Customer First - Expertise across industries

• Use Case: Predict and reduce customer churn by analyzing usage patterns, billing issues, customer support interactions, and competitor offers.
• Maturity of Solution: 70% (Well-established but still improving with new data sources like social media sentiment).
• Causal Factors: Network issues, tariff changes, service downtimes, and customer support responsiveness.
• Expected Accuracy: 80-85% (using models like Double Machine Learning and EconML for treatment effect estimation).
• Timeline to Build: Initial model: 3-4 months; Optimization: 6-12 months (data-driven feature engineering and A/B testing for interventions).
• Improvement Areas: Incorporating real-time data feeds, improving customer segmentation, and personalization of retention offers.

• Use Case: Understand why customers fail to renew policies and predict non-renewal by analyzing premium changes, claim rejections, and customer satisfaction.
• Maturity of Solution: 60% (Some predictive models exist, but causal understanding is still being refined).
• Causal Factors: Premium increase, claim experience, agent interactions, and market competition.
• Expected Accuracy: 70-80% (based on Double Robust Learners or ATE estimation).
• Timeline to Build: Initial model: 4-5 months; Optimization: 8-12 months.
• Improvement Areas: Incorporating customer financial data, competitor analysis, and claim handling satisfaction scores.

• Use Case: Prevent customer drop-off during loan application processing by analyzing delays in approval, credit score changes, and competing offers.
• Maturity of Solution: 65% (Reasonable models exist but need enhancement with more real-time customer data).
• Causal Factors: Processing delays, communication gaps, credit score thresholds, and alternative lender options.
• Expected Accuracy: 75-85% (using Counterfactual inference and Uplift modeling for treatment estimation).
• Timeline to Build: Initial model: 5-6 months; Optimization: 6-10 months.
• Improvement Areas: Integration of real-time credit bureau data, NLP on customer communications, and faster feedback loops from sales teams.

• Use Case: Identify why customers drop out of product or service subscription renewals by analyzing pricing changes, customer satisfaction, and competitors' offerings.
• Maturity of Solution: 75% (Advanced models are in place but can be further optimized).
• Causal Factors: Pricing strategies, value for money, product satisfaction, and ease of cancellation.
• Expected Accuracy: 85-90% (based on Conditional Average Treatment Effect and Longitudinal Data Analysis).
• Timeline to Build: Initial model: 3-4 months; Optimization: 6-8 months.
• Improvement Areas: Personalized pricing recommendations, competitor comparison algorithms, and improving in-app customer journey analytics.

• Use Case: Predict patient drop-off during treatment plans by analyzing cost, perceived effectiveness, insurance coverage, and doctor-patient communication.
• Maturity of Solution: 50% (Still emerging, especially in real-time causal impact modeling).
• Causal Factors: Out-of-pocket expenses, side effects, satisfaction with care, and insurance claims handling.
• Expected Accuracy: 60-70% (due to complexity and individual patient behavior variation).
• Timeline to Build: Initial model: 6-8 months; Optimization: 12-18 months.
• Improvement Areas: Integrating electronic medical records, personalized care pathways, and real-time feedback mechanisms.

• Use Case: Reduce subscriber churn in OTT platforms by analyzing content preferences, pricing models, and competitors' promotions.
• Maturity of Solution: 80% (Advanced models with deep user data are already implemented).
• Causal Factors: Content relevance, subscription cost, and user interface experience.
• Expected Accuracy: 85-90% (using heterogeneous treatment effect estimation and propensity score matching).
• Timeline to Build: Initial model: 3 months; Optimization: 6-9 months.
• Improvement Areas: Improving content recommendation systems, more granular user behavior tracking, and dynamic pricing experiments.

• Use Case: Identify why customers stop using loyalty programs by analyzing reward structure, point redemption issues, and product availability.
• Maturity of Solution: 70% (Some solutions are deployed, but the space is evolving).
• Causal Factors: Difficulty redeeming points, reward relevance, program costs, and customer support issues.
• Expected Accuracy: 80% (using DoubleML or Instrumental Variable techniques for program impact estimation).
• Timeline to Build: Initial model: 4-5 months; Optimization: 8-10 months.
• Improvement Areas: Gamification elements, personalized rewards, and better integration of customer purchasing history with loyalty data.

• Use Case: Understand the root cause of customer non-renewal for SaaS subscriptions by analyzing software usage patterns, customer support interactions, and competitor feature sets.
• Maturity of Solution: 75% (Well-established, but can be fine-tuned).
• Causal Factors: Feature dissatisfaction, high pricing, poor customer support, and slow feature updates.
• Expected Accuracy: 80-85% (based on Incremental Response Models and customer satisfaction scores).
• Timeline to Build: Initial model: 3-4 months; Optimization: 6-9 months.
• Improvement Areas: AI-driven personalization of SaaS features, faster feedback loops on feature requests, and improving customer success touchpoints.

• Use Case: Predict and prevent customer drop-off during hotel bookings by analyzing price comparison behavior, reviews, and booking experience.
• Maturity of Solution: 65% (Limited solutions; many focus only on predictive analytics, lacking causal insights).
• Causal Factors: Price fluctuations, website/app usability, reviews and ratings, and loyalty program influence.
• Expected Accuracy: 75-80% (using Causal Forests and Latent Variable models).
• Timeline to Build: Initial model: 4-6 months; Optimization: 8-10 months.
• Improvement Areas: Real-time pricing optimization, integration of social proof (e.g., reviews), and improving booking funnel experience.