How a leading telco gained $900k in incremental revenue through data-driven customer retention activities.
BACKGROUND
Data Insight’s telco client had a wide range of products and services across its different business units. We had recently undergone a project to merge the different sets of data to give our client a single view of its customers across its entire portfolio of offerings and the client next wanted to identify how this converged dataset could be utilised to quickly add value.
The Di team conducted an ideation workshop with the client where multiple ideas were assessed based on how easily they could be executed and how quickly they would provide value. It was identified that an ideal use for the converged dataset was to understand and predict customer churn – which customers were most likely to leave and why.
THE CLIENT’S CHALLENGEUnderstand a range of aspects relating to customer retention so it could implement tactical marketing activities accordingly:
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OUR APPROACH
The Di team developed a churn model utilising the converged dataset and other applicable datasets. The model was applied in two stages:
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UNDERSTANDING THE CUSTOMER
The first stage was to train the model to understand the factors that lead to customer churn. This provided insights into customer profiles and segments, behaviour patterns and the impact of value-based benefits offered to customers.The client was also able to retrospectively analyse past campaigns and better understand the impact these had on churn. Key churn factors were then explained in a non-technical sense to people from different areas of the business (such as marketing, finance, data engineers) who were then able to design and plan for future initiatives and campaigns.
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CHURN MODEL IMPLEMENTATION
The second stage was implementing the model and incorporating it into other models, for example, segmentation of customers that included their likelihood to churn so that appropriate incentives could be applied to retain these segments. The delivered solution included the ability to refresh the churn model over time to incorporate new data.
THE RESULTS
16%Annualised churn rate before churn modelling |
$600kIncremental revenue gained with just a 2% improvement in churn |
$900kIncremental revenue gained with a 3% improvement in churn |
"We really appreciate the help from the awesome data scientists at Data Insight on our customer retention (churn model). The team at Data Insight are very professional, intelligent, humble and easy to work with."
KEY OUTCOMES
- Ability to identify and predict ‘at risk’ customers and potential contributing factors through profiling
- Clear and actionable insights to create a plan for executing retention strategies
- Deeper understanding of churn behaviours to inform product development and solution bundling
- Data utilised from different business units to enable a full customer view
- Continuously refreshed data to ensure decisions are always based on the most up-to-date information
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