February 25th, 2026
The 25 Best Marketing Analytics Tools in 2026
By Simon Avila · 39 min read
25 Best marketing analytics tools: At a glance
Marketing analytics tools vary widely in what they track, how they connect data, and who they serve best. The list below covers conversational platforms, enterprise business intelligence (BI), data pipelines, and channel-specific tools.
Let’s compare them side by side:
Tool | Best For | Starting price | Key strength |
|---|---|---|---|
AI-powered marketing analysis without SQL | Natural language queries across connected marketing data | ||
Enterprise attribution modeling | Advanced segmentation with predictive analytics | ||
Website traffic analysis | Free | Web and event tracking (Google Analytics 4) | |
CRM-linked marketing reports | Unified marketing and sales data | ||
Enterprise cross-channel reporting | Deep Salesforce ecosystem integration | ||
Advanced marketing dashboards | $75/user/month for a Creator license | Powerful data visualization capabilities | |
Microsoft-based BI teams | Native Microsoft ecosystem integration | ||
Free dashboarding | Free; Pro plan available at $9/user/project/month | No-cost Google data visualization | |
Enterprise data aggregation | 500+ marketing source connectors | ||
Marketing data normalization | Automated data collection and standardization | ||
Executive-level dashboards | Real-time business intelligence across departments | ||
Search-based business intelligence | Natural language search for data exploration | ||
Spreadsheet data pulls | Direct marketing data to Google Sheets and Excel | ||
Automated data pipelines | Reliable scheduled data movement | ||
Agency reporting automation | Multi-client reporting workflows | ||
Visual client reports | Automated white-label client dashboards | ||
KPI tracking dashboards | $159/month, includes 3 data sources | Goal monitoring with mobile access | |
SEO and agency dashboards | SEO-focused client reporting | ||
Product event tracking | Detailed user interaction analysis | ||
Behavioral cohort analysis | Advanced user segmentation and retention tracking | ||
Automatic event capture | No-code event tracking setup | ||
SEO performance analytics | Comprehensive keyword and competitor research | ||
Social media measurement | Unified social media analytics and scheduling | ||
Lifecycle marketing analytics | $75/month, billed monthly | Mobile user engagement and retention tracking | |
SMB multi-source reporting | $48/month (Cloud) | Affordable BI with broad connector support |
1. Julius: Best for AI-powered marketing analysis without SQL
What it does: Julius is an AI data analysis tool. It allows you to run queries on your marketing databases or spreadsheets and get charts, tables, or summaries. Julius lets you save repeatable analyses that run on a schedule, so you can track campaign metrics or performance trends without writing SQL.
Who it's for: Marketing teams who manage data across ad platforms, CRMs, and warehouses, and want flexible analysis without writing SQL.
We designed Julius to answer questions about your marketing data through natural language.
After you connect marketing platforms like Google Ads and Meta Ads, you can ask "Show ad spend by channel last month" or "Compare conversion rates across campaigns" and get answers fast. Julius analyzes your sources and shows the underlying code and data used to produce each result. That transparency helps you verify the numbers before sharing them with stakeholders.
Julius builds an understanding of how your tables connect as you use it. When you ask about conversion rates or ad spend, it returns more consistent answers because it knows where to find the right numbers.
Notebooks let you set up recurring analyses once and get automatic updates. If you check 'weekly revenue by source' every Monday, Julius runs it for you and sends the results to Slack so you don’t need to rebuild reports manually.Key features
Natural language analysis: Ask detailed marketing questions in plain English and get charts, tables, or summaries
Connected marketing sources: Links Google Ads, Meta Ads, Google Sheets, and databases like BigQuery and Postgres
Reusable Notebooks: Set up recurring marketing analyses once and run them again anytime
Scheduled reporting: Send saved analyses to Slack or email on a schedule
Persistent data context: Keeps track of how your marketing tables connect, so answers stay consistent over time
Pros
Quick setup for structured marketing data
Clear charts for campaign analysis
Automated recurring updates
Cons
Built for flexible analysis rather than pre-built attribution dashboards
Requires clean, consistent data
Pricing
Bottom line
2. Adobe Analytics: Best for enterprise attribution modeling
What it does: Adobe Analytics is a platform for web and customer journey analytics. It tracks user behavior on websites and mobile apps. You can also segment audiences, create custom attribution models, and use predictive analytics to forecast trends from past data.
