What is digital marketing analytics? It’s the fundamentals of understanding your digital marketing performance by drilling into the data, with the goal of extracting insights and making optimizations.
Let’s say you start a digital marketing campaign. It’s a simple campaign: a paid advertising campaign through Google. You’re paying $0.02 cents for each click. You spend $20.00. How far did that $20 go?
This is the question that digital marketing analytics helps to answer. Digging into your digital marketing data, you see that out of the people who clicked, most of them bounced from your page. You didn’t get any sales. Why not? Well, often, if you’re getting people clicking your links, it’s because they’re interested in your ad. If they bounce from the page, that means that they didn’t get what they expected from the landing page experience.
What we’ve just demonstrated is a core concept of digital marketing analytics: extract data, conduct analysis, and draw conclusions.
Digital Marketing Analytics across multiple advertising platforms
The example we painted above was a simple one, illustrated by looking at just one digital marketing platform. But what happens when you incorporate multiple platforms into the mix? How should marketers compile all relevant data, and what tactics should they use to analyze that data best?
The process of data ➜ analysis ➜ conclusion stays the same, but with one key difference: for multiple platforms, a lot of the legwork needs to happen up front to compile all data into a central source of truth.
This task can be both time consuming and error-prone. To avoid those pitfalls, you have two options for pulling cross-platform advertising data into a source of truth.
Option #1 Use platform APIs and pull data into a spreadsheet
This option requires a fairly savvy knowledge of APIs and the use of a coding language like PHP, Python or Javascript. The steps that follow will serve as an overview – for more detailed information we recommend you read the source documentation from the ad platforms themselves.
In general, getting data from an API requires you to obtain an API access token, which you can then feed into a cURL HTTP request (or something similar). Without getting into too much detail, you can already see how this process can be a bit of a headache.
Option #2: Use a free tool to extract paid ad data
While there are a lot of tools out there that help you pull data from ad platforms, most of them require some form of payment. Instead, we recommend using a free tool like Joinr (self-promotion, we know), that automatically fetches data for you, and compiles everything into a single source of truth.
With all data connected, you can easily visualize the data and move on to the next phase of the digital marketing process: analysis of the data.
Analyzing Digital Marketing Data in Google Data Studio
Visual data is a great way to begin analyzing trends. With your data in Google Data Studio, the best first step is to create simple charts comparing cross-channel platform performance, including:
- Pie Charts – these comparative charts can show the impact (in % form) that each ad platform has on different metrics, like spend, conversion, impressions and more.
- Line Graphs – perhaps the most effective way to visualize data-over-time, line graphs help you understand how your performance changes daily, weekly, monthly, quarterly or yearly.
- Stacked Combo Charts – Similar to pie charts, stacked combo charts help compare different platforms against a specific metric; however, they can be interpolated with line graphs to present a unique view of data trends.
- Pivot Table with Heatmap – heatmaps help highlight important peaks and troughs in the data, which enables you to laser focus in on potential optimizations.
Here’s a list of all visualizations that Google Data Studio offers, including some extra tools built by 3rd-party companies to give marketers an edge in their analysis.
The best part about Google Data Studio? Templates!
Google Data Studio templates open the door to analysis by combining the best of the visualization tools mentioned above with strategically-built templates that flow seamlessly to answer questions about your marketing data.
One crucial aspect of Google Data Studio templates is that they streamline the analysis phase of your data inquiry, so that you can reach conclusions faster and make data-driven decisions. When dashboards are pre-built for you, your only task as a digital marketer is to look at the data and begin to notice what trends you’re seeing.
Where can you find the best Google Data Studio templates for digital marketing analysis?
There are a few paid ad data connectors out there that offer templates; however, most of these tools require payment upfront, and a lot of the templates are built by members of their community who claim to have deep paid ad data knowledge.
Here’s a list of tools that offer Google Data Studio templates:
- Google Data Studio – Duh! GDS has a report gallery filled with templates from 3rd-party companies and individuals. Unfortunately, with such a vast range of available templates, it’s hard to find a focused set of templates that give you a holistic view of your cross-channel paid ad data.
- Supermetrics – Supermetrics offers a few reports and dashboards for GDS; unfortunately many of them cannot be used with Supermetrics out of the box as the tool requires payment.
- Joinr – Joinr’s entire mission is to empower marketers with better, easy-to-visualize data. That’s why when you connect your ad accounts, you get a gallery of Google Data Studio templates that are finely tuned to answer questions about your favorite ad platforms. Best part? Joinr compiles all your ad data automatically and blends it in a simple, unified data set.
Conclusion: Find your conclusions!
After reading this article, you should now be equipped with tactics for compiling your ad data, and organizing it with templates for deep analysis. The next step is to draw conclusions from the data, which can only be done on a case-by-case basis.
Look for interesting shifts and trends in the data, and try to answer these questions:
- What is the data trying to tell you?
- What external or internal shifts or actions have occurred to impact the data in this way?
- What can be done to capitalize on these shifting trends?
Armed with these insights, you can make informed decisions that drive meaningful impacts for your digital marketing strategy.
