Promos · BigCommerce · Head of E-Com

Promo Effectiveness + BigCommerce: Built for Head of E-Com

Most retailers find Promos problems too late. Ward delivers automated insight cards. What changed, why, and what to do. While there's still time to act. Your E-Commerce team has the data. What they don't have is bandwidth to find what's buried in it.

Promo Effectiveness powered by BigCommerce

Promo Effectiveness is the process of ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.

When connected to BigCommerce, Ward reads orders, products & variants, customers and enriches them with contextual signals to generate promos insight cards. Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.

How Ward delivers Promos insight cards: Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.

Key capabilities

  • 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

Dashboards

Pinned views built from saved data-lake queries.

Revenue vs. forecast +4.2% WoW
Gross margin % −3.2pp
Fill rate, fresh 83%
Shrink, West region +0.8pp

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"
Live product demo, Ward analyzing retail data in real time.

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

Orders
Products & variants
Customers
Inventory
Promotions
Storefront analytics

Impact metrics with BigCommerce

Sell-Through Rate
Velocity tracked live
Slow movers flagged early enough to reallocate inventory.
Customer LTV
Churn risk identified
Cohort analysis surfaces lapsing buyers and re-engagement timing.
Conversion Rate
Buyer vs browser split
Patterns that convert separated from those that just browse.
Inventory Turnover
Reorder cadence optimized
Demand signals calibrate reorder points across the catalog.

Data lake enrichment

Ward enriches BigCommerce data with: Orders & variants, Customer behavior, Marketing data, Returns & exchanges, Competitor pricing

Your online and offline data live in different worlds.

Pain points
  • ×Nobody can see one inventory position across every channel
  • ×Online promo performance is measured separately from in-store
  • ×Customer behavior data is siloed by channel
  • ×BOPIS/BORIS operational complexity is growing unchecked
  • ×Digital marketing attribution stops at the click
How Ward helps
  • Unified insight cards across online and in-store channels
  • Cross-channel promo effectiveness with true attribution
  • Customer journey tracking across digital and physical touchpoints
  • BOPIS fulfillment performance monitoring with exception cards
  • Full-funnel marketing attribution to in-store conversion

Retailers with unified omnichannel data see 30% higher lifetime value per customer. Source: Harvard Business Review

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.

Your online and offline data live in different worlds. Ward solves this with automated insight cards: Unified insight cards across online and in-store channels. Cross-channel promo effectiveness with true attribution. Customer journey tracking across digital and physical touchpoints.

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