Using Transactional Data to Know Your Audience

The Approach

Since the transactional data contained a mix of categorical and continuous variables a two stage approach was used to identify different segments of supporters.

Initially, by means of a clustering algorithm, the volunteer team of analysts grouped all supporters characteristics into a smaller set of components. This has the benefit of reducing the dimensionality in the data.

Finally, once the data had been grouped into a smaller set of variables we applied a hierarchical clustering algorithm. The outputs of this algorithm revealed eight distinct segments.

Once eight segments were identified, the next step was to gain an insight about what makes the segment different from each other. A decision tree provided a set of rules to classify the segments. These rules were coded into an Excel macro so Bloodwise can classify their new supporters into segments.

"We identified quick wins which will allow us to optimise our promotional activities around sporting events" – Insight & Analysis Manager

The Client

Bloodwise, a charity that provides support to the people affected by blood cancer.

The Client's Problem

Bloodwise’s wealth of transactional data presented an opportunity to improve their knowledge about their supporters; they were to keen to understand how many different groups of supporters they had and what makes these groups different from each other.

The Solution

  • Delivered a segmentation solution based on supporters transactional data
  • Identified eight clearly differentiated segments and proposed a set of actions to engage with each one of them
  • Provided a set of rules to classify new supporters according to a set of key characteristics
  • Identified a set of supporters which made up approximately 20 percent of the whole sample but delivered nearly 80 percent of all donations

The Benefits

  • Key insights about what makes each group of supporters different from each other
  • Recommendations about how to communicate effectively to each segment
  • An analytical framework that Bloodwise can re-use to carry out an attitudinal segmentation, including code in R
  • Set of rules, coded in VBA, to classify new supporters

 

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