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The platform
under the conversation

Caisy is the entry point to more than a decade of retail analytics — the depth is here for the reader who wants it.

Advanced Analytics Platform

The foundation everything else sits on: your data harmonised into one model with governed metrics, and every user — internal or external — seeing only what they are permitted to see. How that works →

Report Builder

Build your own view of the business, without waiting for anyone — or ask Caisy to build it for you.
rows columns measures category brand item
drag any dimension against any measure
Free-flow pivot: drag any dimension against any measure, and restructure the view as the question changes.
100% granular selection across store, product, customer and time, down to day of week and hour of day.
Apply any saved attribute or segment, and flexible comparison periods including custom prior periods.
Save, share and export — and every report is also a tool Caisy can run for you in conversation.

Segment Builder

Build a segment, act on it, and measure it — end to end. Caisy can create, activate and evaluate a segment in one conversation.
behaviour demographics activate measure response uplift
build · activate · measure, end to end
Combine any behavioural, demographic or transactional attribute, with dynamic ranges and nesting.
Activate directly with third-party media partners for targeted campaigns.
Close the loop: measure whether the targeted shoppers actually responded.
Save and reuse every segment across the insight suites, or hand it to Caisy.

Customer intelligence

Who shops, what they buy, and why behaviour shifts — in production for years, not a roadmap.
prior new lost trade-up now
where growth actually came from
Source of volume
where sales came from: new buyers, switching, or trade-up.
Switching analysis
who moved between brands, and who you really compete with.
Cross-purchase
what sells with what, strongly enough to plan range around.
Affinity
which products share a basket mission, and where adjacency pays.
Customer profiler
which buyer segments over-index, by loyalty or price sensitivity.
Customer decision tree
how shoppers choose, and what they refuse to compromise on.
Needs unit analysis
the category’s real demand structure, not your trading hierarchy.
New product launch
trial, repeat and incrementality: grown, or just moved?

Business intelligence

Sales, margin, inventory and performance against your own budgets and targets.
sales customers spend penetration frequency basket spend/visit
Sales tree
decompose any KPI into penetration, frequency, basket and spend.
KPI tracker
the numbers that matter, tracked continuously against comparison periods.
Promotion analyser
what a mechanic really delivered, net of cannibalisation.
Assortment prioritiser
what to range and what to cut, store by store.
Inventory
stock on hand, days on hand, out-of-stocks and availability by store.
Performance to budget
sales, margin and inventory against plan and target.

Third-party data

Your own data read in context — brought onto the same model and the same governed metrics.
you market
your performance, in market context
market · competitor · weather · attributes
Market share
your performance against the market, not just against yourself.
Competitor signals
pricing and activity, where you have the feed.
Weather
the demand driver that explains half the anomalies.
Personalised attribution
down to product and store.
Built since 2014. AI made it conversational —
the foundations make it correct.
Architecture & security