Assortment · Shopify · Director Store Ops

Assortment Planning on Shopify, for store operations

Stock what sells. Cut what doesn't. Ward runs it off Shopify, scoped to what a Director Store Ops owns.

Assortment Planning on Shopify, for store operations

Assortment Planning. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.

Managing 800 stores from a spreadsheet is insane. Ward pulls forward the indicators that change a store operations decision.

The mechanism. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.

Setup: OAuth-based connection. Ward reads via Shopify Admin GraphQL API. Real-time webhooks for order and inventory events.

What you get

  • SKU rationalization recommendations
  • Whitespace opportunity detection
  • Planogram optimization inputs
  • Store cluster segmentation
app.getward.ai Live demo
Acme Retail @Merchandising: VP Analyst claude-sonnet default
A

Chat

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

Why did Store 37 miss target last week?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.

SignalFinding
labor_efficiencyRev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak
inventory.freshFresh fill 83%, backroom replenishment lag at 2–4p
promo.liftBOGO crackers cannibalized Brand Y by 28%, net category +6%

Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.

8 parallel queries 3 sources cited confidence 0.92
Show me how to fix the staffing mismatch.
You · 9:43 AM
Labor Agent · drafting schedule diff
Querying labor_scheduling
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. forecast
$48.2M
+4.2% WoW
Gross margin %
24.1%
−3.2pp
Fill rate, fresh
83.4%
−4.1pp
Shrink, West region
2.41%
+0.8pp
Revenue vs. forecast 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
sap_pos_transactionsimport2m ago
sap_inventory_shrinkageimport2m ago
sap_labor_schedulingimport14m ago
retail_inventory_weeklyimport1h ago
retail_google_ads_dailyimport1h ago
retail_meta_ads_dailyimport1h 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-shrinkagepermitModel::"inventory_shrinkage"
vendor-blockedforbidModel::"labor_*"
region-west-onlypermitTenant::"acme"
Assortment on Shopify data, live product demo.

What Ward has eyes on.

Ward reads straight from Shopify rather than replacing it. Orders & line items, product catalog, inventory levels come across on a read-only connection, get enriched with contextual data, and come back as cards. Your Commerce is untouched.

The assortment model runs every morning, not on a reporting calendar. It spots the pattern, explains what caused it, and attaches a recommended action before the number reaches a review deck.

Why this combination
is its own problem.

Shopify / Shopify Plus was built for transactions, not for store operations decisions. The data is right there and it is in the wrong shape. Ward ingests orders & line items and product catalog and rewrites them as a decision.

What the first 90 days
actually look like.

  1. 01

    Week 1: read-only connection

    Ward reads from Shopify / Shopify Plus with read-only credentials and begins ingesting orders & line items and product catalog. No config changes on your side. First findings arrive within 48 hours.

  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: operating rhythm

    Findings 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 Shopify / Shopify Plus

Ward connects to Shopify and Shopify Plus via the Admin API. Orders, products, inventory, and customer data power Ward insight cards for omnichannel retailers.

Setup: OAuth-based connection. Ward reads via Shopify Admin GraphQL API. Real-time webhooks for order and inventory events.

Data Ward reads from Shopify

Orders & line items
Product catalog
Inventory levels
Customer profiles
Discount usage
Fulfillment data

Impact metrics with Shopify

Sell-Through Rate
Slow movers reallocated
Order velocity tracked; underperformers flagged before markdowns.
Return Rate
Return-prone patterns spotted
Behavioral signals identify high-return product and buyer combos.
Customer LTV
Re-engagement timed right
Purchase cadence and cohort data surface lapsing customers.
Inventory Turnover
Reorder points tightened
Demand signals optimize safety stock across the catalog.

Data lake enrichment

Ward enriches Shopify data with: Order & line items, Customer behavior, Marketing attribution, Returns & exchanges, Competitor pricing

Managing 800 stores from a spreadsheet is insane.

Pain points
  • ×Morning check-ins rely on phone calls and email chains
  • ×No single view of which stores need attention today
  • ×Labor scheduling is disconnected from demand signals
  • ×Planogram compliance is checked manually, quarterly
  • ×Exception management is reactive and inconsistent
How Ward helps
  • Morning brief delivered at 06:47 with prioritized action list
  • Estate-wide heat map of store performance, updated hourly
  • Staffing recommendations correlated with predicted traffic
  • Planogram compliance anomalies detected and flagged
  • Consistent exception handling with recommended actions

Poor labor allocation and inconsistent execution cost multi-store retailers 3–5% in lost sales. Source: RSR Research

Frequently asked questions

OAuth-based connection. Ward reads via Shopify Admin GraphQL API. Real-time webhooks for order and inventory events. Data points include: Orders & line items, Product catalog, Inventory levels, Customer profiles, Discount usage, Fulfillment data.

Managing 800 stores from a spreadsheet is insane. Ward solves this with automated insight cards: Morning brief delivered at 06:47 with prioritized action list. Estate-wide heat map of store performance, updated hourly. Staffing recommendations correlated with predicted traffic.

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 assortment 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.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
Your contact info