Like pizza? Like content analytics data?
Our newsletter delivers both.
Measuring the ROI of Content Marketing with True Engagement Metrics
Return on investment (ROI) from content marketing efforts is notoriously difficult for marketers to measure and track. To generate return on investment over time, content depends on a series of micro conversions—from newsletter sign-ups to asset downloads and eventual purchase events. If you fail to track one of those micro conversions, you won’t know the true ROI of your content efforts.
Google Analytics doesn’t help content marketers looking to pinpoint their return on investment. While Google Analytics is great for tracking direct-touch marketing, like ads, it wasn’t built to parse the complexity of content’s role in revenue generation. Trying to get an accurate ROI calculation for content from Google Analytics is like attempting to play a Queen classic on a rubber chicken: it can be done, but not accurately.
Below, we explain the weaknesses of tracking content marketing ROI in Google Analytics and how content-specific analytics can make it much easier to measure and track meaningful content ROI data.
Table of Contents
- What is content marketing ROI?
- How to (Sort of) Measure Content Marketing ROI with Google Analytics
- Content Analytics Offer a Better Way to Track ROI
- Beyond ROI & Conversion: Engaged Times
- Track content marketing ROI with Parse.ly content analytics
What is content marketing ROI?
Content marketing return on investment (ROI) is the measure of revenue your business earns (compared with what you spent) that can be attributed back to content marketing efforts.
That’s a pretty straightforward definition, so why the complexity with measuring content marketing ROI? Because the journey from content to revenue isn’t always straight or quick. Content marketing is a long-term game, and, unlike something like pay-per-click ads, attribution can be harder to determine.
How to (Sort of) Measure Content Marketing ROI with Google Analytics
Google Analytics wasn’t built to measure content. It is best used for tracking the effectiveness of ads, like pay-per-click, search, and Google Ads.
But, if it’s how your company prefers to measure ROI, there are a couple of work-arounds you can leverage to get a general picture of your content’s bottom line:
- Enable ecommerce tracking in Google Analytics: Once enabled, go to Behavior » Site Content » All Pages, and then manually calculate the Page Value. The Page Value calculation will show you the average value of a page that a user visited before completing a transaction or Analytics Goal. This stat gives you a general idea of which pages on your site contribute more to your site’s revenue, but it’s complicated to work out and gives you only a general idea of ROI.
- Enable Assisted Conversions: Assisted Conversions measure the number of conversions that a channel (such as paid ads or organic search) assisted with at any point along the customer’s journey. You then manually figure out the relationship between conversions and actual bottom-line profit for your company.
- Set Up Conversion Events in Tag Manager: Google’s Tag Manager is often used to measure direct revenue from ads. Tag Manager Events can be rejiggered to track different types of conversions from content, including demo requests or newsletter sign-ups, but you’ll need a content team with some developer knowledge to implement this right.
Once you’ve got these tracking work-arounds set up, you can apply the standard ROI formula to get a rough idea of how each piece of content assists in generating eventual revenue.
Calculate the cost of producing your content, add the cost of distribution, and subtract that total from the top-line profit made over the same period. Simple, right?
Problem is, there are a couple of glitches with calculating content ROI like that.
The problem with Calculating ROI In Google Analytics
Google Analytics only offers a stop-gap solution for content marketers looking to calculate ROI. The reason is threefold:
Google Analytics doesn’t have an attribution model that works for content marketing. Ever hear about the content marketer who spent 18 months setting up an attribution model she was happy with? Stories like that speak to the fact that Google’s first-touch or last-touch models are fine for tracking ad spend, but they fail to show you how each piece of content assisted in a multi-touch conversion journey. And content is almost always part of a multi-touch journey as users move from awareness to action over time.
Google Analytics fails to properly measure how engaged site users are. That may seem tangential to ROI, but it’s not. The longer a user is actively engaged, the higher the chance that they take at least one conversion action (even if that’s not an actual purchase conversion). The failure to measure active users creates a gap in your ROI and engagement data and a bias in your aggregate metrics. Content marketers know that successful content can’t be measured by action alone.
