In a focused Proof of Value (PoV) engagement, a team of AI and data science experts collaborated closely with the Swiss mobile operator's marketing and analytics teams. The engagement followed a structured methodology designed to rapidly demonstrate the potential of AI-powered personalized offer generation.

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Phase 1: deep dive into data and uncovering hidden opportunities

The initial phase focused on understanding the operator's existing data infrastructure, customer segmentation models, and business objectives. This involved:

• Comprehensive data landscape analysis: A thorough review of all relevant customer data sources was conducted, identifying opportunities to integrate and enrich the data to create a more holistic view of each customer.
• Customer segmentation reimagined: Existing segmentation models were assessed, and a roadmap was developed for implementing advanced AI-driven techniques, such as behavioral clustering and micro-segmentation, to achieve more granular and insightful customer segmentation.
• Identifying high-impact use cases: Through collaborative workshops and data analysis, specific use cases for personalized offer generation were identified, focusing on areas with the greatest potential to reduce churn and drive revenue growth.

‍Phase 2: developing and showcasing a tailored AI solution

This phase centered on building a working prototype of an AI-powered offer generation engine, demonstrating the core capabilities and potential impact of the solution:

• Unified customer profile: A unified customer profile was designed, consolidating data from disparate sources to provide a comprehensive 360-degree view of each customer, including demographics, service usage, interaction history, and predicted behaviors.
• Predictive modeling for churn and offer propensity: Leveraging the operator's historical data, advanced machine learning models were developed to:
  • Predict customer churn, identifying at-risk customers and understanding the underlying drivers of churn for different segments.
  • Predict the likelihood of individual customers accepting specific offers, enabling more precise targeting and offer optimization.
• Generative AI for dynamic offer creation and communication: A large language model (LLM) was integrated into the prototype to demonstrate the ability to:
  • Dynamically generate tailored offers, including personalized discounts, bundled services, and loyalty rewards, based on individual customer profiles and predicted propensities.
  • Craft compelling and personalized messages for each offer, delivered through the optimal channel (email, SMS, in-app), using language and tone aligned with the customer's preferences and communication history.

‍Phase 3: demonstrating value and defining the path forward

The PoV culminated in a live demonstration of the prototype to the operator's leadership, showcasing the capabilities of the AI-powered offer generation engine and its potential impact on key business metrics.

• Realistic scenario simulation: The demonstration showcased how the system would analyze customer data, identify churn risks, generate tailored offers, and personalize communications in real-time.
• Data-driven performance projections: Based on historical data and model performance, projections were presented for key performance indicators (KPIs), including:
  1. Offer acceptance rate: An estimated 15-20% improvement in offer acceptance rates compared to existing generic promotions.
  2. Churn rate reduction: A projected 10-15% reduction in customer churn, driven by proactive and personalized retention efforts.
  3. Average revenue per user (ARPU) uplift: An anticipated 5-8% increase in ARPU, resulting from more effective upselling and cross-selling through targeted offers.
• Strategic roadmap: A high-level roadmap for full-scale implementation was presented, outlining the key steps, technology considerations, integration requirements, and organizational implications.

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