As a member of the marketing team you already know that data is power. However, it’s not always easy to get, and even when you can get the data you need, it’s not always easy to understand, analyze, interpret and consume.Luckily, we have tools in our martech toolbox that can help us find answers in our data. In this example, we look at several popular marketing technology tools that generate data that can be mined for insights. We’ll use Panoply for the data warehouse, since that’s what we know, and it connects to popular BI tools like Stitch, Chartio, and Tableau for dashboarding.
Some example questions we’re trying to answer:
- Between Twitter and Facebook, which channels contribute to increased engagement on our website?
- Are the paid channels or the organic channels providing a greater impact? And if it’s paid, what’s the ROI on that?
- Are Twitter and Facebook ads worth it, or should we focus on a purely organic approach?
And of course, we’re trying to answer these questions in real time. Let’s look at what data we can get.
You already know that you can grow your Twitter audience, build brand awareness, and drive traffic to your other digital properties (like landing pages!) using Twitter ads.
But how does your spend impact what you are seeing from that channel? Ideally, you could pull in the following data and correlate it with advertising data from other channels, for example:
This collects all the data you have from Twitter ads. Connecting your data to a data warehouse is simple—all you need to do is click the green “Login With Twitter” button to access the data generated by all of your Twitter ads.
Now that we’ve covered paid, let’s look at organic Twitter. Understanding your overall Twitter metrics means that you want to understand the growth driven by organic as well as paid acquisition—what if paid acquisition doesn’t perform any better than organic? Do you want to set money on fire? By putting your data into a data warehouse like Panoply, you’re looking at all of the data through one coherent view.
You can get metrics from your twitter feed—tweets, retweets, likes and followers. To pull this information into your Panoply data warehouse, just select Twitter in the data sources and click the login button.
Facebook Ads and Pages
If you’re spending on Twitter, you’re probably also spending on Facebook. Which channel is better for your social or acquisition programs? You can do comprehensive paid social analysis by combining Twitter ads and Facebook ads. Given how Facebook is set up, connecting your account is slightly more involved:
First, login to Facebook for both the Ads and Pages data sources.
- Select the ad account
- Select the date range you want (the default is the last 30 days)
- Click to connect your Facebook Ads account
To ingest your organic Facebook page data, you follow a similar process with the Facebook Pages data source.
Google Analytics is a common data source to correlate against the rest of your data. While Panoply does provide preset metrics based on what’s popular, you can easily customize what you collect from GA based on your particular business questions.
The date range that data can be collected against include preset dates or all of the historical data. If you’re feeling ambitious and want to look at data from specific date ranges, you can do that by querying against all of the historical data.
Don’t forget that you can schedule your data collection (for more details, take a look at this post)
Finally, you want to correlate your organic and paid social data to your marketing automation data for greater insights.
For example, with Hubspot, you can collect contact details, account details, custom fields, and more.
Storing all of your data in one place will allow you to compare and analyze your data so you know where the data came from, what it means and can have confidence in your data story. You can now answer questions like which channels contribute to increased engagement on our website, the impact of paid channels, and otherwise explore your data for deeper insights.
Dear readers, we’ve set up the problem so it’s up to you to take it and apply this to your own data sets and see what you can discover. Happy analyzing!