MEASURING WILLINGNESS TO PAY FOR INTANGIBLE VALUES

Every product or service creates value for its customers through its features. The customer benefits that are created can be tangible or intangible and customers value is defined as the benefits minus the price paid by the customer. If we want to measure customers’ willingness to pay, we need to first understand the perceived level of customer benefits that our product features generate.

For tangible benefits we often can calculate directly the economic impact for the customer. Nevertheless, for intangible value this is not possible. In this article I am showing a recent example of how we created a simple survey to measure willingness to pay for intangible customer value.

How to value sustainably produced, locally-sourced meat over regular meat?

A short background: We have been working on a project for sustainability in the meat value chain, financed by the City of Barcelona through their “Impulsem” program aimed at promoting sustainable consumption patterns. Our goal has been to develop a business model that can help local farmers to make extensive animal farming (free-range) economically sustainable, while promoting the locally produced, organic and cruelty free high-quality meat to consumers.

To get an idea about consumer demand, we needed to measure the price premium we can potentially charge for these superior, locally produced, sustainable meat products. So essentially, we were trying to measure the - mostly intangible - value we bring to the customer compared to cheaper meat that is not locally and sustainably produced. We started by creating a very short survey, which consisted of a MAXDIFF question to help us understand which attributes customers value and a 3-step Van Westendorp Price Sensitivity Meter (PSM).

Our Experiment Design

In our MAXDIFF question respondents had to select from a list of 9 attributes their 3 most and 3 least valued. This helped us understand the relative importance of different sustainability attributes as well as their importance compared to other attributes such as price. We also used the responses to segment our sample.

Our MAXDIFF question setup. The different attributes were displayed in random order to respondents.

We then used a Price Sensitivity Meter (PSM) to measure how much customers would pay for the premium product versus a basic product as reference point. Providing a reference is important. People tend to be reasonably good at estimating relative value and generally are terrible at estimating absolute values. Our PSM was set up using three questions asking respondents to:

1)    provide a price that they consider good value, compared to the reference product.

2)    tell us what price they would consider expensive but would still make them prefer the premium product over the basic product.

3)    give us a price that they consider excessively high and would make them NOT buy the premium product.

We analyzed the survey in three steps: 1) calculating the MAXDIFF scores 2) segmenting our sample 3) calculating demand curves to estimate willingness to pay separately for each segment.

MAXDIFF Scores

To calculate the MAXDIFF scores we counted the number of times an attribute has been mentioned as most important and subtracted the number of times the same attribute has been mentioned as least important. Then we divided by the number of total mentions for that attribute. So, the scores are between -1 to +1, where -1 is an attribute that was mentioned as unimportant by everyone who mentioned it, and +1 being the opposite for an attribute considered important.

Our MAXDIFF scores of the entire sample.

We see that most sustainability attributes generally rank high, while price appears to be of medium importance. Local production appears to be by far the strongest attribute, while other sustainability attributes score lower. In particular, biodegradable packaging appears to be completely unimportant to our respondents. Nevertheless, these full-sample averages can be very misleading. We therefore split our sample to see differences between consumer segments.

Segmentation

We segmented our sample by respondents’ value drivers and created 3 groups.

Group A: Respondents who care about sustainability and not about price.
Group B: Respondents who care about price and not about local sourcing.
Group C: Respondents who care about price and at least one sustainability attribute.

Recalculating the MAXDIFF scores for each of these three groups, we are able to see how each segment uniquely assigns value across the different attributes.

Group A: This group specifically considers price a less important attribute in their MAXDIFF score, while rating at least one sustainability attribute as important.

Group B: This group specifically indicates price as being an important attribute and local production as being a least important attribute.

Group C: This group considers price being an important attribute, but also indicate one of our sustainability attributes as important in their purchasing decision.

Our value proposition really is not very attractive to Type B customers (approximately 1/3 of our sample), these customers are not willing to pay significantly more for sustainably farmed, locally produced premium meat. Types A and C both are valuing local production parameters but differ in price sensitivity and the value they assign to organic / ecologic and cruelty free farming attributes.

