New collection spend cannibalizes core. Attribution that knows the difference.
Forecasts that learn drop cadence. Return-adjusted ROAS. Creative intelligence that maps which visuals convert by collection type and season.
Each phase wants a different spend shape. Felix models the calendar from your historical drop data; Sam tests the choreography before you commit budget. Same total budget, different total revenue captured. Exact ramp depends on your category and drop cadence. (architecture target)
Four reasons fashion math doesn't survive collection cycles.
Drops, markdowns, returns, and seasonal swings all break standard attribution. These four failure modes compound the further the brand scales.
Seasonal budgets are guesswork
Pre-launch, launch week, markdown, inter-season. Each phase needs different spend; the team works from last year's spreadsheet against this year's market.
New collection spend cannibalizes core
Push new arrivals on Meta; the dashboard can't tell whether the lift was incremental or stole from existing customer purchases.
Returns destroy reported ROAS
Apparel return rates run 20 to 40 percent. Reported 3.5x ROAS becomes 2.1x net; the feedback loop takes 30 to 60 days.
Creative fatigue hits faster with trend cycles
UGC for the summer drop dies by week three. Without pattern data across seasons, every test starts at zero.
Forecast the drop. Reconcile the returns.
Felix forecasts collection demand and ramp. Olivia reads creative by collection type. Sam tests new-arrival cannibalization. Dana unifies returns into the margin layer.
Felix
Forecasting
Forecasts launch ramp velocity, post-drop decay, and markdown demand by collection type. Architecture target: ramp budgets follow projected curves, not arbitrary calendars.
Drop-aware demand forecasting.
Olivia
Creative Intelligence
Reads creative performance by collection type, audience, and platform. Identifies whether studio versus UGC versus model content drives revenue for which moment. Architecture target: format selection that earns its placement.
Format intelligence by collection.
Sam
Scenario Testing
Models new-arrival cannibalization scenarios before money commits. Quantifies whether the drop is acquiring new-to-brand customers or accelerating existing-customer purchases. Architecture target: launch budgets that respect cannibalization.
New vs core, modeled.
Dana
Unified Data
Builds the unified margin layer with returns priced in. Reconciles Shopify orders, returns, and ad spend per collection. Architecture target: net-margin reporting from day one, not 60 days later.
Returns priced into the margin layer.
The other three agents fill out the workforce. See all seven →.
Concrete deltas. Architecture targets for fashion.
Four metrics targeted by the 14-day pilot structure. Exact numbers depend on collection cadence, return rates, and channel mix.
Architecture target: Felix models optimal launch ramp from the brand's historical velocity.
Architecture target: Parker (via Dana's layer) reports return-adjusted ROAS from day one.
Architecture target: Sam quantifies the share of new-arrival lift that's incremental vs accelerated existing-customer demand.
Architecture target: Olivia and Dex catch fatigue inside 24 hours, before the next drop's budget compounds the loss.
Questions fashion teams ask
How does Cresva handle seasonal fashion marketing?
Felix learns the brand's drop cadence, sale windows, and inter-season dynamics. Architecture target: budget recommendations adjust automatically for pre-launch, launch week, markdown, and quiet periods.
Can Cresva track attribution across collections?
Parker attributes revenue at the collection and SKU level, separating new collection adoption from core repurchase. Architecture target: see whether ad spend is acquiring new-to-brand or accelerating existing demand.
How does Cresva help with fashion creative testing?
Olivia analyzes performance across lifestyle imagery, flat lays, UGC, and model content, segmented by collection type and season. Architecture target: predict which formats convert for which moment.
Does Cresva account for return rates in attribution?
Yes. Parker reports return-adjusted ROAS. Architecture target: surface the 20-to-40 percent return rate's impact on net contribution from day one.
How fast can a fashion brand get started?
Five minutes via OAuth: Shopify, Meta, Google, TikTok. First insights inside 48 hours. Forecasts sharpen across one full collection cycle.
Fashion view not the right fit?
Ready when you are
See it work on a single drop.
Pilot connects Shopify and ad platforms in five minutes. First collection-level view inside 48 hours.
Looking for a deep dive? See Felix forecasts →, Olivia reads creative → or Sam tests scenarios →.