KiE Square - Telecom

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Telecom Excellence:

We work with leading telecom companies in building transactional predictive models especially in the areas of churn and campaign effectiveness and integrating the same across enterprise systems.

Experiental understanding of customers backed with an analytical framework for understanding behavior results in superior customer interaction strategies that positively impact customer satisifaction, customer revenue and customer profitability.

Our Market Ready Solution

Segmentation Strategy :

Identify Gaps in Traditional Segmentation: Recognize that pure value-based segments often lack field-level identifiability, while demographic segments fail to explain customer behavior or intent. Develop Dual-Objective Segmentation Models: Use machine learning to build hybrid models that blend transactional value metrics (e.g., ARPU, churn risk) with demographic markers (e.g., age, location) for practical execution. Implement Customer Scoring Frameworks: Deploy predictive scoring models to continuously rank customers by revenue potential, engagement, and risk—enabling proactive targeting and retention strategies. Enable Field Deployment via Actionable Tags: Convert model outputs into intuitive customer tags or micro-segments (e.g., “High ARPU, Low Churn Risk – Urban Youth”) for easy adoption by sales, marketing, and service teams.

Customer Lifetime Value Optimization :

Model True Lifetime Value (LTV): Use behavioral, transactional, and usage data to predict each customer’s long-term value—not just short-term profitability. Refine Acquisition & Retention ROI: Allocate marketing budgets based on expected lifetime returns, ensuring you don’t overspend on short-term gainers or underspend on long-term loyalists. Segment by LTV Trajectories: Identify segments with low current value but high future potential, and vice versa, to guide differentiated customer strategies. Align Campaign Goals with LTV: Tailor acquisition, retention, win-back, and migration campaigns using LTV insights to improve decision-making and maximize impact per rupee spent.

Proactive Churn Management :

Predictive Churn Modeling:: Implement AI/ML-driven models to identify early behavioral signals of potential churn—well before customers mentally check out or competitors lure them away. Early Retention Triggers: Activate personalized retention strategies at least 2–3 months ahead of predicted churn for significantly higher success rates in customer save campaigns. Behavior-Based Segmentation:: Move beyond post-facto churn analytics—build micro-segments based on customer usage patterns, engagement, and complaint silence to anticipate dissatisfaction. Experience Personalization: Deliver tailored product/service benefits or loyalty incentives based on the predicted churn triggers—boost perceived value and stickiness.

Industry sector : Telecom