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It wasn’t always this way. In 1904 Jell-O started printing Jell-O “best seller” recipes and distributing them for free. Jell-O credited its distribution of these free recipes with over $1 million in sales from 1904 to 1906. At a sales price of $0.10, that’s over 10 million units.
The books were physically printed however, with no way to connect people who purchased or influenced a purchase of Jell-O to people who received the recipes. So the company actually had no way of attributing these sales to the cookbook.
Given the lack of data for the campaign, practitioners in today’s world of content analytics and metrics would chuckle that Jell-O not only felt good about the recipe campaign, they attributed a significant amount of their revenue during those two years to it. Today the Jell-O recipe campaign is still remembered as a founding content marketing effort.
Currently, content marketers have the opposite problem: an overwhelming amount of trackable data in the digital space. As digital strategies evolve, the metrics we use to track the success of our programs are constantly playing catch up.
33 percent of B2B marketers and 41 percent of B2C marketers cite an inability to measure as a significant challenge, according to the Content Marketing Institute. Robert Rose, CMI Chief Content Advisor, recounts a related conversation he had with a CMO at Content Marketing World 2016:
During our discussion about the event (and how great it was), he said, “Robert, you know the thing that I’m missing is how we’re ever going to draw a line from content marketing to top-line revenue. If I can’t do that,” he said, “then I’m not sure we actually should do content marketing.
In a 2009 McKinsey survey, two-thirds of marketers said their respective companies had either a very weak link or no link at all between marketing performance and financial incentives. While not everyone is yet at the point where they can draw a line from content to revenue, we’ve all come a long way in our methodology for content analytics.
Many marketing teams now prioritize training on data analysis. Data is now analyzed and applied in increasingly diverse ways.
Here’s a look at some of the ways measurement has changed over the years, and how we’re getting better.
2012: Inbound Links and Search Engine Traffic
One example of a CMI post written in 2012 shows how that year was arguably when online content measurement truly became sophisticated. The focus was on site traffic and keeping potential customers on your website, interacting with your content. Key metrics suggested by CMI and other expert content sites included:
- Page views
- Search engine traffic
- Bounce rate
- Conversion rate
- Inbound links
- Time on site
What We’ve Learned Since Then
Inbound links are great, but it’s hard to determine how they should be measured. They do demonstrate that your content is interesting/useful, and help improve SEO and page reach. Though more inbound links are usually better, take their quality into account when analyzing how successful your content is. Rand Fishkin argues that:
If there’s a lot of links from Wikipedia and DMOZ and the site has high PageRank, lots of inbound links and blog links, there’s clearly some value to getting a link. Just make sure you judge based on the data, not the numerical score.
Best Takeaways of 2012
Page views and conversions rates are an important way of measuring content marketing success.
2013: Mobile and Geo-Targeting
In 2013, marketing measurement shifted to mobile and geolocation analytics as platforms such as Google and Facebook changed the ways measuring took place. Google changed their terminology. “Visits” and “unique visitors” became “sessions” and “users,” respectively. As the popularity of mobile devices rapidly spread, along with the ability to track IP addresses from page to page, marketers developed more advanced and targeted measuring capabilities.
Key metrics included:
- Mobile readership
- Bounce rate
- Heatmaps and click patterns
- Page views
- Social sharing
What We’ve Learned Since Then
Focusing on comments and social sharing became more popular as social media continued to increase in importance. However, the popularity of social platforms such as Snapchat that don’t allow for comments or sharing mean such measurements have slipped slightly in relevance. Google’s change in the words used for search, though probably more accurate, are indicative of a larger problem: the large and increasing fragmentation of data sources with little standardization in format and taxonomy.
What was Right
While perhaps ahead of their time, heat maps, click patterns, and geographic tracking have laid the groundwork for more sophisticated marketing, including automated workflows and automated campaigns based on location.
2014: Big Data Waits in the Wings
Forbes named 2014 the year of digital marketing analytics. According to Forbes, “37 percentof companies surveyed said that they desperately needed staff with serious data chops.” The focus for 2014 was strengthening collection capabilities for big data. Here are some of the things marketers were thinking about in 2014.
- Page views, newsletter subscribers and similar measurements were labeled “vanity metrics”
- Who is converting, what’s converting them, and what conversions are driving revenue
Big data isn’t quite there for content marketing just yet. Though marketers can collect a lot of valuable information, collection methods and the analysis applied to big data for marketing currently falls a little short. A reliably accurate marketing attribution model has yet to exist and journey mapping is as complex as ever.
Best Takeaways of 2014
2014 was the beginning of a shift away from tracking for the sake of tracking and toward looking at analytics in terms of how they’re contributing to revenue. CrazyEgg founder Neil Patel said of analytics: “Measure what matters. With metrics, it’s easy to get caught up in vanity metrics.”
2015: Using Content Analytics to Determine ROI
As sales and marketing alignment becomes more important, marketing teams are looking at measurement to prove their efforts add value and achieve positive ROI. The amount of content analytics available increased, but marketers weren’t yet focusing on their content’s ROI. CMI’s Robert Rose argues:
… goals such as incremental sales revenue, cost per lead generated, cost per sale generated, and cost of a new customer are not returns on an investment; neither are they even goals. These are accountability metrics toward a particular business goal (e.g., higher revenue, decreased costs).
Mobile was more important than ever in 2015, with Google updating its search algorithm to increase the visibility of mobile-friendly websites.
Black Ink forecast that predictive analytics and marketing automation would receive increased focus in 2015. These continue to be a focus in marketing analytics and are informing other technology-focused marketing trends today, such as artificial intelligence and machine learning.
2016: New Metrics Arrive
The way we think about content analytics is increasingly thoughtful. This includes a greater focus on branded search, sales accepted leads, and share of voice.
2016 was a year of introspection. The popular metrics were rethought and new, more powerful metrics put in their place. Some of the changes industry experts peddled in 2016 included:
- Conversions over clicks.
- Scroll depth over time on page. Why? We calculate time on page using an average based on users who didn’t bounce. Users leaving within that time do not figure in the calculated average.
2017 and Beyond
In 2017 the technology we use to understand content analytics continues its relentless advance. The increasing power of artificial intelligence (AI) is driving content intelligence where AI and big data increasingly automate content and incorporate predictive analytics. As AI improves, marketers will leverage it to determine what content to create, when and who to serve it to, and at what time.
Further in the future, measurement will become increasingly automated by machine learning. While this may be years away, content marketing platforms are already available that track marketing efforts directly to attributable revenue. Not quite the exact ROI we’re talking about in the first paragraph—but not far off. The landscape will become more complex, data more granular, and there will be a growing focus on accountability.
To learn more about content analytics, download Curata’s Comprehensive Guide to Content Marketing Analytics & Metrics.