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Beauty & Skincare

AI That Learns Ingredients, Routines, and Skin Concerns

Beauty customers don't buy products. They buy routines, results, and ingredient stories. Your marketing intelligence should understand the difference between a shade-match return and a dissatisfied customer, between a sample convert and a one-time buyer.

See it with your data Meet the agents
Routine-aware attributionIngredient-led creative intelSample-to-full tracking

Beauty Marketing Runs on Broken Signals

Longer journeys. Higher return complexity. Ingredient-driven decisions. Standard marketing tools weren't built for how beauty customers actually buy.

Long consideration windows break attribution

Skincare customers research for 14-30 days before buying. Standard 7-day click attribution misses 40-60% of the journey, making awareness campaigns look like they don't work.

14-30 daysavg skincare consideration window

Sample economics are invisible

You spend $18 to acquire a $12 sample customer. If 35% convert to full-size at $48, your true CAC is $51. But you don't see that for 60-90 days. By then, you've scaled or killed the wrong campaigns.

60-90 dayssample-to-full conversion visibility lag

Ingredient stories don't have consistent ROI

Retinol content drives clicks. Peptide stories drive purchases. Hyaluronic acid generates shares but low conversion. Without creative-level attribution by ingredient angle, you're optimizing for vanity metrics.

3-5xvariance in ROAS by ingredient messaging angle

Shade-match returns inflate ROAS

Foundation and concealer campaigns show strong ROAS until 15-25% of orders come back as shade exchanges. Your 3.8x ROAS is really 2.9x, but the feedback loop takes 30+ days.

15-25%shade-mismatch return rate for color cosmetics
Beauty Intelligence Stack

AI Agents That Understand Beauty

Not generic analytics. Intelligence that learns routines, ingredient stories, and the economics of sample conversion.

Parker

Routine-Aware Attribution

True incremental ROAS that understands beauty's extended consideration windows, shade-match returns, and sample-to-full conversion paths.

Extended attribution windows (14-30 days) for skincare journeys
Return-adjusted ROAS that strips shade exchanges and dissatisfaction
Sample-to-full-size conversion tracking with true CAC
Separates new-to-brand from routine restocking behavior
Compound learning: Beauty brands typically discover their true CAC is 30-50% higher than platform-reported once returns and sample economics are factored in.

Olivia

Ingredient-Led Creative Intelligence

Learns which ingredient stories, content formats, and visual approaches drive incremental revenue for your specific audience and product categories.

Ingredient-angle performance analysis (retinol vs. peptide vs. HA)
Before/after vs. texture shot vs. routine content comparison
UGC vs. clinical vs. lifestyle format ROAS by audience segment
Creative fatigue prediction for beauty-specific content cycles
Compound learning: Olivia finds that clinical ingredient content drives 2.4x higher AOV than lifestyle UGC for serums, but UGC drives 1.9x better new customer acquisition for cleansers.

Felix

Routine-Cycle Forecasting

Revenue forecasts that learn replenishment cycles, seasonal skincare shifts, and launch cadence patterns unique to beauty brands.

Replenishment-cycle revenue prediction by product category
Launch window forecasting based on your brand's velocity patterns
Seasonal skincare shift modeling (SPF summer, retinol winter)
Routine-expansion revenue projections for cross-sell campaigns
Compound learning: After tracking two replenishment cycles, Felix predicts repurchase revenue within +-7% and identifies optimal reactivation timing.

Sam

Launch & SKU Scenario Planning

Model new product launches, shade range expansions, and sample program economics before committing spend.

Simulate launch budgets across channel combinations
Model sample program ROI with projected conversion rates
Test shade expansion impact on return rates and net ROAS
Compare hero SKU vs. bundle vs. routine set marketing scenarios
Compound learning: Instead of spending $25K to test a sample-to-full funnel on Meta, Sam models likely conversion economics from your historical product-level data.

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

Your Beauty Marketing, Transformed

What changes when intelligence learns your routines and customers.

Attribution
Before

7-day click window misses 40-60% of skincare journeys. Awareness campaigns look like waste.

After

Parker uses extended windows that match actual skincare buying behavior. Credit goes where it's earned.

Sample Programs
Before

Spend $18 to acquire a $12 sample customer. No idea if they'll convert for 90 days.

After

Parker tracks sample-to-full conversion. Sam models program economics before you scale.

Creative Strategy
Before

Test 5 angles. Hope retinol content works. Kill clinical content that actually drives higher AOV.

After

Olivia shows which ingredient stories drive revenue by category. Clinical content vs. UGC, mapped to outcomes.

Launch Spend
Before

Over-spend on launch week. Under-spend on restocking. No model for replenishment timing.

After

Felix predicts replenishment revenue and optimal reactivation windows. Launch budgets follow real velocity curves.

Returns
Before

Report 3.8x ROAS to the team. Discover net ROAS is 2.9x after shade-match returns arrive 30 days later.

After

Parker shows return-adjusted ROAS from day one. Scale only campaigns with positive net contribution.

Frequently Asked Questions

Cresva's AI agents learn ingredient-led purchasing behavior, routine building patterns, and the long consideration windows unique to skincare. Felix forecasts around launch cycles and seasonal skincare shifts. Olivia analyzes which ingredient stories and before/after content drive the highest incremental ROAS.

Yes. Parker attributes the full customer journey from sample or discovery set through to full-size repurchase, showing the true LTV of acquisition campaigns rather than just initial conversion value.

Yes. Skincare purchases often have 14-30 day consideration windows. Parker's attribution model accounts for these extended journeys rather than forcing short click-based windows that under-credit awareness campaigns.

Olivia analyzes creative performance by ingredient angle, showing that retinol content might drive clicks while peptide stories drive purchases. She learns which formats (clinical, UGC, before/after) convert best for each product category.

Connect Shopify, Meta, Google, and TikTok in under 5 minutes. First insights within 24 hours. After two replenishment cycles, forecasts and attribution reach peak accuracy for your specific product cadence.

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See It Learn Your Routines

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

Book a Demo See Pricing
Connects in 5 min
First insights in 24 hrs
Ingredient-led intelligence