Pricing · Power BI

Your Power BI feed, read as pricing

Ward reads power BI REST API datasets, underlying SQL/Azure data, dataflow outputs from Microsoft Power BI, then combines them with weather, events, and demographics to produce pricing insight cards.

Running price optimization off Microsoft Power BI

Price Optimization. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.

Under the hood. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.

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

Key capabilities

  • Real-time elasticity measurement
  • Category-level price sensitivity
  • Competitive price monitoring
  • Margin-volume tradeoff modeling
app.getward.ai Live demo
Acme Retail @Merchandising: VP Analyst claude-sonnet default
A

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.

SignalFinding
labor_efficiencyRev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak
inventory.freshFresh fill 83%, backroom replenishment lag at 2–4p
promo.liftBOGO 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

Reporting

Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.

7d13w52w
Revenue vs. forecast
$48.2M
+4.2% WoW
Gross margin %
24.1%
−3.2pp
Fill rate, fresh
83.4%
−4.1pp
Shrink, West region
2.41%
+0.8pp
Revenue vs. forecast 13 weeks actual, 6 weeks forecast, 80% interval
Actual Forecast 80% interval
% of plan 106 94 100 forecast → W−13 W−5 today +6wk
Holt-Winters + weather regressor MAPE 4.1% at 4wk Backtested 24 months Crosses plan in 3 weeks
Forecast error by horizon MAPE, 24-month backtest
1wk 2.1%
2wk 3.0%
4wk 4.1%
8wk 6.3%
13wk 8.9%
Accuracy bar for promo decisions: ≤5% at 4wk
Models in production Every forecast ships a model card
ModelHorizonMAPE
holt_winters4wk4.1%
arima_sarimax13wk8.9%
gbm_demand1wk2.1%
bayes_hiernew store11.4%

Sources

Connect external systems to the data lake.

NameTypeLast sync
sap_pos_transactionsimport2m ago
sap_inventory_shrinkageimport2m ago
sap_labor_schedulingimport14m ago
retail_inventory_weeklyimport1h ago
retail_google_ads_dailyimport1h ago
retail_meta_ads_dailyimport1h ago
retail_ga4_website_dailyimport1h ago

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
merch-read-defaultpermitModel::*
finance-read-shrinkagepermitModel::"inventory_shrinkage"
vendor-blockedforbidModel::"labor_*"
region-west-onlypermitTenant::"acme"
Pricing on Power BI data, live product demo.

What Ward has eyes on.

The pricing model runs each day, not on a reporting calendar. It picks up the pattern, attributes the driver, and attaches what to do about it before the number reaches a review deck.

The connection to Microsoft Power BI is read-only and runs on your schedule. Ward pulls from power BI REST API datasets, underlying SQL/Azure data, and the rest of the feed, then stitches together it with external context your BI does not carry: weather, local events, competitor pricing.

Why this combination
is its own problem.

Most pricing projects stall at data access. This one does not, because Power BI already exposes power BI REST API datasets, underlying SQL/Azure data, dataflow outputs through an API Ward reads directly.

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

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.

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.

See what pricing problems Ward catches.

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

Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly

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.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
Your contact info