From episodic content to data journalism – content marketing trends

From episodic content to data journalism – content marketing trends

Episodic content: why editorial series attract readers

The idea of episodic content isn’t alien to content marketers; recent figures show that articles released as part of an editorial series receive 124% more page views than standalone posts. It’s just episodic contentthat many are yet to deploy the concept to its fullest potential.

There’s so much to love about episodic content. You can finish with a dramatic cliff-hanger, develop an idea or theme to which the customer can relate, and establish a routine that brings your audience back at regular intervals for the next edition.

Introducing an episodic element to your content strategy can help you improve:

  • Retention
  • Credibility
  • SEO
  • Email sign-ups

When done properly, episodic content can have your audience hanging on your every word, waiting for the next edition.

You can use an episodic approach to create a standalone series, or you could apply it to a ‘hub and spoke’ content strategy. Use a powerful, coherent long-form piece as the hub, then release bitesize episodic content as the spokes that point towards it.

“Where could I find a fabulous example of episodic content?” – you’ve probably just bellowed, unabashed, across the office. Well, look no further than your old pal Content Marketing Trends. See you next week!

Use data journalism to hone your content strategy

Content marketers must begin drawing on data journalism techniques to provide potential customers with more insightful content.

Massive datasets are inherently complex, but the concept of data journalism is not. The idea is to use quantitative and qualitative data to uncover emerging trends and create a story that offers genuine insight to your intended audience.

Brands now have a wealth of data at their disposal; industry data, website analytics, qualitative feedback and a whole host of other potentially useful resources. However, they simply don’t factor the majority of these resources into their content strategy.

An example of what can happen when you ignore data occurred just a couple of months ago. Nate Silver, a leading proponent of data journalism, was asked to predict the outcome of the US presidential primaries. He concluded that Trump would lose despite leading the other nominees in the polls.

Whether Silver realised it or not, there was a wealth of supporting data from non-traditional sources that predicted victory for Trump. Data from Google Trends for example, was in line with the polls: it showed that Trump was discussed three to four times as often as his rival candidates.

This doesn’t necessarily predict Trump’s victory, but it does indicate the relative prominence of his profile during the campaign, which should have been factored into a data-led analysis of his chances of success.

Data sources:

Additional sources can provide a more detailed picture:

  • Keyword analysis: Comparing search volumes between related queries can yield interesting insights – for example, simply looking at how many people searched terms relating to Trump becoming President, compared to similar searches for rivals. In fact, Google’s search data can sometimes prove more reliable than traditional data sources, as was the case with the recent Brexit vote.
  • Google predictive search: The typing a query into Google can be a useful source of qualitative data. Generated by popular search queries, these offer a useful insight into the general direction of a conversation (see fig. 1, below).
  • Answer the Public: This resource offers an in-depth insight into the range of questions people are asking Google about a particular subject
  • Lava: ‘The Emotional Search Engine’ identifies whether discussions are seen in a positive or negative light (see fig.2, below)
  • Industry trends: Most industries have their own data sources that can be mined for info about trends, consumer behaviour and opinion.

Fig. 1: Predictive search query beginning ‘How likely is Donald’

How likely is Donald Trump ... data analysis

Fig. 2: Lava sentiment analysis for ‘Donald Trump’

Lava sentiment analysis - trump. Data anlaysis

It’s not just journalists who can benefit from a data-led approach to content. Marketers can use the same resources and techniques to create topical, relevant pieces that provide real insight into complex subject matter.