Customer Behavior for Specialty on Looker, built for technology
Ward pulls from looker API for query results, underlying database (direct), lookML model metadata from Looker / Looker Studio, scans continuously customer throughout 5,000+ specialty SKUs, and delivers the technology read every morning.
The full picture: specialty customer, Looker data, Technology decisions
Specialty Retail changes the scale of the problem: 5,000+ SKUs, every one of your boutiques. High-consideration purchases, curated assortments, and customer lifetime value. Ward tracks the metrics that matter for margin-rich retail.
The business wants AI. You sign off on the architecture. Ward filters to what a Head of IT can act on and drops the rest.
Customer Behavior, in one sentence. Ward tracks basket composition shifts, daypart patterns, and customer segment migration.
How it runs. Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
Setup: Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
What you get
- Customer segment migration
- Cross-sell opportunity detection
- Basket composition trends
- Daypart behavior modeling
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 Customer matters for Specialty retail
A loyal specialty customer is worth an order of magnitude more than a one-time buyer. Ward tracks the signals that predict long-term value: purchase frequency acceleration, category expansion, and associate-influenced purchasing, identifying which customers are becoming loyalists and which are at risk.
What Ward has eyes on.
Coverage is store by store, category by category. Ward keeps a running read on CLV, conversion rate, units per transaction over 5,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the estate is fine and knowing which seven boutiques are not.
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 daily cards. Your BI is untouched.
The customer model runs daily, not on a reporting calendar. It detects the pattern, traces the driver, and attaches a recommended move before the number reaches a review deck.
At the metric level. Ward tracks purchase frequency trajectory, category exploration patterns, price tier migration, associate attachment, and at-risk signals like declining visit frequency or narrowing category purchases.
Why this combination
is its own problem.
A Head of IT does not need the customer 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 Emerging loyalist signals (frequency growth + category expansion + tier trade-up) typically appear 60-90 days before the customer reaches loyal-purchase patterns; missing this window costs 30-50% conversion rate to loyalty.
- 02 Associate attachment is a powerful retention signal in specialty but rarely appears in CRM analytics because POS data doesn't carry associate attribution.
Benchmarks. Specialty top-decile customers typically generate 50-70% of revenue at 5-15x median LTV. Emerging-loyalist conversion when targeted within 60-90 days of signal: 35-55%; missed window drops to 15-25%.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward reads from Looker / Looker Studio with read-only credentials and begins ingesting looker API for query results and underlying database (direct). No config changes on your side. First insight cards arrive in two days.
-
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 sends daily cards daily, each with the cause and a recommended action. Volume settles at a level a single person can read over coffee. The measure of success is not how many 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
The business wants AI. You sign off on the architecture.
- ×Business sponsor already chose the vendor. You inherit the security review
- ×Every AI vendor wants write access and a copy of the production data
- ×Model lock-in means rewriting the stack when GPT or Claude moves again
- ×Audit trail is an afterthought. Compliance has nothing to pull on
- ×Data lake project keeps getting bumped for the next thing the business wants
- ✓Federated query: data stays in your warehouse. No copies, no shadow lake
- ✓Read-only credentials. Cedar policies enforce least-privilege per agent
- ✓LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
- ✓Every query, every model, every source logged. SIEM-ready audit output
- ✓VPC peering, PrivateLink, SOC 2 II. Your security review is short
74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. Source: Gartner
Specialty KPI impact
Frequently asked questions
Ward tracks basket composition shifts, daypart patterns, and customer segment migration. 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 analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
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.
The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.
Ward delivers daily insight cards covering CLV, Conversion rate, Units per transaction, tailored for Technology decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks purchase frequency trajectory, category exploration patterns, price tier migration, associate attachment, and at-risk signals like declining visit frequency or narrowing category purchases.
Ward identifies a cohort exhibiting "emerging loyalist" behavior: increasing visit frequency, trading up in price tier, and expanding from their original category into new ones. Historical modeling shows this pattern strongly predicts top-decile lifetime value. Ward recommends personalized outreach, tasting events, staff recommendations, curated selections, and the targeted cohort shows substantially higher retention than a matched control group.
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 Specialty customer 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.