Content is king, but how can you make sure yours stands out from the crowd?

Lucy Wimmer
3rd April 2017 0 Comments

We’re all aware of the fact “content is king” and we’re now in an age where anyone with an internet connection can create and publish content across video platforms, blogs, news sites, and social networks. However, there’s a limited amount of content that people are able to consume and brands, their competitors, individuals and new sites are now all vying for a few seconds of people’s attention. So how do you make sure you get the right content in front of the right people, at the right time?

The second part of our PYLON for LinkedIn Engagement Insights blog series focuses on content. With LinkedIn Engagement Insights, you can gain a more in-depth understanding of the topics, types of content and creative that resonate most with your key audiences. With these insights you can better shape and inform content marketing strategies on LinkedIn, create more compelling content and effectively measure the success of your content on the platform. There are three key areas that LinkedIn Engagement Insights can help you with when it comes to revamping your content:

  • Topic analysis
  • Content discovery
  • Content analysis

Topic analysis

With so much noise on social networks, targeting your audience with content on the issues that they care about has never been more important. Topic analysis with LinkedIn Engagement Insights is used to understand a topic in-depth, identifying related sub-topics, interests and entities of interest to your audience. As explained in a previous post, LinkedIn Engagement Insights dissects and scans articles shared on the platform for entities using Named Entity Recognition. This process identifies organizations, locations, products, countries, cities and people mentioned in the content and makes this data available for filtering and analysis.

The information surfaced can then be leveraged to refine existing content and explore new content ideas, build richer more successful campaigns, and drive engagement on LinkedIn.

For example, a marketer building campaigns around the topic of energy will want to gain a greater understanding of the top entities found in energy-related articles, as well other relevant topics linked to these entities. The topic analysis diagram for energy below shows how entities relate to each other. If you are particularly interested in the climate change entity because you market environmental products, for example, you can now see related concepts people are interacting around and incorporate them into your content such as greenhouse gas, sustainable energy and carbon cycle.  The analysis also surfaces concepts you might not have considered in your energy content such as talking about pharmaceuticals and vaccines.

Graph1

Content discovery

Content discovery analysis is used to identify the topics and content that are driving engagement for your target audience. This analysis provides an understanding of the most popular articles and the key topics within those articles for your audience. You can discover new content that drives engagement and increase your reach by incorporating new topics into your content and campaigns on LinkedIn. As well as featuring newly discovered topics, you can also adopt some of the features of successful content to increase the impact of your campaigns.

In the example below, the table on the left highlights the article titles receiving more interaction for the key audience of program and project management function in the manufacturing industry vs normal.

The technique being applied to focus on the comparison of interaction vs normal is called baselining. It enables you to gain insight beyond mere volume metrics. It’s an additional calculation you would apply to query results but is perfectly suited for LinkedIn data.

On this right-hand chart, you can see the top topics within the top articles for this audience, identified by LinkedIn Engagement Insights. As with the left-hand chart, the technique of baselining is applied to understand the topics receiving the highest interaction for this audience vs normal. Using baselined analysis in combination with volume metrics enables you to learn from the content generating the most engagement for your audience (volume) but also the most effective content (highest interaction vs normal).

graph2

Influential media analysis

Influential media analysis is used to identify which channels and pieces of content are influencing an audience across LinkedIn. The analysis provides an understanding of what content and media sources are driving the most engagement from your audience. You can identify which media sources are the most popular with your audience and what content is capturing their attention. With this insight, a brand can then choose to approach influential channels and bloggers to better reach their target audience. These are fantastic insights for your media planning and buying needs.

In the example below, the analysis examines who influences an audience of engineers in the high-tech industry sector. The left hand chart shows the top articles, as well as the domains where they appear for this particular audience, providing an understanding of the overall article content and sources that appeal to the target demographic.

The right-hand charts filter the top articles and domains to focus on blog content and sources. In this case, you can see the ability to draw distinction between content that might be more mainstream in nature (the overall top articles) and content that could be considered more personally influential. This kind of analysis enables you to gain insight into what is influential to your audience for different types of content. You can then use that understanding to craft more effective content.

graph3

Do you want to re-energize your content and produce more successful campaigns? Give our free trial a go today and see what LinkedIn Engagement Insights surfaces for you!

Lucy Wimmer

Written by Lucy Wimmer

Lucy Wimmer is global senior director of marketing and communications at DataSift.

Share This