Benchmark Property and Casualty Rate Trends with Competitive Pricing data

Introduction

Insurance leaders have always sought a clear line of sight into the ebb and flow of property and casualty pricing across primary markets and the global reinsurance ecosystem. Yet for decades, visibility into true rate movements by carrier, sub-line, and geography was limited, delayed, and often anecdotal. Decisions about underwriting appetite, renewal strategy, and portfolio steering were made with partial information and long lags, leaving even the most sophisticated organizations reacting to the market rather than shaping it. Today, a new world of pricing data and market intelligence is changing that dynamic, allowing teams to track market conditions monthly—and sometimes weekly—across regions, industries, and risk segments.

Historically, insurance and reinsurance professionals relied on actuarial triangles, annual statutory reports, broker surveys, and memo-driven internal commentary to infer market rate shifts. Before widespread digital record-keeping, many carriers born in an analog era depended on underwriter judgment and word-of-mouth intelligence gathered during renewal seasons. Even after electronic systems arrived, much of the data was fragmented in policy administration platforms, claims systems, spreadsheets, and emails, making it difficult to compile a coherent, timely picture of pricing by sub-line or territory.

Long before organizations could ingest high-frequency external signals, leaders waited quarters for aggregated indices, or they called peers and market contacts for guidance. An underwriter might learn about rate hardening in a specific state or a softening trend in a niche segment through a broker lunch or a conference panel. This approach worked only up to a point. By the time signals coalesced into consensus, the market had often moved on. The opportunity to adjust underwriting rules, rebalance exposure, or recalibrate reinsurance purchasing had already narrowed.

Then came the proliferation of software in every workflow: quoting portals, digital distribution platforms, policy and claims systems designed around event logs, reinsurance bordereaux repositories, and structured regulatory submission pipelines. The rise of connected devices and sensors in both personal and commercial lines—telematics in vehicles, IoT sensors in buildings, and property intelligence from geospatial imagery—expanded the universe of pricing-relevant signals. Coupled with improved data governance and more standardized reporting, this data explosion created a foundation for continuous insight into rate adequacy, loss cost trends, and capacity constraints.

Today, organizations harness rich, curated external data to track pricing in near real time. Datasets mapping premiums, rate changes, and losses down to city or county help isolate where competitive pressure is rising, which carriers are pushing rate, and how reinsurance terms are cascading into primary pricing. With the right data pipelines, market watchers can monitor shifts monthly by product line and distribution channel, rather than waiting for a year-end look-back.

In this article, we’ll explore multiple categories of data that unlock insight into property and casualty pricing and reinsurance market dynamics. We’ll discuss how these datasets originated, why their volume and granularity are accelerating, and the specific strategies business professionals can deploy to translate pricing data into action—across underwriting, capital management, portfolio optimization, and competitive intelligence. As companies sharpen their data search strategies and stitch together complementary sources, they’re transforming reactive processes into proactive, data-driven playbooks.

Regulatory Filings and Statutory Reporting Data

Regulatory filings data has been a cornerstone of insurance transparency for decades. State-mandated submissions, statutory statements, and rate filings provide structured visibility into premiums, losses, and—at times—rate changes. Historically, actuaries, compliance teams, and market analysts combed through annual reports to benchmark carrier performance, while consultants used aggregated views to assess line-of-business trends. These sources were powerful but slow, often trailing the market by many months.

Technological advances changed the game. Digital submission systems, standardized templates, and centralized repositories have improved consistency and comparability across carriers and geographies. The ability to ingest, normalize, and reconcile records at scale means the same underlying filings that once required manual parsing can now be analyzed monthly or quarterly, with state- and county-level segmentation, sub-line breakouts, and even insights by distribution channel in some contexts.

Over time, the scope of reporting expanded. Beyond top-line premiums and losses, filings can include rate, rule, and form changes; exposure measures; and, when consolidated with public records, indicators of market entry and exit by carriers. Analysts in carriers, reinsurers, brokerages, investment firms, and market research shops use these datasets as the backbone of competitive intelligence—cross-referencing regulatory data with internal experience to calibrate pricing strategies.

The volume and granularity of regulatory data are accelerating. More frequent updates, enhanced digital pipelines, and improved cross-referencing with geographic boundaries allow monthly tracking of pricing movements by city and county. By weaving in macroeconomic indices and exposure data, teams can discern how much of a rate move is driven by loss cost inflation versus competition versus reinsurance cost cascades.

