Demand Forecasting on BigCommerce, for procurement
Ward reads straight from orders, products & variants, customers from BigCommerce, runs demand on it, and hands you the procurement read on a daily cycle.
Demand Forecasting on BigCommerce, for procurement
Here is demand forecasting in plain terms. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
Merchandising wants Ward. You sign the contract. Ward filters to what a Head of Procurement can act on and drops the rest.
How Ward sends Demand insight cards: Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
Setup: Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.
What you get
- Event and holiday modeling
- Automatic reorder point recalculation
- Store-SKU-day level precision
- Weather-driven adjustment
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO 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.
labor_scheduling…
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 |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
What Ward has eyes on.
The demand model runs every morning, not on a reporting calendar. It picks up the pattern, explains what caused it, and attaches what to do about it before the number reaches a review deck.
Ward pulls from orders, products & variants, customers from BigCommerce on a read-only connection. Nothing is written back, and your Commerce configuration does not change. BigCommerce stays the system of record.
Why this combination
is its own problem.
Demand Forecasting needs orders and products & variants at minimum. BigCommerce 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.
-
01
Week 1: read-only connection
Ward sits on top of BigCommerce with read-only credentials and begins ingesting orders and products & variants. No config changes on your side. First insight cards arrive within 48 hours.
-
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.
-
03
Weeks 4 to 12: steady state
Ward delivers findings daily, each with root cause 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 BigCommerce
Ward connects to BigCommerce for omnichannel retailers running headless or traditional storefronts. Orders, catalog, and customer data drive insight cards.
Setup: Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.
Data Ward reads from BigCommerce
Impact metrics with BigCommerce
Data lake enrichment
Ward enriches BigCommerce data with: Orders & variants, Customer behavior, Marketing data, Returns & exchanges, Competitor pricing
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
Frequently asked questions
Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events. Data points include: Orders, Products & variants, Customers, Inventory, Promotions, Storefront analytics.
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.
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
See what demand 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.