VOL. XCIV, NO. 247
★ WIDE MOAT STOCKS & COMPETITIVE ADVANTAGES ★
PRICE: 0 CENTS
Thursday, January 8, 2026
The Progressive Corporation
PGR · New York Stock Exchange
Weighted average of segment moat scores, combining moat strength, durability, confidence, market structure, pricing power, and market share.
Request update
Spot something outdated? Send a quick note and source so we can refresh this profile.
Overview
Progressive is a U.S. property-and-casualty insurer centered on personal and commercial auto, with smaller specialty and homeowners/renters offerings. It reports two operating segments: Personal Lines (~85% of 2024 net premiums written) and Commercial Lines (~15%). Core moat mechanisms include data-driven underwriting and pricing (including telematics/UBI), scale-enabled operating efficiencies, and a large claims operation supported by a nationwide repair network. These advantages are tempered by intense price competition, easy price comparison, and state-by-state regulatory constraints.
Primary segment
Personal Lines
Market structure
Oligopoly
Market share
16.7% (reported)
HHI: —
Coverage
2 segments · 6 tags
Updated 2026-01-04
Segments
Personal Lines
U.S. private passenger auto insurance (dominant) plus specialty personal lines and homeowners/renters (small portion)
Revenue
85%
Structure
Oligopoly
Pricing
moderate
Share
16.7% (reported)
Peers
Commercial Lines
U.S. commercial auto insurance (dominant) plus related commercial lines
Revenue
15%
Structure
Competitive
Pricing
moderate
Share
15% (reported)
Peers
Moat Claims
Personal Lines
U.S. private passenger auto insurance (dominant) plus specialty personal lines and homeowners/renters (small portion)
Learning Curve Yield
Supply
Learning Curve Yield
Strength
Durability
Confidence
Evidence
Large-scale risk segmentation and pricing advantage supported by telematics/UBI and extensive data gathering/analysis; continuous model updates improve matching rate-to-risk.
Erosion risks
- Telematics becomes commoditized across carriers
- Privacy or regulatory limits on data use
- Model error in regime shifts (loss severity inflation, EV repair costs)
Leading indicators
- Personal auto combined ratio vs peers
- Policy retention / churn
- Telematics adoption rate and loss ratio lift
Counterarguments
- Comparative raters make switching easy and keep pricing pressure high
- Major peers also invest heavily in telematics and data science
Brand Trust
Demand
Brand Trust
Strength
Durability
Confidence
Evidence
High consumer awareness and trust, reinforced by sustained marketing, supports quoting volume and retention in a price-competitive product.
Erosion risks
- Rising customer acquisition costs
- Brand dilution from claims friction or service issues
Leading indicators
- Quote conversion rate
- Net promoter score / complaint ratios
- Advertising spend efficiency (growth per $)
Counterarguments
- Insurance is often bought on price; brand alone does not ensure margin
- Competitors can match ad spend and narrow awareness gaps
Service Field Network
Supply
Service Field Network
Strength
Durability
Confidence
Evidence
Scaled claims operations (physical plus virtual) and repair network support cycle time, customer satisfaction, and loss control.
Erosion risks
- Repair capacity constraints and parts inflation
- Adverse weather spikes causing claims backlogs
Leading indicators
- Average claim cycle time
- Severity trend vs industry
- Customer satisfaction after claims
Counterarguments
- Repair networks and virtual claims are available to many large insurers
- Severity inflation can overwhelm process improvements
Suite Bundling
Demand
Suite Bundling
Strength
Durability
Confidence
Evidence
Bundling auto with property and other products can improve retention and claims economics; agent tooling and pricing/quoting workflows encourage multi-product adoption.
Erosion risks
- Competitors replicate bundles with similar discounts
- Property cat losses reduce appetite for bundled property growth
Leading indicators
- Bundle penetration rate
- Retention differential: bundled vs mono-line
- Property combined ratio volatility
Counterarguments
- Bundles are common and discounts can be competed away
- Customers can unbundle when shopping via aggregators
Commercial Lines
U.S. commercial auto insurance (dominant) plus related commercial lines
Learning Curve Yield
Supply
Learning Curve Yield
Strength
Durability
Confidence
Evidence
Ongoing investment in classification, segmentation, and product model rollouts improves pricing accuracy and risk selection in a loss-sensitive line.
Erosion risks
- Social inflation / nuclear verdicts driving severity
- Competitors narrow pricing sophistication gap
- Adverse selection if rate filings lag loss trends
Leading indicators
- Commercial auto combined ratio trend
- Rate change vs loss trend (severity/frequency)
- Fleet policy growth and retention
Counterarguments
- Commercial auto remains fragmented with many specialists; data advantages can be competed away
- Profitability is highly sensitive to litigation and macro loss-cost trends
Operational Excellence
Supply
Operational Excellence
Strength
Durability
Confidence
Evidence
Scale and operating discipline (expense control, efficiency) support competitive pricing and underwriting profit through the cycle.
Erosion risks
- Expense ratio creep from technology and claims costs
- Aggressive competitor pricing in soft markets
Leading indicators
- Expense ratio trend
- Underwriting margin (combined ratio)
- New business vs retention mix
Counterarguments
- Size can create operational complexity and slower change cycles
- Competitors may sacrifice margin for share in soft markets
Service Field Network
Supply
Service Field Network
Strength
Durability
Confidence
Evidence
Shared claims infrastructure and repair network support cycle time and loss control for commercial auto as well.
Erosion risks
- Higher litigation and medical inflation in commercial claims
- Cat events and staffing constraints affecting claim cycle time
Leading indicators
- Claim closure speed
- Average severity vs competitors
- Customer satisfaction for claims
Counterarguments
- Claims networks are not exclusive and can be replicated by other large carriers
- External loss-cost drivers may dominate operational improvements
Evidence
We rely heavily on technology ... data gathering and analysis.
10-K links pricing accuracy to technology, data analysis, and Snapshot/UBI know-how.
10-K cites brand recognition/confidence and advertising as key competitive factors.
nationwide network of about 3,700 third-party repair shops
10-K describes scale of claims repair network supporting claim handling.
10-K describes bundling strategy; bundled customers stay longer and have lower claims costs.
PROGRESSIVE GRP ... market share 16.72
NAIC market share table lists Progressive Group at 16.72% for Total Private Passenger Auto (2024).
Showing 5 of 11 sources.
Risks & Indicators
Erosion risks
- Telematics becomes commoditized across carriers
- Privacy or regulatory limits on data use
- Model error in regime shifts (loss severity inflation, EV repair costs)
- Rising customer acquisition costs
- Brand dilution from claims friction or service issues
- Repair capacity constraints and parts inflation
Leading indicators
- Personal auto combined ratio vs peers
- Policy retention / churn
- Telematics adoption rate and loss ratio lift
- Quote conversion rate
- Net promoter score / complaint ratios
- Advertising spend efficiency (growth per $)
Curation & Accuracy
This directory blends AI‑assisted discovery with human curation. Entries are reviewed, edited, and organized with the goal of expanding coverage and sharpening quality over time. Your feedback helps steer improvements (because no single human can capture everything all at once).
Details change. Pricing, features, and availability may be incomplete or out of date. Treat listings as a starting point and verify on the provider’s site before making decisions. If you spot an error or a gap, send a quick note and I’ll adjust.