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GfK Category Purchase Tree

Smoothen shopper choices and revenue by shelf optimization

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When buying Fast-Moving Consumer Goods (FMCG), shoppers have to choose from an overwhelming variety of products to put in their shopping carts.

Shopping decision processes are not easy to understand and changes over time, as market parameters, consumer needs and preferences are constantly changing. Understanding the hierarchy of the purchase criteria in a category is an important step to implement the right strategy for manufacturers and retailers including product portfolio and shelf layout.
Evaluating the latest shopping patterns is key to meeting shoppers’ needs and creating new business opportunities.

The Category Purchase Tree (CPT) is a core element of the assortment and shelf optimization process, answering several business questions:

GfK Category Purchase Tree: shopper choices & shelf-optimization data
How do shoppers decide in my category?
Which purchase criteria are relevant?
 
Which criteria rank first?
 
Which product or brands are bought in parallel?
GfK Category Purchase Tree: shopper choices & shelf-optimization data
What does it mean for my category strategy?
Which segments drive value for my category?
 
Which segments drive frequency?
 
Which segments attract valuable target groups?
GfK Category Purchase Tree: shopper choices & shelf-optimization data
How should I organize the shelf?
Which products should stand together because they build one segment?
 
Which segments are most important and should be in the focus of the shoppers?
 
What is a logical order for the segments in the shelf from a shopper's point of view?
GfK Category Purchase Tree: shopper choices & shelf-optimization data
Do I have a strong position and the right portfolio?
Do I have products in every segment?
 
What segments are white spaces in my portfolio?
 
What segments should be strengthened?
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GfK Category Purchase Tree: shopper choices & shelf-optimization data

Our CPT reveals shopper decision criteria using real life data.
As our insights are derived from real shopping behavior and not respondents, we display natural purchase behavior from customers in their usual routine.
As we measure shopping preferences over long periods, we can identify trends and combine the insights with our powerful panel KPIs, such as quantifying cluster potentials.

CPT reveals product affinity patterns and prioritizes the strength of the identified product relationships

GfK Category Purchase Tree: shopper choices & shelf-optimization data
Identify levels of duplication between products

We analyze purchase behavior of all category shoppers


We reveal levels of duplication between products among these category shoppers

GfK Category Purchase Tree: shopper choices & shelf-optimization data
Identify groups of products by strong relation

We analyze mutual duplication of all products to one another


We reveal groups of products that have a strong relationship with each other (index ranking)

GfK Category Purchase Tree: shopper choices & shelf-optimization data
Prioritize the strength of the relationship

We use the hierarchical clustering technique to create a product affinity tree that prioritizes the strength of the relationship.


The tree represents the shoppers purchase hierarchy

Category Purchase Tree

  • Purchase criteria & hierarchy (up to 5th tree level, more levels for clusters with high market shares)
  • Product allocation
  • Product cluster relevance
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The Potentials

  • Deep dives on focus clusters to evaluate Brand performance
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  • Product cluster overviews on shopper KPIs
  • Identification of potential clusters
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Find out how business for good is becoming a growth driver – and see how behavior is changing

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GfK Category Purchase Tree: shopper choices & shelf-optimization data
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