No more customer surprises. Ward sees them first.
Your Home data holds the answers. Ward finds them.
Why customer matters
in home retail.
The intelligence opportunity lies at the transition points, when a DIY customer starts behaving like a Pro by buying larger quantities, visiting more frequently, and shifting to trade-grade materials. These customers represent the highest lifetime value opportunity in the vertical.
Industry benchmarks
Home improvement Pro customers typically have 4-8x the LTV of DIY at 30-50% gross margin instead of the 28-35% on DIY tail SKUs. DIY-to-Pro conversion rate when targeted within 60 days of trade-up signal: typically 25-45%; missed window drops to under 10%.
DIY-to-Pro migration detection
Ward identifies loyalty customers whose purchasing patterns have shifted in the past 90 days: visit frequency up sharply, basket values climbing, and product mix moving from consumer-grade to professional-grade materials. These customers are likely scaling into major renovation or investment property work. Ward recommends targeted Pro account outreach with volume pricing and project support, and a meaningful share of the flagged customers convert to Pro accounts within 60 days.
What Ward actually tracks
Ward tracks Pro/DIY segmentation migration, project basket identification, seasonal activation patterns, and trade-up indicators, shifts from consumer to professional product tiers signal high-value customer evolution.
Data signals
POS with loyalty IDs, basket compositions and product-tier metadata, visit cadence and seasonality, Pro account roster, and project-basket linkage.
Three pitfalls Ward catches
in home customer.
- 01 DIY-to-Pro migration is a 2-4 month signal window that closes once the customer establishes a competitor relationship; chains that detect at 6 months miss the conversion entirely.
- 02 Trade-up signals (consumer to pro tier) get lumped into general spending growth; the specific tier-shift signature is what predicts Pro conversion.
- 03 Seasonal-only customers get retention treatment when they're actually structurally lower-LTV than year-round Pro accounts; misallocated marketing spend follows.
How Ward runs customer
for home retailers.
-
01
Build trade-up and frequency-shift detectors
Ward flags customers with sustained 60-90 day shifts in visit frequency, basket size, and product-tier mix, the signature of DIY-to-Pro migration.
-
02
Cluster by lifecycle stage
Cards segment customers by lifecycle: emerging Pro, established Pro, seasonal DIY, episodic DIY, each requiring different retention plays.
-
03
Trigger targeted Pro account outreach
For trade-up signals, Ward recommends Pro account onboarding with volume pricing, project support, and dedicated rep, typically within the 60-day signal window.
What a Ward card looks like.
Evening shoppers (6-9 PM) adding 22% more ready-to-eat items vs last quarter. Deli adjacency planogram opportunity identified.
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO crackers cannibalized Brand Y by 28%, net category +6% |
Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.
labor_scheduling…
Dashboards
Pinned views built from saved data-lake queries.
Models
Browse, search, and manage data–lake model definitions for your tenant.
| Name | Namespace | Version |
|---|---|---|
retail_pos_transactions | retail | 1.0 |
retail_inventory_snapshot | retail | 1.2 |
retail_labor_scheduling | retail | 1.0 |
retail_promo_calendar | retail | 1.1 |
retail_supplier_performance | retail | 1.0 |
sap_inventory_shrinkage | sap | 1.0 |
ga4_daily_events | marketing | 1.0 |
meta_ads_ad_level | marketing | 1.0 |
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Architecture
Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.
sap.possnow.inventoryPipelines
Move data from sources into models on a schedule.
| Name | Source | Model | Status | Schedule |
|---|---|---|---|---|
sync_sap_pos_transactions | sap_pos_transactions | pos_transactions | enabled | hourly |
sync_sap_labor_scheduling | sap_labor_scheduling | labor_scheduling | enabled | daily |
sync_sap_inventory_shrinkage | sap_inventory_shrinkage | inventory_shrinkage | enabled | daily |
sync_retail_inventory_weekly | retail_inventory_weekly | inventory_weekly | enabled | weekly |
sync_retail_google_ads_daily | retail_google_ads_daily | google_ads_daily | enabled | daily |
sync_retail_ga4_website_daily | retail_ga4_website_daily | ga4_website_daily | enabled | daily |
Streams
Real-time ingestion pipelines.
pos.txnstore_037, basket $42.18inv.movedc_west → store_104labor.clockstore_022 shift_startpos.txnstore_211, basket $19.04
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Entities
Principals and resources referenced by Cedar policies.
| Entity UID | Type | Tenant |
|---|---|---|
Tenant::"acme" | Tenant | acme |
Model::"sap.pos_transactions" | Model | acme |
Model::"sap.inventory_shrinkage" | Model | acme |
Model::"sap.labor_scheduling" | Model | acme |
Model::"retail.toast_pos_daily" | Model | acme |
Model::"retail.ga4_website_daily" | Model | acme |
Providers
Manage LLM API keys and the model profiles that use them.
| Name | Provider | Used by | Created |
|---|---|---|---|
anthropic-default | Anthropic | 3 profiles | Apr 22 |
openai-default | OpenAI | 2 profiles | Apr 22 |
gemini-default | Gemini | 1 profile | Apr 22 |
ollama-onprem | Ollama | 2 profiles | Apr 22 |
LLM-agnostic. Bring your own key, route per task. No lock-in.
Settings
Manage your dashboard preferences and account.
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Home customer:
the shift.
- ×Project basket identification
- ×Seasonal pre-positioning
- ×Long-tail inventory
- ✓Basket composition trends
- ✓Daypart behavior modeling
- ✓Customer segment migration
Home KPI impact.
Ward requires 6\u201312 months to baseline seasonal categories. Pro vs DIY segment separation is critical for accurate modeling.
Questions about home customer.
The intelligence opportunity lies at the transition points, when a DIY customer starts behaving like a Pro by buying larger quantities, visiting more frequently, and shifting to trade-grade materials. These customers represent the highest lifetime value opportunity in the vertical.
Ward identifies loyalty customers whose purchasing patterns have shifted in the past 90 days: visit frequency up sharply, basket values climbing, and product mix moving from consumer-grade to professional-grade materials. These customers are likely scaling into major renovation or investment property work. Ward recommends targeted Pro account outreach with volume pricing and project support, and a meaningful share of the flagged customers convert to Pro accounts within 60 days.
Ward tracks Pro/DIY segmentation migration, project basket identification, seasonal activation patterns, and trade-up indicators, shifts from consumer to professional product tiers signal high-value customer evolution.
First customer insight cards arrive within 48 hours. Robust home baselines form within two weeks. Ward requires 6\u201312 months to baseline seasonal categories. Pro vs DIY segment separation is critical for accurate modeling.
More Home insight cards.
Home retailers: see what customer problems Ward catches.
Root causes, not just alerts. See it on your data.
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.