When brands devise a content marketing strategy, it is done for the masses, hoping that it would go viral. However, rather than shooting in the dark, and basing the success of the content marketing strategies on how viral the content has gone, the current scenario demands a more targeted approach.

Usually, while creating the content marketing plan, businesses hardly pay much heed to who their target audience is, what type of content they are looking for and which content will offer best results in terms of new customers.

Of course, it is not the recommended approach, but in the rush of doing more with less, brands create a diverse content marketing plan and hope that it will stick with their target audience.

Amid the rush of producing more and more content to please search engine bots for improved ranking, to some extent, this approach is inevitable. However, today, we have big data to help brands narrow down their targeting even with the aforementioned rushed approach.

But, what exactly is this big data?

Big Data is the sheer amount of information that users are generating every second online as well as offline. With each click, swipe, like or share, some amount of data is being generated that can be used by brands and businesses to make informed decisions on how users are using their services.

All this data is giving us a small bit of information on consumer behavior, their likes, their dislikes, etc., which can be used by businesses and brands to better target them. Now, rather than wasting resources and puttering randomly created content, brands should use data at their disposal to target the content more efficiently towards users who really matter.

Before we delve into use case of big data in content marketing, first let us look at the amount in terms of numbers that will allow you to understand its magnitude.

  • More than 90% of the data in the world has been created in the last two years alone.
  • Currently, we are outputting 2.5 quintillion (1012 million) bytes of data every day.
  • Americans use 2,657,700 GB of internet data every minute.
  • Wikipedia users publish 600 new page edits every minute
  • YouTube users watch 4,146,600 videos every minute in 2017.

Related Read: Various Metrics to Measure Content Marketing Success

It is estimated that just 12% of the overall data being generated is currently used by data scientists and the rest 88% is available out there to be used as valuable information.

For example, take Facebook. It has the world’s largest database of information about consumers and their likes. It uses this data so that the brands and businesses can better plan their campaigns on the social networks. So if Ford company wants to launch a campaign for their upcoming car, Facebook would allow Ford to just target users that are car enthusiasts, or looking for a car. This way the content marketing strategy will work more efficiently and will offer better ROI.

That said, here is how you can use Big Data in accomplishing three main goals of content marketing:

Finding targeted audience

If you know who is more likely to read your content or even might be interested in the product/ service that you are offering, you are more likely to get better results on your content marketing plan. You can track and monitor the progress of certain metrics which will help you offer better insights on who to target with your content marketing plan. These metrics include:

  • Search for brand/business on search engines
  • Followers on social media
  • Mentions on social media
  • Traffic sources
  • Overall Visibility

By examining this data, you can analyze the behavior of your target audience, what they are talking about and which type of content will be most effective on them.


There is no denying the fact that content marketing plays a larger role in conversion during the purchase cycle of a user. Big data helps in deducing and mapping how long a prospective customer stays in the purchase cycle and what type of content will convert them. By analyzing this information, brands can easily figure out consuming behavior of the user, allowing them to convert easily.