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All too often companies have only the vaguest idea about what kind of data they’re holding; because such data is very often hidden deeply away in a variety of databases and fragmented across different departments. We identify this data and bring it to light, making it visible, cohesive, comparable and easy to understand so that it really does support YOU in making the right decisions. And if need be, we can also identify any lacking data and define a concept to fill in the gap.

MaxDiff modelling for product feature optimization

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Objective

To understand which product features or applications are most important to a particular market.

Approach

An online quantitative survey was conducted that included a MaxDiff exercise in which 40 different product features were evaluated on what is best and worst among 15 sets with 4 features in each set.

MaxDiff modelling was used over traditional rating scales for several reasons:

  • MaxDiff is free from scale use bias
  • Better differentiation than rankings
    • Rankings don’t show the difference between choices, e.g. how better is first than second, second than third, and so on.

MaxDiff presents a less difficult and more engaging task for respondents to perform than a full sort and rank.

MaxDiff overcomes halo effects that are encountered in using rating scales due to respondents giving overly good ratings to all features because of the overall liking of the product.

Product feature evaluation was derived using the hierarchical bayesian estimation at a respondent’s level and then, aggregated tat an overall level to find the impact.

Impact

The client was able to assess the product feature order based on relative importance.

Venugopala Rao Manneni

A doctor in statistics from Osmania University. I have been working in the fields of data analysis and research for the last 14 years. My expertise is in data mining and machine learning – in these fields I’ve also published papers. I love to play cricket and badminton.

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