Role Overview
We are looking for a data-driven Senior Manager – CLM Analytics to drive customer lifecycle insights and enable targeted growth interventions. This role will own cohort design, deep-dive behavioral analytics, and channel performance understanding to improve acquisition quality, drive upgrades, and maximize customer lifetime value.
The role requires strong analytical rigor, hands-on data capability, and the ability to translate insights into actionable business recommendations.
Key Responsibilities
1. Customer Cohorting & Lifecycle Analytics
- Design and maintain customer cohorts based on usage, tenure, ARPU, product mix, and behavioral signals
- Track cohort performance across lifecycle stages: acquisition, onboarding, engagement, upgrade, and retention
2. Acquisition & Upgrade Drivers
- Analyze drivers of acquisition quality (channel, campaign, offer constructs, pricing, etc.)
- Identify triggers and patterns that lead to customer upgrades / cross-sell / upsell
- Translate insights into actionable targeting strategies for marketing and product teams
3. Channel Performance Analytics
- Evaluate channel effectiveness (digital, assisted, partner-led, etc.) across acquisition and upgrade funnels
- Provide recommendations on channel mix and targeting based on performance and ROI
4. Reporting & Insights Delivery
- Build and manage dashboards and recurring reports for CLM performance tracking
- Ensure data accuracy, consistency, and timeliness across reporting systems
- Present insights to senior stakeholders with clear, structured recommendations
5. Data Science Collaboration & Model Validation
- Work closely with data science teams to define, build, and refine predictive models (e.g., propensity to upgrade, churn risk, segmentation)
- Validate model outputs using base data and cohort-level analysis to ensure business relevance and accuracy
- Translate model outputs into deployable business use cases and track performance
Qualifications & Skills
- 8-10 years of experience in analytics / data science / business analytics, preferably in telecom, fintech, or consumer tech
- Strong proficiency in SQL (complex queries, large dataset handling)
- Proficiency in Python (data analysis, automation, basic modeling)
- Experience in customer lifecycle management (CLM) / CRM analytics
- Strong problem-solving and structured thinking ability
- Ability to work cross-functionally with marketing, product, and data science teams
