Grocery customer from RELEX, briefed to loss prevention
Understand the person behind the basket. Ward runs it on RELEX data across your grocery footprint, scoped to loss prevention.
Customer Behavior for Grocery on RELEX, scoped to loss prevention
Shrinkage costs you more than you think. Ward finds out where. Ward returns findings scoped to loss prevention decision-making.
Here is customer behavior in plain terms. Ward tracks basket composition shifts, daypart patterns, and customer segment migration.
A Grocery & Supermarket operator is tracking 30,000+ SKUs across stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
How it runs. Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
How the connection works. Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.
Key capabilities
- Daypart behavior modeling
- Customer segment migration
- Cross-sell opportunity detection
- Basket composition trends
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…
Reporting
Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.
| Model | Horizon | MAPE |
|---|---|---|
holt_winters | 4wk | 4.1% |
arima_sarimax | 13wk | 8.9% |
gbm_demand | 1wk | 2.1% |
bayes_hier | new store | 11.4% |
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 |
relex_replenishment_plan | import | 1h ago |
blue_yonder_forecast_daily | import | 1h ago |
retail_fresh_waste_daily | import | 1h ago |
retail_promo_calendar | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
lp-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
fresh-team-west | permit | Model::"fresh_waste_daily" |
Why Customer matters for Grocery retail
Grocery shopper behavior is deeply habitual, which makes deviations valuable signals. Ward tracks basket composition, visit frequency, daypart migration, and category penetration at the cohort level, detecting when an entire segment starts behaving differently, usually signaling a competitive threat or economic shift.
What Ward has eyes on.
Every one of your stores gets its own baseline. Ward tracks fill rate, shrinkage %, fresh waste % against it and raises only the deviations that hold up. The two that show up most in grocery retail are fresh waste & spoilage and on-shelf availability gaps, and both are baseline problems before they are P&L problems.
The connection to RELEX Solutions is read-only and runs on your schedule. Ward reads straight from demand forecasts, space plans, and the rest of the feed, then stitches together it with external context your Supply Chain does not carry: weather, local events, competitor pricing.
The customer model runs every morning, not on a reporting calendar. It picks up the pattern, traces the cause, and attaches a recommended action before the number reaches a review deck.
At the metric level. Ward tracks basket composition indices, visit cadence changes, daypart migration, category penetration trends, and price-tier shifting. Each metric is benchmarked against seasonal norms to separate signal from noise.
Why this combination
is its own problem.
Customer Behavior behaves differently in grocery retail than it does anywhere else. The estate shape, the SKU count, and the speed of the category all change what counts as a real data point and what is noise. Ward is tuned to the grocery version.
- 01 Cohort segmentation by demographics misses the actual shopping mission; the same household has 4-6 distinct missions per month.
- 02 Trade-down to private label gets read as price sensitivity when it's often a quality reassessment that won't reverse with promotions.
Benchmarks. Grocery shopping cadence averages 1.5-2.5 visits per week per household, with primary stores getting 60-70% of category spend. A 10% basket-size compression sustained over 8 weeks usually maps to a measurable share-of-wallet loss to a specific competitor.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of RELEX Solutions with read-only credentials and begins ingesting demand forecasts and space plans. No config changes on your side. First findings arrive inside the first 48 hours.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: operating rhythm
Daily cards arrive on a daily cycle and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.
How Ward connects to RELEX Solutions
Ward integrates with RELEX for unified retail planning. Ward monitors RELEX forecasts against actuals and surfaces planning exceptions as insight cards.
Setup: Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.
Data Ward reads from RELEX
Impact metrics with RELEX
Data lake enrichment
Ward enriches RELEX data with: RELEX forecasts, POS actuals, Weather & events, Demographic data, Supplier performance
Shrinkage costs you more than you think. Ward finds out where.
- ×Shrinkage lands as a year-end surprise
- ×Cannot distinguish theft from spoilage from admin error
- ×High-shrinkage stores only identified during audits
- ×No correlation between operational changes and loss patterns
- ×Exception-based reporting misses slow-bleed patterns
- ✓Store-level shrinkage tracking with cause attribution
- ✓Anomaly detection flags stores deviating from estate average
- ✓Receiving dock discrepancy patterns identified automatically
- ✓Correlation analysis links operational changes to loss shifts
- ✓Trend analysis catches slow-bleed patterns audits miss
US retail shrinkage hit $112.1 billion in 2022. Up 19.4% year over year. Source: National Retail Federation
Grocery KPI impact
Frequently asked questions
Ward tracks basket composition shifts, daypart patterns, and customer segment migration. For Grocery retail specifically, Ward monitors 30,000+ SKUs across your stores and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Fill rate, Shrinkage %, Fresh waste %, Promo lift, Basket size at the store-category level. Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound. Data points include: Demand forecasts, Space plans, Workforce forecasts, Promotion plans, Replenishment orders.
Yes. Ward reads RELEX data and combines it with contextual signals (weather, events, demographics) to generate Grocery-specific insight cards. No custom development required.
Shrinkage costs you more than you think. Ward finds out where. Ward solves this with automated insight cards: Store-level shrinkage tracking with cause attribution. Anomaly detection flags stores deviating from estate average. Receiving dock discrepancy patterns identified automatically.
Ward delivers daily insight cards covering Fill rate, Shrinkage %, Fresh waste %, tailored for Loss Prevention decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks basket composition indices, visit cadence changes, daypart migration, category penetration trends, and price-tier shifting. Each metric is benchmarked against seasonal norms to separate signal from noise.
Ward detects rising ready-to-eat meal purchases during the evening daypart across urban stores while raw protein and produce decline in the same window. The shift correlates with a new meal-kit competitor entering the market. Ward recommends expanding prepared foods in affected stores and testing a quick-meal bundle priced to undercut the delivery service.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish stable baselines for your specific operation.
No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards, so your team acts on findings instead of hunting for them.
Related solutions
See what Grocery customer problems Ward catches.
Root causes, not just alerts. See it on your data.
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