From PowerPoint Reports to Power BI: Building a CRM Reporting Infrastructure from Scratch

Between 2021 and 2022, I worked within the Customer Relationship Marketing team of a leading Brazilian financial services company serving more than 380,000 customers across private pension, life insurance, premium bond, and rental guarantee products.

At the time, the team was responsible for customer engagement initiatives across newsletters, educational content, lifecycle communications, customer support journeys, and self-service channels. While communication activities were producing large amounts of data, there was no centralised reporting environment capable of transforming that information into actionable insights.

Most reporting existed in the form of isolated PowerPoint presentations, manually created performance summaries, and disconnected datasets maintained by different teams and agencies. Historical information was difficult to access, performance trends were challenging to track, and reporting was primarily used to explain past results rather than guide future decisions.

This case study explores how I designed and implemented the team's first structured reporting infrastructure, transforming fragmented marketing and customer engagement data into a centralised analytics environment that supported decision-making across CRM, content strategy, customer experience, and lifecycle marketing initiatives.

When I joined the team, reporting processes were highly manual and fragmented.

Historical performance data was scattered across PowerPoint presentations, spreadsheets, agency reports, and multiple internal teams. Information needed to be collected from CRM teams, brand teams, channel teams, external agencies, customer support functions, and marketing platforms before any meaningful analysis could take place.

Several challenges quickly became apparent:

• There was no centralised reporting environment.

• No standard KPI framework existed across customer engagement activities.

• Historical reporting was limited and difficult to access.

• Data collection depended heavily on manual processes.

• Reporting periods were inconsistent across sources.

• Customer behaviour could only be analysed at a surface level.

• Decisions were often based on short-term campaign results rather than long-term behavioural trends.

While metrics such as opens and clicks were available, there was limited visibility into deeper questions:

• Which content themes consistently drove engagement?

• Which customer journeys created friction?

• What questions were customers repeatedly asking?

• Which lifecycle stages presented retention risks?

• How did engagement evolve over time?

Without structured reporting, answering these questions required significant manual effort and often relied on assumptions rather than evidence.

To create a foundation for more informed decision-making, I designed and built the team's first structured reporting infrastructure.

The first step involved recovering and consolidating historical data from multiple sources. This included retrieving performance information from archived PowerPoint reports, agency records, CRM platforms, customer feedback systems, website analytics, and customer support channels.

In total, I consolidated twelve fragmented datasets covering:

• Newsletter performance

• Email engagement

• Customer profiles

• Website analytics

• FAQ searches

• Customer complaints

• Customer satisfaction surveys

• Customer support interactions

• Lifecycle communication activity

• Content performance

• Digital engagement metrics

• Partner channel reporting

Rather than maintaining separate reporting files, I organised the information into four centralised databases designed to support long-term analysis and reporting.

To improve consistency and reduce reporting errors, I developed a standardised Excel-based data management system supported by VBA forms, macros, and automated workflows. These tools enabled structured data entry, classification, and maintenance across multiple reporting categories.

I also designed a content classification framework that organised communications by theme, category, subcategory, communication channel, and content type. This created a level of visibility that had not previously existed and allowed engagement to be analysed at both newsletter and individual content levels.

Once the data foundation was established, I developed Power BI dashboards using:

• Data modelling and relationship management

• Power Query transformations

• DAX calculations and custom measures

• KPI frameworks

• Interactive drill-through reporting

• Automated reporting workflows

The objective was not simply to create dashboards, but to provide stakeholders with a reliable source of truth that could support ongoing decision-making.

Turning Data into Decisions

The value of the reporting environment quickly extended beyond performance monitoring.

For the first time, the team could analyse engagement trends over longer periods and identify patterns that would previously have remained hidden.

The newsletter dashboard became one of the most widely used reporting tools. Rather than evaluating newsletters as a single performance metric, engagement could now be analysed at article level, revealing which content themes consistently attracted customer interest.

This insight later supported significant improvements in customer engagement strategy, content planning, and communication scheduling.

The reporting infrastructure also enabled deeper analysis of customer support and self-service behaviour. FAQ searches, customer complaints, satisfaction surveys, and website interactions could be analysed together, helping identify recurring customer pain points and content gaps.

Another important use case emerged within lifecycle marketing.

By analysing customer behaviour across different stages of the customer journey, the team identified specific periods where customers were more likely to disengage or cancel products. The second, third, and eighth months of some customer journeys showed particularly high levels of attrition.

These findings informed the development of targeted educational content, onboarding guides, reminder communications, and retention initiatives designed to improve customer understanding and long-term engagement.

Perhaps most importantly, communication planning became increasingly evidence-based.

Rather than relying on intuition, decisions regarding content strategy, communication frequency, publication timing, and customer education could be supported by data.