content scoring

To Measure the True Level of Engagement of Your Content, Consider Using ‘Content Scoring’

In 2018, counting the number of views or assessing the bounce rate is no longer enough. Maximising your content performance entails a much more qualitative approach focused on the effective consumption of assets and audience behaviour.

What performance outcome should be anticipated from content production? For many marketing executives, answering this question comes down to looking into a crystal ball. This involves a set of data referred to as ‘web analytics’, which, in fact, can prove difficult to leverage. The reason being that, most of the time, this data is poorly cleaned and weakly correlated, providing common indicators which, in themselves, are barely relevant (number of pages viewed, number of sessions, bounce rate). It is such a paradoxical situation that partnering content strategy with reliable measurements seems an urgent necessity.

Several developments are indeed competing to make measuring content performance indispensable. First of all, the drop in engagement continues on social networks, particularly Facebook. This is driving brands to review their production rate and the formats they use in the hope of minimising this reduction. Next, the requirement for websites to comply with the GDPR, particularly the implementation of pop-in activation of cookies, has resulted in a partial loss of data – which makes the data actually collected even more valuable. Finally, there is so much competition for the audience’s attention spans  these days that it is important to maximise the chances of success with carefully optimised content. A constant ‘Test & Learn’ that is impossible to implement without reliable measurements.

The result is that the challenge is no longer a matter of quantifying the audience (in terms of pages viewed or number of sessions), but in assessing the audience’s engagement with the marketing content produced and relative content performance. A measurement which is more qualitative than quantitative and is essential for monitoring content marketing is the identification of the stages in the conversion process for which content should be produced, and the most engaging formats. This is why Wedia has been defending the notion of content scoring for some time now. How can audience engagement be measured in terms of both text and (audio)visual content? Two KPI (Key Performance Indicator) categories may be prioritised: one which measures loyalty and one which assesses actual content consumption.

Measuring the loyalty of your audience involves assessing the regularity of visits as well as their behaviour during these visits through different channels (websites, blogs, social networks). For example, the number of pieces of content consumed per session is a set of data which is more reliable and less ambiguous than time spent or bounce rate. The evaluation of actual consumption of assets can be supported by an analysis of completion rates. For text web content, that means measuring the percentage of the page that the user scrolled through. For an image, downloading a high definition version or clicking to zoom are examples of useful actions to track, whereas for video content, the focus is on the actual viewing duration (and not the number of times a sequence is launched).

Wedia solutions were designed to produce these types of metrics from the outset. The Enterprise Video Platform (EVP) module therefore keeps a log of video completion rates, which enables the rate to be analysed on an overall scale of assets, by content or by a specific category (for example, videos related to a campaign).

All this data feeds into a content score which is vital for managing production, identifying the most engaging format or subjects for audiences, and also the best combinations. For example, ascertaining which channel (social network, newsletter, website) provides the best engagement for a certain type of content.

This scoring may go much further, so that a return on content investment may be estimated. In the context of B-to-C e-commerce, it seems appropriate to list the content that influences the value of a shopping cart or the act itself of putting the product into the cart. In a B-to-B context, having a clear view of which content triggers the completion of a lead-generating form also proves equally valuable. In summary, the objective here is to obtain a measure of awareness – not to solely assess the ‘absolute’ performance of content, but its contribution to income, lead generation, or any other quantifiable objective.

Obtaining content scoring that is both qualitative and attribute-focused calls for several prerequisites. It involves being able to:

  1. Collect data, ideally via a tag or code which allows all content actions to be recorded in real time (viewing, scrolling, clicking, sharing, etc.).
  2. Enrich data, in other words expand it with third party sources (for example, e-commerce data, or DAM metadata) in order to increase relevance and readability/comprehension.
  3. Store data in a secure manner, ensuring that it will not be exploited by third parties for commercial or advertising purposes, and with sufficient historical data to feed comparisons or for AI machine learning or deep learning algorithms to be applied to it (see below).
  4. Calculate metrics and incorporate them according to customised dimensions by activity. This customisation also relies on the ability to categorise or tag content according to a range of criteria.
  5. Recover the measurements in a digestible visual format (data visualisation) in order to facilitate their analysis.
  6. Export the data as necessary in order to feed other data lakes or performance indicators. The implementation of an API to interrogate the data provides immense flexibility in this regard.
  7. Use artificial intelligence to explore correlations, detect anomalies and to see the occurrence of predictive views.

In practice, these services form a data process chain formulated around a data lake which is dedicated to measuring performance. In any event, this is how Wedia has constructed its solution: as a ‘data factory ’ capable of supporting the cycle of continuous improvement of your marketing content.


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