The Data You Cannot
Get From Adapty
Adapty is a powerful tool for managing mobile subscriptions and running paywall experiments. Its dashboards are great for product managers and marketers looking at subscription performance, but it stops short when you need deeper insights into attribution, profitability, and customer value. Here are seven critical metrics you will not get from Adapty alone, and how a data warehouse unlocks them.
True Revenue Attribution
What Adapty shows you:
- Revenue by subscription product, plan, and platform (App Store / Google Play).
- Limited attribution to marketing campaigns.
- Renewal revenue mixed in with new conversions.
What data modelling unlocks:
- Separation of first-time conversions vs. renewals.
- First-click vs. last-click attribution across channels.
- Net revenue stripped of App Store/Play Store fees.
- Cross-channel attribution consistency with Meta, Google Ads, SEO, etc.
Why it matters:
Adapty’s dashboards often count renewals as fresh campaign-driven revenue, which makes campaigns look stronger than they really are. This can lead teams to double down on channels that simply capture recurring revenue, rather than actually acquiring new subscribers. Proper attribution in a warehouse helps you identify which campaigns are driving incremental conversions and which ones are just “riding along” with renewals. Without this, ROI reporting is misleading, and budget allocation decisions are skewed.
Cohort-Based Retention & Churn
What Adapty shows you:
- Aggregate churn and retention metrics.
- High-level subscription funnel breakdown (trial → active → cancelled).
What data modelling unlocks:
- Cohort tables by acquisition date, campaign, or country.
- Retention curves segmented by paywall version or experiment.
- Churn split by voluntary cancellations vs. failed payments.
Why it matters:
Subscription businesses live and die by retention. Aggregate churn rates hide the fact that some cohorts retain beautifully while others churn almost immediately. Without cohort modelling, you cannot tell whether a drop in retention is driven by poor onboarding, a bad campaign audience, or simple billing failures. Warehouse retention analysis provides clarity on where and why users drop off, letting you design targeted retention strategies rather than blanket fixes.
Customer Lifetime Value (LTV)
What Adapty shows you:
- MRR, ARR, and average subscription duration.
- LTV estimates at a very high level.
What data modelling unlocks:
- Transaction-level LTV curves by user, cohort, and acquisition source.
- Profit-adjusted LTV (after platform fees, refunds, ad spend).
- Alignment of CAC with long-term value.
Why it matters:
In subscription apps, averages can kill you. A handful of high-value users can inflate average LTV, masking the fact that the majority churn within a month. Adapty’s native metrics do not give the granularity to see this. Warehouse LTV modelling allows you to identify profitable cohorts and acquisition sources, then double down on them. Without it, you risk overspending on campaigns that bring in one-month churners while missing the opportunity to scale profitable segments.
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Paywall Experiment Performance
What Adapty shows you:
- High-level conversion rates per paywall experiment.
- Revenue uplift compared to baseline.
What data modelling unlocks:
- Long-term retention of users acquired via each variant.
- Impact of paywall experiments on churn and LTV.
- Interactions between experiments and acquisition channels.
Why it matters:
Paywall experiments do not end at the paywall. A variant that maximises week-one conversion could easily attract low-quality subscribers who churn within the trial or first month. Adapty’s native reporting shows immediate conversion success but ignores long-term impact. Warehouse modelling gives the full picture, showing whether an experiment drives sustainable revenue or just short-term spikes. This prevents false positives and ensures you optimise paywalls for lasting growth.
Unified Marketing + Subscription View
What Adapty shows you:
- Revenue from in-app purchases and subscriptions.
- Some campaign tagging if SDKs are integrated.
What data modelling unlocks:
- Full funnel from ad spend → install → trial → conversion → LTV.
- CAC:LTV alignment at the channel, campaign, and ad creative level.
- Holistic view across CRM, paid media, and subscription performance.
Why it matters:
Marketing teams often optimise on cost per install (CPI) or trial conversion rates because that’s what is visible. But without tying acquisition cost to downstream subscription value, you’re making decisions in the dark. Some campaigns that look “expensive” on CPI can be the most profitable once retention is factored in. Warehouse modelling closes the loop between acquisition spend and subscription value, giving you the confidence to scale the right campaigns.
Refunds, Discounts, and Net Revenue
What Adapty shows you:
- Gross revenue, often mixing in discounts and refunds.
- No easy separation of promo codes, grace periods, or refunds.
What data modelling unlocks:
- Clean net revenue by stripping refunds, discounts, and free trials.
- Profitability analysis at the SKU, cohort, and campaign level.
- Comparability with other revenue sources outside subscriptions.
Why it matters:
Gross revenue looks impressive but does not pay salaries. Refunds, failed payments, and promo codes can dramatically change the real revenue picture. If you optimise campaigns or experiments on gross figures, you risk scaling based on false signals. Warehouse modelling ensures that every revenue figure reflects actual money in the bank, enabling decisions that reflect true profitability.
Cross-Platform User Journeys
What Adapty shows you:
- Platform-specific data (iOS vs. Android).
- User-level detail siloed by platform.
What data modelling unlocks:
- A single user profile across devices and platforms.
- Journey analysis combining mobile subscriptions, web activity, and CRM engagement.
- Behavioural segmentation beyond what Adapty supports.
Why it matters:
Most users do not exist solely on one platform. They may start on iOS, switch to Android, and interact with your brand via web or support channels. Adapty alone sees them as fragmented records. Without a unified warehouse model, you miss opportunities for personalised messaging and lifecycle optimisation. With cross-platform modelling, you see the whole customer, enabling strategies like pausing marketing to churn-risk users who just raised a support ticket, the kind of context-aware actions that drive retention and satisfaction.
Conclusion
Adapty is excellent for paywall management and subscription tracking, but its native reporting is not enough for true business intelligence. By modelling the data in a warehouse, you unlock:
- Proper attribution and net revenue.
- Retention and LTV curves that reflect reality.
- Paywall and marketing insights aligned with long-term growth.
- A unified, cross-platform view of the customer journey.
That is how you move from monitoring subscription dashboards to building a profitable, scalable growth engine.
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