Building BI Dashboards for Marketing Teams

Marketing teams juggle data from Google Ads, Facebook, LinkedIn, and Google Analytics. Every week, someone needs a report. You copy numbers into spreadsheets or wait for an analyst. This guide shows you how to build self-updating dashboards without technical skills.

What we'll cover

Why marketing teams need BI dashboards
Where marketing data lives
Getting data: connectors versus manual imports
Google Sheets as a dashboard tool
Metabase: the database approach
Annie by Pandas-AI: connects directly to ad platforms
Which BI tool fits your situation
Getting started with BI for marketing

Why marketing teams need BI dashboards

You probably check five different tools to measure success. Monday meetings mean someone shares their screen, clicks through tabs, and tries to remember which campaign drove what.

This creates two problems: Nobody can answer "which channel brought the most qualified leads last month?" without spending an hour pulling data. And when multiple people need the same information, everyone creates their own analysis—suddenly you have three different answers.

A proper dashboard solves this. One place, automatically updated, where anyone can see what's working.

Where marketing data lives

Marketing data sits in three places:

Advertising platforms like Google Ads, Facebook, and LinkedIn store campaign performance. They have dashboards, but you can't combine them with other sources.
Analytics tools like Google Analytics track website behavior. They show page views and user journeys but don't connect to your CRM to show which visitors became customers.
Business systems like your CRM (Salesforce, HubSpot) and payment processor hold outcome data: who signed up, who bought something, what they paid.

The magic happens when you combine these sources. You can answer "which Facebook campaign drove customers who spent the most money?" But first, you need to get data out of these platforms.

Getting data: connectors versus manual imports

You have two options.

Manual imports mean downloading CSV files from each platform and uploading them to your dashboard tool. It's free and simple. You control exactly what moves where.
- The downside? You have to remember to do it. Every week, you download files, check formatting, and upload again. Miss a week and your dashboard shows old information. One B2B SaaS company with 8 marketers spent two hours every Monday updating dashboards. When someone went on vacation, dashboards didn't get updated.
Automatic connectors pull fresh data on a schedule. Your dashboard updates while you sleep. The catch: connectors cost money or require technical setup. Not every platform has ready-made connectors.

For most teams, use a mix. Connectors for your biggest data sources (checked daily), manual imports for smaller platforms (reviewed monthly).

Google Sheets as a dashboard tool

Many teams start here. Everyone knows Sheets. You can make charts in minutes. Sharing is just sending a link.

Sheets works well for simple tracking. Need a table showing weekly ad spend across three campaigns? Perfect. Add conditional formatting to highlight high-spend weeks, and you're done.

But Sheets breaks down when:

It doesn't refresh automatically. Even with built-in connectors, someone clicks refresh or rebuilds formulas when data changes.
Large datasets make it slow. Load 50,000 rows and Sheets struggles. Add pivot tables and it might crash.
Advanced visualizations aren't possible. Want a map or funnel chart? Sheets offers basic columns and lines.
Everyone can break everything. Share a Sheet and someone accidentally deletes a formula or sorts one column wrong, corrupting your data.

One e-commerce team spent hours building conversion rate formulas. Someone edited a cell and broke everything. They lost a week recovering the formulas.

Sheets is a spreadsheet forced into dashboard duty. For five metrics in a simple table, it's fine. For anything more, you'll fight the tool.

Metabase: the database approach

Metabase is built specifically for creating dashboards from databases. The open-source version is free. Paid cloud options exist if you don't want to manage servers.

What works well:

Setting up basic charts is easy. The "question builder" lets you click through options: pick your table, choose what to count or sum, split by category. You get a chart in minutes.
Dashboards look clean and professional. Arrange multiple charts on a page, add filters, share a link. When someone opens it, they see fresh data from your database.
Charts are interactive. Click a bar and drill down to see underlying data. Team members can investigate anomalies themselves.

Where it struggles:

The question builder only handles basic queries. Want to calculate customer lifetime value or compare month-over-month growth? You'll need SQL.
Metabase includes natural language Q&A—type "show me total revenue by channel last month" and it tries to build the query. In practice, this works inconsistently. Simple questions like "count of customers" sometimes work. Add complexity like "revenue from customers who clicked Facebook ads but signed up through organic search" and it fails.
The feature assumes your database has clear table and column names. If something is named "rev_total_USD_converted," Metabase won't understand "revenue."
Customization is limited in the free version. Charts have standard colors and styles. Want specific brand colors? You need the Pro version.
Performance on large datasets can be slow. If your query takes 30 seconds, your dashboard takes 30 seconds to load.

One team wanted to recreate their tracking spreadsheet—freeze the first column while scrolling, expand horizontally by adding columns each week. Metabase doesn't support this. They kept that table in Sheets. For marketers without technical backgrounds, Metabase means asking for help when you need anything beyond basic charts.

Annie by Pandas-AI: connects directly to ad platforms

Annie solves the problems other tools create. You can say "create a dashboard" and get one. Or be specific: "I need a dashboard showing Facebook and Google Ads performance, with weekly spend, clicks, conversions, and cost per conversion."

How it actually works:

When you connect Google Ads, Facebook Ads, LinkedIn Ads, or upload CSV files, Annie builds the SQL data model automatically. This is the technical foundation that normally requires an engineer.
With Metabase, you need an engineer to set up your data warehouse and write queries. With Sheets, you write formulas yourself. Annie just does it—constructs the database structure and calculates metrics like conversion rates or cost per acquisition.
Then it generates dashboards with filters, charts, and graphs. You have full control through a visual editor—change chart types, adjust filters, modify displays, customize themes and colors.

The native marketing connectors eliminate the biggest pain. With Sheets, you download CSVs manually every week. With Metabase, an engineer sets up ETL pipelines. Annie connects directly to platforms, builds the data model, then generates the dashboard. For marketing teams, this removes two entire steps.

The trade-off:

Annie is built for speed, not exotic customization. You can adjust themes, colors, and standard chart types (bar charts, line graphs, pie charts, tables). But if you need a highly specific visualization—a custom sankey diagram with particular formatting, or a complex multi-axis chart—you'll hit limits.
This is deliberate: quick, simple, effective dashboards versus pixel-perfect bespoke ones. If your team needs answers now more than perfect brand alignment on chart styling, that makes sense.

Which tool fits your situation

Choose Google Sheets if you track fewer than five metrics, update them manually anyway, and don't need automatic refreshing. It's fine for simple tracking that doesn't change much.
Choose Metabase if you have an engineer who can set up a database and help with dashboards. Your team needs to explore data interactively, and you're willing to invest time learning the tool. The open-source version gives you full control if you have technical resources.
Choose Annie if: You want dashboards quickly without technical skills. Your team needs the data model built automatically from Google Ads, Facebook Ads, LinkedIn Ads, and other platforms. You value speed and efficiency over exotic customization—which most marketing teams should, because you need answers today, not after weeks of setup.

Getting started with BI for marketing

Whichever tool you choose, start small. Don't build a comprehensive dashboard covering everything on day one.

Pick one question you ask repeatedly. "Which channel drove the most conversions last week?" Build a dashboard that answers just that. Use it for a week. See what's missing. Add one more thing.

This works regardless of tool. You learn what your team actually needs versus what you thought you needed.

Most teams fail by trying to build everything at once. They spend a month creating something comprehensive that nobody uses because it's too complicated or doesn't answer actual questions.

Start with three metrics that matter right now. Build those. Share them. Listen to questions people ask. Add those next.