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3 Tips Weather Forecasting Can Teach Us About Conjoint Analysis

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.  

President, TRC


Rich brings a passion for quantitative data and the use of choice to understand consumer behavior to his blog entries. His unique perspective has allowed him to muse on subjects as far afield as Dinosaurs and advanced technology with insight into what each can teach us about doing better research.  

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