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BI · Tableau

Tableau plus Ward: customer behavior

Customer Behavior needs tableau Hyper extracts and underlying database (direct). Tableau has both. Ward does the rest.

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

How Ward attaches to Tableau.

Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.

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
Tableau Hyper extractsUnderlying database (direct)Published data source metadata

Ward does not replace Tableau. Ward adds the proactive layer Tableau lacks. When a metric moves, Ward explains why and recommends action.

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 Tableau 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

Understand the person behind the basket.

Ward tracks basket composition shifts, daypart patterns, and customer segment migration.

Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.

  • Basket composition trends
  • Daypart behavior modeling
  • Customer segment migration
  • Cross-sell opportunity detection

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

Evening shoppers (6-9 PM) adding 22% more ready-to-eat items vs last quarter. Deli adjacency planogram opportunity identified.

Customer experience leaders outperform laggards by 80% in revenue growth. — Forrester

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 Tableau 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 Tableau 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 customer behavior off Tableau looks like once it is live. Retail is where Ward is deployed, not what Ward is.

Grocery

30,000+ SKUs · stores

Ward detects rising ready-to-eat meal purchases during the evening daypart across urban stores while raw protein and produce decline in the same window. The shift correlates with a new meal-kit competitor entering the market. Ward recommends expanding prepared foods in affected stores and testing a quick-meal bundle priced to undercut the delivery service.

The grocery build →

Convenience

3,000+ SKUs · locations

Ward reveals a clear split in morning rush transactions: most are coffee-only with low basket value, while the minority adding food have baskets several times larger. Stores with breakfast displayed adjacent to the coffee station convert significantly more coffee-only customers to coffee-plus-food than stores requiring a separate trip down an aisle. Ward recommends a layout test moving grab-and-go breakfast next to the coffee bar at the lowest-converting stores.

The convenience build →

Fashion

15,000+ SKUs · locations

Ward detects meaningful migration from full-price to sale-only purchasing in a high-value customer segment. It correlates the shift with competitor store openings, recent price increases on workwear basics, and declining quality mentions in online reviews. The merchandising team uses the insight to reformulate a core product and adjust pricing on the most price-sensitive items.

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.

Head of E-Com

E-Commerce

Basket composition shifts and segment migration, tracked weekly.

What they see →

VP Merchandising

Merchandising

Daypart-driven adjacency recommendations for planograms.

What they see →

CFO

Finance

ATV and UPT drivers identified by store and segment.

What they see →
06 · What changes

What connecting Tableau actually moves.

MeasureEffectWhy
Time to Insight Cards before dashboards Anomalies explained before anyone opens Tableau.
Anomaly Detection Extract-gap coverage Catches issues between Tableau extract refresh cycles.
Decision Velocity Investigation eliminated Root cause embedded in cards; no ad-hoc queries needed.
Analyst Productivity Detection work offloaded Analysts freed from triage to focus on strategic work.
Questions

What a technical evaluation asks first.

Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.

Ward reads Tableau Hyper extracts, Underlying database (direct) and Published data source metadata. 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 Tableau in place and holds no second copy of your data. Nothing is exported to run Customer Behavior, 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 tracks basket composition shifts, daypart patterns, and customer segment migration. Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.

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

Ward detects rising ready-to-eat meal purchases during the evening daypart across urban stores while raw protein and produce decline in the same window. The shift correlates with a new meal-kit competitor entering the market. Ward recommends expanding prepared foods in affected stores and testing a quick-meal bundle priced to undercut the delivery service.

Explore

Customer Behavior on another system.

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

What else Ward computes off Tableau.

Stockout Prediction Shrinkage Detection Price Optimization Demand Forecasting Assortment Planning Promo Effectiveness Fill Rate Monitoring
Tableau connector → Customer overview → All 540+ connectors → Every pairing →

Point Ward at your Tableau 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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