PULSE CORE — INITIALIZING
REV / SQ FT
$847/wk
+12.4%
AVG BASKET
$63.20
+$4.80
SHRINK RATE
1.84%
-0.3pp
TRAFFIC CONV
34.7%
+2.1pp
OOS EVENTS
847/day
REGION 7
MARGIN LEAK
$2.3M
CRITICAL
→ EXPLORE THE LIVE DEMO

NO FORM. NO FRICTION. SAMPLE DATA PRELOADED.

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Module 01 — Interactive Explorer

One platform. Two lenses.
Zero blind spots.

Toggle between individual store performance and chain-wide regional aggregates. The same data engine, calibrated to your altitude.

247 STORES ACTIVE
STORE ID
LOCATION
REV/SQFT
AVG BASKET
CONV %
MARGIN %
STR-041
Midtown Manhattan
$1,240
$84.20
42.1%
28.4%
STR-107
Chicago Loop
$980
$71.50
38.7%
24.1%
STR-203
Dallas Uptown
$847
$63.20
34.7%
21.8%
STR-318
Atlanta Buckhead
$762
$58.90
31.2%
19.4%
STR-412
Seattle Capitol Hill
$1,104
$77.40
40.3%
26.7%
STR-519
Phoenix Desert Ridge
$634
$49.80
28.9%
16.2%
SHOWING 6 OF 247 STORES — SORTED BY REV/SQFT DESCVIEW ALL →
AI SIGNAL DETECTED

Region 7 (Southeast) margin erosion of -1.4% WoW correlates with elevated OOS events in Protein & Dairy — 847 stockout incidents across 53 stores. Estimated margin impact: $2.3M annualized.

Module 02 — Margin Erosion Engine

Where did $2.3M go?

Pulse decomposes margin variance into actionable root causes — not aggregate P&L lines you already know are wrong.

MARGIN CONTRIBUTION (INDEXED TO BUDGET = 100)
BASEEROSIONRECOVERY
25
50
75
100
TOTAL EROSION
-24.7pp
AI RECOVERY
+3.7pp
NET MARGIN GAP
-21.0pp
ANNUALIZED IMPACT
$2.3M

See this breakdown for your actual categories, SKUs, and regions.

→ RUN THIS ON MY DATA
Module 03 — AI Correlation Engine

The model sees what
your analysts can't.

Pulse's AI maps multi-variable causal chains across 200+ stores simultaneously — surfacing the non-obvious connections between operational inputs and margin outputs.

CAUSAL CORRELATION MAP — REGION 7 / WEEK 07
r=0.87r=0.79r=0.61r=0.54r=0.92r=0.68r=0.41MARGINEROSION-2.8ppOOS RATE+34%FOOTTRAFFIC-8.2%BASKETSIZE-$4.10PROMOOVERLAP214 SKUsWEATHEREVENTFeb 14-16COMPOPENING0.4mi
Strong (r>0.7)
Medium (r 0.5-0.7)
Weak (r<0.5)

Cross-variable causality

6 variables

Pulse identifies 2nd and 3rd-order correlations — weather → traffic → basket → margin — in a single model pass.

🎯

SKU-level precision

847K SKUs

Not category averages. Individual SKU performance surfaces ranked by margin impact, updated hourly.

Thursday-ready cadence

< 6 hrs latency

Weekly variance reports auto-generate by 6am Monday. No analyst hours. No waiting for the data team.

ACCURACY BENCHMARK — 90-DAY TRAILING
94.2%
Margin Forecast
accuracy
89.7%
OOS Prediction
precision
91.4%
Traffic Model
Module 04 — Speed to Insight

From raw feed to
Thursday-ready in 7 days.

No 6-month implementation. No data science team required. Pulse connects to your existing stack and starts delivering margin intelligence this week.

Day 0

Connect your data feeds

POS, ERP, WMS, e-comm platform. Native connectors for SAP, Manhattan, Salesforce Commerce, Shopify Plus. No ETL work on your end.

Day 1

Baseline model calibrated

Pulse ingests 90 days of historical data, establishes store-level baselines, and flags immediate anomalies within 24 hours.

Day 3

First live alerts delivered

Region-level variance alerts, OOS predictions, and basket opportunity signals arrive in your ops team's workflow — Slack, Teams, or email.

Day 7

Full command center live

All 200+ stores visible. Margin waterfall, traffic heatmaps, SKU-level correlation maps. Your Thursday ops review just got 4 hours shorter.

NATIVE CONNECTORS — NO ETL REQUIRED
SAP
MAN
SFc
SHF
ORC
BY
MSF
SFW
+ 40 additional connectors via REST API
< 7days
Time to first insight
200+stores
Max fleet size tested
99.9%
Uptime SLA
→ EXPLORE THE LIVE DEMO

SANDBOX PRE-LOADED WITH SAMPLE RETAIL DATA — NO FORM, NO FRICTION

"Pulse found $1.8M in margin leakage our BI team had missed for 8 months."

Rachel Kim
VP Operations, 340-store fleet

"Category review went from 4 hours to 40 minutes. Region 7 is actually fixed now."

Marcus Webb
Category Director, Grocery Chain