Full price has
an expiry date.
Every week a style sits is margin coming off, and returns take a second bite after the sale is already booked. aiRA reads the warehouse you already have, answers in plain language with the reasoning shown, and hands back the segment or the journey already built.
The shape of it
The clock starts the day a line lands.
Four things about apparel data that make a generic analytics answer the wrong answer.
Frequency
A few times a year, clustered around drops and sale. A customer can be loyal and still be invisible for five months, which makes a standard lapse window fire on people who never left.
Margin
Decided the day a line stops selling at full price. Markdown is the lever everyone reaches for, and returns take a second bite after the sale has already been counted.
Seasonality
Collections set the calendar. It repeats but the assortment never does, so last year's comparison is against product that no longer exists.
What the data misses
Fit and intent. A return meaning the item was wrong and a return from someone who ordered three sizes to keep one look identical in the data, and bracketing inflates apparent demand.
The questions
Asked in fashion and apparel.
Five real shapes of ask in this category's vocabulary. Each comes back answered, with the reasoning shown and the move built, waiting on approval.
Which styles are tracking behind their sell-through curve with four weeks of the season left?
Ad hoc analytics
Run a basket analysis on the launch buyers: what attached, and did it hold margin?
Advanced analytics
Which customers are genuinely lapsing rather than just between seasons?
Predictive models
Build the reactivation audience for members who lapsed after a single full-price order.
Audience builder
Create a win-back for that group that does not open with a discount.
Journey orchestration
What it runs here
Fifteen things aiRA runs on apparel data.
In the order you would actually use them, with what each one gives back.
01
Analyse
See what is happening, and why.
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RCA Deep Dive
The real reasons full-price sell-through or repeat visits shifted.
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Style & Size Affinity
The categories and the fits each shopper keeps coming back to.
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Churn Propensity
Flags the shoppers drifting toward inactive, read against your season, not a rolling window.
-
Data Quality Review
Cleans profiles and purchase history before anyone segments on them.
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Reconciliation
Keeps reward cost balanced across systems while returns are still settling.
02
Decide
Pick the offer and the audience.
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Campaign Strategy
Builds the audience and the offer out of what past launches actually did.
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Visit Milestones
Third and fifth visit rewards, sized on your own repeat curve.
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Seasonal Drop Plan
The new collection aimed at the shoppers most likely to buy it at full price.
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Full-price Protection
Reward-led demand in place of a blanket markdown.
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Tier Early-access
Who gets first look at a drop, and what that early look is worth.
03
Act
Launch it. You approve before it goes live.
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Milestones
Visit rewards built and ready to approve.
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Promotions
Drop and VIP-access offers, built for you.
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Audiences
Segments by style and by fit, saved and reusable.
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Post-purchase Journeys
Cross-sell and replenishment flows that start after the parcel lands.
-
Creatives
On-brand campaign banners generated alongside the plan.
Proof
Head to head with a dedicated analytics agency.
aiRA beat a specialist analytics agency on eight of nine KPIs across a five-week Ramadan campaign.
- SAR 233MNet sales, up 4% year on year
- +29.4%New customers
- +11.6%Revenue per delivered contact
- 2.2 ptsRegional growth gap, closed from 9.6 points behind
- 6.7xLess erosion in average transaction value
Value-fashion retailer, Saudi Arabia. 14.7 million customer base, weeks 34 to 38.
The working session
Bring the question your fashion team is still waiting on.
Forty-five minutes, one real question, your business context, down to what counts as a full-price customer. You leave with the answer and the move built, or you leave knowing aiRA is not the fit. Both are useful.