Who it's for: Enterprise marketing teams that need advanced segmentation and multi-touch attribution across complex customer journeys.
Adobe Analytics performed well when I needed to track user paths across multiple touchpoints. The segmentation tools let you create detailed audience groups based on behavior, purchase history, or engagement levels. I could see how different segments moved through the site and where they dropped off.
The attribution models go beyond basic first-click or last-click tracking. You can set up custom models that assign credit across multiple touchpoints, which helps when you need to understand how different channels contribute to conversions.
Adobe products connect smoothly with each other, but you'll need additional integration tools to pull in data sources outside the Adobe ecosystem.Key features
Multi-touch attribution: Assign conversion credit across multiple marketing touchpoints
Advanced segmentation: Create detailed audience groups based on behavior and engagement
Predictive analytics: Forecast trends using machine learning on historical data
Pros
Deep integration with Adobe ecosystem
Flexible attribution modeling options
Strong predictive capabilities for enterprise use
Cons
Steep learning curve for new users
Higher cost compared to mid-market analytics platforms
Pricing
Bottom line
3. Google Analytics: Best for website traffic analysis
What it does: Google Analytics is a web analytics platform that tracks website and app traffic. With it, you can monitor user behavior, measure conversions, and see how visitors find your site through organic search, paid ads, social media, or direct traffic.
Who it's for: Marketing teams that need detailed website and event tracking without paying for an enterprise analytics platform.
I tested Google Analytics by tracking user behavior across multiple sites. The event-based tracking lets you monitor specific actions like button clicks, video plays, or form submissions. This allows you to see how users interact with your content. The real-time reporting showed me which pages were getting traffic at that moment, which helped during campaign launches.
The multi-channel attribution shows how different traffic sources contribute to conversions, but the data-driven model requires a steady volume of conversions before it produces stable results.
You can connect GA4 with Google Ads to see which campaigns drive the most valuable traffic. The platform also integrates with BigQuery if you need raw, unsampled data for deeper analysis.
Key features
Event-based tracking: Monitor specific user actions like clicks, scrolls, and video plays
Multi-touch attribution: See how different channels contribute to conversions
BigQuery integration: Export raw data for custom analysis without sampling
Pros
Free for most standard tracking needs
Deep integration with Google Ads and other Google products
Real-time reporting for immediate insights
Cons
GA4's interface has a learning curve compared to Universal Analytics
Historical data doesn't carry over from Universal Analytics
Pricing
Bottom line
4. HubSpot Marketing Hub: Best for CRM-linked marketing reports
What it does: HubSpot Marketing Hub is a marketing automation and analytics tool. It links your email campaigns, landing pages, social media, and ads to your CRM data. You can also track how marketing activities affect deal progression. This way, you’ll see which campaigns generate the most leads and deals.
Who it's for: Marketing teams that want unified reporting across marketing campaigns and sales pipeline data.
I ran email campaigns through HubSpot Marketing Hub to test how it tracks lead progression. Because marketing and sales data live in the same system, I could see how email opens, link clicks, and form submissions connected directly to deals in the pipeline without stitching reports together manually.
The dashboard pulls metrics from email performance, landing page conversions, social posts, and ad campaigns into one view. I liked that I could compare channel performance side by side without switching platforms.
HubSpot works best when you use their full suite of tools, so connecting external platforms often requires paid integrations.
Key features
CRM-linked attribution: Connect marketing activities to deal progression and reported revenue in the CRM
Unified dashboard: View email, social, ads, and landing page performance in one place
Automated workflows: Trigger actions based on contact behavior and engagement
Pros
Marketing and sales data live in the same system
Clear contact timeline showing all interactions
Email and landing page builders included
Cons
Advanced features require higher-tier plans
Limited compared to specialized tools for SEO or paid ads
Pricing
Bottom line
5. Salesforce Marketing Analytics: Best for enterprise cross-channel reporting
What it does: Salesforce Marketing Analytics is a platform for marketing insights. It combines data from ads, social media, emails, and web analytics into one dashboard. The platform lets you build custom reports that show performance across multiple channels. You can also connect the data to Salesforce CRM for pipeline and revenue reporting.
