Customer Behavior for Fashion on Looker, built for procurement
Ward reads straight from looker API for query results, underlying database (direct), lookML model metadata from Looker / Looker Studio, keeps a running read on customer over 15,000+ fashion SKUs, and hands back the procurement read each day.
The full picture: fashion customer, Looker data, Procurement decisions
Here is customer behavior in plain terms. Ward tracks basket composition shifts, daypart patterns, and customer segment migration.
For Fashion & Apparel retailers, that means monitoring 15,000+ SKUs over locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
Merchandising wants Ward. You sign the contract. Ward filters to what a Head of Procurement can act on and drops the rest.
The mechanism. Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
The connection itself. Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
Capabilities
- Cross-sell opportunity detection
- Basket composition trends
- Daypart behavior modeling
- Customer segment migration
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled fall denim against last season’s curve at the same selling week. Two causes, both still fixable this week.
| Signal | Finding |
|---|---|
sell_through | Week 6 sell-through 38% vs. 52% plan, concentrated in 3 of 9 door clusters |
size_curve | Waist 30–32 sold out in 41 doors while 36–38 sits at 71% on hand |
markdown.ladder | First markdown is 3 weeks later than LY, weeks-of-supply now 11.4 |
Recommend: transfer 30–32 out of the 12 overstocked doors, hold the ladder on core indigo, and take the first markdown on light wash now while it still clears at 20%.
sell_through_weekly…
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 |
shopify_returns_reasons | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
cegid_store_sales | import | 1h ago |
retail_size_curve_actuals | import | 1h ago |
retail_markdown_ladder | 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-markdown | permit | Model::"markdown_ladder" |
vendor-blocked | forbid | Model::"labor_*" |
ecom-read-returns | permit | Model::"returns_reasons" |
Why Customer matters for Fashion retail
Lifecycle moments, new jobs, size changes, trend adoption, create predictable opportunity windows in fashion. When a customer shifts from full-price to sale-only purchasing, that's a churn signal. Ward tracks these behavioral shifts at the cohort level to inform marketing spend, staffing, and inventory positioning.
What Ward has eyes on.
The connection to Looker / Looker Studio is read-only and runs on your schedule. Ward ingests looker API for query results, underlying database (direct), and the rest of the feed, then joins it with external context your BI does not carry: weather, local events, competitor pricing.
Understand the person behind the basket. 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.
Coverage is store by store, category by category. Ward tracks sell-through rate, markdown %, return rate across 15,000+ 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 locations are not.
At the metric level. Ward tracks purchase frequency cadence, full-price vs markdown mix, category migration, size consistency, and cohort-level churn probability, each benchmarked against seasonal norms and customer lifecycle stage.
Why this combination
is its own problem.
A Head of Procurement rarely logs into Looker. They read what someone else pulled out of it, two days later. Ward removes the two days and the someone else.
- 01 Lapsed-customer reactivation campaigns target everyone past a threshold; without the lapse-cause segmentation, win-back ROI is flat across cohorts.
- 02 RFM scoring lumps customers by recency-frequency-monetary without separating the high-LTV full-price loyalist from the discount hunter who only buys clearance.
Benchmarks. Fashion top-decile customers drive 35-55% of revenue at 5-10x the LTV of median. A 90-day full-price-to-markdown ratio shift greater than 30% in a cohort typically precedes a 6-month churn lift of 20-40%.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of 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 returns daily cards daily, each with the cause and the next step. 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
Merchandising wants Ward. You sign the contract.
- ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
- ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
- ×Multi-year commits with auto-renew. No exit if the pilot stalls
- ×Renewals come back 30% higher, with no room to push back and no benchmark to cite
- ×Security and DPA reviews start after the team has already committed
- ✓MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
- ✓Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- ✓Transparent pricing set by scope and store count
- ✓14-day insight guarantee. If Ward doesn't deliver, month two is on us
- ✓AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature
Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner
Fashion KPI impact
Frequently asked questions
Ward tracks basket composition shifts, daypart patterns, and customer segment migration. For Fashion retail specifically, Ward monitors 15,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Sell-through rate, Markdown %, Return rate, Style velocity, Size accuracy 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 Fashion-specific insight cards. No custom development required.
Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 Type II underway, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing set by scope and store count.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks purchase frequency cadence, full-price vs markdown mix, category migration, size consistency, and cohort-level churn probability, each benchmarked against seasonal norms and customer lifecycle stage.
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.
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 Fashion customer problems Ward catches.
Root causes, not just alerts. See it on your data.
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