Promos · NetSuite · CFO

NetSuite plus Ward: promos for a CFO

Ward reads straight from sales orders, inventory, purchase orders from Oracle NetSuite, runs promos on it, and hands you the finance read every morning.

Promo Effectiveness on NetSuite, for finance

Your P&L surprises are born on the store floor. Ward surfaces the inputs that change a finance decision.

Here is promo effectiveness in plain terms. Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.

How it runs. Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.

Getting the data in. Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance.

The short list

  • Net lift measurement (not gross)
  • Cannibalization quantification
  • Pull-forward detection
  • Promo ROI scorecards
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"
Promos on NetSuite data, live product demo.

What Ward has eyes on.

Know which promos actually work. 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.

The connection to Oracle NetSuite is read-only and runs on your schedule. Ward reads sales orders, inventory, and the rest of the feed, then stitches together it with external context your ERP does not carry: weather, local events, competitor pricing.

Why this combination
is its own problem.

Promo Effectiveness produces a lot of output that is technically correct and operationally useless to finance. Ward filters on whether the finding changes a decision a CFO can actually make.

What the first 90 days
actually look like.

  1. 01

    Week 1: read-only connection

    Ward connects to Oracle NetSuite with read-only credentials and begins ingesting sales orders and inventory. No config changes on your side. First findings arrive inside the first 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: steady state

    Ward sends daily 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.

How Ward connects to Oracle NetSuite

Ward integrates with NetSuite SuiteCommerce, inventory management, and financials. Mid-market retailers get enterprise-grade insight cards.

Setup: Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance.

Data Ward reads from NetSuite

Sales orders
Inventory
Purchase orders
Customer records
Financial summaries
Item fulfillment

Impact metrics with NetSuite

Inventory Accuracy
Discrepancies reconciled live
POS and fulfillment data cross-checked against NetSuite counts.
Order Fill Rate
Stockouts preempted
Demand forecasting layered onto NetSuite purchase orders.
Gross Margin
Margin erosion flagged
Pricing drift and vendor cost creep caught across financials.
Cash Conversion Cycle
Days of supply reduced
Demand-inventory alignment frees tied working capital.

Data lake enrichment

Ward enriches NetSuite data with: Sales orders, Weather & events, Customer segments, Vendor performance, Market pricing data

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

Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance. Data points include: Sales orders, Inventory, Purchase orders, Customer records, Financial summaries, Item fulfillment.

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 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.

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