Promo Effectiveness for VP / Director of Merchandising, read from Looker / Looker Studio
Know which promos actually work. Ward runs it off Looker, scoped to what a VP Merchandising owns.
How a VP Merchandising runs promos off Looker / Looker Studio
Your category managers are drowning in spreadsheets. Ward writes the finding at the altitude a VP Merchandising works at.
Promo Effectiveness, in one sentence. Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.
How Ward hands you Promos daily cards: Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
The connection itself. Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
Capabilities
- Net lift measurement (not gross)
- Cannibalization quantification
- Pull-forward detection
- Promo ROI scorecards
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 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 |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Why this combination
is its own problem.
Most promos projects stall at data access. This one does not, because Looker already exposes looker API for query results, underlying database (direct), lookML model metadata through an API Ward reads directly.
What Ward has eyes on.
The connection to Looker / Looker Studio is read-only and runs on your schedule. Ward pulls from looker API for query results, underlying database (direct), and the rest of the feed, then stitches together it with external context your BI 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.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Looker / Looker Studio. Ward ingests looker API for query results, underlying database (direct), lookML model metadata 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: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
Insight cards arrive each day 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 Looker / Looker Studio
Ward does not replace Looker. Ward watches the same data Looker visualizes and proactively alerts when something changes. Your dashboards stay. Ward adds intelligence.
Setup: Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
Data Ward reads from Looker
Impact metrics with Looker
Data lake enrichment
Ward enriches Looker data with: Looker query results, Underlying database, Weather & events, Competitor data, Customer segments
Your category managers are drowning in spreadsheets.
- ×Promo planning still runs off last year's playbook
- ×Assortment reviews happen quarterly when they should happen daily
- ×Price changes chase the market a week behind it
- ×No visibility into true cannibalization across categories
- ×Vendor negotiations lack real-time sell-through evidence
- ✓Insight cards flag promo cannibalization the day it happens
- ✓Assortment gaps and whitespace opportunities surface automatically
- ✓Price elasticity shifts detected before margin erosion compounds
- ✓Category-level performance cards replace manual spreadsheet reviews
- ✓Vendor scorecards generated from actual fill rate and quality data
Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. Source: McKinsey & Company
Frequently asked questions
Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses. Data points include: Looker API for query results, Underlying database (direct), LookML model metadata.
Your category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.
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 promos problems Ward catches.
Root causes, not just alerts. See it on your data.
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Find out what your data has been hiding.
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