Discover: Bayesian Network Analysis

We created a segmentation using Bayesian Network Analysis

Attitudinal segmentation with large numbers of diverse statements is problematic; it’s often difficult to find focused, cohesive, targetable segments.

Often, variable reduction techniques are used (typically Factor Analysis) to create a smaller number of composite variables which are combinations of all the statements which are then used. 

However unless all the statements  in a sub set are strongly correlated it is possible for consumers to have similar factor scores despite having very different statement scores. This means people can be put in the same segments despite having quite different attitudinal profiles. The result is a set of less cohesive, less homogenous segments.

To avoid this problem we used Bayesian network analysis to create a smaller set of factors for a leading snack producer. This machine learning technique produces a probability based graphical network model detailing the relationship between all variables in the analysis. We applied this to derive a robust consumer segmentation based on attitudes to health, nutrition and lifestyle.

How does it work?

  • The width of the line denotes the strength of the relationship
  • The colour denotes the nature of the correlations: Red = Negative, Blue = Positive
  • 32 statements uncover 10 factors: the key input into our segmentation which resulted in five clear consumer typologies

Consumer typologies:

Bayesian Network Analysis poster

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