Specialty shrinkage from Looker, briefed to merchandising
Ward reads straight from looker API for query results, underlying database (direct), lookML model metadata from Looker / Looker Studio, monitors shrinkage over 5,000+ specialty SKUs, and hands you the merchandising read daily.
How a specialty VP Merchandising runs shrinkage on Looker / Looker Studio
Your category managers are drowning in spreadsheets. Ward filters to what a VP Merchandising can act on and drops the rest.
Shrinkage Detection is a finding type Ward runs continuously. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
A Specialty Retail operator is monitoring 5,000+ SKUs over boutiques. High-consideration purchases, curated assortments, and customer lifetime value. Ward tracks the metrics that matter for margin-rich retail.
How it runs. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
How the connection works. Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
The short list
- Receiving dock anomaly detection
- Pattern recognition across time
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled traffic against conversion by hour for the four flagships. Traffic is fine. The gap is coverage and depth.
| Signal | Finding |
|---|---|
traffic_conversion | Conversion 18.4% vs. 23.1% chain, all of the gap in 12–2p and 5–7p |
labor.coverage | One associate on the floor through both peaks at 3 of 4 doors |
inventory.depth | Top 20 styles at 1.4 units per size, walk-away rate +9% |
Recommend: add floor coverage to both peak windows, deepen the top 20 styles to three per size at the flagships, and route the walk-away list to clienteling.
traffic_conversion…
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 |
|---|---|---|
shopify_orders_daily | import | 2m ago |
lightspeed_store_sales | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
retail_customer_ltv | import | 1h ago |
retail_traffic_conversion | import | 1h ago |
retail_clienteling_log | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
ops-read-default | permit | Model::* |
crm-read-ltv | permit | Model::"customer_ltv" |
associate-pii-blocked | forbid | Model::"customer_pii" |
merch-read-assortment | permit | Model::"sales_by_tier" |
Why Shrinkage matters for Specialty retail
With manageable SKU counts, specialty retail can track inventory discrepancies at the individual item level, revealing patterns tied to specific shelf positions, staffing configurations, or time windows that aggregate reporting would never surface. Per-unit loss is high enough that each incident matters.
Why this combination
is its own problem.
A VP Merchandising does not need the shrinkage model explained. They need to know which stores moved, why, and what to do by end of day. Ward writes the finding at that altitude.
- 01 Specialty per-unit shrink dollar exposure is high enough that loss patterns affecting just 5-15 units per store per quarter still represent meaningful margin loss, but periodic counts can't detect at that frequency.
- 02 High-traffic mall and tourist locations have fundamentally different staffing-to-traffic ratios than standalone stores; loss patterns track to the staffing density rather than the brand or category.
Benchmarks. Specialty retail shrink: 1.0-2.0% standalone, 1.8-3.5% mall and tourist locations. High-value, easily concealable categories (premium fragrance, jewelry, small electronics) drive 40-65% of dollar shrink despite 5-15% of unit volume.
What Ward has eyes on.
The shrinkage model runs every morning, not on a reporting calendar. It spots the pattern, attributes the driver, and attaches the next step before the number reaches a review deck.
Every one of your boutiques gets its own baseline. Ward scans continuously CLV, conversion rate, units per transaction against it and surfaces only the deviations that hold up. The two that show up most in specialty retail are assortment curation and customer lifetime value, and both are baseline problems before they are P&L problems.
Ward reads straight from Looker rather than replacing it. Looker API for query results, underlying database (direct), lookML model metadata come across on a read-only connection, get enriched with contextual data, and come back as cards. Your BI is untouched.
At the metric level. Ward uses item-level tracking feasible at the 5K-SKU scale, maps loss to store layout and traffic flow, and monitors high-value item movement between floor and backroom.
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 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: steady state
Ward hands back daily cards daily, each with what caused it and a recommended action. Volume settles at a level a single person can read over coffee. The measure of success is not how many daily cards arrive, it is how many get acted on.
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
Specialty KPI impact
Frequently asked questions
Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error. For Specialty retail specifically, Ward monitors 5,000+ SKUs across your boutiques and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks CLV, Conversion rate, Units per transaction, Repeat purchase rate, Sell-through by tier at the store-category level. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
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.
Yes. Ward reads Looker data and combines it with contextual signals (weather, events, demographics) to generate Specialty-specific insight cards. No custom development required.
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.
Ward delivers daily insight cards covering CLV, Conversion rate, Units per transaction, tailored for Merchandising decision-making. Each card includes what changed, why it matters, and what to do next.
Ward uses item-level tracking feasible at the 5K-SKU scale, maps loss to store layout and traffic flow, and monitors high-value item movement between floor and backroom.
Ward flags premium fragrance shrinkage running far above average at high-traffic mall locations. The loss concentrates on tester-adjacent units during weekend afternoons when staff-to-customer ratios drop. Ward recommends relocating premium fragrances behind the counter and adding floor coverage during peak windows. Implementation brings shrinkage back toward standalone-store levels.
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
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