A grocery VP Merchandising running customer on RELEX
Your category managers are drowning in spreadsheets. Ward picks up grocery customer movement in your RELEX data early, with root cause and a recommended move attached.
How a grocery VP Merchandising runs customer on RELEX Solutions
Customer Behavior. Ward tracks basket composition shifts, daypart patterns, and customer segment migration.
A Grocery & Supermarket operator is watching 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.
Your category managers are drowning in spreadsheets. Ward writes the finding at the altitude a VP Merchandising works at.
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.
What you get
- 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.
Coverage is store by store, category by category. Ward watches fill rate, shrinkage %, fresh waste % over 30,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the store base is fine and knowing which seven stores are not.
Ward reads RELEX rather than replacing it. Demand forecasts, space plans, workforce forecasts come across on a read-only connection, get enriched with contextual data, and come back as cards. Your Supply Chain is untouched.
The customer model runs on a daily cycle, not on a reporting calendar. It catches the pattern, accounts for the cause, and attaches what to do about it 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.
A VP Merchandising does not need the customer model explained. They need to know which stores moved, why, and what to do by end of day. Ward writes the finding at that altitude.
- 01 Trade-down to private label gets read as price sensitivity when it's often a quality reassessment that won't reverse with promotions.
- 02 Cohort segmentation by demographics misses the actual shopping mission; the same household has 4-6 distinct missions per month.
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: connect
Read-only credentials to RELEX Solutions. Ward pulls from demand forecasts, space plans, workforce forecasts and starts building baselines. First insight cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
02
Weeks 2 to 3: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward delivers daily cards on a daily cycle, each with what caused it and what to do about it. Volume settles at a level a single person can read over coffee. The measure of success is not how many cards arrive, it is how many get acted on.
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
Your category managers are drowning in spreadsheets.
- ×Promo planning still runs off last year's playbook
- ×Assortment reviews happen quarterly when they should happen daily
- ×Price changes chase the market a week behind it
- ×No visibility into true cannibalization across categories
- ×Vendor negotiations lack real-time sell-through evidence
- ✓Insight cards flag promo cannibalization the day it happens
- ✓Assortment gaps and whitespace opportunities surface automatically
- ✓Price elasticity shifts detected before margin erosion compounds
- ✓Category-level performance cards replace manual spreadsheet reviews
- ✓Vendor scorecards generated from actual fill rate and quality data
Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. Source: McKinsey & Company
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.
Your category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.
Ward delivers daily insight cards covering Fill rate, Shrinkage %, Fresh waste %, tailored for Merchandising 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
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