Convenience promos from Power BI, briefed to technology
A convenience Head of IT on Power BI should not be hunting for promos problems. Ward pulls forward them every morning.
How a convenience Head of IT runs promos on Microsoft Power BI
Promo Effectiveness is a finding type Ward runs continuously. Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.
Applied to Convenience & C-Store, the surface area is 3,000+ SKUs across locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
The business wants AI. You sign off on the architecture. Ward hands you insight cards scoped to technology decision-making.
The mechanism. Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
Setup: Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.
What you get
- Net lift measurement (not gross)
- Cannibalization quantification
- Pull-forward detection
- Promo ROI scorecards
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 Promos matters for Convenience retail
Most c-store operators accept vendor-funded promotions without measuring whether they actually improve store economics. Multi-unit deals can cannibalize single-unit margin, and rebates often don't offset the erosion. Ward measures the true P&L impact of each program, giving operators evidence for vendor negotiations.
Why this combination
is its own problem.
A Head of IT rarely logs into Power BI. They read what someone else pulled out of it, two days later. Ward removes the two days and the someone else.
- 01 Vendor funding gets credited against the discount in margin reports, hiding the cannibalization of single-unit margin and adjacent-SKU sales.
- 02 Display labor and reset cost is left out of promo P&L; on tight-staffing nights, store-level cost can exceed vendor funding.
Benchmarks. C-store vendor promo programs typically show 15-50% gross unit lift and 3-15% net margin lift after cannibalization, funding, and labor. Most chains run 100-300 vendor events per year; the bottom quartile is usually negative-net.
What Ward has eyes on.
The connection to Microsoft Power BI is read-only and runs on your schedule. Ward reads straight from power BI REST API datasets, underlying SQL/Azure data, and the rest of the feed, then cross-references it with external context your BI does not carry: weather, local events, competitor pricing.
The promos model runs each day, not on a reporting calendar. It detects the pattern, attributes the driver, and attaches a recommended move before the number reaches a review deck.
Every one of your locations gets its own baseline. Ward watches transactions/hour, attach rate, basket size against it and raises only the deviations that hold up. The two that show up most in convenience retail are daypart demand variation and planogram compliance, and both are baseline problems before they are P&L problems.
At the metric level. Ward tracks unit lift vs margin impact, single-to-multi-unit cannibalization, vendor funding offset accuracy, and post-promo demand suppression, including inventory holding, labor for reset, and displaced revenue from non-promoted items.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of 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 findings arrive in two days.
-
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 findings are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands you cards on a daily cycle, each with what caused it and a recommended move. 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 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
The business wants AI. You sign off on the architecture.
- ×Business sponsor already chose the vendor. You inherit the security review
- ×Every AI vendor wants write access and a copy of the production data
- ×Model lock-in means rewriting the stack when GPT or Claude moves again
- ×Audit trail is an afterthought. Compliance has nothing to pull on
- ×Data lake project keeps getting bumped for the next thing the business wants
- ✓Federated query: data stays in your warehouse. No copies, no shadow lake
- ✓Read-only credentials. Cedar policies enforce least-privilege per agent
- ✓LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
- ✓Every query, every model, every source logged. SIEM-ready audit output
- ✓VPC peering, PrivateLink, SOC 2 II. Your security review is short
74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. Source: Gartner
Convenience KPI impact
Frequently asked questions
Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading. 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 isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
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.
The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Technology decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks unit lift vs margin impact, single-to-multi-unit cannibalization, vendor funding offset accuracy, and post-promo demand suppression, including inventory holding, labor for reset, and displaced revenue from non-promoted items.
A top beverage vendor runs 26 promotional events per year across the chain. Ward reveals that fewer than half generate positive net margin after accounting for cannibalization and margin erosion. Ward provides per-event ROI scorecards the category manager uses to renegotiate: fewer but deeper promotions on high-ROI events, elimination of negative-margin ones, and better vendor funding terms.
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 promos problems Ward catches.
Root causes, not just alerts. See it on your data.
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