10% of drivers account for ~50%
of crashes

The Samsara Compounding Risk Report is a data-driven analysis of how combinations of driving behaviors and contextual factors drive crash risk. Published as part of the Samsara Signals program, it's built on Samsara's Risk Model, using aggregated telematics data from a sample period of July 1 to December 15, 2025, to show how risk factors like mobile use, harsh braking, and distraction compound when they occur together, and how those patterns are visible weeks before a crash.

Four key findings anchor the report:

• Drivers exhibiting combined mobile use, distraction, and harsh braking are 5.4x more likely than the average driver to be classified in the highest risk tier.

• The top 10% of risk-ranked drivers account for 47% of crashes. Risk concentrates.

• The top 30% of risk-ranked drivers account for 76% of crashes. Prioritization is the highest-leverage move a fleet can make.

• When comparing a driver who is subsequently involved in a crash with one who is not, the model prioritizes the crash-involved driver three out of four times. This holds across both the next-day and seven-day evaluation windows.

The report is built for fleet safety managers and other operations leaders who need to decide where to focus limited coaching time and resources. It's especially useful for anyone currently triaging safety events one at a time (harsh-braking alerts, speeding flags, phone-use notifications) and looking for a more reliable way to prioritize which drivers need attention first, rather than reacting to every individual alert.

The report is based on Samsara's proprietary Risk Model (patent pending), trained and evaluated on aggregated telematics data from July 1 to December 15, 2025, spanning drivers and fleets across multiple industries and regions. The model uses approximately 50 features across four categories: driving behavioral patterns, exposure, context, and developmental factors, most measured over rolling 60-day windows. Association-based findings (like the lift figures for behavior combinations) are distinguished from causal findings, which use methods including double machine learning and causal forests to identify aggressive driving, distracted driving, and speeding as credible drivers of crash risk. Full detail is in the report's Methodology section.

Compounding risk is what happens when multiple risky driving behaviors show up together, and crash risk multiplies. Samsara's Risk Model found that drivers exhibiting combined mobile use, distraction, and harsh braking are 5.4x more likely than the average driver to be classified in the highest risk tier, far more than any of those behaviors alone.

On their own, individual behaviors are a weak signal. Even the strongest single predictor, harsh braking, is only associated with a 1.8x increase in crash likelihood compared to drivers without the behavior:

Spread across hundreds of drivers, behaviors in this range flag too many people to act on. A fleet manager triaging one alert at a time ends up with a long list and no clear place to start. The real signal shows up in combination: drivers who exhibit multiple behaviors together, like mobile use, distraction, and harsh braking, are 5.4x more likely to fall into the highest risk tier, a far stronger signal than any behavior measured alone.

On their own, harsh braking, mobile phone use, and distracted driving carry the highest individual crash likelihood, each associated with a 1.7x to 1.8x increase in crash risk compared to drivers without the behavior. But single behaviors are a weak signal for prioritization. The real predictor is the combination: mobile use plus harsh braking pushes a driver to 4.5x more likely to fall into the highest risk tier, and adding distraction or night driving on top of that raises it to 5.4x.

Yes, but its risk shows up differently than behaviors like harsh braking or phone use. On its own, light speeding concentrates risk only modestly (1.3x). Its real impact comes from how common it is across a fleet: because so many drivers speed at least occasionally, even a moderate per-driver effect adds up to a large share of total crashes at scale. Causal analysis in Samsara's Risk Model identifies speeding-related patterns, alongside aggressive driving and distracted driving, as one of three behavior families with a credible, coachable effect on crash risk.

Crash risk concentrates in a small share of drivers. Samsara's Risk Model found that the top 10% of risk-ranked drivers account for 47% of crashes, and the top 30% account for 76%. That concentration is what makes prioritizing coaching toward the highest-risk drivers the highest-leverage move a fleet safety program can make.

Coach behavioral patterns, not isolated events. A single harsh-braking incident is weak evidence on its own; a driver whose harsh braking shows up consistently alongside mobile use or distraction is a much stronger coaching case. Fleet managers get the most leverage by focusing on drivers where behaviors compound, since that's where crash risk concentrates and where coaching can produce a real behavior change.

Coaching Priority is Samsara's proprietary framework for surfacing and prioritizing coaching opportunities across your fleet, the only solution of its kind available today. This feature is underpinned by the Risk Model, whichevaluates approximately 50 factors, including driving history, weather, and behavioral patterns, to give managers a single view that highlights where coaching will have the greatest impact, while drivers who are already building strong habits receive automated self-coaching to keep them on track.

The information provided in this report is for general informational purposes only. Samsara does not guarantee you will achieve any specific results if you follow any advice in the report. It may be advisable for you to consult with a professional such as a lawyer, accountant, architect, business advisor, or professional engineer to get specific advice that applies to your specific situation.