A home Head of Procurement running promos on BigQuery
Know which promos actually work. Ward runs it on BigQuery data over your home store base, scoped to procurement.
Promo Effectiveness for Home on BigQuery, scoped to procurement
Here is promo effectiveness in plain terms. Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.
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.
Merchandising wants Ward. You sign the contract. Ward returns insight cards scoped to procurement decision-making.
Under the hood. Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
How the connection works. Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.
What you get
- Pull-forward detection
- Promo ROI scorecards
- Net lift measurement (not gross)
- Cannibalization quantification
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 Promos matters for Home retail
Home improvement promos drive traffic spikes, but most promotional purchases would have happened at full price within 30 days, the customer was already planning the project. True incrementality comes from triggering project starts, not discounting items already in someone's plan.
Why this combination
is its own problem.
Promo Effectiveness behaves differently in home retail than it does anywhere else. The estate shape, the SKU count, and the speed of the category all change what counts as a real indicator and what is noise. Ward is tuned to the home version.
- 01 Major holiday events get evaluated on weekend revenue lift, missing 30-day pull-forward and post-event demand drag that erode net incrementality.
- 02 Pro customers and DIY customers respond to promos completely differently; chain-wide ROI averages obscure that Pro promos often have lower incrementality than DIY events.
Benchmarks. Home improvement holiday promo events show 40-90% gross weekend lift but typically 5-25% net incrementality after pull-forward and post-event drag. Project-starter bundles usually deliver 2-4x the incremental basket value of single-item percentage-off promos.
What Ward has eyes on.
Ward pulls from any BigQuery dataset, gA4 event exports, ads data transfers from Google BigQuery on a read-only connection. Nothing is written back, and your Data Platform configuration does not change. BigQuery stays the system of record.
Know which promos actually work. 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.
Ward watches 50,000+ SKUs throughout 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 measures project-start incrementality, pull-forward rates by category, Pro vs DIY promotional response differences, and project basket value vs single-item sales. A 30-day pre/post window captures the full demand-shifting effect.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward connects to Google BigQuery with read-only credentials and begins ingesting any BigQuery dataset and gA4 event exports. No config changes on your side. First insight 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: operating rhythm
Daily 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
Merchandising wants Ward. You sign the contract.
- ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
- ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
- ×Multi-year commits with auto-renew. No exit if the pilot stalls
- ×Renewals come back 30% higher, with no room to push back and no benchmark to cite
- ×Security and DPA reviews start after the team has already committed
- ✓MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
- ✓Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- ✓Transparent pricing set by scope and store count
- ✓14-day insight guarantee. If Ward doesn't deliver, month two is on us
- ✓AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature
Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner
Home KPI impact
Frequently asked questions
Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading. 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 isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
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.
Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 Type II underway, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing set by scope and store count.
Ward delivers daily insight cards covering Project basket value, Seasonal accuracy, Long-tail turn, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
Ward measures project-start incrementality, pull-forward rates by category, Pro vs DIY promotional response differences, and project basket value vs single-item sales. A 30-day pre/post window captures the full demand-shifting effect.
The Memorial Day event shows strong weekend revenue lift, but Ward's analysis reveals most of it was pull-forward from purchases that would have happened within 30 days, plus deal-seekers with below-average basket sizes. The highest-incrementality performers were project-starter bundles that triggered new project purchases. Ward recommends shifting future event strategy from broad discounts to project-starter bundles.
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 promos problems Ward catches.
Root causes, not just alerts. See it on your data.
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