A home Head of E-Com running promos on Oracle
A home Head of E-Com on Oracle should not be hunting for promos problems. Ward surfaces them each day.
The full picture: home promos, Oracle data, E-Commerce decisions
Promo Effectiveness, in one sentence. Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.
A Home Improvement operator is 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.
Your online and offline data live in different worlds. Ward filters to what a Head of E-Com can act on and drops the rest.
How it runs. Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
Getting the data in. Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
Capabilities
- 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.
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 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 Oracle Retail is read-only and runs on your schedule. Ward reads sales audit, inventory positions, and the rest of the feed, then stitches together it with external context your ERP does not carry: weather, local events, competitor pricing.
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.
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.
Why this combination
is its own problem.
Oracle Retail is the system of record for most home operators of your size, which means the readings Ward needs are already there. The gap is not data collection. It is that nobody has time to read sales audit and inventory positions every morning throughout every store.
- 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 the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Oracle Retail. Ward ingests sales audit, inventory positions, allocation and starts building baselines. First daily cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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
Insight cards arrive on a daily cycle 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 Oracle Retail
Ward integrates with Oracle Retail Merchandising (RMFCS), Oracle Retail Demand Forecasting, and Oracle Retail Analytics. Full stack visibility.
Setup: Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
Data Ward reads from Oracle
Impact metrics with Oracle
Data lake enrichment
Ward enriches Oracle data with: Sales audit data, Weather & events, Competitor pricing, Demographic data, Supplier scorecards
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 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.
Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments. Data points include: Sales audit, Inventory positions, Allocation, Replenishment, Demand forecasts, Price management.
Yes. Ward reads Oracle 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 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
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Root causes, not just alerts. See it on your data.
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