Assortment Planning that actually works for Grocery retail.
Assortment Planning at scale. Ward handles it across every store.
Why assortment matters
in grocery retail.
Every assortment addition displaces something else, so the real question is incremental contribution after cannibalization and basket effects. Ward clusters stores by demographics, traffic, and competitive set, then benchmarks SKU performance at the cluster level to produce assortment recommendations that go beyond national planograms.
Industry benchmarks
A typical grocery store carries 30,000-45,000 SKUs; the bottom 20% generate under 2% of revenue. Cluster-aware assortment usually frees 5-12% of facings without a revenue drop, which translates to space for 1,500-3,000 better-fit SKUs per store cluster.
Category review, natural/organic section
A category manager reviews the natural/organic section across 300 stores. Ward's analysis reveals three distinct clusters: urban health-conscious stores that should carry more SKUs, suburban stores that match the national plan, and rural locations where organic moves at a fraction of the estate average. The one-size-fits-all planogram leaves revenue on the table in urban stores while it ties up slow-moving inventory in rural ones.
What Ward actually tracks
Ward tracks SKU productivity (revenue per facing), incremental contribution, substitution patterns, and cluster-level demand elasticity, all weighted against supplier fill rates and promotional obligations.
Data signals
POS at SKU-store-week, planogram positions, slot fee schedules, supplier promotional commitments, demographic overlays, and competitive proximity data.
Three pitfalls Ward catches
in grocery assortment.
- 01 Slot fees from CPG vendors lock in SKUs that don't earn their facing, Ward exposes the cost-of-inclusion versus actual revenue per linear foot.
- 02 Cluster definitions based on store size or banner miss the demographic signal; two same-size stores can have completely different shopper profiles.
- 03 New-item performance gets evaluated against chain averages instead of cluster benchmarks, so winners in one cluster are killed because they fail in another.
How Ward runs assortment
for grocery retailers.
-
01
Build store clusters from real shopper signals
Ward clusters using basket composition, daypart mix, and category penetration, not just demographics, producing 4-8 actionable clusters from your store estate.
-
02
Score every SKU per cluster
Productivity, incrementality after cannibalization, and basket-pull are computed per cluster, exposing the SKUs to add, drop, or shift.
-
03
Pilot the cluster planogram
Ward designs the test in 5-10% of cluster-matched stores, tracks revenue and basket size for 6 weeks, and only then recommends rollout.
What a Ward card looks like.
Cluster B stores (urban, high-traffic) underperforming on premium snacks vs Cluster A by 34%. Assortment gap: 12 SKUs missing.
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.
Models
Browse, search, and manage data–lake model definitions for your tenant.
| Name | Namespace | Version |
|---|---|---|
retail_pos_transactions | retail | 1.0 |
retail_inventory_snapshot | retail | 1.2 |
retail_labor_scheduling | retail | 1.0 |
retail_promo_calendar | retail | 1.1 |
retail_supplier_performance | retail | 1.0 |
sap_inventory_shrinkage | sap | 1.0 |
ga4_daily_events | marketing | 1.0 |
meta_ads_ad_level | marketing | 1.0 |
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 |
Architecture
Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.
sap.possnow.inventoryPipelines
Move data from sources into models on a schedule.
| Name | Source | Model | Status | Schedule |
|---|---|---|---|---|
sync_sap_pos_transactions | sap_pos_transactions | pos_transactions | enabled | hourly |
sync_sap_labor_scheduling | sap_labor_scheduling | labor_scheduling | enabled | daily |
sync_sap_inventory_shrinkage | sap_inventory_shrinkage | inventory_shrinkage | enabled | daily |
sync_retail_inventory_weekly | retail_inventory_weekly | inventory_weekly | enabled | weekly |
sync_retail_google_ads_daily | retail_google_ads_daily | google_ads_daily | enabled | daily |
sync_retail_ga4_website_daily | retail_ga4_website_daily | ga4_website_daily | enabled | daily |
Streams
Real-time ingestion pipelines.
pos.txnstore_037, basket $42.18inv.movedc_west → store_104labor.clockstore_022 shift_startpos.txnstore_211, basket $19.04
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Entities
Principals and resources referenced by Cedar policies.
| Entity UID | Type | Tenant |
|---|---|---|
Tenant::"acme" | Tenant | acme |
Model::"sap.pos_transactions" | Model | acme |
Model::"sap.inventory_shrinkage" | Model | acme |
Model::"sap.labor_scheduling" | Model | acme |
Model::"retail.toast_pos_daily" | Model | acme |
Model::"retail.ga4_website_daily" | Model | acme |
Providers
Manage LLM API keys and the model profiles that use them.
| Name | Provider | Used by | Created |
|---|---|---|---|
anthropic-default | Anthropic | 3 profiles | Apr 22 |
openai-default | OpenAI | 2 profiles | Apr 22 |
gemini-default | Gemini | 1 profile | Apr 22 |
ollama-onprem | Ollama | 2 profiles | Apr 22 |
LLM-agnostic. Bring your own key, route per task. No lock-in.
Settings
Manage your dashboard preferences and account.
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Grocery assortment:
the shift.
- ×Fresh waste & spoilage
- ×On-shelf availability gaps
- ×Promo cannibalization
- ✓Store cluster segmentation
- ✓SKU rationalization recommendations
- ✓Whitespace opportunity detection
Grocery KPI impact.
Impact timing depends on perishable mix, supply chain maturity, and data integration depth. Retailers with fragmented POS or ERP systems should expect a longer ramp to baseline accuracy.
Questions about grocery assortment.
Every assortment addition displaces something else, so the real question is incremental contribution after cannibalization and basket effects. Ward clusters stores by demographics, traffic, and competitive set, then benchmarks SKU performance at the cluster level to produce assortment recommendations that go beyond national planograms.
A category manager reviews the natural/organic section across 300 stores. Ward's analysis reveals three distinct clusters: urban health-conscious stores that should carry more SKUs, suburban stores that match the national plan, and rural locations where organic moves at a fraction of the estate average. The one-size-fits-all planogram leaves revenue on the table in urban stores while it ties up slow-moving inventory in rural ones.
Ward tracks SKU productivity (revenue per facing), incremental contribution, substitution patterns, and cluster-level demand elasticity, all weighted against supplier fill rates and promotional obligations.
First assortment insight cards arrive within 48 hours. Robust grocery baselines form within two weeks. Impact timing depends on perishable mix, supply chain maturity, and data integration depth. Retailers with fragmented POS or ERP systems should expect a longer ramp to baseline accuracy.
More Grocery insight cards.
Grocery retailers: see what assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
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.