Best Data analytics report templates for marketing teams and agencies (2024)

Automate marketing reporting with dozens of 100% customizable, white-label Data analytics report templates. Used and made by +10,000 marketers in over 60 countries.

PPC report template for marketing teams and agencies

Optimize your PPC strategy with this report template. Track metrics like conversion rate, ROAS, and CTR across Facebook Ads, TikTok Ads, and Google Ads. Analyze by audience, channel, and time. Ideal for marketing teams to measure campaign performance and achieve objectives efficiently.

See template

Advertising report template for marketing teams and agencies

Optimize your PPC strategy with this Advertising report template. Track CTR, conversion rate, and CPA across Facebook Ads, TikTok Ads, and Google Ads. Analyze demographics, psychographics, and geographics. Segment data by hour, day, or month. Perfect for marketing teams to measure performance and achieve goals using Google Analytics 4 insights.

See template

Omni-channel report template for marketing teams and agencies

Optimize your marketing strategy with this Omni-channel report template. Track metrics like conversion rate, ROAS, and AOV across CRM, E-commerce, Facebook Ads, Google Analytics 4, and LinkedIn Ads. Analyze customer demographics and behavior. Segment by timeframes for actionable insights. Perfect for marketing teams seeking unified analytics and performance measurement.

See template

Acquisition report template for marketing teams and agencies

This Acquisition report template helps marketing teams track key metrics like conversion rate, ROI, and CTR. Analyze dimensions such as target audience and campaign goals. Integrate data from Google Analytics 4, Copy, and PPC. Ideal for measuring performance and refining strategies to meet specific objectives.

See template

Peso model report template for marketing teams and agencies

Analyze key metrics such as leads, conversion rates, and engagement with the Peso model report template. Evaluate campaign performance across Facebook Ads, Instagram Insights, and SEO. Segment by audience demographics and timeframes. Perfect for marketing teams to track e-commerce and social media strategies efficiently.

See template

Store performance report template for marketing teams and agencies

Measure key metrics with the Store performance report template. Analyze total revenue, conversion rates, and traffic sources. Segment by audience, marketing channels, and campaign objectives. Consolidate data from E-commerce, Google Analytics 4, Shopify, Google Ads, Facebook Ads, and SEO. Perfect for marketing teams to track performance and optimize strategies.

See template

DTC report template for marketing teams and agencies

Optimize your strategy with this DTC report template. Track revenue, conversion rates, and cost per acquisition. Blend data from E-commerce, Google Analytics 4, Shopify, Google Ads, and Facebook Ads. Segment by demographics, behavior, and time. Perfect for marketing teams to measure key metrics and enhance E-commerce performance.

See template

Growth marketing report template for marketing teams and agencies

Track and measure key metrics with the Growth marketing report template. Analyze conversion rates, CPA, and ROI. Evaluate email and social media performance. Integrate data from Facebook Ads, Google Ads, and Google Analytics 4. Segment by demographics and behavior. Ideal for marketing teams to assess and optimize campaign performance efficiently.

See template

Demand generation report template for marketing teams and agencies

Optimize B2B demand generation with this report template. Track conversion rates, cost per conversion, and click-through rates. Analyze social media shares and impressions. Integrate data from CRM, E-commerce, Facebook Ads, Google Analytics 4, and LinkedIn Ads. Segment by demographics and time for strategic insights. Ideal for marketing teams focused on performance.

See template

What is a data analytics report?

A data analytics report is a document that consolidates data from multiple sources (e.g., databases, APIs, spreadsheets) to track and display key performance indicators (KPIs) (e.g., data accuracy, processing time, user engagement), enabling teams to monitor data performance and create presentations for stakeholders and executives. 

Data analytics reports are typically created using flexible tools like Google Looker Studio, Power BI, Google Sheets, or platform-specific solutions to enable high customization and integration of multiple data sources.

What to include in a data analytics report?

An actionable data analytics report balances context and specificity based on the audience (executives, managers, and analysts) and their use cases.

Executive data analytics reports

Executive reports for CTOs, CIOs, and stakeholders show data's impact on business outcomes. Reviewed weekly, monthly, or quarterly, they include:

  • Data quality analysis: accuracy, completeness, and consistency of data across systems.
  • Performance metrics: system uptime, response time, and data processing speed.
  • User engagement analysis: user activity, adoption rates, and feedback.
  • Add text for additional context to translate metrics for non-technical audiences. Present in slide decks and simplified Looker Studio reports.

