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Retail Loss Prevention Knowledge Share

From Data to Decisions. From Incidents to Intelligence.

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Power by the RLPKS Fusion Engine

Step 1 — Retailers Submit Incident Data

Participating retailers submit their monthly retail crime incidents using the standardized RLPKS Monthly Incident Submission Template™.

Each submission captures information such as:

  • Incident location

  • Date and time

  • Merchandise targeted

  • Estimated loss

  • Theft method

  • Suspected organized retail crime indicators

  • Violence or threat indicators

  • Store information

Individual customer data remains confidential.

Step 2 — National Intelligence Fusion

All customer submissions are securely combined into a single national intelligence dataset.

The RLPKS Fusion Engine™ analyzes the information to identify:

  • National retail crime trends

  • Geographic hotspots

  • Emerging merchandise targets

  • Organized retail crime indicators

  • Regional changes

  • Theft methods

  • Trend acceleration

  • Seasonal patterns

No customer can view another retailer's confidential incident data.

Step 3 — External Intelligence Fusion

The platform enriches customer data with contextual information to improve situational awareness.

Depending on availability and relevance, this may include:

  • Weather

  • Holidays

  • School calendars

  • Major public events

  • Seasonal retail periods

  • Publicly available crime mapping (used as contextual evidence)

  • Open-source intelligence

  • Relevant retail crime news

These external signals are used to provide additional context. RLPKS distinguishes observed correlation from demonstrated causation and does not attribute retail crime to external factors without supporting evidence.

Step 4 — Intelligence Analysis

The Fusion Engine™

Calculates a series of proprietary intelligence measures, including:

Retail Crime Intelligence Index (RCII™)

An overall indicator of the current retail crime environment.

Intelligence Confidence Rating™

Measures the strength and reliability of each assessment based on data quality, corroboration, historical consistency, and analyst review.

Emerging Merchandise Index™

Identifies merchandise categories showing notable increases in theft activity.

Geographic Context Intelligence Engine™ (GCI™)

Analyzes geographic relationships using customer data together with contextual mapping to identify emerging hotspots and movement patterns.

Organized Retail Crime Assessment

Evaluates incident patterns that may indicate coordinated criminal activity, while distinguishing preliminary indicators from validated findings.

Step 5 — Retail Threat Forecast™

Historical data tells you what happened.

The Retail Threat Forecast™ helps you prepare for what may happen next.

Using historical patterns, current trends, contextual intelligence, and analyst review, the platform produces a 30-day operational outlook that includes:

  • Higher-risk days

  • Higher-risk time periods

  • Geographic priorities

  • Emerging merchandise risks

  • Organized retail crime outlook

  • Operational recommendations

  • Forecast confidence assessment

Forecasts are designed to support operational planning and are accompanied by confidence ratings and analyst commentary.

Monthly Intelligence Publications

Intelligence Publications analysis interface

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  • Executive Dashboard™

  • Monthly Intelligence Report™

  • GIS Intelligence Atlas™

  • National Intelligence Map™

  • Retail Threat Forecast™

  • Executive Briefing™

  • Decision Support Matrix™

  • National Retail Intelligence Bulletin™ (anonymized benchmarking)

Each publication is designed to support executives, loss prevention professionals, investigators, and operational leaders.

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