Pricing · Shopify · CFO

Shopify plus Ward: pricing for a CFO

Ward pulls from orders & line items, product catalog, inventory levels from Shopify / Shopify Plus, runs pricing on it, and sends the finance read on a daily cycle.

How a CFO runs pricing off Shopify / Shopify Plus

Your P&L surprises are born on the store floor. Ward writes the finding at the altitude a CFO works at.

Here is price optimization in plain terms. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.

What Ward does with that: Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.

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

The short list

  • Category-level price sensitivity
  • Competitive price monitoring
  • Margin-volume tradeoff modeling
  • Real-time elasticity measurement
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"
Pricing on Shopify data, live product demo.

What Ward has eyes on.

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.

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 findings. Your Commerce is untouched.

Why this combination
is its own problem.

Price Optimization needs orders & line items and product catalog at minimum. Shopify / Shopify Plus carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.

What the first 90 days
actually look like.

  1. 01

    Week 1: read-only connection

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

  2. 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.

  3. 03

    Weeks 4 to 12: operating rhythm

    Cards arrive on a daily cycle 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

Your P&L surprises are born on the store floor.

Pain points
  • ×Margin erosion only surfaces at month-end close
  • ×Inventory carrying costs are a black box
  • ×Working capital tied up in slow-moving stock nobody is watching
  • ×Same-store sales comps lack decomposition into actionable drivers
  • ×Capex decisions for store remodels lack unit-economics evidence
How Ward helps
  • GMROI tracking by category with weekly insight cards
  • Inventory carrying cost alerts when capital efficiency drops
  • Working capital optimization recommendations based on turnover trends
  • SSS decomposition into traffic, conversion, and basket components
  • Store-level unit economics cards for capex prioritization

Inventory distortion, overstock and out-of-stock combined, costs retailers $1.77 trillion globally. Source: IHL Group

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.

Your P&L surprises are born on the store floor. Ward solves this with automated insight cards: GMROI tracking by category with weekly insight cards. Inventory carrying cost alerts when capital efficiency drops. Working capital optimization recommendations based on turnover trends.

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 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.

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