Data manager reports

Manager reports have cross-system views with drill-downs to see performance by department, project, region, team member, and data source. They help align teams, define strategies, and include:

  • Cross-system reporting: overall data flow, project, department, or region reporting across systems.
  • Goal tracking: compare current performance vs objectives.
  • Data audits for prioritization and spotting issues 
  • Benchmarking for performance and efficiency mapping.
  • Data source and integration analysis

Operational Data Analytics Reports

Operational reports for analysts and data engineers have granular, customizable KPIs to solve technical issues. Monitored hourly, daily, or weekly, they cover:

  • Data processing: ETL performance, data pipeline efficiency, error rates.
  • System monitoring: server load, memory usage, network latency.
  • User activity: login rates, session duration, feature usage.
  • Data integrity: validation errors, data anomalies, reconciliation issues.

Operational data analytics reports are highly customized, built in flexible tools like Google Sheets or Looker Studio to enable data cleaning, blending, annotations, and integrating multiple sources.



How to build a data analytics report?

To build a data analytics report, connect your data sources, choose a template on Looker Studio or Sheets, build your queries by selecting metrics and dimensions, choose charts to visualize your data, customize the report, design and share via link, PDF or email. 

Here’s the breakdown: 

Connect data sources

Define and connect the data sources to bring to your report. Common sources are databases, APIs, CRM systems, and cloud storage for data analytics.

To connect your data sources, go to portermetrics.com, choose the data sources to bring to your report. 

You can follow these tutorials on connecting your data:

Choose a template

Choose from dozens of data analytics report templates in Google Sheets or Looker Studio, designed for use cases like data monitoring, performance tracking, and user engagement analysis. 

Learn to copy Looker Studio templates

While templates are the starting point. Make them specific for your business or agency. Map your specific metrics, especially custom data points, CRM data, and all the fields and metrics that you define as "key performance indicators" and "outcomes".

Depending on your reporting tool—Google Sheets or Google Looker Studio, pick any of the dozens of templates created by our team and customers to solve your data analytics reporting use cases, such as data monitoring, performance tracking, and user engagement analysis. 

Select metrics, dimensions, and charts

Once your report template is downloaded, you may 1)modify it or 2) create a blank page to build it from scratch. Whatever the case, setting up a query always follows these steps: 

  1. Select the data source and the account connected to it
  2. Choose metrics (e.g., data accuracy, processing time, user engagement, etc.). 
  3. Choose breakdowns to segment your data (e.g., by date, department, data source, etc.)

You can follow these tutorials on adding data to your reports

Design

To make your data analytics reports truly white-label you can add logos, colors, fonts, and styling to mirror your brand. 

Follow these tutorials to design your data analytics reports:

Share

Share your data analytics reports via links, PDF, schedule emails, and control permissions.

KPIs to include in a data analytics report?

Data analytics reports should include a mix of data quality, performance, user engagement, and cost metrics and KPIs to fully understand the performance of data systems towards business goals. They include:

Data quality KPIs measure the integrity and reliability of data: 

  • Accuracy metrics: error rates, validation success, data consistency
  • Completeness metrics: missing values, data coverage, record counts
  • Timeliness metrics: data latency, update frequency, processing time

Performance KPIs compare your data processing outputs to the system capabilities, including:

  • Processing speed: data throughput, ETL efficiency
  • System uptime: availability, downtime incidents
  • Resource utilization: CPU, memory, network usage 

User engagement KPIs measure the interaction with data systems:

  • Activity metrics: login rates, session duration
  • Adoption metrics: feature usage, user growth
  • Feedback metrics: user satisfaction, issue reports

Cost KPIs show the financial impact of your data systems:

  • Operational costs: infrastructure, maintenance
  • Efficiency: cost per transaction, cost savings
  • Effectiveness: ROI, cost-benefit analysis

To analyze these data analytics KPIs, segment them by:

  • System: database, application, cloud service
  • Time: Hourly, daily, weekly, monthly
  • Project: phase, objective
  • Business: department, branch, region
  • User: role, location, behavior
  • Data source: type, format, origin