For pricing visibility, the payoff is significant. Regulatory datasets help attribute changes in written premium and rate to specific lines of business—commercial property, general liability, auto, public sector risk pools, and more—and across territories. When normalized and benchmarked, these records offer a credible yardstick for what the market is actually charging, rather than relying on isolated anecdotes.

How regulatory data informs pricing decisions

When paired with internal underwriting and claims experience, regulatory data provides a baseline for market tracking and competitive positioning. By aligning filings with sub-line definitions and geography codes, organizations can establish a recurring monthly dashboard to detect pricing inflections early.

Practical applications

Benchmark rate changes by geography: Compare indicated versus achieved rate across states, cities, and counties to pinpoint where pricing is firming or softening.
Carrier-level competitive intelligence: Track relative growth and pricing posture by carrier and sub-line to detect aggressive expansion or retrenchment.
Distribution channel analysis: Where data supports it, assess rate variation by broker channel, direct, or MGA/MGU relationships.
Regulatory rate actions: Monitor approved filings and form changes to anticipate downstream pricing shifts.
Forecasting loss ratio impacts: Merge regulatory premium and loss views with internal severity/frequency trends to project underwriting results under different rate scenarios.

Policy Placement and Transaction-Level Pricing Data

Policy placement data—spanning quotes, binds, endorsements, and renewals—offers a high-resolution lens on real-world pricing. While carriers and brokers have always maintained records of placements, the historical challenge was fragmentation: different systems for submissions, binders, and endorsements; free-text fields; and limited normalization across carriers and geographies.

Digitization across broker networks and carrier portals has brought order to this complexity. Placement logs now capture effective dates, coverage layers, limits, deductibles, sub-line categorization, and premium/rate outcomes, often at monthly cadence. For certain segments—such as public entities, municipalities, school districts, and other public-sector risks—data coverage can be especially detailed with insured location information, enabling granular geographic analysis.

Industries and roles that rely on placement-level data include underwriting executives, product managers, portfolio strategists, and reinsurance buyers who need a forward-looking view of price adequacy. Investment analysts and consultants also use these data to evaluate market share shifts and predict cyclical inflection points. The richness of the placement data allows slicing by sub-line and attaching attributes like building construction, protection class, auto fleet size, or industry SIC/NAICS codes.

Technology advances—API-enabled quoting, standardized ACORD messages, and cloud data lakes—have not only increased accessibility but also expanded the dimensionality of the data. Event-level logs mean teams can study pricing outcomes along the entire journey: submission to quote to bind to endorsement, and even mid-term adjustments. This granularity accelerates understanding of conversion elasticity relative to price.

The amount of transaction-level data is growing quickly as more distribution moves to digital channels and MGAs adopt modern policy admin systems. With appropriate privacy and compliance controls, this data can be aggregated to surface monthly rate movements by carrier, sub-line, and geography, providing the kind of competitive intelligence that once required months of manual compilation.

With placement data in hand, organizations can precisely monitor their competitive position and identify where to recalibrate rate, deductible structures, or coverage terms to optimize both conversion and profitability.

How placement data powers competitive pricing

Placement-level records bridge the gap between strategic pricing aspirations and real outcomes in the market. By studying cohorts of similar risks across carriers and territories, teams can quantify competitive intensity and calibrate where to push rate or hold ground.

Practical applications

Monthly pricing heatmaps: Visualize rate change by carrier, sub-line, and county to spot hotspots of acceleration or deceleration.
Win/loss and elasticity analytics: Quantify how price changes affect bind probability across segments and channels.
Term structure optimization: Analyze the interplay of deductibles, limits, and sub-limits to identify profitable structures that improve conversion.
Public-sector benchmarking: For municipalities and schools, compare pricing outcomes by region and exposure characteristics to inform bidding strategies.
Rapid competitive scans: Use aggregated results to anticipate competitor moves and pre-position renewal strategies.

Reinsurance Treaty and Market Pricing Data

Reinsurance markets set the tone for many primary insurance pricing decisions. Historically, insights into treaty and facultative pricing were confined to renewal seasons and broker narratives. Market participants tracked macro cycles—hardening after catastrophe years, softening during benign periods—but visibility into monthly movements by region or peril was limited.