Who it's for: Enterprise marketing teams that run campaigns across multiple channels and need centralized reporting within the Salesforce ecosystem.
I tested Salesforce Marketing Analytics by connecting multiple ad platforms, social channels, and web analytics with sample data. The harmonization features let you standardize metrics across platforms, so "clicks" from Google Ads and Meta Ads appear in the same format. I could compare campaign performance side by side without worrying about inconsistent naming conventions.
The integration with Salesforce CRM makes it easier to connect marketing spend to pipeline reporting. You can see which campaigns generate leads and how those leads move through later stages.
The platform works well if you're already using other Salesforce products, though the setup process requires time to map data fields and configure dashboards correctly.
Key features
Cross-channel data aggregation: Pull data from ad platforms, social media, email, and analytics into one view
Data harmonization: Standardize metrics and dimensions across different platforms
Salesforce CRM integration: Connect marketing performance to CRM pipeline reporting
Pros
Deep integration with Salesforce ecosystem
Handles multiple data sources without separate tools
Custom dashboards for different teams or regions
Cons
Setup requires significant configuration and field mapping
Works best within the Salesforce product suite
Pricing
Bottom line
6. Tableau: Best for advanced marketing dashboards
What it does: Tableau is a data visualization platform that lets you build interactive dashboards from multiple data sources. It connects to databases, spreadsheets, cloud platforms, and marketing tools, allowing you to create charts, graphs, and reports that update automatically as your data changes.
Who it's for: Marketing teams that need powerful data visualization and have data already centralized in databases or warehouses.
I connected multiple data sources to test how Tableau handles dashboard design. You can drag fields to build custom visualizations, apply filters, and create calculated metrics without writing code. The interactive elements let viewers drill down into specific campaigns, time periods, or segments directly from the dashboard.
The platform connects to databases like BigQuery, Snowflake, and Postgres and handles large datasets efficiently. Dashboard refreshes run on a schedule to keep reports current, though sharing them requires either Tableau licenses for each recipient or access through Tableau Server.
Tableau focuses on visualization rather than data preparation, so it works best with data that's already cleaned and structured.Key features
Custom visualizations: Build charts and graphs with drag-and-drop controls
Interactive dashboards: Let viewers filter, drill down, and explore data themselves
Multiple data connections: Link to databases, warehouses, and cloud platforms
Pros
Powerful visualization options for complex data
Interactive features for self-service exploration
Handles large datasets efficiently
Cons
Requires data to be cleaned and structured beforehand
Learning curve for advanced dashboard features
Pricing
Bottom line
7. Power BI: Best for Microsoft-based BI teams
What it does: Power BI is a business intelligence platform that lets you connect data sources, build reports, and create interactive dashboards. It integrates with Microsoft products like Excel, Azure, and Dynamics 365, allowing you to visualize marketing data from across your Microsoft ecosystem.
Who it's for: Marketing teams that work primarily within Microsoft tools and need data visualization connected to their existing infrastructure.
I connected Power BI to Excel spreadsheets and Azure databases to see how it handles marketing data. The integration with Excel was straightforward, and I could import existing reports without rebuilding formulas.
The dashboard builder uses a drag-and-drop interface similar to other BI tools. Connecting to Dynamics 365 or Azure happens quickly, and sharing reports through Teams or SharePoint keeps everything in one ecosystem.
The downside is that non-Microsoft data sources require additional configuration, which adds setup time.
