Specialty · BigCommerce · Head of E-Com

BigCommerce plus Ward, for a specialty Head of E-Com

A specialty Head of E-Com running BigCommerce gets CLV, conversion rate, units per transaction monitors on a daily cycle, with the driver attached.

What a specialty Head of E-Com gets from BigCommerce

For Specialty Retail retailers, that means monitoring 5,000+ SKUs over boutiques. High-consideration purchases, curated assortments, and customer lifetime value. Ward tracks the metrics that matter for margin-rich retail.

Your online and offline data live in different worlds. Ward filters to what a Head of E-Com can act on and drops the rest.

How the connection works. Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.

app.getward.ai Live demo
Acme Specialty @Store Ops: Retail Analyst claude-sonnet default
A

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why is conversion down at the flagship boutiques?
You · 9:42 AM
Schema Scout · routed to Store Ops Agent

I pulled traffic against conversion by hour for the four flagships. Traffic is fine. The gap is coverage and depth.

SignalFinding
traffic_conversionConversion 18.4% vs. 23.1% chain, all of the gap in 12–2p and 5–7p
labor.coverageOne associate on the floor through both peaks at 3 of 4 doors
inventory.depthTop 20 styles at 1.4 units per size, walk-away rate +9%

Recommend: add floor coverage to both peak windows, deepen the top 20 styles to three per size at the flagships, and route the walk-away list to clienteling.

8 parallel queries 3 sources cited confidence 0.91
Draft the clienteling outreach list.
You · 9:43 AM
Clienteling Agent · drafting outreach list
Querying traffic_conversion
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. plan
$27.5M
+4.4% WoW
Conversion rate
18.3%
−4.7pp
UPT, flagships
2.14
−0.18
At-risk CLV, top decile
$4.1M
−$310K
Revenue vs. plan 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
shopify_orders_dailyimport2m ago
lightspeed_store_salesimport2m ago
netsuite_inventory_snapshotimport14m ago
retail_customer_ltvimport1h ago
retail_traffic_conversionimport1h ago
retail_clienteling_logimport1h 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
ops-read-defaultpermitModel::*
crm-read-ltvpermitModel::"customer_ltv"
associate-pii-blockedforbidModel::"customer_pii"
merch-read-assortmentpermitModel::"sales_by_tier"
for Specialty on BigCommerce data, live product demo.

What Ward delivers

  • 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

Metrics Ward monitors

CLV
Conversion rate
Units per transaction
Repeat purchase rate
Sell-through by tier

Specialty challenges Ward solves

  • Assortment curation
  • Customer lifetime value
  • Staff selling effectiveness
  • Clienteling optimization
  • Inventory depth vs breadth

What Ward has eyes on.

The connection to BigCommerce is read-only and runs on your schedule. Ward ingests orders, products & variants, and the rest of the feed, then combines it with external context your Commerce does not carry: weather, local events, competitor pricing.

Coverage is store by store, category by category. Ward watches CLV, conversion rate, units per transaction across 5,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the estate is fine and knowing which seven boutiques are not.

Why this combination
is its own problem.

BigCommerce is the system of record for most specialty operators of your size, which means the inputs Ward needs are already there. The gap is not data collection. It is that nobody has time to read orders and products & variants every morning over every store.

What the first 90 days
actually look like.

  1. 01

    Week 1: read-only connection

    Ward reads from BigCommerce with read-only credentials and begins ingesting orders and products & variants. No config changes on your side. First findings arrive in two days.

  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

    Findings arrive each day 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 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

Specialty KPI impact

CLV
Churn risk surfaced
At-risk customers identified before they leave.
Conversion Rate
Assortment + staffing
Cards that help convert high-intent browsers.
Revenue per SKU
Whitespace found
Underperformers identified, gaps in curated assortment.
Overstock
Less capital locked
Demand matching reduces slow-moving inventory.

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.

Yes. Ward reads BigCommerce data and combines it with contextual signals (weather, events, demographics) to generate Specialty-specific insight cards. No custom development required.

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.

Ward delivers daily insight cards covering CLV, Conversion rate, Units per transaction, tailored for E-Commerce decision-making. Each card includes what changed, why it matters, and what to do next.

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 Specialty 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