Promotion Effectiveness and Lift Measurement for Retail
Know which promos actually work. Most retailers find it in a post-mortem. Ward surfaces it while there is still time to act.
Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.
How Ward catches what your reports miss
Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
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…
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. Ward AI maps the schema and drafts the cleaning rules.
| 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.posretail.inventory_weeklyWhat changes for your team
Sample insight card
BOGO on Brand X crackers lifted units 34% but cannibalized Brand Y by 28%. Net category lift: only +6%.
Where this lands hardest
The roles that feel promos first, and what changes for them.
How Ward delivers this
The platform pieces doing the work behind the insight.
Promos by role
Available for every vertical
Promos by integration
Metrics this moves
The KPIs Ward improves when this insight runs.
See it. Or compare it.
Operator stories and the alternatives buyers benchmark against.
Questions about promos.
Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI. Each card explains what changed, the root cause, and the recommended action, at the store-category level, not estate aggregates.
Ward delivers promos insight cards within 48 hours of data connection, with baselines tightening over the first two weeks. Most issues are surfaced before they show up in weekly or quarterly reports.
Ward runs classical statistical and time-series models your planners can audit, backtested against your last 24 months. The model card, MAPE, and confidence interval ride on every answer. The LLM frames the result; the number comes from the math.
Two ways to start.
Run a fixed-fee pilot on your data, or talk to advisory about a broader engagement.
Stop finding Promos problems in the post-mortem.
See what Ward catches, and when.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
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.