Key features
Microsoft ecosystem integration: Connects directly to Excel, Azure, Dynamics 365, and other Microsoft products
Power Query: Clean and transform data before building visualizations
Collaborative sharing: Distribute reports through Teams, SharePoint, or email
Pros
Native integration with Microsoft tools
Built-in data transformation with Power Query
Lower cost compared to enterprise BI platforms
Cons
Non-Microsoft data sources require more setup
Advanced features need premium licenses
Pricing
Bottom line
8. Looker Studio: Best for free dashboarding
What it does: Looker Studio is a free data visualization tool from Google that lets you build dashboards and reports from Google products and other data sources. You can connect to Google Analytics, Google Ads, Google Sheets, and third-party platforms to create shareable reports. Teams that need advanced features can upgrade to Looker Studio Pro.
Who it's for: Marketing teams that need basic dashboards and reporting without a budget for paid BI tools.
I tested Looker Studio by building dashboards that combined data from Google Analytics, Ads, and Sheets. The platform lets you merge metrics from different Google sources into one view and share reports through links that refresh automatically.
The template library helped me start faster, though there are fewer customization options than Tableau or Power BI offer. Connecting non-Google sources also meant using community connectors or routing data through Google Sheets first.
The free version handles basic reporting well, but building complex calculations takes extra work.
Key features
Free data visualization: Build dashboards and reports at no cost
Google integration: Direct connections to Analytics, Ads, Sheets, and other Google products
Shareable reports: Distribute dashboards through links that update automatically
Pros
Completely free for standard use
Easy setup with Google data sources
Templates for common report types
Cons
Limited customization compared to paid BI tools
Non-Google data sources require workarounds
Pricing
Bottom line
9. Improvado: Best for enterprise data aggregation
What it does: Improvado is a marketing data pipeline platform that lets you extract data from over 500 marketing sources and load it into data warehouses, BI tools, or dashboards. It handles data transformation, normalization, and scheduling, allowing you to centralize marketing data without building custom integrations.
Who it's for: Enterprise marketing teams that run campaigns across dozens of platforms and need automated data aggregation without engineering resources.
I connected Improvado to multiple marketing platforms, including ad networks, social channels, and analytics tools. I liked that it pulled data from Google Ads, Meta Ads, and LinkedIn into one place. It also didn't need separate connectors for each. Data transformations happened automatically to standardize naming conventions and metrics across platforms.
The scheduling features kept data current without manual refreshes. I set up pipelines that ran daily, and the data appeared in our warehouse ready for analysis. The platform works well for teams managing large marketing stacks, though the setup requires working with their team to configure pipelines correctly.
Key features
500+ marketing connectors: Extract data from ad platforms, analytics tools, CRMs, and social channels
Automated transformations: Standardize metrics and naming conventions across sources
Flexible destinations: Load data into warehouses, BI tools, or custom dashboards
Pros
Handles complex multi-source marketing stacks
Automated scheduling keeps data current
Professional services help with setup and configuration
Cons
Requires initial setup with Improvado's team
Pricing scales with sources and data volume
Pricing
Bottom line
10. Funnel: Best for marketing data normalization
What it does: Funnel is a marketing data platform that lets you collect data from advertising, analytics, and social media platforms, then normalize it before sending it to your data warehouse or BI tool. It standardizes metrics, currency, and naming conventions across sources, allowing you to compare performance without manual cleanup.
Who it's for: Marketing teams that need clean, standardized data from multiple platforms without writing transformation scripts.
To test Funnel's data normalization, I pulled metrics from Google Ads, Meta Ads, and LinkedIn into one pipeline. The platform standardized the data based on the transformation rules I configured, so metrics like "clicks" and "impressions" appeared in the same format across platforms. That saved me time while I was building reports.
The transformation rules let me map custom fields and create calculated metrics without coding. I set up rules to convert currency and group campaigns by naming patterns. Funnel sent cleaned data to my warehouse on a schedule I controlled. Initial setup required mapping my data structure to define how fields should transform, but once configured, the automation handled ongoing updates without manual work.
