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Data Science AnalyticsTop 10 Best Cryptocurrency Analysis Software of 2026
Ranked review of cryptocurrency analysis software for traders, weighing Dune Analytics, Glassnode, CryptoQuant, plus Nansen and Token Terminal.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Nansen is the strongest enterprise pick if you need labeled wallet and protocol investigations with repeatable watchlists, whereas Token Terminal fits teams doing fast, metric-based protocol research, and CoinGecko is the cheaper entry for quick cross-asset research views and API lookups.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Nansen
Entity attribution labeling that maps wallets and contracts to behavioral labels across supported networks.
Built for fits when traders need labeled wallet and protocol investigations with repeatable watchlists..
Token Terminal
Editor pickCurated protocol-level analytics pages that map market behavior to on-chain activity signals in one view.
Built for fits when traders need fast, repeatable metric-based analysis across tokens and protocols..
CryptoQuant
Editor pickInvestor-flow dashboards that pair exchange flows with derivatives positioning context for single-view signal interpretation.
Built for fits when traders need repeatable exchange and positioning signals for daily monitoring..
Comparison Table
Nansen
enterpriseBlockchain analytics platform with wallet labeling.
Entity attribution labeling that maps wallets and contracts to behavioral labels across supported networks.
Nansen’s core workflow centers on entity attribution and transaction graph navigation, so investigators can start with a wallet or protocol and trace how it interacts with others. The tool’s tag-based labeling supports heuristic tagging for entities, which reduces manual effort when comparing counterparties and time windows. Filters and saved views help teams reproduce the same investigation on recurring questions like inflow surges and intermediary behavior.
A notable tradeoff is that deeper attribution quality depends on labeling coverage, so some newly created contracts or obscure counterparties may require manual cross-checking. Nansen fits best when daily monitoring needs consistent wallet-level narratives, like tracking custody-style actors and recurring DeFi counterparties through changing volumes.
- +Entity attribution labeling shortens wallet-to-behavior investigation cycles
- +Transaction graph navigation keeps multi-hop relationships readable
- +Alert configuration supports recurring watchlists for on-chain events
- +Exportable views preserve analyst findings for reports and handoffs
- –Label coverage gaps can force manual validation for obscure entities
- –Fine-grained programmatic automation needs stronger API and event exports
- –Some graph views can feel dense during high-activity periods
- –Cross-network comparisons require careful normalization of comparable metrics
Crypto trading desks
Whale wallet tracking across token moves
Faster sourcing of trade thesis
Market research teams
DeFi counterparty behavior investigations
Consistent attribution across analysts
Show 2 more scenarios
Risk and compliance analysts
Mixer and bridge-related entity triage
Prioritized reviews with evidence
Apply entity labels to identify suspicious intermediaries and follow chain-hop relationships.
Quant analysts
Alert-driven monitoring of on-chain changes
Lower time-to-signal on anomalies
Configure watch rules to detect behavior shifts and review the underlying transaction context.
Best for: Fits when traders need labeled wallet and protocol investigations with repeatable watchlists.
Token Terminal
enterpriseAnalytics for crypto protocols and applications.
Curated protocol-level analytics pages that map market behavior to on-chain activity signals in one view.
Token Terminal centers on human-readable metric pages for tokens, protocols, and exchanges, with charting and comparison views that reduce the time spent stitching sources. Its core workflow supports monitoring trends, validating hypotheses with specific metrics, and sharing findings through consistent dashboards. That structure also fits repeated analysis cycles such as daily watchlists and periodic portfolio reviews.
A key tradeoff is that the product emphasizes curated metrics and prebuilt views over raw query flexibility, which can limit deep custom extraction for niche research questions. It works best when the goal is fast, metric-driven analysis using standard entity views rather than building bespoke pipelines from node-level data.
- +Curated token and protocol metric pages reduce source stitching time
- +Time-window filtering supports quick trend validation for watchlists
- +Exportable chart data supports spreadsheet and model handoff
- +Consistent entity views speed up cross-token comparisons
- –Less suited to fully custom query workflows and bespoke datasets
- –Prebuilt views can constrain research that needs atypical joins
Active traders
Daily token watchlist validation
Faster decision cycles
DeFi analysts
Protocol health trend checks
Earlier signal detection
Show 1 more scenario
Market research teams
Cross-token metric reporting
Lower reporting overhead
Export consistent metrics for reporting cycles across multiple assets and venues.
