Actionable Insights For Customer Retention [Telco Case Study]


Data Insight
by Data Insight
posted on Wed, 20 October 2021

Data Insights

How a leading telco gained $900k in incremental revenue through data-driven customer retention activities.

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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.


Understand a range of aspects relating to customer retention so it could implement tactical marketing activities accordingly:

  • Whether customers who purchased multiple products were more likely to stay versus customers purchasing a single product
  • If customers stopping using one product could then result in them discontinuing with another
  • The timeframes customers typically left within
  • Which product bundles had the highest and lowest levels of customer retention 


The Di team developed a churn model utilising the converged dataset and other applicable datasets. The model was applied in two stages:

    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.

    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.




Annualised churn rate before churn modelling


Incremental revenue gained with just a 2% improvement in churn


Incremental 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."



  • 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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If you’d like to hear more about how we can help you get better value from your data, reach out to our team today. 


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Data Insight
by Data Insight
posted on Wed, 20 October 2021

Data Insights