Ward runs promos on your Blue Yonder grocery data
Know which promos actually work. Ward runs it on Blue Yonder data over your grocery fleet, without a warehouse project first.
Promo Effectiveness for Grocery & Supermarket, running on Blue Yonder
Promo Effectiveness, in one sentence. Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.
Applied to Grocery & Supermarket, the surface area is 30,000+ SKUs over stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
The mechanism. Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
The connection itself. Ward reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy.
Capabilities
- Pull-forward detection
- Promo ROI scorecards
- Net lift measurement (not gross)
- Cannibalization quantification
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 Promos matters for Grocery retail
Most grocery chains measure promotions by gross lift, ignoring cannibalization, pantry loading, and margin erosion that destroy actual ROI. Ward isolates each effect to calculate true net promotional lift, giving category managers evidence to kill underperformers and concentrate spend where it generates real incrementality.
Why this combination
is its own problem.
A generic promos model applied to a grocery store base produces alerts nobody trusts. The thresholds are wrong, because grocery baselines are wrong for it. Ward learns the baseline from your own stores instead of importing one.
- 01 Halo effects are credited to the wrong promo because chains run 3-5 overlapping events at any time and don't isolate which one drove what.
- 02 BOGO and 2-for events drive pantry loading that shifts demand from the next 2-3 weeks, so the post-promo dip is misread as competitive pressure.
Benchmarks. Grocery promos typically show 30-80% gross lift but only 10-25% net incremental lift after cannibalization and pull-forward. The vast majority of promos are net-margin-positive only after vendor funding; remove the funding and roughly half lose money.
What Ward has eyes on.
The promos model runs on a daily cycle, not on a reporting calendar. It detects the pattern, accounts for the driver, and attaches a recommended action before the number reaches a review deck.
Ward watches 30,000+ SKUs over your stores, at the store-category level rather than the chain roll-up. The metrics under watch include fill rate, shrinkage %, fresh waste %. A roll-up hides a single-store problem inside a healthy average, which is how fresh waste & spoilage stays invisible for a quarter.
Ward reads demand forecasts, replenishment recommendations, allocation plans from Blue Yonder on a read-only connection. Nothing is written back, and your Supply Chain configuration does not change. Blue Yonder stays the system of record.
At the metric level. Ward decomposes promo results into gross lift, cannibalization rate, pantry loading, halo effects, and true incremental margin contribution. It also tracks promo fatigue, when repeated discounts permanently shift baseline demand downward.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of Blue Yonder with read-only credentials and begins ingesting demand forecasts and replenishment recommendations. No config changes on your side. First findings arrive in two days.
-
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: steady state
Ward hands you daily cards every morning, each with the cause 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 findings arrive, it is how many get acted on.
How Ward connects to Blue Yonder
Ward layers on top of Blue Yonder demand planning and replenishment. Ward watches what Blue Yonder recommends and flags when actual diverges from plan.
Setup: Ward reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy.
Data Ward reads from Blue Yonder
Impact metrics with Blue Yonder
Data lake enrichment
Ward enriches Blue Yonder data with: Demand forecasts, POS actuals, Weather & events, Supplier fill rates, Competitor data
Grocery KPI impact
Frequently asked questions
Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading. 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 isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
Ward reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy. Data points include: Demand forecasts, Replenishment recommendations, Allocation plans, Exception alerts.
Yes. Ward reads Blue Yonder data and combines it with contextual signals (weather, events, demographics) to generate Grocery-specific insight cards. No custom development required.
Ward decomposes promo results into gross lift, cannibalization rate, pantry loading, halo effects, and true incremental margin contribution. It also tracks promo fatigue, when repeated discounts permanently shift baseline demand downward.
A major snack vendor proposes a co-op BOGO program across 12 SKUs. Gross lift looks strong, but Ward shows net category lift is minimal after accounting for cannibalization and pantry-loading pull-forward. Several SKUs generate negative net category contribution. Ward provides SKU-level promo scorecards the category manager uses to restructure the deal around the SKUs with genuine incremental lift.
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 promos problems Ward catches.
Root causes, not just alerts. See it on your data.
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.
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