Best for: Fits when traders need fast, repeatable metric-based analysis across tokens and protocols.
CryptoQuant
enterpriseOn-chain data analytics platform for crypto assets.
Investor-flow dashboards that pair exchange flows with derivatives positioning context for single-view signal interpretation.
CryptoQuant’s core workflows revolve around exchange inflow and outflow analytics and derivatives positioning signals built from public market data. The interface is geared toward answering “what changed” on a time window basis rather than requiring users to build custom graphs from raw transactions. Data export provides table-level granularity for reporting and spreadsheet modeling, and the historical views support backtesting of indicator behavior. Coverage spans multiple major EVM-compatible chains through entity and transfer analytics layered onto the monitoring model.
A tradeoff appears when deep transaction graph visualization or custom entity graph exploration is the main goal. In those cases, Dune Analytics and similar query-first systems can be better suited for custom query logic and transaction graph work. CryptoQuant fits daily research routines where exchange and positioning signals need quick interpretation, plus recurring review of behavioral changes across wallets and liquidity movement.
- +Exchange inflow and outflow dashboards support rapid trend checks
- +Open-interest and long short positioning signals map to derivatives sentiment
- +Historical metric views help validate indicator reactions over time
- +Exported tables work directly for spreadsheet and downstream analysis
- –Limited emphasis on customizable transaction graph exploration
- –On-chain investigations can require external tooling for UTXO-level tracing
- –Indicator configuration choices are less flexible than SQL query workflows
- –API and automation depth is narrower than query and warehouse ecosystems
Day traders and prop desks
Monitor flow shifts before entries
Faster signal to execution loop
Quant researchers
Test flow indicators over history
Reusable factor dataset creation
Show 2 more scenarios
Risk managers
Spot leverage build-ups
Earlier hedging decision points
Review long short and open-interest trends to flag potential crowded positioning regimes.
Blockchain analysts
Triage wallet-linked movement
Reduced time to hypothesis
Use chain-linked entities and transfer context to narrow investigation scope around behavioral clusters.
Best for: Fits when traders need repeatable exchange and positioning signals for daily monitoring.
CoinGecko
API-firstCryptocurrency data aggregator and ranking platform.
Address and token transfer pages that keep entity context close to market and project metadata for rapid investigation.
CoinGecko aggregates market, exchange, and on-chain oriented crypto data into a single web experience with strong address and token visibility. Its coin and token pages emphasize practical entity-level views that traders use for watchlists, cross-asset comparisons, and historical performance references.
CoinGecko also offers developer-facing access through an API surface that supports programmatic retrieval of prices, market data, and project metadata. For deeper on-chain workflows, it pairs address and transaction explorer-style views with chain-specific context such as token transfer history where available.
- +High-frictionless coin and token pages for quick entity-level market checks
- +Consistent identifiers and metadata across assets to reduce join errors
- +Usable API endpoints for prices, market snapshots, and asset metadata
- +Address views and token transfer history support investigation without extra tools
- –Limited support for custom on-chain risk scoring and rule-based alerting
- –Not oriented around graph-native analysis workflows for clustering
- –API-focused automation can be constrained for streaming or replay-style tasks
- –Export formats and batch pipelines are not built for large-scale analytics
Best for: Fits when traders need fast cross-asset research views and API-driven lookups without building their own data layer.
CoinMarketCap
API-firstCryptocurrency market cap and ranking platform.
High-throughput CoinMarketCap API retrieval for market-wide asset and metrics at scale.
CoinMarketCap aggregates market-wide cryptocurrency data into a single interface with ranked listings, price and volume history, and market-cap breakdowns. Its core analysis workflows center on watchlists, asset pages, and cross-asset comparisons that help analysts track changes across many listings.
It also provides developer-facing data access via an API that supports programmatic retrieval of market metrics and asset metadata. Automated reporting usually depends on pulling data over REST endpoints and then shaping results in external tools.
