Pattern of Life Analysis: How to Identify Behavioral Trends from Call Data
Learn how pattern of life analysis turns call records into behavioral insights that help investigators identify routines, shifts, and anomalies.
What is Pattern of Life Analysis?
Pattern of life (POL) analysis is a structured methodology used to map the behavioral habits of a subject over time. In the context of call data, it means examining who a person calls, when they call, how long those calls last, and how those behaviors repeat — or deviate — across days, weeks, and months.
Unlike a single data point, a pattern of life gives investigators a baseline. Once you know what "normal" looks like for a subject, anomalies become visible. A sudden spike in late-night calls, a new contact appearing repeatedly, or a complete communication blackout — these are the signals that matter.
Why It Matters in Investigations
Raw call detail records (CDRs) are dense and difficult to interpret at face value. A spreadsheet of thousands of calls tells you very little without context. Pattern of life analysis provides that context by surfacing structure within the data.
Investigators use POL analysis to:
- Establish a subject's routine communication habits
- Identify key contacts and communication hierarchies
- Detect coordination events — periods of unusually high activity
- Pinpoint behavioral shifts that may correspond to significant events
- Corroborate or challenge alibi claims using temporal data
How to Analyze Call Data Step-by-Step
Ingest and Clean the Data
Start by importing your CDR file into a structured format. Remove duplicate entries, standardize phone number formats, and ensure timestamps are consistent. Dirty data produces misleading patterns.
Establish the Time Window
Define the analysis period. A 30-day window is a common starting point, but longer windows (90+ days) reveal more reliable behavioral baselines. Shorter windows are useful for event-specific analysis.
Segment by Time of Day and Day of Week
Group calls into time blocks: early morning (12am–6am), morning (6am–12pm), afternoon (12pm–6pm), and evening (6pm–12am). Then cross-reference with day of week. This reveals the subject's active communication windows.
Identify Top Contacts
Rank contacts by total call volume, total call duration, and frequency of contact. The top 5–10 contacts typically account for the majority of communication. These are your primary nodes for network analysis.
Map Contact Frequency Over Time
Plot call frequency per day across your time window. Look for spikes, gaps, and recurring peaks. A consistent spike every Friday evening, for example, may indicate a regular meeting or coordination event.
Identifying Anomalies and Behavioral Shifts
Once you have a baseline, anomalies become visible. These are the deviations from established patterns that warrant closer examination. Common anomalies in call data include:
Communication blackouts
Sudden multi-day gaps in activity from an otherwise active subject
New contact emergence
A previously unseen number appearing with high frequency
Time-of-day shifts
A subject who normally calls during business hours suddenly active at 2am
Duration spikes
Calls that are significantly longer than the subject's average
Analyze Patterns Faster with CaseTrack
CaseTrack automates the heavy lifting — import CDRs, visualize call frequency, and surface behavioral anomalies without writing a single query. Runs entirely offline.
Ready to Put This Into Practice?
CaseTrack gives investigators the tools to apply these techniques directly — import CDRs, visualize patterns, and manage case files without cloud exposure.