Home pricing without leaving Power BI
Price on elasticity you can measure. Ward runs it on Power BI data across your home fleet, without a warehouse project first.
How Ward turns Power BI data into home pricing
Price Optimization is a insight card type Ward runs continuously. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.
Applied to Home Improvement, the surface area is 50,000+ SKUs over stores. Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.
The mechanism. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
Getting the data in. 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
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled the spring pre-build against the last three seasons and the weather signal for the 22 Southeast stores.
| Signal | Finding |
|---|---|
seasonal_prebuild | Mulch on-hand hit 61% of plan the week temps broke 70°F |
dc_push | DC push started 9 days after the first-warm-week trigger, LY it was 2 |
attach.project | Soil and edging attach fell 18% at stores that gapped on mulch |
Recommend: tie the push trigger to the 10-day forecast instead of the calendar week, pre-position two truckloads at the 8 stores that gapped, and re-set the attach endcap.
seasonal_prebuild…
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 |
|---|---|---|
epicor_pos_transactions | import | 2m ago |
epicor_special_orders | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
retail_seasonal_prebuild | import | 1h ago |
retail_sku_velocity | import | 1h ago |
retail_pro_account_sales | import | 1h ago |
retail_weather_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
pro-team-read-accounts | permit | Model::"pro_account_sales" |
vendor-blocked | forbid | Model::"labor_*" |
supply-read-orders | permit | Model::"special_orders" |
Why Pricing matters for Home retail
A 2x4 has two completely different demand curves depending on who's buying it. Pro customers compare lumber prices daily; DIY customers barely notice per-board differences. Ward segments price elasticity by customer type so recommendations respect Pro sensitivity while capturing margin on DIY transactions.
Why this combination
is its own problem.
Running Ward on Power BI in a home fleet skips the usual first step. There is no ingestion project, because Microsoft Power BI already holds power BI REST API datasets, underlying SQL/Azure data, dataflow outputs. Ward reads straight from what is there.
- 01 Seasonal pricing power isn't modeled; the same SKU has materially different elasticity in peak project season versus the shoulder months.
- 02 Lumber and commodity building material pricing gets treated as a single elasticity number when Pro and DIY customers behave completely differently, Pro is highly elastic, DIY is nearly inelastic.
Benchmarks. Home improvement Pro elasticity on commodity items typically -1.8 to -3.0; DIY on the same SKUs runs -0.3 to -0.8. Pro accounts represent 25-45% of revenue at 4-8x the basket size; protecting Pro pricing while capturing DIY margin is usually worth 200-400 bps of gross.
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 stitches together it with external context your BI does not carry: weather, local events, competitor pricing.
The pricing model runs every morning, not on a reporting calendar. It detects the pattern, attributes the driver, and attaches a recommended move before the number reaches a review deck.
Ward keeps a running read on 50,000+ SKUs over your stores, at the store-category level rather than the chain roll-up. The metrics under watch include project basket value, seasonal accuracy, long-tail turn. A roll-up hides a single-store problem inside a healthy average, which is how project basket identification stays invisible for a quarter.
At the metric level. Ward tracks Pro vs DIY elasticity segmentation, commodity price benchmarking, project basket sensitivity (total project cost matters more than item prices), and seasonal demand multipliers on pricing power.
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 cards arrive within 48 hours.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: steady state
Ward sends insight cards on a daily cycle, each with the driver and a recommended move. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight 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
Home KPI impact
Frequently asked questions
Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume. For Home retail specifically, Ward monitors 50,000+ SKUs across your stores and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Project basket value, Seasonal accuracy, Long-tail turn, Pro customer share, Attachment rate 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 Home-specific insight cards. No custom development required.
Ward tracks Pro vs DIY elasticity segmentation, commodity price benchmarking, project basket sensitivity (total project cost matters more than item prices), and seasonal demand multipliers on pricing power.
Ward reveals that Pro account customers show steep price elasticity on framing lumber while DIY customers are nearly inelastic on the same SKU. Ward recommends maintaining aggressive Pro pricing through the loyalty tier while implementing modest increases on non-loyalty transactions. The increase is invisible to DIY weekend-project buyers but protects the Pro relationship and delivers meaningful annual margin improvement.
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 Home pricing problems Ward catches.
Root causes, not just alerts. See it on your data.
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