India’s Subscription Economy and Consumer Insights: Opportunities, Research Imperatives, and Privacy Obligations

Subscription models have matured beyond OTT and software. Groceries, beauty, FMCG bundles, and service memberships are now mainstream. For brands and researchers, this creates recurring relationships with customers, rich behavioural signals, but also new measurement questions about retention, lifetime value and evolving privacy obligations. Recent market estimates indicate rapid growth in the subscription economy globally and a sizable opportunity in India.

The global subscription economy was valued at nearly USD 492B in 2024, with continued rapid growth projected into 2025–2030.

India’s digital consumption, expanding internet user base, and growing middle class make it fertile ground for subscription expansion across categories (grocery, F&B delivery, beauty, fitness and more). Local reports highlight India’s growth in its digital economy and rising e-commerce adoption, which serves as a backbone for subscription services.

Value and convenience over pure price: Indian consumers increasingly subscribe when the offering simplifies their lives or unlocks convenience.
Trial and retention dynamics: The initial trial period often determines long-term retention; understanding the triggers of churn is critical.
Micro-segmentation matters: Diversity across cities, incomes, and digital literacy levels means a one-size-fits-all subscription is unlikely to succeed.

1. Attribution of value: Subscriptions bundle benefits (discounts, delivery, and exclusives). Measuring incremental LTV requires careful control groups and longitudinal tracking to ensure accurate results.
2. Churn prediction vs. causal analysis: Predictive models can flag at-risk subscribers, but causal work is required to identify which interventions truly move retention metrics.
3. Voice-of-subscriber vs. usage data: Behavioural telemetry (app usage, purchase cadence) must be combined with qualitative feedback to explain “why” subscribers stay or leave. McKinsey and other industry reports emphasise blending behavioural and attitudinal datasets to get a complete picture.

India’s digital privacy regime has evolved rapidly; the Digital Personal Data Protection Act (DPDPA) and draft operational rules have set expectations regarding the collection, processing, and cross-border transfer of personal data. For subscription services and researchers, this means:

• Obtain explicit consent for processing subscription-related personal data.
• Keep transparent records of data uses, retention policies, and transfer justifications.
• Prefer anonymised/aggregated datasets when possible, and keep personally identifiable data isolated and secure.

(Practically: before you feed customer-level data into analytic models or third-party AI, verify contractual safeguards and whether a local/private model is required).

1. Define the business question: acquisition optimisation? Churn reduction? Pricing elasticity?
2. Combine datasets: platform usage logs, transactional history, periodic attitudinal surveys, and qualitative exit interviews.
3. Design longitudinal measurement: cohort tracking, retention curves, and experiment-based causal inference (A/B for pricing changes or retention offers).
4. Predictive and prescriptive analytics: churn models to prioritise interventions, and uplift modelling to choose the most efficient offers.
5. Ethics and governance: Data minimisation, consent management, and a documented data retention policy.

A D2C grocery subscription observed a decline in renewal rates after 6 months. Eklavya’s approach:

• Merged usage logs (order frequency, average basket) with exit surveys.
• Ran uplift tests for two interventions: an early-renewal discount vs. a personalised discount based on basket composition.
• Outcome: Personalised discounts improved 12-month retention by 8% vs. 3% for blanket early-renewal offers, with lower program cost per incremental retained user.

• Treat subscriptions as longitudinal products– measure cohorts, not just snapshots.
• Invest in linked data infrastructure (CRM + transaction logs + survey responses) for richer modelling.
• Use experiments (randomised offers) to discover causal levers for retention.
• Prioritise privacy-by-design: minimise PII used in models and maintain consent records- this reduces future regulatory risk.

• Cohort design and retention analytics: Set up cohort dashboards and lift measurement frameworks.
• Churn and uplift modelling: Build predictive models and design cost-effective retention campaigns.
• Privacy-safe analytics: Implement data governance that balances insight with compliance.

If you’re launching or scaling a subscription service in India, schedule a discovery call with Eklavya to design a measurement plan that drives retention, revenue, and regulatory compliance.