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Conjoint Analysis

conjoint-vs-configuratorWe at TRC conduct a lot of marketing research projects using Conjoint Analysis. Conjoint is a very powerful tool for determining preferences for the various components that make up a product or service. The power of Conjoint comes from having consumers make mental trade-offs in evaluating products against each other. Do they prefer a lower cost product that contains few features, or a higher priced product that provides many benefits? How willing are they to choose a product that meets 2 or 3 of their criteria, but not all? Conjoint forces consumers to make these decisions, and the results can then be simulated to determine purchase preferences in a variety of scenarios.
But not all product development problems can be solved with Conjoint. Conjoint requires certain steps in the development cycle to have already been taken (defined features, some idea of pricing – see my previous blog on the topic.) In some cases, though, you may be at a stage in which Conjoint is feasible, but a different approach may be more appropriate, such as a Configurator. In a Configurator, otherwise known as a "Build-Your-Own" approach, you would use the same product features as in a Conjoint, but instead of pitting potential products against one another, the consumer "builds their own" ideal product.
So why choose one technique over the other? There are many reasons, but here are a few:
1. If determining overall product price sensitivity is the goal – Choose Conjoint. Conjoint will produce scores that assess both the importance of price overall as well as price tolerance for the product as features are included or excluded.
2. If you just want to know which features are the most popular, or which ones are selected when choosing or not choosing other features – Choose Configurator. In an a la carte scenario, respondents can choose which items to throw in their shopping cart and which ones to leave on the shelf. Getting simple counts on which features are popular and which ones are not – and in what combinations – can be very useful information, and it's an easier task for respondents. Keep in mind though that the Configurator works best if each feature is pre-assigned a price (to keep respondents from piling on).
3. If understanding competitive advantage/disadvantage is paramount – Choose Conjoint. Conjoint allows you to include "Brand" as a feature, and the results will link brand to the product price to see if respondents are willing to pay more (or less) for your product vs. that of a key competitor. You can also simulate competitive market scenarios. While you can include Brand in a Configurator, modeling the trade-off between brand and product price is far less robust.
4. If you have a lot of features, or complex relationships between the features - Choose Configurator. It's much easier for a respondent to sift through a long list of features and build their ideal product just once than to choose between products with a gigantic feature list multiple times. Conjoint works best when the features are not dependent on one another; a long list of restrictions on the features can disqualify Conjoint as a viable solution from a design perspective.
There are plenty of times when a technique may present itself as an obvious choice, and other times when the choice may be more subtle. And in those cases, we turn to our senior analysts who use their expertise and understanding of the research objectives to make sound recommendations.

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When working with clients on parameters for a conjoint design, there is often an assumption that the design includes a current product configuration, or base case. This base case provides a benchmark against which new configurations can be compared.  
Having a benchmark can be both useful and comforting when analyzing the conjoint results. Replicating a base case allows us to reference important metrics that are known for that product (for example, market share, CPU, revenue, etc.). As we configure new products and compare their appeal to our base case, we can gain insight into how these key metrics might be impacted.
Aside from establishing a benchmark, having a base case is also critical if there is concern about cannibalization.  If the expectation for the new product is that it will compete in the market with a current configuration it is critical to understand what impact the new product will have on the current landscape.
However, allowing for a base case in the conjoint design is not always warranted. As products become more dissimilar from current offerings it can become difficult to include a base case. Trying to integrate the components of a current and new product that don’t share many characteristics can lead to conjoint parameters that are too complex to administer, or create apples to oranges comparisons. It is not wrong to leave out a base case as long as it is understood there will be no benchmark comparison.
One hybrid solution to consider is to allow for a set choice that reads something like “None of these, prefer the PRODUCTS currently available”. This is similar to a typical “none” option in the conjoint but provides a bit more information; specifically, that they would not leave the category but are not interested in the new, very different product configuration. Of course this solution would not be appropriate in all instances but does provide a good compromise.
Ultimately, the extent to which “real products” are modeled with a conjoint study’s parameters is a function of the specific information needs and the complexity of the design. Most of the time we want to include that dose of “reality” in our design but don’t be afraid to leave it behind if warranted.

conjoint analysis design

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Conjoint Analysis Home buyingDuring my recent first time home buying experience I learned there are many, often competing, factors to consider.   My last blog discussed how I used Bracket™, a tournament-based analytic approach, to determine what homebuyers find most important when considering a home. My list of 13 items did not include standard house stats like # of bedrooms, # of baths, etc. To measure preference for those items I used a conjoint design.

