BigCommerce: Built for Head of LP
Your BigCommerce data holds answers nobody has time to extract. Ward reads it via read-only APIs. Your Loss Prevention team has the data. What they don't have is bandwidth to find what's buried in it.
Ward + BigCommerce for Head of Loss Prevention
Ward connects to BigCommerce and delivers AI-powered insight cards tailored for loss prevention leaders. Ward connects to BigCommerce for omnichannel retailers running headless or traditional storefronts. Orders, catalog, and customer data drive insight cards.
Shrinkage costs you more than you think. Ward finds out where. Ward solves this by reading BigCommerce data — orders, products & variants, customers, inventory — and generating automated insight cards with root cause analysis and recommended actions.
Setup: Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.
What Ward delivers
- Store-level shrinkage tracking with cause attribution
- Anomaly detection flags stores deviating from estate average
- Receiving dock discrepancy patterns identified automatically
- Correlation analysis links operational changes to loss shifts
- Trend analysis catches slow-bleed patterns audits miss
Data Ward reads from BigCommerce
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
Shrinkage costs you more than you think. Ward finds out where.
- ×Shrinkage is a year-end surprise, not a weekly metric
- ×Cannot distinguish theft from spoilage from admin error
- ×High-shrinkage stores only identified during audits
- ×No correlation between operational changes and loss patterns
- ×Exception-based reporting misses slow-bleed patterns
- ✓Store-level shrinkage tracking with cause attribution
- ✓Anomaly detection flags stores deviating from estate average
- ✓Receiving dock discrepancy patterns identified automatically
- ✓Correlation analysis links operational changes to loss shifts
- ✓Trend analysis catches slow-bleed patterns audits miss
US retail shrinkage hit $112.1 billion in 2022 — up 19.4% year over year. — National Retail Federation
What a Ward insight card looks like
Store #37 showing 4.2% shrinkage vs 1.8% estate average. Pattern suggests receiving dock discrepancy, not shoplifting.
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.
Shrinkage costs you more than you think. Ward finds out where. Ward solves this with automated insight cards: Store-level shrinkage tracking with cause attribution. Anomaly detection flags stores deviating from estate average. Receiving dock discrepancy patterns identified automatically.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish robust 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
Insights surface
Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action—not just a chart to interpret.
Insights become actions
Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel—mobile push, text, or email. Not every insight needs a ticket. But when one does, it has an owner.
Your team responds
Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal—Ward learns what worked, what missed, and why.
Outcomes measured
Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.
Agents get sharper
Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.
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