Real-time pricing for Convenience & C-Store.
location-level pricing signals, caught before they compound.
Why pricing matters
in convenience retail.
Customers know exactly what a Coke costs, but the majority of a c-store's SKUs carry no mental reference price. Ward identifies which items have elastic demand and which have inelastic demand, unlocking micro-pricing opportunities across the assortment without triggering price perception issues on the items customers actually compare.
Industry benchmarks
C-store inside-store gross margins run 30-38% on average, with packaged beverages at 35-45%, tobacco at 12-18%, and HBA/automotive often above 50%. Most operators have 200-400 actively priced KVIs; the other 2,500+ SKUs typically have 100-300 bps of unrealized margin headroom.
Micro-pricing test, 200-store chain
Ward segments 3,000 SKUs into price-awareness tiers: KVIs where customers compare, moderate-awareness items, and low-awareness categories like automotive and seasonal. Ward recommends holding KVI prices while implementing small increases on low-awareness items. Pilot stores show zero volume decline on adjusted items with meaningful weekly margin gains.
What Ward actually tracks
Ward tracks item-level price awareness, daypart elasticity differences, competitive proximity impact on sensitivity, and fuel-to-inside attach rate sensitivity.
Data signals
POS at SKU-store-day, competitive fuel and inside prices, basket compositions, daypart traffic, and elasticity history per category.
Three pitfalls Ward catches
in convenience pricing.
- 01 Cigarettes and beverages get over-managed for price perception while automotive, health-and-beauty, and seasonal items are left at default cost-plus margins despite low elasticity.
- 02 Fuel pricing decisions are made independently of inside-store pricing, missing that fuel customers anchor on the canopy price and barely notice inside markups.
- 03 Daypart-uniform pricing ignores that the 6 AM coffee buyer and the 9 PM impulse buyer have completely different price sensitivities.
How Ward runs pricing
for convenience retailers.
-
01
Score every SKU on price awareness
Ward classifies SKUs into high/medium/low awareness tiers using mention frequency, basket position, and elasticity proxy, at the store-cluster level.
-
02
Test increases on low-awareness items first
Cards recommend modest price moves (typically 2-8%) on tail-awareness items in 5-10% of stores with matched controls.
-
03
Roll only after multi-week verification
Ward holds the recommendation until 3-6 weeks of post-test data show no volume erosion at the basket or category level.
What a Ward card looks like.
Dairy category showing -1.4 elasticity this week vs -0.8 baseline. Consumers responding to price changes 75% more than normal.
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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Convenience pricing:
the shift.
- ×Daypart demand variation
- ×Planogram compliance
- ×Impulse category optimization
- ✓Real-time elasticity measurement
- ✓Category-level price sensitivity
- ✓Competitive price monitoring
Questions about convenience pricing.
Customers know exactly what a Coke costs, but the majority of a c-store's SKUs carry no mental reference price. Ward identifies which items have elastic demand and which have inelastic demand, unlocking micro-pricing opportunities across the assortment without triggering price perception issues on the items customers actually compare.
Ward segments 3,000 SKUs into price-awareness tiers: KVIs where customers compare, moderate-awareness items, and low-awareness categories like automotive and seasonal. Ward recommends holding KVI prices while implementing small increases on low-awareness items. Pilot stores show zero volume decline on adjusted items with meaningful weekly margin gains.
Ward tracks item-level price awareness, daypart elasticity differences, competitive proximity impact on sensitivity, and fuel-to-inside attach rate sensitivity.
First pricing insight cards arrive within 48 hours. Robust convenience baselines form within two weeks. Value compounds across multi-site operators. Chains with 100+ locations see the strongest returns. Fuel-dominant locations should expect impact concentrated on forecourt-to-store attach rate.
Convenience pricing
by data source.
More Convenience insight cards.
Convenience retailers: see what pricing 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.