We specializes in solving customer churn across industries by leveraging advanced machine learning and predictive analytics. By identifying key churn indicators and optimizing customer retention strategies, we empower businesses to reduce attrition, increase customer loyalty, and boost long-term profitability, delivering tailored solutions that adapt to diverse market needs.
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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: 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: 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.