The downside is that Funnel stops at normalization. Once your data lands in the warehouse, you'll need another tool to actually analyze it and build dashboards.Key features
Data normalization: Standardize metrics, currency, and naming conventions across platforms
Custom transformation rules: Map fields and create calculated metrics without code
Flexible destinations: Send data to warehouses, spreadsheets, or BI tools
Pros
Automates data cleaning and standardization
Works with most major marketing platforms
No coding required for transformations
Cons
Initial setup requires mapping your data structure
Limited to data collection and transformation
Pricing
Bottom line
Special mentions
I didn't have the space to cover every tool in full detail, but the 15 platforms below address specific marketing needs, budgets, or team setups. If your workflow leans heavily toward SEO, product analytics, agency reporting, or lightweight BI, one of these may be a better fit than the top 10:
Domo: Domo is a cloud-based business intelligence platform that lets you connect hundreds of data sources and build real-time dashboards. I tested it with marketing data from multiple channels and found the executive dashboard templates helpful for quick setup, though the platform works best for teams that need company-wide BI rather than marketing-specific analytics.
ThoughtSpot: ThoughtSpot is a search-based analytics platform that lets you type questions in plain language to get charts and insights. I typed questions about campaign performance and quickly received charts based on the available data. The AI suggestions feature recommended follow-up questions that helped me explore patterns I hadn't considered initially.
Supermetrics: Supermetrics is a data connector that pulls marketing metrics from platforms like Google Ads and Facebook into Google Sheets or Excel. I used it to automate weekly reporting instead of manually downloading CSVs. The scheduled refreshes saved time on routine reporting tasks.
Fivetran: Fivetran is a data pipeline tool that automates movement from marketing platforms to your warehouse. I set up connectors for ad platforms and let Fivetran handle the ongoing sync. The automated updates kept data current without managing ETL scripts.
TapClicks: TapClicks is an agency-focused platform that helps you manage reporting across multiple client accounts. I tested it by setting up reports for three different clients and found the white-label dashboard templates saved setup time. The multi-client workflow features work better than tools designed for single-company use.
Whatagraph: Whatagraph is a reporting tool that creates visual marketing dashboards from connected data sources. I used it to build client-facing reports that pulled from Google Ads, Facebook, and Google Analytics. The drag-and-drop builder made it easy to customize layouts without design skills.
Databox: Databox is a KPI tracking platform with mobile dashboards that update throughout the day. I set goals for conversion rates and revenue, and Databox sent alerts when metrics hit thresholds. The mobile app made it practical to monitor performance without opening a laptop.
AgencyAnalytics: AgencyAnalytics is an SEO and reporting platform with pre-built templates for common marketing metrics. I tested it for SEO campaigns and found the keyword tracking and backlink monitoring features worked well. The tool fits agencies running multiple client SEO projects more than in-house marketing teams.
Mixpanel: Mixpanel is a product analytics platform that lets you track user events and interactions on your app or website. I set up event tracking for button clicks and page views to analyze user behavior. The funnel analysis helped identify where users dropped off during key workflows.
Amplitude: Amplitude is a behavioral analytics platform that helps you analyze user cohorts and retention patterns. I tracked how users from different acquisition channels engaged with features. The cohort comparison views made it clear which channels brought higher-quality users.
Heap: Heap is an automatic event tracking platform that captures user interactions without manual setup. I connected it to a website and it recorded clicks, form submissions, and page views without requiring event tracking code for each element. The retroactive analysis feature let me query events from before I defined them.
Semrush: Semrush is an SEO analytics platform that provides keyword rankings, backlink profiles, and competitive research. I used it to track organic search performance and identify content opportunities. The keyword difficulty scores helped prioritize which terms to target in content planning.
Sprout Social: Sprout Social is a social media management platform that combines scheduling with analytics across Twitter, Facebook, and LinkedIn. I tested it for scheduling posts and tracking engagement metrics. The unified inbox made it easier to respond to comments and messages from one interface.
CleverTap: CleverTap is a mobile marketing platform that helps you track user behavior and lifecycle stages for apps. I set up campaigns that triggered based on user actions like app opens or purchases. The retention analysis showed how engagement changed over time for different user cohorts.
Zoho Analytics: Zoho Analytics is a business intelligence platform that provides reporting, AI-driven insights, and data blending at a lower cost than enterprise tools. I connected spreadsheets and databases to build reports using the drag-and-drop interface. The platform works well for small businesses and larger teams that want affordable BI with advanced features.
