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Donna Moores
Donna Moores 24 May 2018

How to Create a Winning Data-Driven Content Marketing Strategy in 2018

Digital marketers know the role content marketing plays in their strategies. Research shows that it’s the second most effective tactic marketers plan to use in 2018. It is ahead of SEO, email marketing, and martech, and second only to social media marketing.

Even though it’s extremely popular, content marketing is the third most difficult strategy to put in place. Still, marketers are more than willing to try to make content marketing work for them. According to data from the Content Marketing Institute, 91% of B2B marketers use content marketing. Among B2C marketers, the number 86%, but it still shows how widespread is the use of content marketing.

Yet, as we know all too well, doing content marketing is not enough. A variety of factors differentiates successful content marketers from those that dabble. One of them is the use of data.

The Basics of the Data-Driven Approach to Content Marketing

Data, on its own, is not enough to create a successful content marketing strategy. However, data can be useful for every step of development and deployment of a content strategy. Data from past transactions can yield valuable insights. Those insights can fuel the development of new content marketing strategies.

This type of data usage extends way beyond the content marketing world. One of the best examples of how to harness the power of data comes from the world of banking. American Express uses data analysis and sophisticated machine learning to find out when their Australian customers are about to close their accounts.

The company feeds data from historical transactions and 115 variables into an algorithm. That way, the company finds a quarter of accounts that will close within the next four months. At its best, data can help us determine the future performance of what we are observing. For AmEx, it is about customers who are ready to jump the ship.

For content marketers, the focus is on delivering the best possible piece of content to customers. It helps to be able to use their preferred channel, at the time they are most likely to consume the content. All that is done to ensure the most favorable outcome for every separate piece of content. The campaign benefits, as well. And machine learning can really help with crunching all the data for and putting it in a context for digital marketers.

Step 1 — Understanding the Audience

The first step in developing a content marketing strategy involves researching the audience for specific insights. Marketers have the option of creating customer personas and use them as the ideal potential recipient of the content. That is a sound and time-honored strategy. However, it can be further improved by using more variables to describe every segment of the audience.

So, the first question is "who is my audience?" The answer is likely to include their gender, age, education, geographic region, employment and an assessment of income. Those six different variables tell a lot more than it seems. One of the benefits of generational targeting, for example, is that it allows marketers to know which channels to use to reach the members of specific age groups.

The more marketers dig into consumer behavior, the more variables they will be able to gather. Engagement metrics are invaluable when trying to understand consumer intent. Customers' sharing habits and content preferences provide valuable insights. Marketers use them to understand what the consumers are looking for and how far away they are from getting it.

Step 2 — Discovering Content Ideas

Marketers can use engagement metrics to determine the topics consumers are interested in. They can do this while performing competition research, for example. Engagement metrics can also help marketers gauge the performance of their own content. High-performing content can show marketers the way forward.

Keyword research can let marketers know what interests their consumers. Long-tail keyword research can be very valuable for this. But keyword research in general for content topics is a valid data-driven content marketing technique. It is especially effective for gathering information about the needs of consumers before they enter the sales funnel.

For the customers who are already somewhere in the funnel, marketers can adopt a different approach. British Airways launched its data-driven customer analytics program in 2011. Named "Know Me," the program compiles customer data from a variety of sources. Social media to the customer information the company has from its loyalty programs are only some of them.

Using that data, BA is able to achieve a high degree of customization. Everything from the way the passengers are greeted when they get onto a plane, to the special offers and deals the company offers can be customized. The lesson here is simple. It pays to integrate the information marketers get across channels.

Step 3 — Distributing Content

Content creation is a step in the content strategy that takes cues from both the discovery and the distribution. While the discovery is concerned with the topics of the content, the distribution is more concerned with the form and audience.

Distribution channels have their sets of constraints when it comes to the type of content they can distribute. Marketers will have a hard time using YouTube or Instagram to distribute articles, for example. On LinkedIn, however, articles are an important part of the content landscape. Email marketing is text-driven, but marketers are starting to include video in there as well.

The same goes for demographics. Facebook is ubiquitous — everyone uses it, and it is good for every form of content. Pinterest, on the other hand, has a mostly female user base. YouTube has a mostly male user base. Marketers who want to reach seniors on social media should turn to Facebook instead of Instagram, for example. The preferred channel also determines the type of content marketers will create.

When creating a content marketing calendar, marketers should time their posting for maximum engagement. For example, Instagram is a social network with native analytics that allows marketers to see when their audience is active the most. It also has an API that lets marketers schedule when their posts appear.

To create a successful, data-driven content marketing strategy, marketers should understand how to extract value from data. On their own, the large datasets content marketers work with are unmanageable bundles of numbers and letters. However, when put into context and analyzed, those same datasets become a source of essential insights. That is how good data-driven content marketing strategies are created. Mining data for insights, and using those insights for informed decision-making every step of the way is winning strategy.

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