Assortment Planning: Home retail, BigQuery data, E-Commerce decisions
Stock what sells. Cut what doesn't. Ward runs it on BigQuery data over your home store base, scoped to e-commerce.
The full picture: home assortment, BigQuery data, E-Commerce decisions
Your online and offline data live in different worlds. Ward hands back daily cards scoped to e-commerce decision-making.
Assortment Planning, in one sentence. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
Home Improvement changes the scale of the problem: 50,000+ SKUs, every one of your stores. Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.
How Ward hands back Assortment cards: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Setup: Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.
What you get
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
- Planogram optimization inputs
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.
Every one of your stores gets its own baseline. Ward tracks project basket value, seasonal accuracy, long-tail turn against it and surfaces 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.
Ward pulls from BigQuery rather than replacing it. Any BigQuery dataset, gA4 event exports, ads data transfers come across on a read-only connection, get enriched with contextual data, and come back as insight cards. Your Data Platform is untouched.
Stock what sells. Cut what doesn't. Ward runs the model continuously rather than on a reporting cycle, which is why a finding lands the morning the pattern starts instead of at the end of the period.
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.
Running Ward on BigQuery in a home store base skips the usual first step. There is no ingestion project, because Google BigQuery already holds any BigQuery dataset, gA4 event exports, ads data transfers. Ward reads straight from what is there.
- 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 reads from Google BigQuery with read-only credentials and begins ingesting any BigQuery dataset and gA4 event exports. 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: operating rhythm
Cards arrive daily and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.
How Ward connects to Google BigQuery
Ward queries BigQuery using your existing datasets. GA4 exports, POS data, CRM exports. Ward reads it where it lives.
Setup: Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.
Data Ward reads from BigQuery
Impact metrics with BigQuery
Data lake enrichment
Ward enriches BigQuery data with: Any BigQuery dataset, GA4 event exports, Weather & events, Demographics, Custom feeds
Your online and offline data live in different worlds.
- ×Nobody can see one inventory position across every channel
- ×Online promo performance is measured separately from in-store
- ×Customer behavior data is siloed by channel
- ×BOPIS/BORIS operational complexity is growing unchecked
- ×Digital marketing attribution stops at the click
- ✓Unified insight cards across online and in-store channels
- ✓Cross-channel promo effectiveness with true attribution
- ✓Customer journey tracking across digital and physical touchpoints
- ✓BOPIS fulfillment performance monitoring with exception cards
- ✓Full-funnel marketing attribution to in-store conversion
Retailers with unified omnichannel data see 30% higher lifetime value per customer. Source: Harvard Business Review
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.
Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule. Data points include: Any BigQuery dataset, GA4 event exports, Ads data transfers, Custom ETL outputs.
Yes. Ward reads BigQuery data and combines it with contextual signals (weather, events, demographics) to generate Home-specific insight cards. No custom development required.
Your online and offline data live in different worlds. Ward solves this with automated insight cards: Unified insight cards across online and in-store channels. Cross-channel promo effectiveness with true attribution. Customer journey tracking across digital and physical touchpoints.
Ward delivers daily insight cards covering Project basket value, Seasonal accuracy, Long-tail turn, tailored for E-Commerce 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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Root causes, not just alerts. See it on your data.
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