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KPI Monitoring

Product · KPI Monitoring

Your KPIs erode quietly
between reports.

Fill rate slips, shrinkage drifts, GMROI drops, slowly, across hundreds of stores. Ward watches every metric, every store, every day and flags what’s off before it compounds.

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The slow-bleed problems aggregate reports miss

A 2% fill rate decline at one store vanishes in a chain-wide average. Across 300 stores, those quiet declines add up fast. Ward baselines every metric at every location and surfaces deviations as insight cards, each with root cause and a recommended action. Thresholds are statistical: z-score against a 90-day rolling baseline, anomaly at p<0.01.

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Acme Retail @Merchandising: VP Analyst claude-sonnet default

Chat

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

Today

Why did Store 37 miss target?

3 turns · live

Q4 promo cannibalization

5 turns · 1h ago

Margin compression, private label

4 turns · 3h ago

Earlier

Shrinkage drift, West region

2 turns · yesterday

DC West fill-rate slip vs SLA

7 turns · 2d ago

Stockout root cause, SKU 8821

6 turns · last week

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.

Signal	Finding
`labor_efficiency`	Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak
`inventory.fresh`	Fresh fill 83%, backroom replenishment lag at 2–4p
`promo.lift`	BOGO 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

AI Insights

Agents run against your baselines overnight. These are what they flagged without being asked.

3 flagged 8 queries run 3 sources swept 04:00, acme-retail

Act now labor_efficiency 0.94

Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak

Schema Scout · routed to Merchandising Agent Pin Ask Ward Investigate

Review inventory.fresh 0.89

Fresh fill 83%, backroom replenishment lag at 2–4p

Promo Agent · routed via Merchandising Pin Ask Ward Investigate

Watch promo.lift 0.81

BOGO crackers cannibalized Brand Y by 28%, net category +6%

Margin Agent · routed via Finance Pin Ask Ward Investigate

Recommended

Re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.

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

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

Model	Horizon	MAPE
`holt_winters`	4wk	4.1%
`arima_sarimax`	13wk	8.9%
`gbm_demand`	1wk	2.1%
`bayes_hier`	new store	11.4%

Models

Browse, search, and manage data–lake model definitions for your tenant.

Search models…

Name	Namespace	Version
`sap_pos_transactions`	sap	1.0
`sap_inventory_shrinkage`	sap	1.2
`sap_labor_scheduling`	sap	1.0
`retail_inventory_weekly`	retail	1.1
`retail_google_ads_daily`	retail	1.0
`retail_meta_ads_daily`	retail	1.0
`retail_ga4_website_daily`	retail	1.0

Sources

Connect external systems to the data lake.

Search sources…

Name	Type	Last sync
`sap_pos_transactions`	import	2m ago
`sap_inventory_shrinkage`	import	2m ago
`sap_labor_scheduling`	import	14m ago
`retail_inventory_weekly`	import	1h ago
`retail_google_ads_daily`	import	1h ago
`retail_meta_ads_daily`	import	1h ago
`retail_ga4_website_daily`	import	1h ago

Architecture

Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.

Your systems · read-only

SAP Retail

Snowflake

BigQuery

Shopify

NCR Voyix

Ward Gateway

TLS 1.3 · AES-256

Querying live · data stays put

Federated answers

SELECT * FROM sap.pos

JOIN retail.inventory_weekly

WHERE store_id = 37

→ insight cards

Ward Data Lake

→ baselined per store

TLS 1.3 in transit AES-256 at rest Read-only credentials SOC 2 Type II underway VPC peering · PrivateLink

Pipelines

Move data from sources into models on a schedule.

Name	Source	Model	Status	Schedule
`sync_sap_pos_transactions`	sap_pos_transactions	pos_transactions	enabled	hourly
`sync_sap_inventory_shrinkage`	sap_inventory_shrinkage	inventory_shrinkage	enabled	daily
`sync_sap_labor_scheduling`	sap_labor_scheduling	labor_scheduling	enabled	daily
`sync_retail_inventory_weekly`	retail_inventory_weekly	inventory_weekly	enabled	weekly
`sync_retail_google_ads_daily`	retail_google_ads_daily	google_ads_daily	enabled	daily
`sync_retail_meta_ads_daily`	retail_meta_ads_daily	meta_ads_daily	enabled	daily

Streams

Real-time ingestion pipelines.