Google Analytics forces you to find work-arounds like those above, leading to manual setup errors and mistracking. Broken tracking Events or wrongly configured Goals lead to dirty data and, in the worst-case scenario, the inability to make data-informed decisions on how to spend the marketing budget in the future.
All that to say, there’s a better way for content marketers to calculate ROI.
Content Analytics Offer a Better Way to Track ROI
Content-specific analytics tools are specifically designed for content marketers and content-driven businesses to track their efforts. For example, at Parse.ly, we leverage two content-team-specific features to measure and track content marketing ROI more accurately: linear attribution models and content-specific conversion categories.
An Attribution Model Built for Content, Not Ads
As we wrote back in 2019, “not every attribution model is going to work for every business.” Content attribution requires a custom-built model that maps how content generates revenue over time and on a granular article-by-article level.
In Parse.ly, you can leverage the linear model. This model gives credit to every page viewed within 30 days of a conversion except the page where the final conversion occurred. That differentiates linear from the default last-touch attribution models offered in Google Analytics, which gives 100% of the credit to the page on which a conversion occurred.
With this model, content marketers can see a full mapping of all the content visited along the path to conversion. This allows you to identify each page’s individual role in generating revenue. With this model, content marketers can see a full mapping of all the content visited along the path to conversion.
Take Parse.ly’s own pricing page. When we use last-touch attribution, we see that only two users converted on the pricing page itself:
But when we combine that with data from the linear model, we see that the pricing page was the second-most-visited page before site users made a conversion:
This may seem like a no-brainer for a pricing page. But imagine that thelinear model reveals that you have a handful of blog posts that the majority of visitors read before converting. That’s a powerful way to understand a piece of content’s impact on the bottom line.
Content-Specific Conversion Categories
Content supports the sales cycle at lots of different points. Prospects who interact with your content will not always go directly to a sale, but they may take baby steps toward an eventual purchase each time they visit your blog.
That’s why content teams need to track various types of conversion from content, not just conversion to purchase. A newsletter sign-up conversion from a top-of-funnel piece of content could be a leading indicator of eventual revenue. Parsing the impact of content at different milestones throughout the sales cycle gives a more accurate view of content’s return on investment.
Parse.ly helps content teams track the different steps in the sales cycle with out-of-the-box conversion categories designed for publishers and content teams. Alongside purchase event conversions, in Parse.ly you can use custom conversion categories to measure:
- Subscription sign-ups
- Newsletter sign-ups
- Lead capture form-fills
- Link clicks (useful if you want to track outbound affiliate clicks)
- Any other event that matters for your business’s bottom line
While you won’t see a dollar amount of “revenue won” next to each of these conversion events, you will have a true picture of how each piece of content moves prospects toward a purchase. You’ll be able to parse what kind of content journeys result in the highest-value sales and optimize future content investment accordingly.
Beyond ROI & Conversion: Engaged Times
Content-specific attribution modeling and conversion tracking will give you a more accurate picture of how content assists in generating revenue. But that’s not the whole story.
A better measure of content success is a user’s true engagement with your content. True engagement measures not only whether or not a user clicked but also how long visitors spend actively engaged with your content.
Our tool, Parse.ly, can show you true engagement. We use a “heartbeat” pixel to check in every few seconds and track whether the browser tab is open and whether the user is currently engaging with the page. That means our data isn’t reliant on “exit events” in the same way Google is. And we’ve created a proprietary algorithm that’s able to accurately track the length of a page visit from the initial page view to the end of a session.
Track content marketing ROI with Parse.ly content analytics
Simply put, Google Analytics wasn’t built for content marketing teams. That’s why marketers have to jump through tons of hoops and custom dimensions just to get some sense of content ROI.
Instead, marketers should turn to an analytics solution that’s designed for content marketing from the ground up. With Parse.ly, you can more accurately and meaningfully define content marketing ROI and rely on that data to make content strategy decisions and improve your content’s performance.