Fashion · Pricing · Head of Procurement

Head of Procurement: fashion pricing in daily cards

Ward surfaces pricing findings scoped to procurement, over every one of your fashion locations.

What a fashion Head of Procurement sees in pricing

Merchandising wants Ward. You sign the contract. Ward writes the finding at the altitude a Head of Procurement works at.

Price Optimization is a finding type Ward runs continuously. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.

Applied to Fashion & Apparel, the surface area is 15,000+ SKUs throughout locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.

How it runs. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.

The short list

  • Competitive price monitoring
  • Margin-volume tradeoff modeling
  • Real-time elasticity measurement
  • Category-level price sensitivity
app.getward.ai Live demo
Acme Apparel @Merchandising: Planning Analyst claude-sonnet default
A

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why is fall denim sell-through behind plan?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled fall denim against last season’s curve at the same selling week. Two causes, both still fixable this week.

SignalFinding
sell_throughWeek 6 sell-through 38% vs. 52% plan, concentrated in 3 of 9 door clusters
size_curveWaist 30–32 sold out in 41 doors while 36–38 sits at 71% on hand
markdown.ladderFirst 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%.

9 parallel queries 3 sources cited confidence 0.89
Draft the size-curve transfer list.
You · 9:43 AM
Allocation Agent · drafting transfer list
Querying sell_through_weekly
Ask anything, Ward routes to the right agent. Cmd+K

Reporting

Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.

7d13w52w
Revenue vs. plan
$31.7M
+3.1% WoW
Full-price mix
61.4%
−5.1pp
Sell-through, fall denim
38.2%
wk6, −9pp vs plan
Markdown rate
19.6%
+2.8pp
Revenue vs. plan 13 weeks actual, 6 weeks forecast, 80% interval
Actual Forecast 80% interval
% of plan 106 94 100 forecast → W−13 W−5 today +6wk
Holt-Winters + weather regressor MAPE 4.1% at 4wk Backtested 24 months Crosses plan in 3 weeks
Forecast error by horizon MAPE, 24-month backtest
1wk 2.1%
2wk 3.0%
4wk 4.1%
8wk 6.3%
13wk 8.9%
Accuracy bar for promo decisions: ≤5% at 4wk
Models in production Every forecast ships a model card
ModelHorizonMAPE
holt_winters4wk4.1%
arima_sarimax13wk8.9%
gbm_demand1wk2.1%
bayes_hiernew store11.4%

Sources

Connect external systems to the data lake.

NameTypeLast sync
shopify_orders_dailyimport2m ago
shopify_returns_reasonsimport2m ago
netsuite_inventory_snapshotimport14m ago
cegid_store_salesimport1h ago
retail_size_curve_actualsimport1h ago
retail_markdown_ladderimport1h ago
retail_ga4_website_dailyimport1h ago

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
merch-read-defaultpermitModel::*
finance-read-markdownpermitModel::"markdown_ladder"
vendor-blockedforbidModel::"labor_*"
ecom-read-returnspermitModel::"returns_reasons"
Pricing for Fashion, live product demo.

Why Pricing matters for Fashion retail

Fashion pricing is a one-way ratchet: mark down too early and you leave money on the table, too late and you're stuck with deep clearance. Ward monitors style-level sell-through velocity against time remaining in season and recommends optimal markdown depth and timing to maximize total margin dollars across the selling window.

What Ward has eyes on.

Coverage is store by store, category by category. Ward tracks sell-through rate, markdown %, return rate throughout 15,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the footprint is fine and knowing which seven locations are not.

Price on elasticity you can measure. 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.

At the metric level. Ward tracks style-level sell-through vs plan, weeks-of-supply remaining, size fragmentation, competitive markdown timing, and price sensitivity by brand tier. It models the full-season margin curve, not just immediate clearance math.

Signals · POS by style-size-color-store-day, planned vs actual sell-through, current inventory positions, competitor markdown observations, and historical end-of-season clearance curves.

Why this combination
is its own problem.

A Head of Procurement does not need the pricing 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 Competitive markdown calendars are tracked by brand, not by category, so seasonal markdown wars in adjacent categories aren't modeled.
  • 02 Calendar-driven markdowns (week 8: 25% off, week 12: 50% off) ignore that hot styles still sell at full price and slow ones need a 40% cut earlier.

Benchmarks. Fashion ends-season markdown rates: 20-35% basics, 35-55% trend, 50-70% denim, 60-80% outerwear in mild winters. Operators with style-level markdown cadence typically capture 200-500 bps of additional gross margin versus chain-uniform calendars.

What the first 90 days
actually look like.

  1. 01

    Week 1: read-only connection

    Ward pulls from your existing systems on a read-only connection. Nothing is written back. First findings arrive in two days.

  2. 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 findings are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: steady state

    Ward returns insight cards daily, each with the driver 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.

Merchandising wants Ward. You sign the contract.

Pain points
  • ×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
How Ward helps
  • 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

Markdown Rate
Shallower, earlier
Slow movers detected before deep clearance is the only option.
Sell-Through
More at full price
Style velocity cards flag underperformers early enough to reallocate.
Size Accuracy
Fewer size gaps
Size curves recalibrated by store cluster and season.
Return Rate
Better matching
Right size, right store means fewer returns.

Frequently asked questions

Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume. 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 continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.

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 style-level sell-through vs plan, weeks-of-supply remaining, size fragmentation, competitive markdown timing, and price sensitivity by brand tier. It models the full-season margin curve, not just immediate clearance math.

Mid-season, Ward identifies dozens of styles selling below plan and segments them by severity: some need immediate deep markdown, others need moderate discounts, and a group should hold price because they're trending toward natural clearance. This tiered approach recovers significant margin versus the standard blanket-markdown playbook.

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.

See what Fashion pricing 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.

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What are your goals?
Step 2 of 3
About your operation
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