Getting the Most from Facebook Topic Data: Part Four

24th August 2016 0 Comments

In our previous ‘getting more from Facebook topic data’ blog post, we looked specifically at Facebook, the non-public network with the most users and the richest insight available. To really fulfill the promise of PYLON as a privacy-first social analytics platform, a partnership with Facebook was of the highest importance.

In this post we will explain why the partnership was so important, and focus on how Facebook topic data has far exceeded what social analytics had been able to achieve before.

Limitations of social analytics before Facebook topic data

Facebook topic data enables brands to go way beyond their current understanding of consumer preferences. They can address an entirely new set of business challenges that have been unresolved until now, by uncovering rich and deep audience insights from the data.

Even before the availability of Facebook topic data, social data analytics had been widely adopted – almost two-thirds of US companies use it for campaign tracking, 48% for brand analysis and 40% for competitive intelligence. But the analysis had been limited to the available public social networks, with no-one able to access the richer insight to be gleaned from non-public networks, in particular, Facebook.

Traditionally, social data analytics has been mostly focused on listening and monitoring what is said about a company, a product or a brand on public networks. This is satisfactory for simple use cases, such as measuring a brand’s share of voice using simple text analysis of keywords. Although, it could be argued that such results are not much more than vanity metrics, with very little actionable insight.

New Facebook topic data use cases

Not only does Facebook topic data enable analysts to drastically improve results from such activity, it also makes it possible to go way beyond traditional use cases and expand into areas such as content creation, media planning and audience segmentation. This in turn allows social analysts to move beyond the limited budgets previously allocated to them and stake a much larger claim to the previously inaccessible big budgets owned by the advertisers and creative teams.

Here are some of the new use cases that Facebook topic data can address:

Content and media analysis – identifying links, content and topics that resonate with an audience will help analysts to understand the most popular pieces of content being shared, how the content is shared and the source varies by audience demographic, and which personalities and topics drive engagement.

Brand analytics – monitoring and analysis of the audience mentioning a brand, products and competitors to assess the performance of that brand and that of its competitors. The audience can also be broken down by highly specific demographic groups and time to see how it has evolved.

Industry and topic research – categorization of industries, topics and influencers for vertical marketing analysis using topics, categories and topics attributes. Facebook topic data also allows the identification of audience demographics, which topics are trending, and who the influencers are in a specific industry.

Market research for product development – leverage DataSift’s classification engine (VEDO) to classify products and features and analyze their performance in the market for product development. Marketers can identify which products and features are driving engagement, evaluate the sentiment towards them, and determine how well they’re performing within each demographic group.

The main challenge for analysts, now Facebook topic data enables them to tackle both traditional social media use cases, as well as opening up entirely new ones, is how should they prioritize? By ranking Facebook topic data use cases on three criteria, DataSift makes the following recommendations:

  1. The ability to differentiate from existing use cases supported by public networks
  2. The ability to access bigger budgets in new functions
  3. The current popularity among our partner network

Our next post in the series will look at how PYLON for Facebook Topic Data works in practice and will reveal a number of techniques that will help extract maximum insight from Facebook topic data.

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