MaxDiff Analysis

MaxDiff analysis is a technique for robust ranking of items. It can be used for ranking:

  • Flavours of your product by consumer preference
  • Usage occasions by frequency
  • Aspects of your brand by customer satisfaction
  • Features of a product by importance

MaxDiff is a statistical relative of conjoint analysis. It derives its name from “maximum difference” scaling, also called best-worst scaling.

Traditionally, MaxDiff treats each product as an individual item, whilst conjoint treats products as a combination of attribute levels. As such, conjoint analysis produces rankings for particular products by summing the preference scores for each attribute level of that product whilst MaxDiff produces rankings by polling the respondents directly. However, Conjoint.ly’s novel robust approach to MaxDiff allows for:

  • Testing of multiple attributes in the same survey
  • Brand-Specific combinations of attributes for when each brand is substantially different (To enable that, first create a Brand-Specific Conjoint and then convert it into the MaxDiff variety)
  • Simulation of preference shares, at a highly indicative level

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Outputs

Relative importance of attributes

(do people care about price, data, international calls, or text messages?)

Conjoint.ly estimates how important each attribute is relative to the other attributes in customers’ decision-making process. For example, imagine you are investigating price (specifically $30, $50, or $70 per month) and SMS inclusion (300 messages or Unlimited) for mobile phone plans. If the variation in price sways customers three times as much as variations across SMS inclusions, the relative importance score of price will be thrice as high as that of SMS inclusion.


Relative value by levels

(is 300 min of international calls much better than 90 min?)

Each level of each attribute is also scored for its performance in customers’ decision-making. For example, if low price ($30/mo) is seen favourably (relative to the other pricing options), it will show as positive. High price ($70/mo) can be least favourite and will be showing up as negative, while moderate price ($50/mo) can be in the middle showing as either low positive or low negative. The sign of the performance score of each attribute is only relative to the other options respondents faced: $70/mo can be negative when compared with $30/mo and $50/mo, but might show up as positive relative to $90/mo.


Ranked list of product constructs

Ranked list of product constructs

(list all possible level combinations and rank them by customers' preference)

Conjoint.ly forms the complete list of product constructs using all possible combinations of levels. They are ranked them based on the relative performance of the levels that they combine. This module allows you to find the best product construct that your customers will prefer over others.


Segmentation example: Segment 1 (~27%) cares more about SMS, segment 2 (~73%) care more about data. Both are price-conscious.

Segmentation of the market

(find out how preferences differ between segments)

With Conjoint.ly, you can split your reports into various segments using the information collected automatically by our system, respondents' answers to additional questions (for example, multiple choice), or GET variables. For each segment, we provide the same detailed analytics as described above.