Head of Procurement: specialty pricing, read from Looker / Looker Studio
Merchandising wants Ward. You sign the contract. Ward detects specialty pricing movement in your Looker data before it compounds, with the driver and a recommended action attached.
How a specialty Head of Procurement runs pricing on Looker / Looker Studio
Price Optimization is a card type Ward runs continuously. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.
In a Specialty Retail fleet the job is 5,000+ SKUs over boutiques. High-consideration purchases, curated assortments, and customer lifetime value. Ward tracks the metrics that matter for margin-rich retail.
Merchandising wants Ward. You sign the contract. Ward writes the finding at the altitude a Head of Procurement works at.
How it runs. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
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
- Competitive price monitoring
- Margin-volume tradeoff modeling
- Real-time elasticity measurement
- Category-level price sensitivity
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 Pricing matters for Specialty retail
Specialty pricing lives or dies on value perception. The opportunity lies in separating products with "curation premium" tolerance, where customers won't compare, from items cross-shopped against Amazon where a gap triggers showrooming. Ward segments pricing power by product and customer segment to maximize margin without triggering comparison behavior.
Why this combination
is its own problem.
A generic pricing model applied to a specialty estate produces alerts nobody trusts. The thresholds are wrong, because specialty baselines are wrong for it. Ward learns the baseline from your own boutiques instead of importing one.
- 01 Associate-driven upsell categories (consultative beauty, jewelry, custom furniture) tolerate higher prices than self-service categories, but pricing rules don't differentiate.
- 02 Exclusive collaborations and artisan products often have 30-60% pricing headroom that gets left on the table because chain pricing logic uses cross-category averages.
Benchmarks. Specialty showrooming-vulnerable SKU share: 15-30%. Exclusive and curated products typically carry 100-300 bps of unrealized margin. Associate-driven categories tolerate 5-15% higher pricing than self-service equivalents because of the service value.
What Ward has eyes on.
Every one of your boutiques gets its own baseline. Ward keeps a running read on CLV, conversion rate, units per transaction against it and pulls forward 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.
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 stitches together it with external context your BI does not carry: weather, local events, competitor pricing.
The pricing model runs on a daily cycle, not on a reporting calendar. It flags the pattern, traces the cause, and attaches a recommended move before the number reaches a review deck.
At the metric level. Ward tracks showrooming risk by SKU, curation premium tolerance, customer segment sensitivity, and associate-driven upsell effectiveness, staffed departments tolerate higher prices because of the service component.
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 cards arrive in two days.
-
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 cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
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 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
Specialty KPI impact
Frequently asked questions
Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume. 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 continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
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.
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 CLV, Conversion rate, Units per transaction, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks showrooming risk by SKU, curation premium tolerance, customer segment sensitivity, and associate-driven upsell effectiveness, staffed departments tolerate higher prices because of the service component.
Ward identifies a minority of the assortment, branded items available on Amazon, being actively showroomed. The majority, including exclusive collaborations and artisan products, has near-zero price sensitivity because customers can't comparison shop. Ward recommends matching online pricing on showroomed SKUs while implementing increases on non-comparable items, delivering a net margin improvement with better competitive perception on the items that actually get compared.
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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Root causes, not just alerts. See it on your data.
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