What marketing roles does Cresva support?

Cresva supports growth teams, marketing executives (CMOs, VPs, directors), agencies, and brand teams with AI-powered intelligence that learns from each team's specific decisions and KPIs.

What types of ecommerce businesses does Cresva work with?

Cresva works with DTC brands, multi-channel retailers, subscription brands, high-SKU catalog businesses, and brands selling on both marketplaces and their own store. The AI learns business-model-specific patterns like seasonal trends, churn curves, SKU profitability, and channel dynamics.

How accurate are Cresva's forecasts?

Cresva's forecasts learn from each brand's specific seasonality, channel mix, and decision history. Architecture target: forecasts sharpen across the pilot's onboarding period and continue to improve with each cycle. Exact accuracy depends on data history and category.

Can agencies use Cresva for multiple clients?

Yes, agencies can manage 10+ clients in one dashboard. Each client gets smarter individually, and the system learns patterns across your entire portfolio for cross-client insights.

How does Cresva help subscription brands optimize LTV?

AI agents learn your subscriber cohort patterns, churn timing, and acquisition channel quality. Felix forecasts LTV per acquisition channel. Parker shows which channels drive high-LTV subscribers vs. quick churners.

Can Cresva predict subscriber churn?

Yes. By learning your cohort patterns, Cresva identifies churn risk signals weeks before cancellation. Combined with channel-level LTV analysis to optimize acquisition toward retention.

How does Cresva's AI improve subscription analytics over time?

Every subscription cycle adds signal. Churn prediction accuracy and LTV forecasts compound as the system learns your specific subscriber behavior patterns.

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Subscriptions

First-order ROAS hides retention. Acquire the subscribers who stick.

LTV-weighted attribution. Per-channel, per-cohort retention curves. Churn signals caught before they show up in the monthly review.

Book a pilot How agents work →

Promo-driven cohort

Organic cohort

Felix models per-cohort decay curves so acquisition can weight the subscribers who stick, not the ones who signed up. Architecture target; exact curves depend on category and replenishment cycle.

Where dashboards break for subscriptions

Four reasons retention stays a black box.

Subscription economics depend on month four through twelve, but ad platforms only see month one. The gap is where retention quietly destroys LTV projections.

High CAC, low first-order value

Acquisition cost runs hot against month-one revenue. Without LTV-weighted ROAS, the team kills the campaigns acquiring the best long-term subscribers.

Churn eats LTV projections

Linear LTV models miss the cliffs at month three and month six. Real cohort decay is nonlinear and category-specific.

Trial vs loyal looks identical at acquisition

Promo-driven and organic subscribers are indistinguishable in week one. By the time the difference shows up, the team has already optimized for the wrong audience.

Channel-level LTV is invisible

Overall LTV is known. Per-channel LTV is not. Meta subscribers might retain twice as well as TikTok, but allocation treats every channel the same.

The four agents

Predict LTV. Catch churn early. Acquire what stays.

Felix models per-channel decay curves. Maya holds the cohort memory. Parker reconciles LTV-weighted ROAS. Dana unifies the subscription data layer.

Felix

Forecasting

Models per-channel, per-cohort decay curves. Predicts LTV inside the architecture target. Architecture target: LTV predictions tighten as the system reads two replenishment cycles.

Per-channel LTV predictions, modeled.

Maya

Memory & Context

Holds the cohort memory across acquisition channels. Recalls which campaign signatures drove which retention curves last quarter. Architecture target: institutional memory of cohort behavior across the brand's history.

Cohort memory that compounds.

Parker

Attribution

Reconciles acquisition spend against subscription LTV, not first-order revenue. Surfaces the campaigns acquiring high-retention subscribers. Architecture target: LTV-weighted ROAS by channel and campaign.

LTV-weighted ROAS, debiased.

Dana

Unified Data

Unifies subscription, acquisition, and platform data into one layer. Reconciles Recharge or Skio with Shopify and ad platforms. Architecture target: one source of truth across the subscription stack.

Subscription stack, unified.

The other three agents fill out the workforce. See all seven →.

Targeted by the 14-day pilot

Concrete deltas. Architecture targets for subscriptions.

Four metrics targeted by the 14-day pilot structure. Exact numbers depend on category, replenishment cycle, and current retention.

LTV prediction accuracy

Linear assumptions→Per-channel curves

Architecture target across the 9-month pilot structure. Felix models decay curves per channel and cohort.

Acquisition quality

First-order ROAS→LTV-weighted

Architecture target: Parker shows which campaigns acquire subscribers who stay, not just buyers who convert.

Churn signal detection

Monthly review→Same-day alert

Architecture target: catch retention drops in new cohorts inside 24 hours, not in the monthly business review.

Channel allocation

Volume-driven→Retention-weighted

Architecture target: Sam reallocates budget toward channels that produce subscribers who stay across the curve.

Common questions

Questions subscription brands ask

How does Cresva help subscription brands reduce churn?

Felix learns per-cohort decay curves and identifies which channels acquire subscribers who retain. Parker shows LTV-weighted contribution by channel, so budget can shift toward sources that produce lasting customers.

Can it predict subscriber LTV by channel?

Yes. Felix models per-channel, per-cohort LTV using your retention data. Architecture target: LTV predictions tighten as the system reads two replenishment cycles.

How does it tell promo subscribers from organic?

Parker tracks subscriber quality by acquisition source, distinguishing promo-driven from organic. Architecture target: surface the retention gap between segments early enough to reallocate.

Does it work with Recharge or Skio?

Cresva integrates with Shopify and the subscription ecosystem above it. Combined with Meta, Google, TikTok ad data, the workforce builds one view from acquisition cost through retention curve.

How fast can we start?

Five minutes via OAuth. First cohort insights inside 48 hours. Retention-adjusted LTV predictions sharpen across the first two replenishment cycles.

Ready when you are

See LTV by channel on your cohorts.

Pilot connects subscription, acquisition, and platform data in five minutes. First cohort view inside 48 hours.