Convenience · CFO

What a CFO in convenience retail stops missing

Ward tracks 3,000+ SKUs across your convenience store base and returns the finance read on a daily cycle.

The finance read on a convenience fleet

In a Convenience & C-Store fleet the job is 3,000+ SKUs across locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.

Your P&L surprises are born on the store floor. Ward writes the finding at the altitude a CFO works at.

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

Chat

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

Why is the morning daypart down at the Route 9 sites?
You · 9:42 AM
Schema Scout · routed to Store Ops Agent

I pulled the 6–9a daypart for the 14 Route 9 sites against the chain baseline. Two causes, one of them scheduling.

SignalFinding
daypart_sales6–9a revenue −11% vs. chain, coffee units −18%
labor_schedulingSecond associate clocks in at 7:30a, peak starts 6:40a at 9 of 14 sites
foodservice.wasteBreakfast sandwich waste 14%, hold times past 4 hours at 6 sites

Recommend: move the second open to 6:15a at those nine sites, cut the breakfast batch by one tray, and re-check attach in two weeks.

7 parallel queries 3 sources cited confidence 0.91
Show me the schedule change by site.
You · 9:43 AM
Labor Agent · drafting shift diff
Querying daypart_sales
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
Inside-store sales
$12.9M
+2.4% WoW
Foodservice margin
47.3%
−2.1pp
Attach, fuel-to-store
31.2%
−1.6pp
Shrink, cigarettes
1.94%
+0.6pp
Inside-store sales 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
ncr_pos_transactionsimport2m ago
pdi_fuel_transactionsimport2m ago
verifone_forecourt_eventsimport14m ago
ncr_planogram_auditimport1h ago
retail_daypart_salesimport1h ago
retail_foodservice_wasteimport1h ago
retail_labor_schedulingimport1h 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::*
lp-read-shrinkagepermitModel::"inventory_shrinkage"
vendor-blockedforbidModel::"labor_*"
fuel-team-forecourtpermitModel::"fuel_transactions"
for Convenience, live product demo.

What Ward delivers

  • GMROI tracking by category with weekly insight cards
  • Inventory carrying cost alerts when capital efficiency drops
  • Working capital optimization recommendations based on turnover trends
  • SSS decomposition into traffic, conversion, and basket components
  • Store-level unit economics cards for capex prioritization

Metrics Ward monitors

Transactions/hour
Attach rate
Basket size
Planogram compliance
Daypart mix

Convenience challenges Ward solves

  • Daypart demand variation
  • Planogram compliance
  • Impulse category optimization
  • Fuel attach rate
  • Labor scheduling

What Ward has eyes on.

Coverage is store by store, category by category. Ward keeps a running read on transactions/hour, attach rate, basket size throughout 3,000+ 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 locations are not.

Why this combination
is its own problem.

A CFO in convenience retail owns numbers that move faster than the reporting cycle that covers them. Transactions/hour, attach rate, basket size shift store by store, each day. A monthly pack cannot represent that. Your P&L surprises are born on the store floor.

Your P&L surprises are born on the store floor.

Pain points
  • ×Margin erosion only surfaces at month-end close
  • ×Inventory carrying costs are a black box
  • ×Working capital tied up in slow-moving stock nobody is watching
  • ×Same-store sales comps lack decomposition into actionable drivers
  • ×Capex decisions for store remodels lack unit-economics evidence
How Ward helps
  • GMROI tracking by category with weekly insight cards
  • Inventory carrying cost alerts when capital efficiency drops
  • Working capital optimization recommendations based on turnover trends
  • SSS decomposition into traffic, conversion, and basket components
  • Store-level unit economics cards for capex prioritization

Inventory distortion, overstock and out-of-stock combined, costs retailers $1.77 trillion globally. Source: IHL Group

Convenience KPI impact

Attach Rate
Impulse adjacencies
Daypart-specific cross-sell opportunities surfaced.
Daypart Revenue
Weak hours identified
Which hours and categories underperform, and why.
Planogram Compliance
Sales-correlated flags
Deviations flagged once they start costing revenue.
Shrinkage
Slow-bleed detection
Transaction-level anomalies that periodic audits miss.

Frequently asked questions

Your P&L surprises are born on the store floor. Ward solves this with automated insight cards: GMROI tracking by category with weekly insight cards. Inventory carrying cost alerts when capital efficiency drops. Working capital optimization recommendations based on turnover trends.

Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Finance 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 Convenience 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.

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