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Fashion & Apparel

AI That Learns Your Collections, Seasons, and Customers

Fashion marketing moves in cycles. Your AI should too. Forecasts that learn your drop cadence, attribution that separates new collection hype from core repurchase, and creative intelligence that knows which visuals convert for your audience.

See it with your data Meet the agents
Shopify + Meta + Google + TikTokLearns your seasonal patternsNo engineering required

Fashion Marketing Has a Data Problem

Trend cycles are faster. Return rates are higher. Platform attribution doesn't understand collections, seasons, or the difference between a new customer and a loyalist buying early.

Seasonal budgets are a guessing game

Pre-launch, launch week, markdown, inter-season. Every phase needs different spend levels but you're working from last year's spreadsheet in a market that's shifted.

4-6distinct budget phases per collection cycle

New collection spend cannibalizes core

You push new arrivals hard on Meta but can't tell if those sales would've happened anyway. Platform ROAS blends new-to-brand acquisition with existing customer acceleration.

25-45%of attributed new collection sales are non-incremental

Returns destroy reported ROAS

Fashion return rates hit 20-40%. Your attributed ROAS of 3.5x becomes 2.1x after returns, but you don't see that for 30-60 days. By then, you've already scaled the wrong campaigns.

20-40%avg fashion ecommerce return rate

Creative fatigue hits faster with trend cycles

UGC that worked for the summer drop dies by week 3. Lifestyle imagery for fall converts differently than flat lays. You're testing blind without pattern data across seasons.

2-3 weeksavg creative lifespan in fashion ads
Fashion Intelligence Stack

AI Agents That Understand Fashion

Not generic marketing tools. Agents that learn collections, seasons, and style-driven buying behavior.

Felix

Seasonal Forecasting

Learns your collection cadence, pre-launch velocity, markdown patterns, and inter-season dips. Forecasts that actually understand fashion timing.

Revenue forecasts by collection, season, and category
Pre-launch vs. launch week vs. markdown budget recommendations
Learns YOUR seasonal curves, not generic retail averages
Accounts for weather, trends, and promotional calendars
Compound learning: After one full season, Felix predicts launch week revenue within +-8% and recommends optimal pre-launch ramp spend.

Parker

Return-Adjusted Attribution

True incremental ROAS that accounts for fashion's unique return patterns, size exchanges, and browse-to-buy windows.

Return-adjusted ROAS: sees through 20-40% return rates
Separates new-to-brand from existing customer reactivation
Collection-level attribution across Meta, Google, TikTok
Identifies which campaigns drive full-price vs. markdown sales
Compound learning: Fashion brands typically discover 25-40% of platform-reported conversions are non-incremental once returns and existing customer overlap are factored in.

Olivia

Creative Intelligence

Learns which visual formats, styling approaches, and content types drive incremental sales for your specific audience and collection type.

Lifestyle vs. flat lay vs. UGC vs. model performance analysis
Creative fatigue prediction before engagement drops
Seasonal creative pattern recognition across collections
Format recommendations by audience segment and platform
Compound learning: Olivia identifies that your UGC outperforms studio content by 2.3x for new customer acquisition but studio drives 1.8x higher AOV from existing customers.

Sam

Collection Scenario Planning

Model budget scenarios for launches, seasonal transitions, and markdown periods before committing spend.

Simulate launch week budgets across channel combinations
Model markdown spend vs. holding price with projected outcomes
Test new platform entry (TikTok, Pinterest) risk-free
Compare collection-level scenarios side-by-side
Compound learning: Instead of over-spending on a launch that underperforms, Sam models likely outcomes from your historical collection data and cross-platform performance.

Fashion brands also get Maya (institutional memory), Dana (unified data), and Dex (automated delivery) - meet all 7 agents

Your Fashion Marketing, Transformed

What changes when intelligence learns your collections and customers.

Launch Budget
Before

Copy last season's spend. Over-invest in week 1, scramble to adjust by week 3.

After

Felix models optimal launch ramp from your historical velocity. Budget follows demand curves.

Attribution
Before

Meta says 4x ROAS on the new drop. After returns, it's 2.1x. You scaled the wrong campaigns.

After

Parker shows return-adjusted, incremental ROAS. Scale what actually drives net revenue.

Creative
Before

Rotate 5 assets. Hope something works. Kill winners too late, keep losers too long.

After

Olivia predicts fatigue before it hits and recommends formats by audience and collection type.

Channel Mix
Before

Testing TikTok costs $20K for signal. No idea if it's incremental or cannibalizing Meta.

After

Sam models TikTok entry scenarios with your real data. Decide before spending.

Markdown
Before

Slash prices, blast ads. No idea if markdown spend is profitable or just accelerating inevitable sales.

After

Sam + Parker show true markdown incrementality. Spend only where it creates new demand.

Frequently Asked Questions

Felix learns your brand's seasonal cycles, collection drops, and sale windows. After one full season, forecasts account for your specific patterns rather than generic industry averages. Budget allocation recommendations adjust automatically for pre-launch, launch week, markdown, and inter-season periods.

Yes. Parker attributes revenue at the collection and SKU level, showing which campaigns drive new collection adoption vs. core repurchase. This helps identify whether ad spend is acquiring new-to-brand customers or accelerating purchases that would have happened organically.

Olivia analyzes creative performance across lifestyle imagery, flat lays, UGC, and model content. She learns which visual formats drive the highest incremental ROAS for your specific audience, with insights segmented by collection type and season.

Yes. Parker adjusts attributed ROAS for actual return rates, showing net revenue contribution rather than gross. This is critical for fashion where 20-40% return rates can make a 3.5x ROAS campaign actually perform at 2.1x.

Connect Shopify, Meta, Google, and TikTok in under 5 minutes via OAuth. First insights within 24 hours. After one full collection cycle, forecasts and attribution reach peak accuracy for your specific seasonal patterns.

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See It Learn Your Collections

30-min demo with your Shopify and ad data. Live.

Book a Demo See Pricing
Connects in 5 min
First insights in 24 hrs
Return-adjusted attribution