Ward
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Convenience
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Power BI
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Director Store Ops

Convenience · Power BI · Director Store Ops

Power BI + Convenience Retail: Built for Director Store Ops

Convenience retailers have 3,000+ SKUs and blind spots hiding in every store. Ward watches them all and delivers the findings your team doesn't have bandwidth to find. Your Microsoft Power BI data holds answers nobody has time to extract. Ward reads it via read-only APIs.

Get a demo → Get started

Overview

Ward + Power BI for Convenience & C-Store

Convenience & C-Store retailers running Microsoft Power BI get AI-powered insight cards without custom development. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.

How it works: Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.

Ward monitors 3,000+ SKUs across your locations and delivers automated insight cards covering Transactions/hour, Attach rate, Basket size, and more.

app.getward.ai Live demo

Acme Retail @Merchandising: VP Analyst claude-sonnet default

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why did Store 37 miss target last week?

You · 9:42 AM

Schema Scout · routed to Merchandising Agent

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.

8 parallel queries 3 sources cited confidence 0.92

Show me how to fix the staffing mismatch.

You · 9:43 AM

Labor Agent · drafting schedule diff

Querying labor_scheduling…

› Ask anything, Ward routes to the right agent. Cmd+K

Dashboards

Pinned views built from saved data-lake queries.

Revenue vs. forecast +4.2% WoW

Gross margin % −3.2pp

Fill rate, fresh 83%

Shrink, West region +0.8pp

Models

Browse, search, and manage data–lake model definitions for your tenant.

Search models…

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.

Search sources…

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.

Your systems · read-only

SAP Retail

Snowflake

BigQuery

Shopify

Toast POS

Ward Gateway

TLS 1.3 · AES-256

Querying live · data stays put

Federated answers

SELECT * FROM sap.pos

JOIN snow.inventory

WHERE store_id = 37

→ insight cards

Ward Data Lake

→ baselined per store

TLS 1.3 in transit AES-256 at rest Read-only credentials SOC 2 II in progress VPC peering · PrivateLink

Pipelines

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.

0events / min

0streams active

0% delivered

pos.txn store_037, basket $42.18
inv.move dc_west → store_104
labor.clock store_022 shift_start
pos.txn store_211, basket $19.04

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II

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.

Search UIDs…

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.

API Keys Model Profiles

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.

Appearance

Theme • Light ° Dark

Light and dark themes are available. Your choice is remembered per browser.

Account

NameAdmin

Emailadmin@acme.io

Tenantacme-retail

Live product demo, Ward analyzing retail data in real time.

What Ward delivers

Morning brief delivered at 06:47 with prioritized action list
Estate-wide heat map of store performance, updated hourly
Staffing recommendations correlated with predicted traffic
Planogram compliance anomalies detected and flagged
Consistent exception handling with recommended actions

Metrics Ward monitors

Transactions/hour

Attach rate

Basket size

Planogram compliance

Daypart mix

Convenience challenges Ward solves

Daypart demand variation
Planogram compliance
Impulse category optimization
Fuel attach rate
Labor scheduling

Integration

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

Power BI REST API datasets

Underlying SQL/Azure data

Dataflow outputs

Impact metrics with Power BI

Time to Insight

Push, not pull

Insight cards delivered without waiting for someone to look.

Anomaly Detection

Between-refresh coverage

Issues surfaced before the next scheduled Power BI review.

Decision Velocity

Cause analysis included

No drill-down investigation; cards carry root cause context.

Report Efficiency

Ad-hoc requests reduced

Proactive cards answer questions before analysts get asked.

Data lake enrichment

Ward enriches Power BI data with: Power BI datasets, Underlying SQL/Azure data, Weather & events, Demographics, Custom feeds

For Store Operations

Managing 800 stores from a spreadsheet is insane.

Pain points

×Morning check-ins rely on phone calls and email chains
×No single view of which stores need attention today
×Labor scheduling is disconnected from demand signals
×Planogram compliance is checked manually, quarterly
×Exception management is reactive and inconsistent

How Ward helps

✓Morning brief delivered at 06:47 with prioritized action list
✓Estate-wide heat map of store performance, updated hourly
✓Staffing recommendations correlated with predicted traffic
✓Planogram compliance anomalies detected and flagged
✓Consistent exception handling with recommended actions

Poor labor allocation and inconsistent execution cost multi-store retailers 3–5% in lost sales. Source: RSR Research

Sample insight

What a Ward insight card looks like

Ward · Convenience06:47 AM

7 stores need your attention. 793 are clean. Priority: Stores 22 and 37, fresh availability below threshold. Replenishment already raised.

✓ Action recommendedConvenience context appliedPower BI data

Expected impact

Convenience KPI impact

Attach Rate

Impulse adjacencies

Daypart-specific cross-sell opportunities surfaced.

Daypart Revenue

Weak hours identified

Which hours and categories underperform, and why.

Planogram Compliance

Sales-correlated flags

Deviations flagged when they affect revenue, not just visuals.

Shrinkage

Slow-bleed detection

Transaction-level anomalies that periodic audits miss.

FAQ

Frequently asked questions

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.

Managing 800 stores from a spreadsheet is insane. Ward solves this with automated insight cards: Morning brief delivered at 06:47 with prioritized action list. Estate-wide heat map of store performance, updated hourly. Staffing recommendations correlated with predicted traffic.

Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Store Operations decision-making. Each card includes what changed, why it matters, and what to do next.

First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish robust 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.

Explore

See what Convenience problems Ward catches.

Root causes, not just alerts. See it on your data.

デモを予約 →

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