Using this segmentation, we have been able to also measure other differences to better understand our target segments. For example, Type A customers on average consume meat 16% less frequently than the price-driven Type B.

We also think that the product range can potentially be segmented to optimally serve Type A and C with different choices, so we are satisfied with our simple segmentation exercise and move on to the hard part: measuring willingness to pay.

How to Draw a Demand Curve from Survey Data?

It is relatively easy to draw a demand curve whenever asking a large enough number of people how much they would pay for a particular product. In our case, we first sorted responses into price-point “bins”, counting how many of our respondents would pay a particular price, or less. Next, we calculated a cumulative distribution for each bin. This basically means that if 5% responded that they would pay less than 13Eur (our first bin) and 4% between 13-15Eur (Bin 2), the cumulative value is 5% for Bin 1 and 9% (5%+4%) for Bin 2, and so on. The cumulative value of the highest bin has to add to 100%.

Then we inverted the values by subtracting them from 100%, so Bin 1 would have a value of 95% (100%-5%) and Bin 2 would have 91%, and so on. Once we drew these values in a graph, we basically constructed a very simple demand curve for our offering.

Measuring Willingness To Pay

So, we could have just asked everyone to tell us how much they would pay for our meat and then drawn the demand curve. Instead, we asked our three questions to build the PSM, which allowed us to take into account that consumers assign a price range to products and perceive a different value depending on whether they are at the upper or lower end of this range. A classic Van Westendorp PSM would add a fourth question to ask respondents at which low price point they would have doubts about the product quality. For our study we did not deem this to be relevant, as we are trying to price a premium feature and we decided to omit the fourth question.

We therefore measured only three price responses from each of our segments: Value (great deal), High (but customers still would purchase), Excessive (no purchase). Next, we plotted the inverted cumulative curves of the Value and High price responses, as described earlier. We also plotted the cumulative curve for the Excessive price response, but we did not invert the cumulative values. This is another benefit of the PSM, because we can use the intersection of the Excessive curve (not inverted) with the other two inverted curves for Value and High to determine an optimal price point.

Below are the plots for our three customer segments. The price premium horizontally on the X-axis, so the percentage value customers would pay on top of the regular product price for sustainably, locally farmed meat. On the Y-axis we plot the % of customers who would purchase at this price premium level.

Group A shows a reasonable price range to be at around a 45-55% premium. At a 45% premium about 20% of customers consider the price to be great value, about 50% consider it high, but would still purchase the product and about 20% of customers consider the price excessive and would refrain from buying the product at this premium.

Group B shows a reasonable price range to be at around a 20-35% premium. At a 20% premium about 22% of customers consider the price to be great value, about 60% consider it high, but would still purchase the product and about 22% of customers consider the price excessive and would refrain from buying the product at this premium.

Group C shows a reasonable price range to be at around a 40-50% premium. At a 40% premium about 20% of customers consider the price to be great value, about 40% consider it high, but would still purchase the product and about 20% of customers consider the price excessive and would refrain from buying the product at this premium.

What We Learned

Looking at the graphs and applying a few additional statistics measures, we are able to draw lot of conclusions. Let’s discuss our price conscious Segment B first. This group does not perceive a lot of value from our sustainability attributes, and this is very clearly reflected in how much they would be willing to pay extra for it, somewhere between 20-35% to be precise. Also, Segment B has the highest variability in its responses, as they are clearly struggling to assign a value to these attributes.

Our target segments A and C on the other hand are much better at valuing sustainability attributes and are willing to pay a more significant premium. Our sustainability conscious, not-price-driven Segment A seems to be happy to pay around 45-55% extra, while our C’s, being somewhat more price conscious seem to consider an extra 40-50% to the appropriate price range.

These differences also highlight the importance of segmentation. If we had not first segmented, we would have ended up pricing at a 35-45% premium, a range that simultaneously is too high to draw any relevant demand from low-price Segment B, while leaving value on the table for both target segments A and C.

Obviously, large consumer studies can become infinitely more complex, but I wanted to demonstrate that even with a simple couple of questions and some basic data analysis it is possible to draw very concrete conclusions about how to price a product, even if there is a large intangible value component involved.