0events / min

0streams active

0% delivered

pos.txn store_037, basket $42.18
inv.move dc_west → store_104
labor.clock store_022 shift_start
pos.txn store_211, basket $19.04

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II

Policy ID	Effect	Resources
`merch-read-default`	permit	Model::*
`finance-read-shrinkage`	permit	Model::"inventory_shrinkage"
`vendor-blocked`	forbid	Model::"labor_*"
`region-west-only`	permit	Tenant::"acme"

Entities

Principals and resources referenced by Cedar policies.

Search UIDs…

Entity UID	Type	Tenant
`Tenant::"acme-retail"`	Tenant	acme-retail
`Model::"sap.pos_transactions"`	Model	acme-retail
`Model::"sap.inventory_shrinkage"`	Model	acme-retail
`Model::"sap.labor_scheduling"`	Model	acme-retail
`Model::"retail.inventory_weekly"`	Model	acme-retail
`Model::"retail.google_ads_daily"`	Model	acme-retail

Providers

Manage LLM API keys and the model profiles that use them.

API Keys Model Profiles

Name	Provider	Used by	Created
`anthropic-default`	Anthropic	3 profiles	Apr 22
`openai-default`	OpenAI	2 profiles	Apr 22
`gemini-default`	Gemini	1 profile	Apr 22
`ollama-onprem`	Ollama	2 profiles	Apr 22

LLM-agnostic. Bring your own key, route per task. No lock-in.

Settings

Manage your dashboard preferences and account.

Appearance

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Account

NameAdmin

Emailadmin@acme.io

Tenantacme-retail

Revenue, margin, fill rate, shrink, every KPI baselined per store, per category.

Five domains, continuous monitoring

Revenue, stores, finance, inventory, supply chain. Each domain has a dedicated agent tracking its KPIs around the clock and flagging deviations before they compound.

Revenue, Growth & Stores

Growth: CAC, channel mix, promo ROI, same-store sales, basket size, conversion. Stores: location performance, staffing, shrink, planogram compliance.

Finance & Inventory

Finance: gross margin, P&L variance, category profitability, elasticity, markdown rate. Inventory: fill rate, stockouts, turnover, carrying cost.

Supply Chain

Demand forecasting, weather shifts, supplier lead times, and DC throughput.

KPI glossary

Definitions, formulas, and benchmarks for every retail metric Ward tracks.

Fill Rate

On-Shelf Availability

Percentage of expected SKUs physically present and available for purchase.

SSS

Same-Store Sales

Year-over-year revenue comparison for stores open 12+ months.

ATV

Average Transaction Value

Revenue per transaction. Measures basket size and upsell effectiveness.

UPT

Units Per Transaction

Average item count per basket. Key cross-sell and adjacency indicator.

Shrinkage

Inventory Loss Rate

Percentage of inventory lost to theft, damage, spoilage, or admin error.

GMROI

Gross Margin Return on Inventory

Gross profit per dollar invested in inventory.

ITR

Inventory Turnover Ratio

How many times inventory is sold and replaced in a period.

Promo Lift

Promotional Uplift

Incremental sales from a promotion vs baseline, net of cannibalization.

MD%

Markdown Rate

Revenue sold at discount vs original price.

NPS

Net Promoter Score

Customer loyalty metric. Promoters minus detractors.

CVR

Conversion Rate

Percentage of visitors who purchase.

Basket Penetration

Percentage of baskets containing a specific category/SKU.

Fixes powered by KPI Monitoring

Where KPI Monitoring
does the work.

The insight types that lean on kpi monitoring to deliver value.

Shrinkage

Shrinkage Detection

Find the leak before it drains you.

Demand

Demand Forecasting

See demand before it arrives.

Promos

Promo Effectiveness

Know which promos actually work.

Fill Rate

Fill Rate Monitoring

Every empty shelf is a lost sale.

WHO

Roles that depend on it

Built for the teams
that catch problems first.

KPI

Metrics it moves

The KPIs that change
when this runs.

Shrinkage

Inventory Loss Rate

Ward attributes shrinkage to cause categories and benchmarks each store.

GMROI

Gross Margin Return on Inventory

Ward ranks categories by GMROI to identify inventory tying up capital.

ITR

Inventory Turnover Ratio

Ward flags when turn slows below vertical benchmark, signaling overstock.

Fill Rate

On-Shelf Availability

Ward monitors fill rate at the store-category-hour level and triggers insight cards when it drops below your threshold.

See it. Or compare it.

Operator stories
and the alternatives.

PLAY

Plays in production

How operators run kpi monitoring on Ward today.

PBI

Ward vs Power BI

Why exec dashboards miss what costs you money.

Two ways to start.

Run a fixed-fee pilot on your data, or talk to advisory about a broader engagement.

Get a demo → Talk to advisory