Product Configuration: Evidence for Effectiveness

This white paper looks at the examples from one product configuration study, the kinds of information that can be derived and the possibilities provided by statistical analysis.

 

In the companion piece [Configuration: An Approach for the Times] the basics of configuration were explained. It is an effective approach that mimics the real world of customer driven product design to obtain simple yet deep understanding into consumer decision making, and its implications for the practicalities of new product design. In this piece we will look at an example from one study, the kinds of information that can be derived and the possibilities provided by advanced statistical analysis. The latter are particularly interesting, as such capabilities (utilities, simulation) have till now been the province of methods such as conjoint analysis.

 

An Example

The example in question is in the auto insurance industry. The topic is interesting for a few different reasons. All drivers need it and most adults choose their own provider. It can be customized for a driver and it has some complexity built into the process, especially with regard to differential pricing. The decision-making may not be straightforward with rules being used to arrive at an optimal product. It is often renewed every six months, providing an opportunity to re-visit the decision-making process with somewhat high frequency. And, of course, it is quite amenable for a configuration exercise.

The study was set-up as a task for choosing auto insurance for oneself. A basic product (largely hewing to state mandated minimums) was described followed by the configuration exercise where respondents were offered choices on six features. Each feature had three to four options including a base option and respondents could choose to stay with the base product or shift to one of the other offered options. Some options would increase the total price, while others would lead to a lower price. Given the customized pricing used in auto insurance, we kept the task realistic by asking respondents for their current expenditure and using that as a basis for building the price for the overall product. Respondents build their ideal product from the choices provided as they proceed through the exercise.

Basic Results Of the 822 respondents in the study only 20% chose the base option in every feature. (Figure 1) In other words, the vast majority of study respondents chose to alter the base product to fit their specifications showing both their inclination with regard to auto insurance and their level of engagement with the exercise. As shown in Figure 2, in every feature there are considerable proportions of people choosing to upgrade (and sometimes trade down) from the base product. For example, with Collision Deductible, a fifth of respondents show a willingness to lower it down to $250 even though it adds $125 to the overall premium. Another fifth would rather lower their premium by choosing higher levels ($1000 or $2000) of Collision Deductibles.

Almost half of respondents opt for some form of Accident Forgiveness option while about that proportion indicate they would prefer policy terms longer than 6 months. In both cases respondents are showing that they are willing to pay for such amenities, thus providing an auto insurance company with valuable input on pricing these kinds of innovative features.

Profiling people by the choices they make also provides interesting information. This is clarified more when we run a segmentation analysis on the choices that people make when building the product. Using a Neural Network based segmentation method (called SelfOrganizing Maps) we can identify segments with clearly distinguishing characteristics.

 

• Segments are differentiated mainly based on deductible preferences


• A somewhat smaller high-deductible segment of consumers who are relatively affluent, educated and younger. They are much more interested in unusual offers like very high deductibles and more likely to indicate a willingness to buy the product that they have built.


• A low deductible segment that is older, almost as affluent, has more children, generally prefers dealing with an agent and doesn’t use the Internet to shop as much. Unusually low deductibles are especially attractive to this group, perhaps because they are more risk averse than other segments.


• There are also segments which tend to go with the base product offer not showing much inclination to customize the product. They do have some clear differences among them in terms of variables like Accident Forgiveness, but it is clear that they are quite different from those who seek high or low deductibles.

 

Advanced Results

The primary information that comes from a configuration exercise is simple, intuitive and very useful. But we don’t have to stop there. Advanced econometric modeling can be applied to the data to draw out conjoint-like insights even though the design is not set up accordingly. While the problem is quite complex because of the design flexibility, it is possible to derive individual-level utilities or attractiveness scores for every option in every feature. Of course, this provides us the same level of flexibility on the back end that has been the hallmark of conjoint designs. In essence, we overcome the front-end design constraints of conjoint while availing ourselves of its back-end flexibility. In technical terms this is called having your cake and eating it too.

That is all great but where is the proof that the utilities calculated through this method are accurate? We use validation to show that this is the real deal. After the configuration exercise was completed we asked respondents to indicate their willingness to buy a few pre-specified products. If the individual utilities we calculated are accurate they should identify what is important for individual respondents. Using that information we should be able to predict the willingness to buy for each respondent and compare it with what they actually said in the survey. Doing this calculation for the example data, we are able to correctly predict the buy/no buy status of 81% of the respondents. Clearly, if the utilities are not properly calculated this kind of result would not be possible.

So what does it practically mean to have individual level utilities? Since utilities express the desirability of every option for every respondent, we can make all kinds of calculations like take-rates of any product we choose to design, or preference shares of any groups of products. We don’t have to restrict ourselves to only products that were actually designed by respondents. Since individual-level preferences are now available, any product combination from the universe of possibilities (often running into tens of thousands of products) can be created.

For example, if a high-deductible product and a low-deductible product with varying levels of accident forgiveness and policy terms were introduced how will the market react to it? Which demographic groups would be more likely to choose one over the other? Who are the people who prefer high bodily injury liabilities? All these kinds of questions can be answered because we now know preferences at the individual respondent level. In fact, we can build a custom simulator that would allow all these scenarios to be played out to get a full understanding of how consumers make choices in the market place. See Figure - 3 for what such a simulator would look like.

For simplicity, this example uses only six features and three to four levels per feature. In reality, the configuration exercise can handle far more features and levels, bounded only by respondent engagement and your ability to develop price constraints. And of course, this method can be applied in a variety of industries.

 

In Summary

Product configuration is a deceptively simple and engaging way of gathering information from consumers by having them build their ideal product. In the process of building they provide a lot of insight into their preferences allowing companies to design products that are much more likely to resonate in the market place. In many situations this method has the potential to surpass existing methods of preference elicitation (such as discrete choice conjoint) while at the same time providing an engaging and enjoyable experience for the respondent.

 

Rajan Sambandam is Chief Research Officer at TRC and Pankaj Kumar is Managing Director of Quantellingence, the marketing analytics division of TRC. TRC is a market research company located near Philadelphia, PA. Visit their website at www.trchome.com.