Shrinkage Detection for Head of E-Commerce / Digital, read from Tableau
Your online and offline data live in different worlds. Ward picks up shrinkage movement in your Tableau data and attributes what caused it.
How a Head of E-Com runs shrinkage off Tableau
Your online and offline data live in different worlds. Ward pulls forward the signals that change a e-commerce decision.
Shrinkage Detection, in one sentence. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
What Ward does with that: Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
The connection itself. Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.
Capabilities
- Pattern recognition across time
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
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" |
What Ward has eyes on.
The connection to Tableau is read-only and runs on your schedule. Ward pulls from tableau Hyper extracts, underlying database (direct), and the rest of the feed, then combines it with external context your BI does not carry: weather, local events, competitor pricing.
Find the leak before it drains you. 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.
Why this combination
is its own problem.
Shrinkage Detection produces a lot of output that is technically correct and operationally useless to e-commerce. Ward filters on whether the finding changes a decision a Head of E-Com can actually make.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Tableau. Ward pulls from tableau Hyper extracts, underlying database (direct), published data source metadata and starts building baselines. First insight cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: operating rhythm
Insight cards arrive every morning 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 Tableau
Ward does not replace Tableau. Ward adds the proactive layer Tableau lacks. When a metric moves, Ward explains why and recommends action.
Setup: Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.
Data Ward reads from Tableau
Impact metrics with Tableau
Data lake enrichment
Ward enriches Tableau data with: Tableau data sources, Underlying database, Weather & events, Competitor pricing, Customer data
Your online and offline data live in different worlds.
- ×Nobody can see one inventory position across every channel
- ×Online promo performance is measured separately from in-store
- ×Customer behavior data is siloed by channel
- ×BOPIS/BORIS operational complexity is growing unchecked
- ×Digital marketing attribution stops at the click
- ✓Unified insight cards across online and in-store channels
- ✓Cross-channel promo effectiveness with true attribution
- ✓Customer journey tracking across digital and physical touchpoints
- ✓BOPIS fulfillment performance monitoring with exception cards
- ✓Full-funnel marketing attribution to in-store conversion
Retailers with unified omnichannel data see 30% higher lifetime value per customer. Source: Harvard Business Review
Frequently asked questions
Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context. Data points include: Tableau Hyper extracts, Underlying database (direct), Published data source metadata.
Your online and offline data live in different worlds. Ward solves this with automated insight cards: Unified insight cards across online and in-store channels. Cross-channel promo effectiveness with true attribution. Customer journey tracking across digital and physical touchpoints.
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 shrinkage problems Ward catches.
Root causes, not just alerts. See it on your data.
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.