Each year Cardinal Path (one of the world’s leading analytics agencies) releases a report on analytics used by brands. They skew towards big brands, and at the moment focus on site analytics. But it’s a good starting point, this is where most of us reading this post have/are starting.
You probably have site analytics, such as Adobe, or Google – but now you’re in search of something more specific, to help with content. We have found that these traditional page analytics tools are great for your website – but, when content is a key part of your business, they have some clear gaps. This post explores what you likely need to think about in a content marketing analytics platform.
How do you use analytics to measure the success of content marketing?
As we covered in the ROI of content, you do need to establish the yard post for how well you are doing. This is a bit of a cliche, ‘what gets measured gets managed’ – but it’s true, so we’ll use it.
Analytics enables you to understand if you are making measurable progress against your content goals.
There is a difference between measurement and analytics. A common gripe amongst old school data analytics folks. Measurement is simply measuring that something has happened. Analytics is adding a framework and directionality of the measurement. In other words analytics helps you make decisions from data.
For example, let’s say I measure 100 views on a piece of content – analytics would tell me that there were 80 views the day before, and 120 the day after. What it boils down to is, analytics should enable you to make better business decisions.
Different types of content analytics
Built-in tools, i.e. platform-specific analytics, to help analyse social posts (e.g. Facebook Insights).
External tools, e.g. Nudge, to get meaning across many different types of content (built for content marketing, branded content, and content distribution).
Creative analysis, i.e. heat maps, or similar. Like RealEyes.
Other examples within the wide spectrum of content analytics tools include data/text mining, pattern matching, visualization, semantic analysis, neural networks, and complex event processing, just to name a few.
What considerations do you need to make when selecting content marketing analytics?
Firstly, do make sure you have a solid understanding of your content strategy and how you drive ROI. These can dictate to a degree your needs in this area. Here are some schools of thought we see in the market.
1) The best tool is the one you use
Very very valid. In picking a tool, are you using it, do you think you will use it? Can you do a trial to play with it?
Think of it like you would say a step tracker or health watch. Pick something you want to use each day if you have to. And reliable enough to get the job done in the background whilst your busy.
At Nudge, we see that users use us up to 6 hours/week. Sign up here.
2) Third-party measurement is vital
Some of the bigger platforms include their own measurements. A third-party tool is built with you, the end-user in mind. So it tends to be more user-friendly, simplifies usage and builds features based on your needs.
Also, external measurement helps keep other platforms in check. i.e. Facebook has had measurement issues before.
Finally, when you use external tools, you are making sure you are competing against the market. Not just that specific platform.
Benchmarks are an exceedingly brilliant invention. These help keep you in check – and ensure you know how you are positioned in the market. Consider tools that help you benchmark against yourself and the market. Contextualize your performance and figure out how your content can work even better.
4) Time to insight
Ensure that the data you are going to get is actionable. What business decisions do you need to make? What do you need to communicate with stakeholders? i.e.
- Did our content work?
- Is it delivering ROI?
- Should we spend more money? Or not?
- Where should we invest more money?
Data for data sake doesn’t help anyone.
5) Distribution impacts success too, so make sure it is measured effectively
We had a finance client who just wasn’t hitting their goals. We looked at the data and saw that all of their content was promoted in areas that meant that 60% of users left the content within 5 seconds.
So the content had ZERO chance of delivering any impact. Poor team!
In picking a tool, think about how you will measure your distribution, and use that to make your overall campaign a success. Content analytics has to go beyond the content and go to how it is shared, seen and distributed. See more in our Content ROI post and our post on the Factors which drive content ROI.
6) A good tool will help improve your old content too
This is often forgotten, but in calculating the return on investment consider what you have invested in content in the last three years. A new system should help improve that content too. Copy our snazzy ROI calculator here.
Site analytics vs content analytics
My very first website had a little counter at the bottom. It would count how many visits I had to my page. If you refreshed, I got another count, yuss! It was the best.
Site analytics are good for understanding the consumption and user journey of your website, from all types of pages.
Content Analytics looks at the content first and seeks to understand its impact.
The two are intertwined, you can’t have one without the other (in most use cases). And this is very common; marketers today use multiple tools to deliver on their goals.
What is the future of content marketing analytics?
- Diversity in specialized tools. It is common to use 1-2 tools. As organizations become more digitally-centric, this will only increase, as business demands do. At some point, it will consolidate but not in the next 2-3 year horizon.
- Privacy-centric or privacy respectful tools. Marketers are finding that this isn’t a trade-off that is productive for either the brand or the consumer.
- Better workflows. In recent history, marketers have aimed to get data into dashboards, the next trend will be towards, getting data into action, through integration into marketing workflows.
- Increased maintenance. With more digital tools, every organization will need to increase its technical maintenance to keep all their data up to date and reflective of the organization’s needs.
- Tighter feedback loops with paid distribution of traffic. I.e. Google Adwords has begun to integrate more data from Google Analytics.
- Digital content expanding into connected TV and digital out of home. Our content will now branch out from the regular screens. Requiring new ways of understanding content success.
- The merging of brand and performance. All brand advertising and marketing should perform. All performance should grow the brand. They still have different roles but will be more integrated than in the past.
- Machine learning democratizes insights, data collection and the feedback loop to creating more effective content. Now small businesses can really compete with big businesses.
- The shift from data scientists to insight curators, people that understand the data and how it is created but can then link it to business objectives.
- The intersection of content, personalization, and distribution will begin to blur. As they all get further integrated.
The evolution of content analytics
Content marketing analytics has to go beyond the page view. Today there are more platforms, devices, types of content than ever before.
To frame it up, for one brand, we saw over 30,000 different browser combinations in accessing their content. Wild. Remember your customer isn’t you, and doesn’t always have the best device, the best internet connection – and/or the time.
What are the best practices for content marketing analytics?
If we were to distill it, it would look like this:
- The best tool is the one you use.
- Use tools built for purpose.
- Does the company itself dog food? I.e. use the tool themselves.
That should get you ahead of the pack.