- +Broad coin and market coverage with consistent rank and supply fields
- +API access for market metrics supports scheduled data pipelines
- +Asset pages consolidate price, volume, and historical charts in one view
- +Watchlists and comparisons reduce time spent switching between assets
- –Limited on-chain attribution and clustering compared with on-chain analytics specialists
- –Alerting and custom risk logic are constrained outside external tooling
- –Historical and ranking views need careful normalization across different exchanges
- –Integration work is required to model entities and relationships
Best for: Fits when teams need cross-asset market metrics, watchlists, and API pull workflows.
LunarCrush
SMBSocial intelligence for cryptocurrency markets.
Community-attention metrics on per-asset pages with threshold alerts for tracking signal shifts.
LunarCrush focuses on social and market signals for crypto assets and pairs them with trader-facing dashboards and alerts. It provides asset pages with sentiment-style metrics, community activity indicators, and price-linked context designed for watchlists and event-driven monitoring.
LunarCrush also supports analytics workflows through exports and an integration surface intended for automation around watchlist changes and signal thresholds. Data coverage and signal granularity are strongest when the goal is to correlate attention and activity with market moves rather than to run custom on-chain graph analysis.
- +Social-signal dashboards connect community activity to market context quickly
- +Alerting supports threshold-driven monitoring for assets on watchlists
- +Export-friendly outputs help move signals into external spreadsheets and tooling
- +Asset-level pages reduce manual research time for recurring watch items
- –On-chain entity attribution and transaction graph analysis are not the primary focus
- –Signal interpretation depends on the platform’s heuristics rather than custom models
- –Automation requires using the available integration surface rather than direct full-data access
- –Cross-chain tracing style workflows are limited compared with chain-native analytics tools
Best for: Fits when trading workflows rely on social attention signals plus market context and need alert-based monitoring.
Glassnode
enterpriseOn-chain and market intelligence platform for digital assets.
Investor-focused valuation and holder-cohort metrics, packaged as repeatable time series for exchange flow and wallet state analysis.
Glassnode focuses on on-chain analytics built from structured blockchain datasets and investor-oriented metrics. Its core workflow centers on wallet and entity valuation indicators, exchange flow stats, and chain activity snapshots that support ongoing monitoring.
The system also provides programmatic access for pulling time series and derived signals into trader tooling and internal dashboards. Glassnode is a good fit when on-chain signals must stay consistent across research, alerting, and reporting.
- +Consistent investor metric framework across major on-chain time series
- +Export-ready metrics that support repeatable research and reporting workflows
- +Clear entity-style views for tracking wallet cohorts over time
- +API access for integrating curated signals into external monitoring
- –Less flexible than graph-first tooling for custom relationship discovery
- –Advanced tagging and attribution depend on model definitions rather than raw graph building
- –UI-heavy analysis can slow down batch workflows versus code-first pipelines
- –Alerting controls can feel narrower than dedicated monitoring products
Best for: Fits when teams need consistent, investor-grade on-chain signals and API pull-through for alerts and dashboards.
Dune Analytics
API-firstSQL-based blockchain data exploration and visualization.
Community marketplace of saved queries and dashboards built on indexed contract events for fast metric assembly.
Dune Analytics focuses on query-driven crypto research with a SQL workflow over on-chain data. It is known for community-built dashboards, reusable datasets, and fast iteration for metrics like protocol usage and token flows.
Core capabilities include smart contract event indexing, historical block querying, and export-ready results for downstream analysis. Automation and integration are supported through an API and programmable query execution for repeatable reporting.
- +SQL-first workflow with shared dashboards for repeatable research
- +Event-level indexing enables precise protocol and token-flow metrics
- +Exports support granular results for external modeling and visualization
- +API supports programmatic query runs for scheduled reporting
- –Onboarding requires SQL skill and dataset discovery to avoid dead ends
- –Heavy, large-range queries can hit throughput limits and slow down iteration
- –EVM-specific coverage can require extra logic when mixing multiple chains
- –Address clustering and entity attribution quality depends on the available heuristics
Best for: Fits when traders and analysts need SQL-driven dashboards with programmatic refresh for on-chain KPIs.
