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We think this is a great way to share our knowledge and discuss best practices in market research.

Take a look at some of your recent questions (and our answers):

I agree that choices tend to get more differentiated response but how can I be sure they are in fact more accurate?
TRC answer: In research we know that the more realistic the research the better the quality of the data. Choice is inherently more realistic than, say rating scales, as it is truer to how consumers act in the marketplace. Thus, studying choice is way to get more accurate information.

I love using choice methods, but I'm unclear on how it might apply to segmentation.
TRC answer: Since choice based methods tend to provide greater variance (or discrimination) in the data and because that variance is a key for good segmentation, choice based methods are a particularly good input for this analysis. You could use choice based methods such as Max-Diff or discrete choice conjoint to conduct effective segmentation analysis.

Isn't the product configurator method same thing as Adaptive Conjoint?

TRC answer: No. Adaptive conjoint is an interactive method but it is still a conjoint. That is respondents react to products they see on screen. In a configuration exercise respondents build the product on the screen so they are fundamentally different approaches.

How can I most accurately measure brand value?
TRC answer: Using discrete choice conjoint is a good way to get at brand value since the structure of the model is such that it isolates the impact of brand beyond the impact of the other tangible features included in the study.

I know existing data can be very important to econometric modeling, but my company has several disparate systems so it'll be nearly impossible for us to pull it all together. Are there certain kinds of data that are more important than others, i.e. 'must haves'?
TRC answer: Give the objectives market research seeks to satisfy, generally the most important data we need are consumer data. It could be in the form of surveys or database information. Beyond that company marketing data in the form of sales, advertising and promotional efforts are useful to identify the linkage with consumer data. Company financial metrics form the next layer when trying to connect research data with consumer experiences. Lastly employee and cost data can also be incorporated into the analysis if available.

I'm often asked to 'prove' the value of marketing research by connecting it to business outcomes. Have you done this successfully for any of your clients?
TRC answer: Yes. Linking customer experience information to company financial metrics is an effective way of showing the importance of research to senior level executives in metrics they care about. It allows executives to see how spending on specific programs can change the customer experience and therefore change the company's bottom line.

I'm a huge proponent of choice-based questions for initiatives like product development, and even segmentation, but how does it fit with satisfaction and loyalty related initiatives?
TRC answer: The basic question we have to ask ourselves as marketing researchers is what are we doing research for. It is to help companies market better and if they do, then the proof will be seen in the market place in the choices consumers make. So ultimately it is all about choice, regardless of the type of research we do. We may study customer satisfaction, but if it is not useful in understanding and influencing the future choices customers make what is the point of studying it? Satisfaction is a means to an end. So a choice based approach to satisfaction research would emphasize connecting the dots from survey research to repurchase to ensure that the results are actionable and appropriate from a business perspective.