Here’s a quiz. Suppose you own a retail store and want us to help predict which customers that come through the door will buy. You want to focus your sales efforts on the best prospects. We do our market research and offer you a fancy “predictive analytics” model. Alas, it offers zero percent accuracy in identifying which customers will buy. Would you purchase our fancy model? Of course you should!
Posts Tagged ‘conjoint’
If you ever have trouble convincing your managers and executives about the value of market research, tell them this: The one billion dollar settlement that Samsung will pay Apple for patent infringement was based on a carefully designed market research study using conjoint analysis.
According to colleagues at Sawtooth Software, a company that develops the statistical software we use for conjoint analysis, Apple commissioned online conjoint studies to help estimate the economic cost of the patent infringement. Trial records document a series of qualitative interviews with consumers, which were then used to design choice-based conjoint studies. Their goal was to simulate willingness to pay and “shares of preference for Samsung’s devices with and without the alleged patent-infringing technology.” The result? Apple’s estimated loss was $2.5 billion, of which the jury awarded just over $1 billion. (more…)
The New York Times is one of the few organizations trying to push our industry further in developing better data presentation and visualization techniques. Sometimes they do a good job, introducing rich, informative, engaging, and interactive charts that would make even Edward Tufte, the contemporary pioneer in data visualization, proud. Sometimes they do a not-so-good job; and indeed we can learn from that as well.
Here is a chart they printed several weeks ago on the op-ed page:
MaxDiff is a powerful method and it is increasingly popular among market researchers. But it is not always the best choice for measuring the importance of attributes, and here’s why.
Suppose you want to measure the importance of 12 attributes for a new product or service. If you know ahead of time that consumers are going to say that all 12 are extremely important to them, then MaxDiff is an excellent method for differentiating among the attributes so you can focus on the top two or three that matter most.
But what if you don’t know that all 12 attributes are extremely important? Maybe none of them are. Maybe they run the gamut from unimportant to extremely important. The problem with MaxDiff is that it only tells you the importance of attributes relative to each other, but it won’t tell you whether the attributes are important. The MaxDiff model will assign ratio-level numbers so that you can rank and quantify the importance of each attribute vis-à-vis the others. But it will not anchor the attributes in a meaningful way. (more…)
MaxDiff is a survey method used to measure the importance of product features. Subsets of features are presented, and respondents are asked to select which feature is most important and which feature is least important. Its advantage over other techniques is that by forcing a choice from among multiple features, it more strongly differentiates the features if customers are prone to say that all features are important or attractive.
The hardest part of quantitative market research is not that it involves numbers, math, or even statistics, but that it involves complex problems in probability.
Over the past several years, psychologists have been documenting how difficult it is for us humans to solve even “simple” probability problems. One fascinating example is a puzzle known as the Monty Hall dilemma based on the 1960’s game show Let’s Make A Deal. Monty would offer his contestants three doors to choose from, one of which had a valuable prize behind it. After the contestant chose, Monty would open one of the other two doors, deliberately choosing one that had no prize behind it. Then he offered the contestant an option of staying with the original choice, or switching to the other unopened door. Which should the contestant do? (more…)
Versta Research just hit a magic number: 100. That’s the number of articles we have written to help our clients and their colleagues keep abreast of important trends in market research. If your market research supplier is not providing ongoing thought leadership in design, methods, and analytics, then what are the chances they are bringing ongoing and deep insight to your specific research needs?
To celebrate, we’re serving up a sampler of our five best articles. How did we decide they are the best? Our clients told us. These are the articles that they write to us about, forward to their colleagues, and for which they return to our website time and again. These are also the articles for which we get requests for print-ready PDF versions. (Just let us know if you want one!) (more…)
Our March 2011 newsletter focuses on conjoint research, which we consider to be one of the most clever and powerful techniques of survey research. Why? Because it allows us to build working models of decision-making.
Conjoint works by presenting people with scenarios that are more like the real-life trade-offs they always make. For example, instead of just asking a respondent about the importance of price, we ask them to make decisions about price, where price varies based on other attributes that are important to them. (more…)
In a recent survey we fielded among B2B decision-makers, respondents told us how much they liked participating in the study compared to other research studies they have done. They said it was “real” and interesting because it was confronting them with questions that reflect the kinds of decisions and trade-offs they make every day in their work.
The technique we used for that study is called Adaptive Conjoint. If you want to know how people in your target audience make decisions—how they weigh the pros and cons of your product or service versus others— adaptive conjoint can be a powerful technique that provides robust and insightful data at the same time it really engages the participants. (more…)
A client had a surprising experience this week when a member of our multivariate analysis team showed up online to watch a live in-depth interview with a registered nurse about how prescribing decisions are made. “Who is that online with us?” the end-client inquired, not recognizing the name. The qualitative manager answered, “He’s on our multivariate team.”
It must have seemed strange to have a statistician taking a keen interest in the qualitative work. Strange, because too often the qualitative and quantitative sides of research do not inform each other in the rich ways they can and should.