Santiment
SMBCrypto market intelligence with social and on-chain data.
Behavior-focused entity attribution and clustering baked into dashboards reduces time spent translating raw on-chain data into trader signals.
Santiment produces crypto on-chain analytics built around address and entity behavior signals, then packages them into trader-facing dashboards and reports. It emphasizes heuristics like wallet clustering and behavioral attribution, plus market-wide metrics for exchange flows, stablecoin movement, and whale activity.
The system supports alerts and scheduled datasets so analysts can track metric changes over time without manual export cycles. Data access and automation are handled through documented export and API integration so research workflows can feed external notebooks and internal tooling.
- +Entity and behavior signals reduce manual labeling for watchlists
- +Alert rules support ongoing monitoring without spreadsheet refreshes
- +Exports provide practical granularity for analyst workflows
- +API access fits research automation and repeatable data pulls
- –Advanced graph exploration depends on understanding its entity layer
- –Some niche token and contract decoding tasks require deeper setup
Best for: Fits when analysts need automated entity attribution signals and alert-driven monitoring across major assets.
Bitquery
API-firstGraphQL blockchain data API for developers.
A unified query interface for contract events and transaction-graph patterns, backed by an API for automation at query level.
Bitquery focuses on query-driven cryptocurrency and on-chain analytics for EVM-compatible networks, with a data retrieval layer built around flexible graph patterns. It supports historical indexing use cases such as contract event analysis, token movement queries, and entity-level address behavior checks.
Bitquery also exposes an API surface designed for programmatic analytics, plus automation patterns like scheduled extraction and downstream data export. Compared with other on-chain analysis tools, the differentiator is how far the workflow can be pushed through parameterized queries instead of fixed dashboards.
- +Query-first analytics covers contract events and token flows in one interface
- +API access enables automated extraction for trader workflows and research jobs
- +Graph-style transaction queries support address relationships across hops
- +Export granularity supports downstream modeling into data pipelines
- –Query authoring requires schema familiarity and careful query shaping
- –Deep entity attribution can depend on heuristic coverage and query design
- –Throughput limits can constrain large backfills without batching
- –Advanced alerting and RBAC controls are less direct than workflow-first tools
Best for: Fits when teams need repeatable on-chain research via programmable queries and scheduled exports for EVM assets.
Conclusion
After evaluating 10 data science analytics, Nansen stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right cryptocurrency analysis software
Cryptocurrency analysis software turns on-chain and off-chain signals into trader-ready views that support investigation, monitoring, and export. This guide covers Nansen, Dune Analytics, Glassnode, CryptoQuant, and other major platforms that separate entity labeling, query assembly, and exchange-flow dashboards.
The evaluation focuses on how each tool handles integration depth through API and data export, automation through scheduled refresh or alert-driven monitoring, and control depth through configuration that fits trading workflows. Nansen prioritizes entity attribution labeling for repeatable watchlists, while Dune Analytics centers SQL-driven dashboards built on indexed contract events.
Cryptocurrency analysis software for on-chain and exchange-driven signal workflows
Cryptocurrency analysis software provides structured access to blockchain data and market-relevant signals using dashboards, labeled entities, and queryable datasets. It typically combines protocol-level metrics with wallet or contract context so trading teams can move from raw activity to decision signals without rebuilding their own data layer.
Nansen focuses on entity attribution labeling that maps wallets and contracts to behavioral labels across supported networks, which shortens multi-hop investigation cycles. CryptoQuant emphasizes exchange inflow and outflow dashboards paired with derivatives positioning context, which supports single-view monitoring for daily exchange-and-position signals.
Core evaluation criteria for cryptocurrency analysis software
Cryptocurrency analysis software earns selection priority when it turns blockchain activity into repeatable trader signals using integrations, query automation, and export controls. These features determine whether research cycles stay fast during market volatility and whether teams can operationalize insights into monitoring.
This guide emphasizes integration depth through API and data export, automation through scheduled refresh or alert workflows, and control depth through configuration that fits recurring trading playbooks. The strongest tools also reduce time spent rebuilding the same joins across watchlists, exchanges, and labeled entities.
