Convenience pricing from Power BI, briefed to loss prevention
Ward reads power BI REST API datasets, underlying SQL/Azure data, dataflow outputs from Microsoft Power BI, scans continuously pricing across 3,000+ convenience SKUs, and returns the loss prevention read daily.
How a convenience Head of LP runs pricing on Microsoft Power BI
Here is price optimization in plain terms. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.
Applied to Convenience & C-Store, the surface area is 3,000+ SKUs throughout locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
Shrinkage costs you more than you think. Ward finds out where. Ward hands you insight cards scoped to loss prevention decision-making.
Under the hood. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
Setup: Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.
The short list
- Real-time elasticity measurement
- Category-level price sensitivity
- Competitive price monitoring
- Margin-volume tradeoff modeling
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled the 6–9a daypart for the 14 Route 9 sites against the chain baseline. Two causes, one of them scheduling.
| Signal | Finding |
|---|---|
daypart_sales | 6–9a revenue −11% vs. chain, coffee units −18% |
labor_scheduling | Second associate clocks in at 7:30a, peak starts 6:40a at 9 of 14 sites |
foodservice.waste | Breakfast sandwich waste 14%, hold times past 4 hours at 6 sites |
Recommend: move the second open to 6:15a at those nine sites, cut the breakfast batch by one tray, and re-check attach in two weeks.
daypart_sales…
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 |
|---|---|---|
ncr_pos_transactions | import | 2m ago |
pdi_fuel_transactions | import | 2m ago |
verifone_forecourt_events | import | 14m ago |
ncr_planogram_audit | import | 1h ago |
retail_daypart_sales | import | 1h ago |
retail_foodservice_waste | import | 1h ago |
retail_labor_scheduling | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
ops-read-default | permit | Model::* |
lp-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
fuel-team-forecourt | permit | Model::"fuel_transactions" |
Why Pricing matters for 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, so you can price at the item level without triggering price perception issues on the items customers actually compare.
What Ward has eyes on.
The connection to Microsoft Power BI is read-only and runs on your schedule. Ward reads power BI REST API datasets, underlying SQL/Azure data, and the rest of the feed, then combines it with external context your BI does not carry: weather, local events, competitor pricing.
The pricing model runs daily, not on a reporting calendar. It catches the pattern, attributes the driver, and attaches a recommended action before the number reaches a review deck.
Ward keeps a running read on 3,000+ SKUs throughout your locations, at the store-category level rather than the chain roll-up. The metrics under watch include transactions/hour, attach rate, basket size. A roll-up hides a single-store problem inside a healthy average, which is how daypart demand variation stays invisible for a quarter.
At the metric level. Ward tracks item-level price awareness, daypart elasticity differences, competitive proximity impact on sensitivity, and fuel-to-inside attach rate sensitivity.
Why this combination
is its own problem.
Price Optimization produces a lot of output that is technically correct and operationally useless to loss prevention. Ward filters on whether the finding changes a decision a Head of LP can actually make.
- 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 Daypart-uniform pricing ignores that the 6 AM coffee buyer and the 9 PM impulse buyer have completely different price sensitivities.
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.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward plugs into Microsoft Power BI with read-only credentials and begins ingesting power BI REST API datasets and underlying SQL/Azure data. No config changes on your side. First insight cards arrive inside the first 48 hours.
-
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 insight cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands back cards daily, each with the cause and a recommended move. 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 Microsoft Power BI
Ward sits alongside Power BI. Your dashboards visualize. Ward detects and explains what changed. No dashboard login needed for your morning brief.
Setup: Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.
Data Ward reads from Power BI
Impact metrics with Power BI
Data lake enrichment
Ward enriches Power BI data with: Power BI datasets, Underlying SQL/Azure data, Weather & events, Demographics, Custom feeds
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
Convenience KPI impact
Frequently asked questions
Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume. For Convenience retail specifically, Ward monitors 3,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Transactions/hour, Attach rate, Basket size, Planogram compliance, Daypart mix at the store-category level. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched. Data points include: Power BI REST API datasets, Underlying SQL/Azure data, Dataflow outputs.
Yes. Ward reads Power BI data and combines it with contextual signals (weather, events, demographics) to generate Convenience-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 Transactions/hour, Attach rate, Basket size, tailored for Loss Prevention decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks item-level price awareness, daypart elasticity differences, competitive proximity impact on sensitivity, and fuel-to-inside attach rate sensitivity.
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.
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 Convenience pricing problems Ward catches.
Root causes, not just alerts. See it on your data.
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