I framed up the conjoint exercise by asking homebuyers to imagine they were shopping for a home and to assume it is located in their ideal location. Using our online panel of consumers, we showed recent or soon-to-be homebuyers 2 house listings side by side, plus an “I wouldn’t choose either of these” option. Each listing included the following:

        • Number of bedrooms: 1, 2, 3 or 4
        • Number of bathrooms: 1 full, 1 full/1 half, 2 full, 2 full/1 half or 3 full
        • House style: Single Family, Townhouse, Condominium, or Multi-Family
        • House condition: Move-in ready, Some work required or Gut job
        • Price: $150,000, $200,000, $250,000, $350,000 or $450,000

I felt a conjoint was best suited here, because in addition to importance, I wanted to see what trade-offs homebuyers were willing to make between these 5 items that are highly important in home buying. Are homebuyers willing to give up a bedroom to get the right price? Are they willing to do some sweat equity to get the number of bedrooms and/or bathrooms they want?

We found the top three most important factors are # of bedrooms, price and house condition. This made perfect sense to me as I would not consider any house with less than 3 bedrooms. Price and house condition were the next two key pieces. Was the house in my price range? How much work was needed? Did the price give me enough wiggle room for repairs? I was curious to see the play between price and house condition among the recent and soon-to-be homebuyers we interviewed.

Using the simulator I selected a 3 bedroom , 2 full baths, Single Family home. I picked 3 price points ($150,000, $300,000, $450,000) and then varied the house condition. Overall, homebuyers are less interested in a "gut job" compared to "move-in-ready". However, at the $150,000 price point, share of preference drops more drastically going from "move-in-ready/some work required" to "gut job" compared to higher price points.


conjoint analysis blizzardHere in Philly we are recovering from the blizzard that wasn’t. For days we’d been warned of snow falling multiple inches per hour, winds causing massive drifts and the likelihood of it taking days to clear out. The warnings continued right up until we were just hours away from this weather Armageddon. In the end, only New England really got the brunt of the storm. We ended up with a few inches. So how could the weather forecasters have been this wrong?

The simple answer is of course that weather forecasting is complicated. There are so many factors that impact the weather…in this case an “inverted trough” caused the storm to develop differently than expected. So even with the massive historical data available and the variety of data points at their disposal the weather forecasters can be surprised.  

At TRC we do an awful lot of conjoint research…a sort of product forecast if you will. It got me thinking about some keys to avoiding making the same kinds of mistakes as the weather forecasters made on this storm:

  1. Understand the limitations of your data. A conjoint or discrete choice conjoint can obviously only inform on things included in the model. It should be obvious that you can’t model features or levels you didn’t test (such as say a price that falls outside the range tested). Beyond that however, you might be tempted to infer things that are not true. For example, if you were using the conjoint to test a CPG package and one feature was “health benefits” with levels such as “Low in Fat”, “Low in carbs” and so on you might be tempted to assume that the two levels with the highest utilities should both be included on the package since logically both benefits were positive. The trouble is that you don’t know if some respondents prefer high fat and low carbs and others the complete opposite. You can only determine the impact of combinations of a single level of each feature so you must make sure that anything you want to combine are in separate features. This might lead to a lot of “present/not present” features which might overcomplicate the respondent’s choices. In the end you may have to compromise, but best to make those compromises in a thoughtful and informed way.
  2. Understand that the data were collected in an artificial framework. The respondents are fully versed on the features and product choices…in the market that may or may not be the case. The store I go to may not offer one or more of the products modeled or I may not be aware of the unique benefits one product offers because advertising and promotion failed to get the message to me. Conjoint can tell you what will succeed and why but the hard work of actually delivering on those recommendations still has to be done. Failing to recognize that is no better than recognizing the possibility of an inverted trough.
  3. Understand that you don’t have all the information. Consumer decisions are complex. In a conjoint analysis you might test 7 or 8 product features but in reality there are dozens more that consumers will take into account in their decision making. As noted in number 1, the model can’t account for what is not tested. I may choose a car based on it having adaptive cruise control, but if you didn’t test that feature my choices will only reflect other factors in my decision. Often we test a hold out card (a choice respondents made that is not used in calculating the utilities, but rather to see how well our predictions do) and in a good result we find we are right about 60% of the time (This is good because if a respondent has four choices random chance would dictate being right just 25% of the time). Weather forecasters are not pointing out that they probably should have explained their level of certainty about the storm (specifically that they knew there was a decent chance they would be wrong).