Entity attribution and labeled investigation paths
Nansen provides entity attribution labeling that maps wallets and contracts to behavioral labels across supported networks, which shortens multi-hop investigation cycles. Santiment also bakes behavior-focused entity attribution and clustering into dashboards but relies more on its entity layer than raw graph exploration.
Protocol-level metric assembly from indexed events
Dune Analytics uses a SQL-first workflow with event-level indexing to assemble protocol and token-flow metrics from precise contract events. Token Terminal focuses on curated protocol-level analytics pages that map market behavior to on-chain activity signals in a single view.
Exchange flow and derivatives context for monitoring
CryptoQuant pairs exchange inflow and outflow dashboards with open-interest and long short positioning signals for single-view interpretation. Glassnode packages investor-focused valuation and holder-cohort time series designed to support exchange flow and wallet state analysis.
Automation surface for recurring research and alert workflows
Glassnode emphasizes export-ready metrics that support repeatable research and reporting workflows for alert-driven dashboards. Santiment adds alert rules that enable ongoing monitoring without spreadsheet refreshes.
Graph-native exploration versus prebuilt views
Nansen keeps transaction graph navigation readable for multi-hop relationships and investigation. Token Terminal reduces stitching time with curated metric pages but becomes less suited when research requires fully custom query workflows and bespoke datasets.
Scalable market metrics API for cross-asset pipelines
CoinMarketCap targets teams that need high-throughput market-wide asset retrieval with consistent rank and supply fields through its API. CoinGecko supports fast cross-asset research views with consistent identifiers and metadata, but it lacks dedicated graph-native workflows for clustering and risk-rule alerting.
How to choose cryptocurrency analysis software for trading workflows
Selection should start from how signals get operationalized, not from which dataset looks best on a dashboard. Teams that repeat the same investigations need stable entity logic and repeatable exports, while teams that iterate on custom hypotheses need programmable query depth.
The decision forks below separate tools optimized for labeled investigations, SQL-driven protocol metrics, and exchange-plus-derivatives monitoring. Each fork uses differences visible in how tools package investigation views, automate monitoring, and support custom workflows.
Choose the signal backbone: labeled entities or metric-ready dashboards
If recurring research starts from “what is this wallet or contract doing” and needs consistent labeling across networks, Nansen provides entity attribution labeling that maps wallets and contracts to behavioral labels. If recurring research starts from token and protocol metrics in prebuilt views, Token Terminal provides curated protocol-level analytics pages with time-window filtering.
Pick the query model: SQL-first event indexing or programmable query building
If building and sharing custom dashboards requires a SQL-first workflow over indexed contract events, Dune Analytics supports programmatic refresh for on-chain KPIs. If research workflows must combine contract events and transaction-graph patterns through a unified query interface backed by an API, Bitquery supports API automation at query level.
Match monitoring focus: exchange flows with derivatives context or investor-cohort time series
If daily monitoring depends on “what are exchanges moving” plus “what are derivatives traders implying,” CryptoQuant pairs exchange inflow and outflow dashboards with open-interest and long short positioning signals. If daily monitoring depends on investor-grade holder and valuation views that plug into dashboards and alerts, Glassnode delivers a consistent investor metric framework with export-ready time series.
Confirm whether custom graph exploration or bespoke joins are required
If research requires deep relationship discovery and graph-driven iteration, Nansen supports transaction graph navigation for multi-hop relationships. If research stays within a curated set of metric views, Token Terminal’s prebuilt pages can reduce source stitching but can constrain atypical joins.
Validate how much customization is worth trading off for speed
If fast cross-asset lookups and metadata consistency matter more than custom on-chain risk scoring, CoinGecko provides frictionless coin and token pages with consistent identifiers and metadata. If market-wide metric pipelines matter more than on-chain attribution, CoinMarketCap offers high-throughput CoinMarketCap API retrieval for market-wide asset and metrics at scale.
Who cryptocurrency analysis software fits best
Different teams use cryptocurrency analysis software for different reasons, and those differences show up in how they build repeatable signals. Some teams need labeled entity attribution to speed multi-hop investigation, while others need SQL-driven metric assembly or exchange-and-derivatives monitoring.
