Head of Procurement: home assortment in daily cards
Home operators find assortment problems in the post-mortem. A Head of Procurement finds them on Ward the morning they start.
Assortment Planning for Home procurement
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 brings up the signals that change a procurement decision.
Assortment Planning, in one sentence. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
Under the hood. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
What you get
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
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.
Coverage is store by store, category by category. Ward tracks project basket value, seasonal accuracy, long-tail turn across 50,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the store base is fine and knowing which seven stores are not.
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.
The procurement problem in home retail is not missing data. It is that project basket value, seasonal accuracy, long-tail turn live in different systems on different refresh schedules, and reconciling them is a person-week. Ward does the reconciliation and returns the two-line version.
- 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 your existing systems on a read-only connection. Nothing is written back. First daily cards arrive within 48 hours.
-
02
Weeks 2 to 3: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the insight cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
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.
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 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.
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 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
See what Home assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
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