Harnessing Predictive Choice Modelling to Optimise Telco Service Rollout Strategy
Industry: Telecommunications
Challenge: New Product Launch, Customer Acquisition and Customer Insights
Solution: Machine Learning and Predictive Choice Modelling
Technology: Python
Situation and Challenge
A leading telecom provider, already offering “triple play” services (broadband, landline, and TV), sought to enhance customer loyalty and revenue through high-value add-on services, including on-demand video and domestic security monitoring. The critical challenge was to determine an effective rollout strategy for customer acquisition, necessitating:
- Accurate understanding of customer behaviour and choices
- Prediction of potential impact on demand and product adoption
- Informed planning for accelerating customer acquisition
Insight and Action
- Commissioning quantitative market research to gauge customer interest, price sensitivity, and competitive landscape
- Applying decision tree probabilistic algorithms to model relationships between variables and predict outcomes
- Leveraging data fusion methods to extrapolate research findings and calculate probable responses to different choices for each customer
- Controlling for response biases to ensure accuracy of predictions
Our intervention delivered substantial benefits:
– Achieved 93% accuracy in predicting customer behaviour
– Enabled an 8% uplift in new service subscriptions
– Facilitated a 12% improvement in targeted upselling and cross-selling to existing customers
– Provided a five-year market demand forecast for new services
– Generated revenue projections and churn predictions for each customer
Key Achievements:
– Developed a highly accurate predictive choice model for complex customer behaviour
– Identified priority areas for phased rollout of new service offerings
– Enabled data-driven decision-making for product launch and marketing strategies
– Provided long-term market insights for strategic planning
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