A habit this regular
tells you when it breaks.
No other kind of retail sees its customer this often, so a change in behaviour shows up in days rather than quarters. 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 richest record in retail, and the thinnest identity.
Four things about grocery data that make a generic analytics answer the wrong answer.
Frequency
Weekly, often more. The upside is the richest behavioural record in retail. The cost is volume: every question is asked against millions of small transactions rather than a few large ones.
Margin
Among the thinnest anywhere. Promotional depth and supplier funding decide the quarter, so the real question is rarely whether a promotion sold units, but whether it sold units that were not coming anyway.
Seasonality
A weekly rhythm sitting under the annual one: the big shop, the top-up, the weekend. Fresh and ambient move on different clocks, and an average across both describes neither.
What the data misses
Baskets are rich and identity is thin. Unscanned trips, one card shared across a household and cash at the kiosk all mean the person behind the basket is often inferred rather than known.
The questions
Asked in grocery and supermarket.
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 of last week's promotions grew units, and which pulled forward sales we were going to get anyway?
Ad hoc analytics
Run a basket analysis on shoppers who bought the front-page line: what else moved with it, and at what margin?
Advanced analytics
Which households have missed their normal weekly shop three weeks running?
Predictive models
Find the households that switched to own label in one category but stayed with brands everywhere else.
Audience builder
Set up a win-back for the three-week absentees and hold it for my sign-off.
Journey orchestration
What it runs here
Fifteen things aiRA runs on grocery 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 baskets or visits moved, not just the fact that they did.
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Category Affinity
Which aisles a shopper has never bought from, and which ones usually travel together.
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Churn Propensity
Flags the members whose weekly rhythm is slowing before the gap shows in a report.
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Data Quality Review
Cleans the messy data first, so you are not acting on a broken join.
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Fraud Detection
Spots points and coupon abuse early, by store.
02
Decide
Pick the offer and the audience.
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Campaign Strategy
Picks the audience and the offer most likely to work, with the reasoning shown.
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Basket-builder Promotion
Spend-more and buy-more mechanics aimed at basket size rather than footfall.
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Category Cross-sell
Bonus points built to move a shopper into an aisle they have never used.
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Private-label Push
Targeted offers to grow own label without discounting the brands that fund you.
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Redemption Forecast
Estimates uptake and cost before you launch, not after.
03
Act
Launch it. You approve before it goes live.
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Promotions
The offer built and waiting for your sign-off.
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Personalised Coupons
Codes on the items each shopper actually buys most.
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Win-back Journey
A flow that triggers when the weekly shop stops.
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Audiences
The exact segment, built conversationally and saved.
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SmartJourneys
The lifecycle view, with the next step named for each stage.
The working session
Bring the question your grocery team is still waiting on.
Forty-five minutes, one real question, your business context, down to what counts as an active household. You leave with the answer and the move built, or you leave knowing aiRA is not the fit. Both are useful.