom line, these are not worthwhile to offer at all at any price. When evaluating price and willingness to pay, it helps to consider price in the context of multiple other product features that consumer evaluate.
Conjoint analysis is a pricing technique used to investigate the value consumer’s place on various features that make up a product, including price.
With this technique, you can understand the demand levels for competitive product sets or for different bundle configurations for a product; you can investigate the relationship between price and demand and you can determine how much a particular feature or product attribute (like price) drives consumer preference.
In a conjoint analysis exercise, respondents are shown a set of product profiles and asked to choose their preferred one. This type of exercise is more realistic in how consumers shop and make purchase decisions compared to directly asking in a monadic design: On a scale of 1 to 5 how likely are you to buy this product for $9.99? When choosing between alternative sets of product options, a conjoint analysis exercise forces respondents to make tradeoffs between features. Price is only one component of a product offer. Respondents go through a set of a dozen such questions, receiving a new set of product profiles each time. This allows us to collect enough data to build a model that quantifies how much consumers value each product feature.
If you wanted to buy a computer and these would be your only options, which would you choose?
|Computer 1||Computer 2||Computer 3||None: I would not choose any of these|
|Processor Speed||20 GHz||4 GHz||30 Hz|
|Memory||12 GB||4 GB||8 GB|
|HDD Storage||2000 GB||1000 GB||750 GB|
Conjoint analysis data is used to model consumer preferences with a market simulator. This allows you to generate “what if” scenarios, mixing and matching different features to build your own product. You may also pit multiple product configurations against each other in a competitive set. In each case, preference shares are calculated for all the product profiles that you create in your custom marketplace. Then you can see how consumer interest changes when you vary pricing and product options.
Conjoint analysis has been rigorously researched and tested by the marketing and academic community alike. It is a robust technique for gaining insights into the complexity of pricing, product preferences, product feature options, and competitive dynamics. But it requires specialized expertise and understanding to set up and execute. Conjoint analysis projects will be more costly because of this.
Understanding price is a key element to successful product development and market positioning. Researchers have long used surveys to investigate how different pricing options may appeal or impact consumer behavior. Most common pricing questions can be addressed utilizing one of the four techniques discussed here. Whether you are just beginning to explore the marketplace or want to gauge customer reaction to a new pricing model a survey can be the right starting point of investigation for you.