Ward detects. You decide. Promo Effectiveness for Fashion.
Promo Effectiveness at scale. Ward handles it across every location.
Why promos matters
in fashion retail.
Blanket promotions drive traffic but train customers to wait for sales. Ward measures the full cycle, demand suppression before the event, lift during, and pull-forward decline after, to reveal which promotions build revenue and which merely shift it around the calendar.
Industry benchmarks
Fashion promo events show 40-100% gross weekend lift but typically 5-25% net incrementality after pre-event suppression and pull-forward. Operators that convert blanket events to targeted-acquisition offers usually recover 200-600 bps of gross margin.
Friends & Family event post-mortem
Marketing declares the annual Friends & Family event a win based on weekend revenue lift. Ward's full-cycle analysis shows substantial pre-event demand suppression and post-event pull-forward decline that cut net incrementality roughly in half. New customer acquisition during the event ran well below non-promo weekends. Ward recommends replacing the blanket discount with targeted acquisition offers that actually grow the customer base.
What Ward actually tracks
Ward measures the full promo cycle: pre-event suppression, event lift, post-event decline, new vs returning customer mix, and margin-per-unit impact. It also calculates customer-level promo dependency scores to flag at-risk segments.
Data signals
POS at customer-style-day where loyalty data exists, full promo calendar with stack rules, marketing send and email engagement data, and competitor promo observations.
Three pitfalls Ward catches
in fashion promos.
- 01 Promo ROI gets calculated on event-window revenue only, missing the pre-event demand drag and post-event pull-forward decline that erode net incrementality.
- 02 Customer-level promo dependency is invisible at the cohort level; the top 20% of "loyalists" can be 80% promo-dependent without the chain noticing.
- 03 Margin-per-unit erosion compounds when stack-on offers (employee discount + cart promo + free shipping) trigger together, typical promo P&L doesn't track stacked effective discount.
How Ward runs promos
for fashion retailers.
-
01
Build the multi-week baseline
Ward establishes a 30-day pre/post baseline for each event, controlling for adjacent events, weather, and macro signals.
-
02
Decompose lift into components
Each promo is split into incremental, suppressed, pulled-forward, and cannibalized volume, at the brand and category level.
-
03
Score customer-level promo dependency
Ward flags cohorts whose full-price-to-promo ratio has shifted permanently and recommends segmented offers rather than blanket events.
What a Ward card looks like.
BOGO on Brand X crackers lifted units 34% but cannibalized Brand Y by 28%. Net category lift: only +6%.
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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Fashion promos:
the shift.
- ×Markdown timing
- ×Size curve misallocation
- ×Style velocity prediction
- ✓Net lift measurement (not gross)
- ✓Cannibalization quantification
- ✓Pull-forward detection
Fashion KPI impact.
Ward requires at least 2 full selling cycles to baseline style velocity and markdown timing. Results vary between basics and trend-driven categories.
Questions about fashion promos.
Blanket promotions drive traffic but train customers to wait for sales. Ward measures the full cycle, demand suppression before the event, lift during, and pull-forward decline after, to reveal which promotions build revenue and which merely shift it around the calendar.
Marketing declares the annual Friends & Family event a win based on weekend revenue lift. Ward's full-cycle analysis shows substantial pre-event demand suppression and post-event pull-forward decline that cut net incrementality roughly in half. New customer acquisition during the event ran well below non-promo weekends. Ward recommends replacing the blanket discount with targeted acquisition offers that actually grow the customer base.
Ward measures the full promo cycle: pre-event suppression, event lift, post-event decline, new vs returning customer mix, and margin-per-unit impact. It also calculates customer-level promo dependency scores to flag at-risk segments.
First promos insight cards arrive within 48 hours. Robust fashion baselines form within two weeks. Ward requires at least 2 full selling cycles to baseline style velocity and markdown timing. Results vary between basics and trend-driven categories.
More Fashion insight cards.
Fashion retailers: see what promos problems Ward catches.
Root causes, not just alerts. See it on your data.
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