How I tested these marketing analytics tools
I evaluated marketing analytics tools by connecting sample data sources and running the queries marketing teams actually use daily. I pulled metrics from Google Ads, Facebook Ads, email platforms, and CRM systems to see how each tool handled multi-channel reporting, data refresh processes, and cross-platform analysis.
Here’s what I considered:
Data source connections: I tested how quickly each platform connected to common marketing tools and whether the integrations maintained data accuracy during sync. Some tools required custom field mapping, while others pulled data correctly on the first try.
Query flexibility: I ran standard marketing analyses like campaign ROI calculations, attribution modeling, and cohort comparisons to see which platforms gave accurate results without extensive configuration. Tools that required SQL or complex formulas got marked down for accessibility.
Dashboard creation speed: I tracked how long it took to build functional marketing dashboards from scratch. Some platforms offered templates that worked immediately, while others needed hours of customization before displaying useful metrics.
Learning curve for non-technical users: I evaluated whether business users could generate reports independently or needed constant data team support. Platforms that required training sessions or documentation review to perform basic tasks rated lower for usability.
Report sharing and collaboration: I tested how teams could share insights with stakeholders who didn't have platform access. Email delivery, Slack integration, and export options all factored into the evaluation.
Which marketing analytics tool should you choose?
The right choice of marketing analytics tool depends on the marketing channels you track, how technical your team is, and whether you need basic reporting or advanced attribution modeling. Choose:
Julius if your work includes connected marketing data and you want conversational analysis, charts, and recurring reports without writing SQL.
Adobe Analytics if you run enterprise campaigns that need advanced attribution modeling, predictive analytics, and deep segmentation across multiple touchpoints.
Google Analytics if you primarily track website and app traffic without paying for analytics software.
HubSpot Marketing Hub if you need marketing analytics that connect directly to your CRM, so you can track how campaigns influence deals and revenue.
Salesforce Marketing Analytics if you manage cross-channel enterprise reporting and need deep integration with the Salesforce ecosystem.
Tableau if you want highly customizable dashboards with powerful visualization capabilities and your team has the technical skills to build complex reports.
Power BI if you work in a Microsoft environment and need native integration with Office, Azure, and Dynamics tools.
Looker Studio if you want free dashboarding that connects to Google marketing tools without paying for BI software.
Improvado if you need to aggregate data from hundreds of marketing sources for enterprise-scale reporting.
Funnel if you need automated data collection and normalization across multiple marketing platforms before warehouse storage.
My final verdict
I noticed during testing that marketers running attribution modeling usually pick Adobe Analytics or Salesforce Marketing Analytics, while teams focused on dashboard creation lean toward Tableau or Power BI. Improvado and Funnel fit companies that need data pipeline automation before analysis happens.
Julius takes a different approach by letting you query connected marketing data conversationally and get charts without building dashboards or writing SQL. The platform learns your database structure over time, so follow-up questions pull from the right tables automatically. I think this approach gives marketing teams faster access to insights when they need quick answers rather than formal reports.Want to analyze marketing data conversationally? Try Julius
Marketing analytics tools often require dashboard building or SQL knowledge to explore campaign performance, attribution, and ROI. With Julius, you can query connected marketing data in plain English and get charts, insights, and recurring reports without technical skills.
Julius is an AI-powered data analysis tool that connects directly to your marketing data sources and delivers insights, charts, and reports quickly.
Here’s how Julius helps:
Direct connections: Link databases like Postgres, Snowflake, and BigQuery, or integrate with Google Ads and other business tools. You can also upload CSV or Excel files. Your analysis can reflect live data, so you’re less likely to rely on outdated spreadsheets.
Smarter over time with the Learning Sub Agent: Julius's Learning Sub Agent learns your database structure, table relationships, and column meanings as you use it. With each query on connected data, it gets better at finding the right information and delivering more accurate answers without manual configuration.
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
Recurring summaries: Schedule analyses like weekly revenue or delivery time at the 95th percentile and receive them automatically by email or Slack.
One-click sharing: Turn a thread of analysis into a PDF report you can pass along without extra formatting.