So, with all these limitations is conjoint worth it? Well, I would suggest that even though the weather forecasters can be spectacularly wrong, I doubt many of us ignore them. Who sets out for work when snow is falling without checking to see if things will improve? Who heads off on a winter business trip without checking to see what clothes to pack? The same is true for conjoint. With all the limitations it has, a well executed model (and executing well takes knowledge, experience and skill) will provide clear guidance on marketing decisions.  

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You may have heard about the spat between Apple and Samsung. Apple is suing Samsung for alleged patent infringements that relate to features of the iphone and ipad. The damages claimed by Apple? North of 2 billion dollars. The obvious question is how Apple came up with those numbers? The non-obvious answer is, partly by using conjoint analysis – the tried and tested approach we often use for product development work at TRC.    

Apple hired John Hauser, Professor of Marketing at MIT’s Sloan School of Management to conduct the research. Prof. Hauser is a very well known expert in the area of product management. He has mentored and coauthored several conjoint related articles with my colleague Olivier Toubia at Columbia University. For this case, Prof. Hauser conducted two online studies (n=507 for phones and n=459 for tablets) to establish that consumers indeed valued the features that Apple was arguing about. Details about the conjoint studies are hard to get, but it appears that he has used Sawtooth Software (which we use at TRC) and used the advanced statistical estimation procedure known as Hierarchical Bayes (HB) (which we also use at TRC) to get the best possible results. It also appears that he may have run a conjoint with seven features, incorporating graphical representations to enhance respondent understanding.

There are several lessons to be learnt here for those interested in conducting a conjoint study. First, conjoint sample sizes do not have to be huge. I suspect they are larger than absolutely necessary here because the studies are being used in litigation. Second, he has wisely confined the studies to just seven attributes. We repeatedly recommend to clients that conjoint studies should not be overloaded with attributes. Conjoint tasks can be taxing for survey respondents, and the more difficult they are, the less attention will be paid. Third, he is using HB estimation to obtain preferences at the individual level, which is the state of the science approach. Last, he is incorporating graphics wherever possible to ensure that respondents clearly understand the features. When designing conjoint studies it is good to take these (and other) lessons into consideration to ensure that we get robust results.

So, what was the outcome?

As a result of the conjoint study, Prof. Hauser was able to determine that consumers would be willing to spend an additional $32 to $102 for features like sliding to unlock, universal search and automatic word correction. Under cross examination he acknowledged that this was stated preference in a survey and not necessarily what Apple could charge in a competitive marketplace. This is another point that we often make to clients both in conjoint and other contexts. There is a big difference between answering a survey and actual real world behavior (where several other factors come into play). While survey results (including conjoint) can be very good comparatively, they may not be especially good absolutely. Apple used the help of another MIT trained economist to bring in outside information and finally ended up with a damage estimate of slightly more than $2 billion.

Recent comment in this post - Show all comments
  • Ed Olesky
    Ed Olesky says #
    how interesting! thanks for sharing this, Dr. Sambandam. i wonder how many price points they tested. and was it subsidized price,

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