Over time, reinsurance data became more structured. Treaty program summaries, ceded premium records, layer structures, attachment points, and rate-on-line metrics started to flow into digital bordereaux and analytics platforms. The rise of capital markets solutions, including insurance-linked securities, added public signals such as spread movements and placement dynamics for catastrophe risk.

Roles across the ecosystem—ceding officers, reinsurance brokers, actuarial capital teams, and portfolio managers—use this data to manage risk transfer and capital efficiency. Investment firms and consultants also monitor reinsurance pricing data to gauge the downstream impact on primary rate adequacy in property, casualty, and specialty lines.

Technology advances enabled timely, standardized capture of treaty terms and pricing signals. APIs, secure data rooms, and common schemas for exposure and loss experience have increased comparability and throughput. As a result, reinsurance pricing data is available more frequently and with more granularity across perils and geographies.

The volume of reinsurance market data is expanding as more cedants and markets share aggregated outcomes, and as alternative capital channels provide additional transparency. Monthly and quarterly snapshots of rate-on-line changes by region help forecast how capacity constraints and capital costs will cascade into primary pricing.

With these insights, carriers can anticipate reinsurance-driven pressure on certain sub-lines and geographies—adjusting rate, retention, and product design ahead of competitors, rather than reacting after renewal seasons conclude.

How reinsurance data guides primary pricing

Mapping treaty cost changes to primary portfolio segments allows organizations to quantify pass-through requirements and to identify where reinsurance economics will most influence street pricing.

Practical applications

Rate-on-line to primary rate translation: Attribute percentage changes in treaty cost to necessary primary rate adjustments by peril and region.
Capacity heatmaps: Identify geographies or sub-lines where tightening reinsurance capacity signals imminent primary price hardening.
Optimization of retentions: Use treaty pricing trends to reassess deductible structures, attachment points, and net retentions for profitability.
Cat bond and ILS proxies: Track spreads as a leading indicator for catastrophe-exposed property rate movements.
Scenario planning: Model outcomes for different renewal environments to pre-commit pricing posture and deployment of limit.

Claims and Loss Development Data

Claims and loss development data is the actuarial bedrock of pricing. Prior to modern data infrastructure, many organizations operated with quarterly or annual snapshots, high-level severity/frequency trends, and delayed visibility into development patterns by sub-line and geography. That delay made it hard to separate pricing inadequacy from adverse loss development or new forms of social inflation.

With digitized claims systems, granular loss runs, and structured bordereaux, claims data now offers a near real-time window into emerging trends. Metadata attached to claims—from cause of loss and litigation status to supplier cost indices—illuminates the drivers of loss cost inflation and where pricing needs to respond.

Actuarial teams, reserving committees, underwriting leaders, and product managers are the primary consumers of loss development analytics. But capital markets participants, consultants, and risk engineers rely on this data as well to contextualize market pricing moves with actual loss experience at segment level.

Technology has unlocked more frequent updates, machine-readable coding of loss details, and easy linkage to external benchmarks. The result: a faster feedback loop between market pricing changes and observed loss performance, enabling monthly recalibration of risk selection and rate.

As more sensors, telematics, and third-party attributes are tied to claims, the volume and explanatory power of claims datasets are growing. These enrichments help pinpoint not just that loss costs are rising, but precisely why—in which counties, among which classes of business, and due to which causal factors.

With robust claims and loss development data, pricing teams can confidently set targets that reflect both emerging severity drivers and the litigation environment, ensuring that primary and reinsurance pricing stay aligned with risk reality.

How claims data sharpens price adequacy

Loss information translated into geographic and sub-line lenses provides the necessary grounding for rate actions. It also supports credibility when communicating with distribution partners and clients about the need for changes.

Practical applications

Monthly severity and frequency dashboards: Track shifts by county and class to isolate where pricing must move.
Development factor refresh: Update LDFs more frequently for fast-changing segments to reduce reserve and pricing drift.
Social inflation monitors: Measure litigation and settlement trends tied to claims to anticipate pricing pressure.
Repair and replacement cost indices: Link claim payments to parts, labor, and materials inflation to refine rate indications.
Cat event attribution: Separate catastrophe-driven losses from attritional experience to avoid over- or under-reacting in pricing.

