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Data Platform · BigQuery

Your BigQuery feed, read as promos

Ward reads any BigQuery dataset, gA4 event exports, ads data transfers from Google BigQuery, then combines them with weather, events, and demographics to produce promos findings.

Request access BigQuery connector reference
Read-only by default No copy of your data Ward AI drafts, you approve
01 · The connection

How Ward attaches to Google BigQuery.

Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.

app.getward.ai Live demo
Acme Retail @Merchandising: VP Analyst claude-sonnet default
A

AI Insights

Agents run against your baselines overnight. These are what they flagged without being asked.

3 flagged 8 queries run 3 sources swept 04:00, acme-retail
Act now labor_efficiency 0.94

Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak

Schema Scout · routed to Merchandising Agent Pin Ask Ward Investigate
Review inventory.fresh 0.89

Fresh fill 83%, backroom replenishment lag at 2–4p

Promo Agent · routed via Merchandising Pin Ask Ward Investigate
Watch promo.lift 0.81

BOGO crackers cannibalized Brand Y by 28%, net category +6%

Margin Agent · routed via Finance Pin Ask Ward Investigate
Recommended

Re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.

Sources

Connect external systems. Ward AI maps the schema and drafts the cleaning rules.

Search sources…
NameTypeLast sync
sap_pos_transactionsimport2m ago
sap_inventory_shrinkageimport2m ago
sap_labor_schedulingimport14m ago
retail_inventory_weeklyimport1h ago
retail_google_ads_dailyimport1h ago
retail_meta_ads_dailyimport1h ago
retail_ga4_website_dailyimport1h ago

Pipelines

Move data from sources into models on a schedule. Describe the sync; Ward AI builds it.

NameSourceModelStatusSchedule
sync_sap_pos_transactionssap_pos_transactionspos_transactionsenabledhourly
sync_sap_inventory_shrinkagesap_inventory_shrinkageinventory_shrinkageenableddaily
sync_sap_labor_schedulingsap_labor_schedulinglabor_schedulingenableddaily
sync_retail_inventory_weeklyretail_inventory_weeklyinventory_weeklyenabledweekly
sync_retail_google_ads_dailyretail_google_ads_dailygoogle_ads_dailyenableddaily
sync_retail_meta_ads_dailyretail_meta_ads_dailymeta_ads_dailyenableddaily
Sources, then what Ward AI drafted from them, then what it computed. Click between the panels.
What Ward reads
Any BigQuery datasetGA4 event exportsAds data transfersCustom ETL outputs

Ward queries BigQuery using your existing datasets. GA4 exports, POS data, CRM exports. Ward reads it where it lives.

Scope

Credentials are read-only and locked to the objects you name. Ward cannot reach a table you did not grant it, and write access is opt-in per object rather than implied by the connection.

Residency

Ward queries BigQuery in place and keeps no second copy. Your region, your retention policy, and no staging warehouse in the middle.

Drift

Every connector carries a schema contract. A source that changes shape fails the contract and stops the pipeline, rather than quietly writing wrong rows until someone notices the numbers moved.

02 · What it computes

Know which promos actually work.

Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.

Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.

  • Net lift measurement (not gross)
  • Cannibalization quantification
  • Pull-forward detection
  • Promo ROI scorecards

Definitions resolve through the semantic layer, so a number here reconciles with the dashboard your team already publishes instead of disagreeing with it quietly. How the layer works →

Sample output

BOGO on Brand X crackers lifted units 34% but cannibalized Brand Y by 28%. Net category lift: only +6%.

Up to 72% of trade promotions fail to break even on net margin. — Nielsen

03 · Scope and audit

What the agent may read, and what it did last Tuesday.

Agents on your own data fail the security review, not the demo. The model is never the problem. The problem is that nobody can say what the agent is allowed to reach, what it already did, or what happens when it is wrong.

01

The agent sees the BigQuery objects you declared on the source and nothing else. Scope is enforced at query time, not in the client.

02

Read-only until you grant a write action explicitly, per object. A grant leaves a record naming the agent, the caller and the rows it touched.

03

One audit log covers every person and every agent in the tenant, so a reviewer answers one question against one system rather than correlating two.