The segments below map directly to the tools that match each workflow, based on how each platform structures investigations and monitoring signals.
Traders who run repeated wallet and contract investigations
Nansen fits when labeled entity attribution needs to shorten wallet-to-behavior investigation cycles for watchlists across supported networks.
Analysts who assemble protocol metrics using queryable event indexing
Dune Analytics fits when SQL dashboards must refresh on indexed contract events and when shared dashboards reduce repeated metric assembly.
Teams monitoring exchange activity with derivatives positioning context
CryptoQuant fits when exchange inflow and outflow signals must be interpreted alongside open-interest and long short positioning for daily monitoring.
Operations and research workflows focused on investor-grade holder and valuation time series
Glassnode fits when consistent investor metric frameworks need to export into repeatable research and reporting dashboards for alert workflows.
Monitoring workflows that prioritize social attention thresholds alongside market context
LunarCrush fits when per-asset social-signal dashboards and threshold alert monitoring drive decisions more than on-chain graph attribution.
Common pitfalls when buying cryptocurrency analysis software
Buying mistakes usually happen when research requirements are described in terms of “on-chain data” instead of in terms of operational workflows. The tools differ sharply in whether they support labeled investigations, SQL-first metric building, or exchange-plus-derivatives monitoring, so mismatches create slow cycles.
The pitfalls below focus on the concrete failure modes that show up when a team selects a tool for the wrong research shape.
Choosing a curated metrics dashboard when the team needs bespoke query joins
Token Terminal can constrain research that needs atypical joins because it centers curated token and protocol metric views. Dune Analytics or Bitquery fits better when custom query assembly is a requirement for the research workflow.
Relying on prebuilt entity attribution without validating label coverage gaps
Nansen can require manual validation for obscure entities when label coverage has gaps. Teams should test label coverage for their specific token sets and wallet clusters before standardizing watchlists.
Assuming graph-native exploration exists at the same depth as exchange dashboards
CryptoQuant emphasizes exchange inflow and outflow dashboards and derivatives sentiment, and it puts less focus on customizable transaction graph exploration. Teams needing UTXO-level tracing or deep entity relationship discovery should plan for external tooling or select a graph-native platform.
Building alert logic in a tool that does not support rule-based monitoring for on-chain risk logic
CoinGecko is not oriented around custom on-chain risk scoring and rule-based alerting. Teams that need automated risk logic should prioritize platforms with alert rules or export-ready metrics designed for alert-driven dashboards.
How We Selected and Ranked These Tools
We evaluated Nansen, Dune Analytics, Glassnode, CryptoQuant, and the remaining tools by weighting features at 40%, ease of use and day-to-day iteration at 30%, and overall value at 30%. Features scoring emphasized how the product turns on-chain and market activity into repeatable trading signals through entity attribution, indexed event metrics, and exchange-flow monitoring views.
Ease of use scoring emphasized how quickly teams can move from investigation to monitoring without heavy manual stitching across sources. Value scoring emphasized whether exports and operational workflows reduce recurring effort, and Nansen separated itself through entity attribution labeling that maps wallets and contracts to behavioral labels across supported networks while keeping transaction graph navigation readable.
Frequently Asked Questions About cryptocurrency analysis software
How do Dune Analytics and Bitquery differ for contract event indexing and historical block replay?
Which tools are best for labeled wallet and entity attribution during transaction investigations?
How does CryptoQuant handle investor-flow metrics compared with Glassnode's wallet and valuation indicators?
When do address and token transfer pages in CoinGecko matter more than chart-first market dashboards in CoinMarketCap?
What breaks if an organization tries to rely on token-level dashboards in Token Terminal instead of protocol event indexing?
How do SSO and RBAC controls typically affect team workflows in Dune Analytics versus Nansen?
What level of API coverage and rate limits should be planned for when automating market data pulls from CoinMarketCap and CoinGecko?
How does Dune Analytics export granularity compare with Glassnode when building alerting pipelines?
When does WebSocket streaming or real-time monitoring influence the choice between Nansen and LunarCrush?
How should data migration be approached when moving from exchange-centric metrics to on-chain entity attribution across tools?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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