Customer · Lightspeed · Head of E-Com

Lightspeed plus Ward: customer for a Head of E-Com

Your online and offline data live in different worlds. Ward spots customer movement in your Lightspeed data and accounts for what caused it.

How a Head of E-Com runs customer off Lightspeed Retail

Your online and offline data live in different worlds. Ward delivers cards scoped to e-commerce decision-making.

Here is customer behavior in plain terms. Ward tracks basket composition shifts, daypart patterns, and customer segment migration.

Under the hood. Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.

Setup: Ward connects via Lightspeed Retail API. OAuth-based authentication. Read-only access.

Key capabilities

  • Cross-sell opportunity detection
  • Basket composition trends
  • Daypart behavior modeling
  • Customer segment migration
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"
Customer on Lightspeed data, live product demo.

What Ward has eyes on.

Understand the person behind the basket. 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.

Ward reads Lightspeed rather than replacing it. Sales transactions, inventory counts, customer profiles come across on a read-only connection, get enriched with contextual data, and come back as findings. Your POS is untouched.

Why this combination
is its own problem.

A Head of E-Com does not need the customer model explained. They need to know which stores moved, why, and what to do by end of day. Ward writes the finding at that altitude.

What the first 90 days
actually look like.

  1. 01

    Week 1: connect

    Read-only credentials to Lightspeed Retail. Ward ingests sales transactions, inventory counts, customer profiles and starts building baselines. First cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.

  2. 02

    Weeks 2 to 3: baselines

    Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the daily cards are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: operating rhythm

    Findings arrive every morning 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 Lightspeed Retail

Ward integrates with Lightspeed Retail POS for specialty and boutique retailers. In-store transaction data powers customer behavior and assortment insight cards.

Setup: Ward connects via Lightspeed Retail API. OAuth-based authentication. Read-only access.

Data Ward reads from Lightspeed

Sales transactions
Inventory counts
Customer profiles
Purchase orders
Employee performance

Impact metrics with Lightspeed

Customer LTV
Lapsing buyers flagged
Purchase cadence and segment data reveal re-engagement windows.
Conversion Rate
Staffing-timing link found
Transaction data reveals which staff and hour combos convert.
Revenue per SKU
Whitespace identified
Underperformers and assortment gaps surfaced from sales data.
Inventory Accuracy
Count-to-velocity matched
Inventory counts reconciled against POS sell-through live.

Data lake enrichment

Ward enriches Lightspeed data with: Sales transactions, Customer profiles, Weather & events, Competitor data, Demographic data

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 Lightspeed Retail API. OAuth-based authentication. Read-only access. Data points include: Sales transactions, Inventory counts, Customer profiles, Purchase orders, Employee performance.

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 customer 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