Shrinkage · Looker · VP Merchandising

Shrinkage Detection + Looker: Built for VP Merchandising

Most retailers discover Shrinkage problems too late. Ward delivers automated insight cards — what changed, why, and what to do — while there's still time to act. Your Merchandising team has the data. What they don't have is bandwidth to find what's buried in it.

Shrinkage Detection powered by Looker / Looker Studio

Shrinkage Detection is the process of ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.

When connected to Looker / Looker Studio, Ward reads looker api for query results, underlying database (direct), lookml model metadata and enriches them with contextual signals to generate shrinkage insight cards. Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.

How Ward delivers Shrinkage insight cards: Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.

Key capabilities

  • Cause-level shrinkage attribution
  • Store-vs-estate benchmarking
  • Receiving dock anomaly detection
  • Pattern recognition across time
app.getward.ai
Live product demo — Ward analyzing retail data in real time.

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

Looker API for query results
Underlying database (direct)
LookML model metadata

Impact metrics with Looker

Time to Insight
Proactive, no login
Explains why metrics moved before anyone checks a dashboard.
Anomaly Detection
Inter-refresh coverage
Catches deviations between Looker dashboard refresh cycles.
Decision Velocity
Root cause attached
Every anomaly card includes cause analysis; no drill-down needed.
Data Utilization
Unused models activated
LookML dimensions and measures queried beyond built dashboards.

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.

Pain points
  • ×Promo planning relies on last year's playbook, not this week's data
  • ×Assortment reviews happen quarterly when they should happen daily
  • ×Price changes are reactive, not predictive
  • ×No visibility into true cannibalization across categories
  • ×Vendor negotiations lack real-time sell-through evidence
How Ward helps
  • 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. — McKinsey & Company

What a Ward insight card looks like

Ward · Shrinkage06:47 AM

Store #37 showing 4.2% shrinkage vs 1.8% estate average. Pattern suggests receiving dock discrepancy, not shoplifting.

✓ Action recommendedLooker data

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 robust 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.

Ward
Insight
Dispatch
Feedback
Evaluate
Learn
01

Insights surface

Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action—not just a chart to interpret.

Real-time detection Root cause + recommendation
02

Insights become actions

Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel—mobile push, text, or email. Not every insight needs a ticket. But when one does, it has an owner.

Tickets created automatically Dispatched to the right person
03

Your team responds

Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal—Ward learns what worked, what missed, and why.

Vote up / down Ticket completed Reasoning attached
04

Outcomes measured

Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.

KPI impact tracked Results vs. prediction scored
05

Agents get sharper

Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.

Cycle repeats, sharper each time
$1.8T
Projected global AI market by 2030
0
×
Customer acquisition lift for data‑driven orgs
0
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Foundation models shipped since 2022
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Guarantees any single model stays on top

See what shrinkage problems Ward catches.

Root causes, not just alerts. See it on your data.

Get a demo

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.

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