[Webinar Recap] Anchored MaxDiff Scaling

If you weren’t able to make it to the kick off of our conversation around Anchored MaxDiff then we have got you covered. We were joined by Scott & Chris from DeckChair Data to talk about MaxDiff Scaling and introducing the concept of “Anchoring” to give better meaning to the MaxDiff share of preference calculations & models. Anchored Max-Diff allows us to scale the relative scoring model down to an absolute scoring model.

Key takeaways

MaxDiff is a great model for “discriminating” between a set of options. It has low cognitive stress on respondents and most folks can tell easily what’s their “best” and “worst” option given a set of choices. It also has a high discrimination value.
Anchoring allows us to determine a practical scale – what % of people would actually get affected / will buy / or will NOT buy. Creating logical benchmarks for the study.
The way you ask the “anchoring” question does matter. Eg: What amongst these options is a MUST HAVE. i.e Something that you cannot live without?

Qual before Quant – make sure to use a qualitative exercise before running a large quantitative model to determine all the options that don’t have implicit bias. Avoid the red-bus/blue bus problem; IIA (Independence of Irrelevant Alternatives)

LEARN ABOUT: Implicit Data