Retail Customer Behavior Analytics
Understand the person behind the basket. Most retailers find it in a post-mortem. Ward surfaces it while there is still time to act.
Ward tracks basket composition shifts, daypart patterns, and customer segment migration.
How Ward catches what your reports miss
Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO crackers cannibalized Brand Y by 28%, net category +6% |
Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.
labor_scheduling…
Dashboards
Pinned views built from saved data-lake queries.
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
What changes for your team
Sample insight card
Evening shoppers (6-9 PM) adding 22% more ready-to-eat items vs last quarter. Deli adjacency planogram opportunity identified.
Where this lands hardest
The roles that feel customer first, and what changes for them.
How Ward delivers this
The platform pieces doing the work behind the insight.
Available for every vertical
Customer by integration
Customer by role
Metrics this moves
The KPIs Ward improves when this insight runs.
See it. Or compare it.
Operator stories and the alternatives buyers benchmark against.
Questions about customer.
Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement. Each card explains what changed, the root cause, and the recommended action, at the store-category level, not estate aggregates.
Ward delivers customer insight cards within 48 hours of data connection, with baselines tightening over the first two weeks. Most issues are surfaced before they show up in weekly or quarterly reports.
Ward runs classical statistical and time-series models your planners can audit, backtested against your last 24 months. The model card, MAPE, and confidence interval ride on every answer. The LLM frames the result; the number comes from the math.
Two ways to start.
Run a fixed-fee pilot on your data, or talk to advisory about a broader engagement.
Stop finding Customer problems in the post-mortem.
See what Ward catches, and when.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.