Governance and policy →
How a scope gets set

Describe the job in plain English. Ward AI proposes the agent and the exact permissions it would need against your BigQuery objects, and the proposal sits there until a person approves it. Nothing runs on a draft.

Every finding it returns carries the SQL that produced it. If the query is wrong you can see that it is wrong, which is the only way a data team ever trusts one of these.

04 · Where it runs

The same pipeline, in three operating contexts.

What promo effectiveness off BigQuery looks like once it is live. Retail is where Ward is deployed, not what Ward is.

Grocery

30,000+ SKUs · stores

A major snack vendor proposes a co-op BOGO program across 12 SKUs. Gross lift looks strong, but Ward shows net category lift is minimal after accounting for cannibalization and pantry-loading pull-forward. Several SKUs generate negative net category contribution. Ward provides SKU-level promo scorecards the category manager uses to restructure the deal around the SKUs with genuine incremental lift.

The grocery build →

Convenience

3,000+ SKUs · locations

A top beverage vendor runs 26 promotional events per year across the chain. Ward reveals that fewer than half generate positive net margin after accounting for cannibalization and margin erosion. Ward provides per-event ROI scorecards the category manager uses to renegotiate: fewer but deeper promotions on high-ROI events, elimination of negative-margin ones, and better vendor funding terms.

The convenience build →

Fashion

15,000+ SKUs · locations

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.

The fashion build →
05 · Who consumes it

You stand it up. They read the output.

Your platform team owns the connection, the definitions and the policy. These are the teams that consume what comes out of it.

VP Merchandising

Merchandising

True net lift after cannibalization and pull-forward. Not gross.

What they see →

CFO

Finance

Promo ROI scorecards. The money-losing campaigns surfaced before renewal.

What they see →

Head of E-Com

E-Commerce

Online promo lift quantified against offline cannibalization.

What they see →
06 · What changes

What connecting BigQuery actually moves.

MeasureEffectWhy
Time to Insight No staging required GA4, POS, and CRM datasets queried in place.
Marketing Attribution Online-offline linked GA4 events joined with in-store POS to close attribution gaps.
Data Activation Historical data made queryable Years of unqueried BigQuery data brought into analysis.
Anomaly Detection Speed Always-on monitoring Deviations caught between scheduled dashboard reviews.
Questions

What a technical evaluation asks first.

Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.

Ward reads Any BigQuery dataset, GA4 event exports, Ads data transfers and Custom ETL outputs. Credentials are read-only and scoped to the objects you name, so Ward cannot reach a table you did not grant it.

No. Ward queries BigQuery in place and holds no second copy of your data. Nothing is exported to run Promo Effectiveness, and your residency and retention policy are unchanged by connecting Ward.

Every connector carries a schema contract. When a source changes shape the contract fails loudly and the pipeline stops, rather than writing wrong rows into your warehouse for a week before anyone notices the numbers moved.

Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading. Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.

Yes. Every figure Ward reports carries the query that produced it, resolved through the semantic layer, so it reconciles with the dashboard your team already publishes rather than disagreeing with it quietly.

Only what you declared. An agent is scoped to named tables and cannot reach past them, it is read-only unless you grant a write action explicitly, and every query, tool call and result is logged against the agent identity that made it.

Typically VP / Director of Merchandising, CFO / VP Finance and Head of E-Commerce / Digital. They read the output; your platform team owns the connection, the definitions and the policy that produced it.

A major snack vendor proposes a co-op BOGO program across 12 SKUs. Gross lift looks strong, but Ward shows net category lift is minimal after accounting for cannibalization and pantry-loading pull-forward. Several SKUs generate negative net category contribution. Ward provides SKU-level promo scorecards the category manager uses to restructure the deal around the SKUs with genuine incremental lift.

Explore

Promo Effectiveness on another system.

SAP Oracle Shopify NetSuite Snowflake Blue Yonder RELEX Looker Power BI Tableau NCR BigCommerce Lightspeed Epicor

What else Ward computes off BigQuery.

Stockout Prediction Shrinkage Detection Price Optimization Demand Forecasting Assortment Planning Customer Behavior Fill Rate Monitoring
BigQuery connector → Promos overview → All 540+ connectors → Every pairing →

Point Ward at your BigQuery and see what comes back.

Deploying is free. You pay 5% of the model compute it uses, and nothing else. No call required to find out whether this works on your data.

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