Data-Driven Strategies for Content Marketing: A Marketing Analytics Perspective

Data interpretation is a threshold of knowledge. Understanding the why, what, and how behind marketing analytics. Having this knowledge not only pivots the mind of a strategic thinker but brings insight into everything they do.

I am a strategic thinker. No matter the activity I will find core strategies behind sports, games, and hobbies. Being strategy driven helps define problems, and then seeks out solutions in real time. Without this way of thinking I don’t think I would have ever been as passionate about analytics as I am now. So, let's dive right into viewing, interpreting, and improving analytics to bring value to personal and work life.

Viewing Analytics

The first step to understanding marketing analytics is understanding metrics. Every product and promotion have some method of measurement. Whether it is a print promotion, social media post, or newsletter. Each of these containers have MULTIPLE ways of mapping metrics and establishing base parameters for content. To make things easier to digest, I will be using the example of website and what analytics are necessary to really understand what’s happening with your content.

Definition: Events are user interaction with the page/website

Website Analytics

• Views

• How many views the website has

• Unique Views

• How unique viewers have viewed the website

• User Frequency

• Views divided by Unique viewers – Frequency of times unique viewers look at the website. (repeat visitors)

• Bounce Rate (%)

• The amount of people who leave the website without triggering a session or event

• Engagement Rate

• Specified amount of time people pass measured as a conversion

• Ex. Passed 10 seconds, counts as an event for engagement

• Conversion Rate

• A potential user turning into a user. This could be a call-to-action conversion, email, or tracked events on a website.

• Views Per User

• How many times a user has viewed the website

• Engaged Sessions Per User

• ratio of engaged sessions to the total number of users

• Average Session Duration

• Average time a user is on the page

Each of these metrics has unique measuring features that can pinpoint the issues with the current product. The more information you have, the better you can distribute and promote content for future iterations. Now that we have the analytics, the next step is to interpret the analytics.

Interpreting Analytics

Having a clearer idea as to what metrics we can track in relation to a website; it should be easier to provide ways to interpret the information. Given these metrics, we can look for improvements and justifications in the data. However, we first need to establish what the Marketing Goal of the website is first.

• Informational Based Website?
• Landing Page?
• Other conversion metrics?

Each of these conversion goals will depend on what kind of outcome is expected in the analytics. With landing pages, key metrics might include:

• low bounce rate
• low views per user
• High overall views
• NA average engagement rate

While an informational site might have completely different metrics to keep track of:

• High bounce rate
• High views per user
• High overall views
• High average engagement rate

Having a clear marketing objective can transfer to clearer marketing analytics. If the communication objective for a Landing Page campaign is to inform users about a new product there are some considerations to make. We want to have high traffic. Have a high conversion ratio. Keep average engagement lower. And have a low bounce rate. These metrics might point to a beneficial campaign for that landing page. However, if these marketing analytics goals are not achieved, strategizing the “why” around their performance is the next step. This is where we find ways to improve and evaluate the campaign so that future iterations are better than the last.

Improving Evaluation Analytics

Once the campaign has had a couple weeks to set off, we might want to consider what metrics are lacking, and which ones are doing well. In this situation I like to step into the marketing funnel to identify what metrics might fit in which part of the funnel:

• Awareness – Views
• Knowledge – scroll depth (engaged sessions per user)
• Liking – engagement with website, clicks
• Preference – repeat visitors/ high frequency

We will use an informational site to unpack these types of analytics. I am going to give some example analytics in reference to a website with the objective of a long informational site with a contacting page towards the end. We have 2 weeks of information to pull data from. Then I will unpack what these metrics might mean in relation to these objectives.

• Views: 500
• Frequency: 1.25
• Scroll Length: 2 scrolls
• Average Session Duration: 40 seconds
• Bounce Rate: 60%

Views

Thinking about the strategy behind these analytics is key to improving them. Let's begin with Views. We have no reference to whether 500 views dictate high or low awareness. If you assume 500 viewers is a generally high number of viewers, this could be detrimental to the campaign. If your audience consists of 50,000 people, this website only gets 1% of potential viewers. This means we are seeing a low amount of awareness in our marketing funnel.

Frequency

Since we have a low frequency, this could be good or bad for an informational site. If people can find the information they are looking for quickly, then a low frequency is our desired result. But if we want to encourage viewers to look at all the information that is available and contact us at the end, this might not be the desired result. High frequency could also mean higher interest with the content, meaning better engagement with liking and preference for the marketing funnel

Scroll Length, Average Session Duration, Bounce Rate

In association with frequency, we might want these metrics to be unique depending on the objectives of our informational website. Low scroll length and low average session duration are good metrics for sites that want to give users quick information, but poor for sites that have lots of information to digest.

In conclusion, marketing analytics needs foundational context regarding the marketing objective you are trying to achieve. Without a clear understanding of the outcomes, you are trying to achieve; these metrics become hard to follow and can lead campaigns in the wrong direction. Thank you for reading this blog and hopefully I will be doing more soon!

If you would like to read more about content marketing analytics, this article might give some more insight.

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