Property Attributes, Hazard, and Geospatial Data

Understanding exposure is essential to pricing. Before high-resolution geospatial intelligence, property underwriting leaned on coarse proxies: ZIP-code averages, broad protection classes, and limited building details. Two blocks apart, two commercial properties could face very different flood or wildfire exposures—yet receive similar pricing due to data limitations.

The advent of satellite imagery, aerial lidar, parcel records, and hazard models changed that. Today, geospatial data offers per-parcel building attributes, defensible space assessments, roof conditions, elevation, and distance to hazards. Hazard layers quantify wildfire, flood, hail, wind, and earthquake intensity at extremely granular levels.

Risk engineers, catastrophe modelers, underwriters, and pricing actuaries have embraced these datasets to price precisely and avoid adverse selection. Brokers, MGAs, and reinsurance underwriters use the same insights to structure towers, allocate aggregates, and negotiate terms.

Technology advances—cloud-native geospatial processing, rapid updates after hazard events, and APIs for address-to-attribute enrichment—have led to accelerating adoption. The cadence of updates is increasing as imagery refresh cycles shorten and hazard models incorporate new climate signals.

As more property-level attributes are captured and refreshed, the volume of data relevant to pricing grows exponentially. Monthly or even event-driven refreshes give teams the ability to adjust pricing where risk changes rapidly—particularly in catastrophe-exposed regions.

With geospatial and hazard data embedded in rating engines, organizations can differentiate pricing by sub-line and micro-geography, improve selection, and articulate risk-based pricing transparently to stakeholders.

How geospatial data elevates pricing granularity

When blended with placement and claims data, property and hazard attributes let teams separate price from peril with uncommon precision, supporting both growth and profitability.

Practical applications

Micro-territory pricing: Move beyond ZIP codes to pricing at parcel or block level based on hazard intensity and mitigation features.
Aggregate management: Track exposure accumulation within specific hazard footprints to guide rate and capacity deployment.
Event response pricing: Adjust underwriting guidelines and rates in regions where recent events change expected loss costs.
Underwriting pre-fill: Enrich submissions with verified property attributes to reduce leakage and improve accuracy.
Reinsurance-backed modeling: Align primary pricing with hazard-driven treaty costs to avoid mismatch between net and gross views.

Macroeconomic, Cost Inflation, and Industry Benchmark Data

Not all pricing pressure is born in the insurance market. Macroeconomic and industry cost data—wage growth, materials indexes, parts and labor for auto repair, construction costs, and medical inflation—flow directly into claim severity. Historically, pricing teams used broad national averages and lagging indicators, which often under-estimated regional or segment-specific cost realities.

With more granular, timely indices and sector-specific cost trackers, organizations can incorporate monthly inflation signals into pricing by sub-line and geography. Construction cost indices tied to local markets, auto parts price trackers, and labor rate datasets now offer the precision that pricing engines need.

Users of this data include pricing actuaries, reserving teams, product managers, and investor strategists. By aligning rate changes with cost inflation at a local level, carriers can maintain margin without over-correcting, while reinsurers can price reinstatement and aggregate protections with a clearer view of severity trends.

Technology has made these datasets easier to integrate—via APIs, standardized geographic schemas, and harmonized time series—so analysts can quickly blend them with claims and placement records. As a result, the frequency and breadth of macro-cost data leveraged in pricing has increased dramatically.

The rapid expansion of alternative cost indicators—supplier invoices, marketplace transaction data, and localized wage trackers—has further enriched inflation monitoring. This acceleration enables proactive monthly adjustments rather than annual step changes.

With macro and cost data in the loop, pricing models better reflect real-world replacement and repair dynamics, stabilizing combined ratios even as external costs evolve.

How macro and cost data drive rate precision

Integrating cost inflation at a fine-grained level ensures that rate changes are targeted, defensible, and responsive to real conditions.

Practical applications

Local inflation overlays: Apply city- or county-level cost indices to adjust base rates in near real time.
Severity forecast enhancements: Blend macro series with historical claims to improve next-12-month loss cost projections.
Repair cycle analytics: Track parts and labor rates to refine auto and property severity assumptions.
Medical cost trend integration: For casualty lines, incorporate regional medical inflation to fine-tune bodily injury pricing.
Reinsurance alignment: Coordinate primary rate actions with macro-driven shifts in expected loss for treaty negotiations.

