Microsoft Power BI is a data visualization platform used primarily for business intelligence purposes. It lets you combine data from various sources, visualize and discover what's important, and share the results with anyone you want. Its key features include:
In this article, we will go through the steps of building a template dashboard for Facebook Ads in Power BI, using data funneled by Dataddo—a free data integration and automation tool that connects analytics tools to business intelligence apps and storages. The article will also cover the steps of connecting Facebook Ads to Power BI. Let’s get started.
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A Power BI dashboard is a single page, often called a canvas, that conveys a story through visualizations. Due to its single-page format, a well-crafted dashboard features only the key points of that story.
Many beginners find it challenging to distinguish between dashboards and reports. Dashboards are limited to one page and cannot be filtered or sliced, while reports can span multiple pages and offer extensive filtering, highlighting, and slicing options. However, dashboards can integrate data from multiple reports and semantic models, whereas reports are based on a single semantic model.
Now that we understand what a dashboard is, let's make one for Facebook Ads. We'll start by connecting our Facebook Ads data with Dataddo.
Dataddo is a no-code data integration tool that can transfer data from any source to any destination, including Power BI. And it's really easy to use, as you'll see below.
If you don't already have an account, go to Dataddo.com and create an account for free by clicking Login.
After you log in, you'll see a screen like this:
Now, let's connect Facebook Ads to Power BI.
This section is divided into three simple parts. Let’s go over them one by one.
Now that our source data is ready, let’s create our destination.
Once your flow is complete, a popup will appear in the Dataddo platform with some simple instructions on how to connect your data to Power BI.
If you get stuck, refer to the steps in our documentation on Power BI.
Now that you've connected your data to Power BI, it’s time to create visuals for our dashboard.
This section will be divided into two parts:
Let’s dive in.
Clicks, impressions, and click-through rates (CTR) are key metrics for evaluating the performance of online marketing campaigns:
We'll create three cards in Power BI to display total clicks, total impressions, and CTR. Following that, we'll construct a line chart to show clicks and CTR over time.
The total clicks for an advertisement provide quick insight into sales. The card visual in Power BI is useful for displaying metrics like total sales and total count. Here’s what we need to do:
The steps for total impressions are similar, but this time, the column will be impressions instead of clicks. Here’s how it will look after adding the total impressions:
To display CTR on the card, we need to sum the clicks and impressions, divide the total clicks by total impressions, and then multiply by 100. This will require us to create a new measure. Let’s see that step-by-step:
Total Clicks = SUM('dataset'[Clicks])
Like so:
Overall CTR = DIVIDE([Total Clicks], [Total Impressions], 0) * 100This creates the Overall CTR measure as shown below:
Here are the steps to create the line chart showing clicks and CTR over time, which provides insights on interest over impressions.
Now let's talk about conversion rates and cost.
A conversion rate records the percentage of users who have completed a desired action. Cost per conversion (also known as cost per acquisition or CPA) measures the cost of acquiring one conversion. These are important metrics for judging an ad campaign.
We will create three cards in Power BI to display conversions, conversion rates, and conversion costs. Then, we'll construct a line chart to show conversions and conversion costs over time.
Note: Since the steps will repeat, we will only discuss the steps that have changed.
To display the conversions:
Here’s how to display the conversion rate in the dashboard:
Conversion Rate = DIVIDE(SUM('dataset'[Conversions]), SUM('dataset'[Clicks]), 0) * 100
The formula here is condensed because it does not create any extra measure like before. Here is the result:
For this, we need to:
Cost Per Conversion = DIVIDE(SUM('dataset'[Spend]), SUM('dataset'[Conversions]), 0)
Here are the steps to create a line chart showing conversions and conversion rates over time:
Now that we have completed the main part of the project, let’s add some nice touches to the dashboard.
A slicer is a visual element that filters the other visuals on a report page. When using Power BI reports, you'll find various types of slicers (learn more about slicers here). In this dashboard, we will filter the data by date. Here's how to do it:
Now, we can filter our dashboard by date to create a nicer look.
In this section, we will enhance the dashboard's visual appeal by adding a title, adjusting color schemes, and refining the overall design. These improvements will ensure the dashboard is informative and visually engaging for the audience.
Adjustments to the background and text colors have significantly enhanced the dashboard's aesthetics.
Analytics of online ads are so important for marketing teams to be able to validate their processes. However, keeping data updated and accurate is a time-consuming challenge.
Dataddo solves these problems by syncing analytics-ready data to Power BI at the intervals of your choice. And not just from Facebook Ads, but from all your other online sources.
It also includes built-in tools for data quality control, data blending, data security (like the ability to exclude personally identifiable information), and storage (eliminating the need for a separate data warehouse).
Sign up for a free Dataddo account now and get your data flowing to Power BI in minutes 👇
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Continue reading the Dataddo blog for more Power BI tips, tricks, and templates!