Forecast Financial Modelling – Forecast

Case study

Covid-19 Behavioural Machine Learning Model

Media, Telecommunications

The final model provided key finance decision makers with up to date sports churn propensities for immediate use across a number of potential work streams, ranging from provisioning as a result of lost revenue, mitigation of risk, and defining appropriate communication strategies.

 Client

Our client is a leading telecommunications and technology company, offering a full range of communications services and competing in all telecommunications markets within the United Kingdom.

Purpose 

The client required a detailed model to identify customers at risk of pausing or cancelling their sports product holding as a result of COVID-19. The client has over 3.5m active sports customers on their book and were looking for a model to predict the potential decay of this base. Forecast delivered a propensity model for the entire customer base within a tight deadline.

 Key deliverables and functionality of the model included:

  • Propensity scores split by the three target outcomes–Do Nothing, Cancel, or Pause their subscriptions.
  • Strategic, regularly maintained and updated data tables allowing for the timely identification of new pause/cancel customers.
  • Analysis of the predictive ability of the modelling suite with evaluation metrics back tested using a short period of actuals.
  • The ability to create and investigate customer cohorts for each of the three target outcomes.
  • Automation of the end-to-end process to identify active sports customers who have not yet paused or cancelled, predict the risk profile of these customers, and update strategic tables with this risk profile.
  • The use of strategic platforms and tools (specifically cloud computing and modelling) to ensure reusability and ongoing consumption.

Approach

Forecast constructed a data pipeline feeding strategic data from Google Big Query onto the GCP AI Platform for further exploratory analysis. Forecast then built and fine tuned a Machine Learning approach that took in the most identifying features to predict propensities on a customer level for the target classes. We delivered a full modelling solution that can be projected on the entire customer base, and re-run frequently to assess the changing circumstances. Forecast delivered this project on a very strict deadline of 3 daysand continues to provide support to the client’s decision-making team.

Outcome

The final model provided key finance decision makers with up to date sports churn propensities for immediate use across a number of potential work streams, ranging from provisioning as a result of lost revenue, mitigation of risk, and defining appropriate communication strategies. These decision makers helped the client navigate the fast paced changes in their customer base due to the unexpected scenario and stay ahead of the curve.

More like this

Case study

Energy, Retail

Demand & Margin Forecast

Case study

Telecommunications

Demand Forecast Model

More case studies Contact Us