Distribution Channel, Quoting, and Digital Conversion Data

Distribution dynamics shape pricing outcomes. Before the rise of digital channels, insight into quoting volume, quote-to-bind conversion, and channel-specific price sensitivity was limited and lagged. Brokers and agents shared periodic feedback; carriers inferred trends from internal results without a clear external benchmark.

Digital distribution and quoting platforms now produce structured data on submissions, quotes, and binds by channel and product. Aggregated views reveal where price competition is most intense, which segments face rising declines, and how channel mix affects achievable rate change.

Underwriting leaders, distribution executives, MGAs, and marketing teams use these signals to optimize appetite, pricing, and tactical campaigns. Investors and consultants also monitor channel dynamics to gauge competitive pressure and growth potential by segment.

Technology advances—tracking of funnel stages, cookie-less attribution methods, and standardized event schemas—enable monthly or even weekly visibility into quoting and conversion. As more distribution migrates online, the volume of channel data grows, increasing its value as a leading indicator for pricing adjustments.

With structured channel data, organizations can calibrate pricing to balance conversion and margin, setting rules that vary by geography, sub-line, and distribution partner.

Moreover, channel analytics help separate the impact of rate from non-price factors—coverage breadth, service levels, and submission responsiveness—that often drive competitive outcomes.

How channel and quoting data informs pricing strategy

By quantifying funnel behavior, teams can tune pricing to meet the market where it’s moving—without sacrificing profitability.

Practical applications

Elasticity by channel: Measure how much rate can move before conversion drops in specific geographies and segments.
Declination analytics: Identify where price or appetite mismatches are causing lost opportunities and adjust accordingly.
Appetite maps: Align underwriting guidelines with channel strengths to capture rate in the right places.
Competitor posture signals: Infer competitive pricing from sudden changes in conversion and quote volume across markets.
Monthly market pulse: Track shifts in digital quoting as an early signal of tightening or softening markets.

Litigation, Legal Environment, and Social Inflation Data

The legal environment exerts a powerful influence on casualty pricing. Historically, teams leaned on high-level legal climate indices and anecdotal reports of “nuclear verdicts” to gauge social inflation. The lag between courtroom trends and pricing action often resulted in underestimation of severity.

Modern datasets capture filings, settlements, verdicts, attorney activity, and litigation funding signals at granular geographic levels. Combined with claims metadata, they reveal where litigation risk is rising and which case types are seeing higher awards.

Users include casualty underwriters, claims executives, actuaries, reinsurers, and investors who need to anticipate severity trends before they fully appear in paid losses. Consultants and market researchers synthesize these datasets to advise on pricing posture by jurisdiction.

Technology advancements—natural language processing of court documents, structured tagging of case attributes, and linkages to insured geographies—allow monthly scoring of legal environments. As digitization spreads across court systems, the pace and depth of available signals increase.

The accelerating availability of litigation analytics helps align pricing and underwriting with emerging legal realities. It also supports reinsurance negotiations by evidencing jurisdictional severity shifts.

With legal and social inflation data incorporated into pricing frameworks, insurers and reinsurers can more accurately reflect risk by geography and line, preventing adverse surprises and sharpening competitive differentiation.

How legal environment data de-risks pricing

Early detection of litigation trends translates into proactive rate and underwriting action where it matters most.

Practical applications

Jurisdictional severity flags: Adjust pricing in counties showing rapid growth in awards or settlement values.
Attorney network monitoring: Track high-activity plaintiff firms as a proxy for rising litigation pressure.
Defense cost projections: Incorporate legal cost inflation into pricing for professional and general liability segments.
Venue risk segmentation: Refine underwriting appetite in venues prone to outsized verdicts.
Reinsurance advocacy: Use objective litigation metrics to support program structure and rate discussions.

Bringing It All Together: A Unified Pricing Intelligence Stack

Each dataset category tells part of the story. The real power emerges when organizations integrate these sources into a cohesive pricing intelligence stack. By blending regulatory filings, placement transactions, reinsurance treaty signals, claims development, geospatial hazard attributes, macro-cost indices, channel dynamics, and legal environment data, teams gain a 360-degree view of pricing—by carrier, sub-line, and geography—on a monthly cadence.

As companies invest in data engineering and governance, they improve normalization across sources: mapping sub-lines to common taxonomies, aligning geographies to counties and tracts, and reconciling rate definitions. With a well-architected pipeline, pricing dashboards shift from static reports to dynamic, forward-looking tools that track leading and lagging indicators in unison.

