To connect marketing data with business reporting, it is necessary to integrate Google Analytics with other company systems such as CRM, ERP or the financial system within one analytical environment. In practice, this means using Power BI and Google Analytics to create a unified data model that links website traffic with actual revenue.

Google Analytics data alone shows:

• number of users and sessions,
• traffic sources and campaigns,
• conversions and events,
• user behavior on the website.

However, this is only part of the picture. Without connecting it to sales and financial data, you can’t see:

• which campaigns generate real revenue,
• what the margin is on acquired customers,
• how much it truly costs to acquire a customer,
• how marketing impacts the company’s bottom line.

Power BI enables the integration of these data sources into one dashboard, so reporting moves beyond just traffic and clicks and begins to include key business metrics.

What is Power BI and what role does it play in the analytics environment?

Power BI is an analytics tool that allows you to combine data from multiple systems into one consistent model and turn it into clear management reports. In the context of Power BI Google Analytics, it acts as an integrating layer — connecting marketing data with sales performance and financial results.

Power BI as a tool for reporting and data analysis

Power BI makes it possible to create interactive reports and dashboards that update automatically and display data in near real time.

Key capabilities of the platform:

• integration with multiple data sources: ERP, CRM, financial systems, Excel, marketing automation platforms, Google Analytics,
• data modeling and building relationships between tables,
• creation of custom measures and KPIs tailored to business needs,
• 24/7 access to reports — on desktop and mobile devices,
• controlled access to data at the user and department level.

Thanks to this, Power BI is more than just a visualization tool. It is an analytics environment where marketing, sales and financial data start speaking the same language.

What can Power BI present in the context of marketing and sales?

After integrating Power BI and Google Analytics, it becomes possible to build reports that connect user activity with the company’s actual performance.

Example analyses:

• website traffic vs number of generated leads,
• campaign cost vs generated revenue,
• conversions by channel, campaign or product,
• user paths linked with CRM data,
• ROI and ROAS metrics in a management-friendly format,
• full sales funnel – from clicking an ad to issued invoice.

This approach allows companies to move from operational reporting to business reporting. Instead of analyzing only clicks and sessions, the organization sees the real impact of marketing on sales.

Google Analytics as a data source – capabilities and limitations

Google Analytics is one of the primary sources of marketing data. It provides detailed information on what happens on the website and how users interact with content.

Available data includes:

• users and sessions,
• traffic sources and marketing campaigns,
• user behavior on the website,
• events and conversions (e.g., form submissions, file downloads, purchases).

This is valuable information, but limited to the online world.

Key limitations of Google Analytics:

• no direct link to financial data and ERP systems,
• limited ability to combine data with CRM without additional integration,
• marketing silo reporting without a full sales perspective.

As a result, a company may see how many leads were generated, but not which of them brought real revenue and what their value was.

That’s why Power BI Google Analytics integration becomes essential. Only by combining marketing data with sales and financial systems can you create a complete picture of performance.

Power BI Google Analytics – what does the integration look like in practice?

The integration of Power BI and Google Analytics in practice is not just about connecting a single data connector. It is a process aimed at creating a consistent analytical environment that combines marketing data with sales and financial information.

Data sources in one model

The first step is importing data from Google Analytics – most often through:

• GA4 API,
• exporting data to BigQuery,
• indirect integrations via a data warehouse or Microsoft Fabric.

Next, the data is combined with other systems used within the company:

• CRM – leads, sales opportunities, MQL/SQL statuses,
• ERP system – revenue, invoices, margin,
• marketing automation platform – campaigns, scoring, acquisition sources,
• Excel sheets – budgets, sales plans, additional summaries.

As a result, a single source of truth is created, where website traffic can be linked to the sales pipeline and actual revenue.

Data modeling

The integration itself is only the beginning. Data modeling in Power BI plays a crucial role.

At this stage:

• a unified data model covering marketing, sales and finance is created,
• relationships between campaigns and revenue are built,
• custom measures and KPIs are defined,
• definitions of terms such as “conversion,” “lead acquisition cost,” or “campaign revenue” are standardized.

The data model determines whether the report will be merely a visualization of statistics or a true decision‑making tool.

What dashboards can be built in a Power BI Google Analytics scenario?

Once the integration is complete and the data model is built, Power BI Google Analytics enables the creation of reports tailored to the needs of different business areas.

Marketing dashboard

A marketing report allows the analysis of campaign effectiveness from both operational and cost perspectives.

It may include:

• traffic by source and campaign,
• campaign cost vs number of generated leads,
• MQL/SQL conversion,
• changes in the number of conversions month‑over‑month or year‑over‑year,
• channel performance (organic, paid, social, AI).

Thanks to CRM integration, data doesn’t end at the form submission — further stages of the process are clearly visible.

Sales dashboard

In this view, marketing data is linked to the sales pipeline and revenue.

Typical elements include:

• pipeline value by acquisition source,
• revenue generated by specific campaigns,
• average sales cycle duration by channel,
• effectiveness of leads from each acquisition source.

This is the point at which traffic analysis stops being the goal on its own.

Executive dashboard

At the executive level, financial and efficiency metrics are most important.

The report may present:

• ROI of marketing activities,
• customer acquisition cost (CAC),
• marketing’s contribution to achieving the sales budget,
• the relationship between marketing spend and generated revenue.

Such dashboards make it possible to move from analyzing clicks to assessing the real impact of marketing on business performance.

Summary – from marketing reports to real business decisions

The integration of Power BI and Google Analytics is not just a technical system connection. It is a step toward a data‑driven organization, where marketing and sales decisions are based on solid metrics rather than fragmented reports.

Combining Google Analytics with CRM, ERP and financial systems enables a shift:

• from operational reporting (traffic, clicks, conversions),
• to executive reporting (revenue, margin, ROI, CAC).

This represents a fundamental change in perspective. Instead of analyzing user activity alone, the company can evaluate the real impact of marketing activities on financial results.

Data consistency plays a crucial role here. When marketing, sales and management work on one unified data model:

• metric definitions are standardized,
• customer acquisition source is unambiguous,
• financial performance can be directly linked to campaigns,
• reports no longer require manual data merging in Excel.

As a result, Power BI Google Analytics stops being a tool for traffic analysis and becomes an integral part of performance management.

The conclusion is simple: marketing data only has real value when it is directly connected to revenue, margin and profitability. Only then does a report cease to be a collection of statistics and become a foundation for business decisions.