VP Merchandising: home assortment, read from Microsoft Power BI
Your category managers are drowning in spreadsheets. Ward spots home assortment movement in your Power BI data while it is still fixable, with what caused it and a recommended action attached.
Assortment Planning for Home on Power BI, scoped to merchandising
Your category managers are drowning in spreadsheets. Ward brings up the signals that change a merchandising decision.
Assortment Planning is a card type Ward runs continuously. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
For Home Improvement retailers, that means tracking 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.
How Ward hands you Assortment insight cards: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
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.
Capabilities
- SKU rationalization recommendations
- Whitespace opportunity detection
- Planogram optimization inputs
- Store cluster segmentation
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 Assortment matters for Home retail
The top 2,000 SKUs generate the bulk of revenue, but the remaining 48,000 are what makes you a project destination. Drop a niche fitting and you lose the entire project basket. Ward identifies which tail SKUs are project-basket anchors worth keeping and which are truly dead weight that should be rationalized.
What Ward has eyes on.
The assortment model runs each day, not on a reporting calendar. It detects the pattern, accounts for root cause, and attaches the next step before the number reaches a review deck.
Every one of your stores gets its own baseline. Ward tracks project basket value, seasonal accuracy, long-tail turn against it and pulls forward only the deviations that hold up. The two that show up most in home retail are project basket identification and seasonal pre-positioning, and both are baseline problems before they are P&L problems.
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 cross-references it with external context your BI does not carry: weather, local events, competitor pricing.
At the metric level. Ward tracks long-tail project basket affinity, Pro vs DIY assortment dependency, seasonal SKU activation cycles, and revenue-per-linear-foot by department and planogram section.
Why this combination
is its own problem.
Microsoft Power BI was built for transactions, not for merchandising decisions. The data is right there and it is in the wrong shape. Ward reads power BI REST API datasets and underlying SQL/Azure data and rewrites them as a decision.
- 01 Seasonal activation cycles mean a SKU "dead" for 9 months can be critical for 3; chain-wide 90-day velocity cuts kill seasonal anchors prematurely.
- 02 SKU rationalization based on standalone velocity destroys project-basket anchors and drives customers to competitors for the entire job, net assortment economics turn negative.
Benchmarks. Home improvement long-tail SKUs (the bottom 60-70% of catalog by velocity) typically generate 8-15% of standalone revenue but anchor 25-35% of project basket value. Pro customers depend disproportionately on the tail, losing tail items typically costs 2-4x the standalone revenue impact in Pro retention.
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 daily cards arrive in two days.
-
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 delivers findings daily, each with the driver and a recommended action. Volume settles at a level a single person can read over coffee. The measure of success is not how many daily 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
Your category managers are drowning in spreadsheets.
- ×Promo planning still runs off last year's playbook
- ×Assortment reviews happen quarterly when they should happen daily
- ×Price changes chase the market a week behind it
- ×No visibility into true cannibalization across categories
- ×Vendor negotiations lack real-time sell-through evidence
- ✓Insight cards flag promo cannibalization the day it happens
- ✓Assortment gaps and whitespace opportunities surface automatically
- ✓Price elasticity shifts detected before margin erosion compounds
- ✓Category-level performance cards replace manual spreadsheet reviews
- ✓Vendor scorecards generated from actual fill rate and quality data
Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. Source: McKinsey & Company
Home KPI impact
Frequently asked questions
Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. 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 clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
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.
Your category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.
Ward delivers daily insight cards covering Project basket value, Seasonal accuracy, Long-tail turn, tailored for Merchandising decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks long-tail project basket affinity, Pro vs DIY assortment dependency, seasonal SKU activation cycles, and revenue-per-linear-foot by department and planogram section.
Plumbing carries thousands of SKUs, hundreds with zero sales in 90 days. Ward's project basket analysis reveals that many of those "dead" SKUs appear alongside high-velocity project items, a specialty elbow fitting with minimal standalone sales is still critical to a complete project basket. Deleting it sends the customer to a competitor for the entire job. Ward separates true orphaned SKUs from project-basket anchors and recommends cutting only the former.
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
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