Finding and assembling the right external ingredients is faster than ever with modern data search platforms. These tools accelerate discovery across many types of data while streamlining validation, contracting, and delivery. The result: shorter time-to-insight and greater confidence in pricing decisions.

Advanced analytics, including AI-assisted modeling, can further enhance signal extraction—provided the underlying data is robust and well-governed. For organizations exploring model development, curating high-quality training data is critical to reliable results and responsible deployment.

With an integrated approach, pricing leaders move from retrospective market reading to proactive market shaping—setting strategy with confidence and precision.

Conclusion

The journey from opaque, lagging market anecdotes to real-time pricing intelligence has transformed how organizations navigate primary and reinsurance markets. What once required months of waiting and inference can now be tracked monthly by carrier, sub-line, and geography. Regulatory filings, placement data, reinsurance pricing signals, claims development, geospatial hazard attributes, macro-cost indices, channel dynamics, and litigation analytics together provide a comprehensive map of the market.

Embracing these datasets empowers underwriters, actuaries, and reinsurance buyers to act earlier and with greater precision—aligning rate with risk, anticipating capacity constraints, and allocating capital where it earns the highest return. Decision cycles compress, renewal strategies become sharper, and portfolio outcomes improve.

Organizations that invest in discovery, integration, and governance of external data build a durable edge. The ability to rapidly evaluate new sources across relevant categories of data, test them against internal outcomes, and operationalize them in pricing workflows is now a core competency for market leaders.

Data monetization is also reshaping the landscape. Many corporations are learning how to responsibly monetize their data, turning operational exhaust into market intelligence that benefits the broader ecosystem. Insurance, with its deep history of record-keeping, is no exception: decades of policy, loss, and exposure records, when aggregated and de-identified, can inform better pricing and risk selection across the market.

Looking ahead, more frequent and granular signals will emerge. Expect richer event-driven hazard updates, deeper visibility into supply chain costs that influence claims, and enhanced distribution analytics as digital channels mature. As organizations apply Artificial Intelligence thoughtfully—anchored by carefully curated training data—they will unlock patterns that are impossible to spot manually.

The bottom line is clear: pricing power is data power. By continuously enriching your pricing intelligence stack with best-in-class sources and leveraging modern data search to discover what’s next, you can move from guessing the market to guiding it—month after month, geography by geography, sub-line by sub-line.

Appendix: Who Benefits and What’s Next

Underwriters and product managers gain the most immediate benefit from high-frequency pricing data. With monthly visibility into rate movements by carrier and sub-line, they can adjust appetite, endorsements, deductibles, and limits to optimize both growth and profitability. Geospatial and hazard data enable fine-grained selection, while claims and litigation analytics help anticipate severity trends before they hit loss ratios.

Actuarial and reserving teams leverage regulatory, claims, and macro-cost datasets to refresh indications and development factors more frequently. This reduces reserve drift and strengthens credibility in pricing proposals. The fusion of external inflation measures with internal loss experience supports quicker, targeted rate actions.

Reinsurance buyers and brokers rely on treaty pricing data and catastrophe exposure analytics to structure programs efficiently. By connecting reinsurance rate-on-line trends to primary pricing plans, they align net and gross views, maintain capital efficiency, and negotiate more effectively with markets.

Investors, consultants, and market researchers use aggregated carrier, sub-line, and geographic pricing signals to model market cycles, identify share shifts, and forecast earnings sensitivity to rate changes. They also track distribution dynamics and litigation trends as leading indicators of pricing pressure or relief.

Risk managers and large insureds benefit from transparency into market conditions, improving budgeting and program design. Understanding where capacity is tight and where pricing is rising helps them time marketing efforts and evaluate alternative risk transfer structures.

Looking forward, the fusion of decades-old documents with modern analytics promises new insight. Digitization of historical filings, scans of legacy bordereaux, and transformation of unstructured notes into structured signals—assisted by carefully governed AI—will surface hidden patterns across time. As firms explore fresh sources, modern external data discovery tools simplify evaluation across many types of data, accelerating time to value. And as more organizations seek to responsibly monetize their data, the market will benefit from novel signals—monthly and granular—fueling smarter pricing decisions for years to come.