Subscription Economy Benchmarks API
An API and dashboard with anonymized aggregate subscription benchmarks across categories — sourced from SubMagician's consumer base, presented as aggregate cohort metrics with explicit privacy boundaries and use restrictions.
- Engagement
- API subscription · monthly benchmark refresh
- Built for
- Subscription product leads · Pricing teams · Strategy
Subscription businesses operate without the cross-industry benchmarks that public-company businesses have always had. What's the median churn rate for video-streaming subscribers in their seventh month? What share of consumers carry multiple subscriptions in a given category? Each subscription business answers these from its own data, with its own blind spots.
What this is
A benchmarking layer built on top of SubMagician's consumer subscription tracker — for the subscription businesses that operate without cross-industry visibility. Three layers:
- Data privacy preservation. Aggregate-only data delivery, k-anonymity-class enforcement, no individual-level leakage. Documented in the privacy architecture.
- Category-level benchmarks. Retention curves, multi-subscription patterns, cross-category affinity, seasonality, per category.
- API delivery. Programmatic access for engineering integration, plus a web dashboard for strategy-team analysis.
How it's built
Aggregate query layer over the SubMagician consumer dataset with differential-privacy-class protection where the category and query call for it. API in FastAPI; dashboard in a React UI. Compliance architecture documented per use case (US, EU, UK separately).
What you get
- API access with documented privacy boundary.
- Dashboard for the strategy team.
- Per-category benchmark coverage with documented sample size and confidence.
- Monthly benchmark refresh.
- The privacy architecture documentation for your compliance team.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- API subscription · monthly benchmark refresh
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · Operations Algorithms Suite
Customer Clustering & LTV Engine
An unsupervised clustering layer over the client's own customer-and-transaction data — surfaces behavioral segments using RFM and broader signal sets, attaches a lifetime-value forecast per segment, feeds marketing and retention decisions.
- Same suite · Operations Algorithms Suite
Real-Time Personalization API
An API that returns the next-best-product, next-best-content, or next-best-offer per user — fed by the client's own behavior data, augmented (with consent) by Subscription Economy Benchmarks contextual signal where the use case warrants.
What buyers ask about this one.
Where does the data come from and how is privacy handled?
Data is sourced from SubMagician's consumer subscription tracker — users who have consented to anonymized aggregate use of their subscription patterns as part of the SubMagician terms of service. The benchmarks are aggregate cohort metrics; no individual-level data leaves the consumer side. The use restrictions are documented in the API terms.
What's the data flywheel that makes this valuable over time?
SubMagician's consumer base includes employees of OP's business clients — when those employees use the consumer app, their aggregate patterns become benchmarks the business clients can use. The overlap is structural: more SubMagician adoption means richer benchmarks aligned to the segments enterprise clients actually want to understand.
What categories are covered?
Media (streaming, audio, news), software (consumer SaaS, productivity, security), retail subscriptions (boxes, food, pet, beauty), services (gym, education, dating, transportation). Category breadth scales with consumer-base size; new categories added as the data depth justifies.
How granular are the benchmarks?
Cohort-level, not individual. Typical benchmarks: median retention curve per category, share of consumers with N subscriptions in a category, cross-category subscription affinity, seasonal patterns. Granularity bounded by privacy preservation.
Pricing?
Tiered by query volume and category breadth. Discovery call covers both.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026