A fashion Head of E-Com running promos on Looker
Your online and offline data live in different worlds. Ward picks up fashion promos movement in your Looker data early, with the driver and a recommended move attached.
How a fashion Head of E-Com runs promos on Looker / Looker Studio
For Fashion & Apparel retailers, that means continuous review 15,000+ SKUs across locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
Your online and offline data live in different worlds. Ward hands you insight cards scoped to e-commerce decision-making.
Here is promo effectiveness in plain terms. Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.
The mechanism. Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
The connection itself. Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
What it does
- Net lift measurement (not gross)
- Cannibalization quantification
- Pull-forward detection
- Promo ROI scorecards
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 Promos matters for Fashion retail
Blanket promotions drive traffic but train customers to wait for sales. Ward measures the full cycle, demand suppression before the event, lift during, and pull-forward decline after, to reveal which promotions build revenue and which merely shift it around the calendar.
Why this combination
is its own problem.
A Head of E-Com 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 Customer-level promo dependency is invisible at the cohort level; the top 20% of "loyalists" can be 80% promo-dependent without the chain noticing.
- 02 Margin-per-unit erosion compounds when stack-on offers (employee discount + cart promo + free shipping) trigger together, typical promo P&L doesn't track stacked effective discount.
Benchmarks. Fashion promo events show 40-100% gross weekend lift but typically 5-25% net incrementality after pre-event suppression and pull-forward. Operators that convert blanket events to targeted-acquisition offers usually recover 200-600 bps of gross margin.
What Ward has eyes on.
Ward keeps a running read on 15,000+ SKUs throughout your locations, at the store-category level rather than the chain roll-up. The metrics under watch include sell-through rate, markdown %, return rate. A roll-up hides a single-store problem inside a healthy average, which is how markdown timing stays invisible for a quarter.
Ward reads 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 insight cards. Your BI is untouched.
The promos model runs every morning, not on a reporting calendar. It detects the pattern, attributes what caused it, and attaches what to do about it before the number reaches a review deck.
At the metric level. Ward measures the full promo cycle: pre-event suppression, event lift, post-event decline, new vs returning customer mix, and margin-per-unit impact. It also calculates customer-level promo dependency scores to flag at-risk segments.
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 cards arrive inside the first 48 hours.
-
02
Weeks 2 to 3: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the insight cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands you findings each day, each with what caused it and what to do about it. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight 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 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
Fashion KPI impact
Frequently asked questions
Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading. 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 isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
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.
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.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for E-Commerce decision-making. Each card includes what changed, why it matters, and what to do next.
Ward measures the full promo cycle: pre-event suppression, event lift, post-event decline, new vs returning customer mix, and margin-per-unit impact. It also calculates customer-level promo dependency scores to flag at-risk segments.
Marketing declares the annual Friends & Family event a win based on weekend revenue lift. Ward's full-cycle analysis shows substantial pre-event demand suppression and post-event pull-forward decline that cut net incrementality roughly in half. New customer acquisition during the event ran well below non-promo weekends. Ward recommends replacing the blanket discount with targeted acquisition offers that actually grow the customer base.
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 promos 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.