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:
How do shoppers decide in my category?
Which purchase criteria are relevant?
Which criteria rank first?
Which product or brands are bought in parallel?
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?
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?
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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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
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
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)
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 cluster relevance
Deep dives on focus clusters to evaluate Brand performance
Product cluster overviews on shopper KPIs
Identification of potential clusters
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