Powering Your Content Strategy from Consumption Analytics

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Over the last few years, there has been a great deal of conversation surrounding the drastic changes in publishing. With the Shift to digital, publishers are increasingly offering new innovative digital solutions in the forms of eBooks, eJournals, or apps, among others.

As the product portfolio expands to the digital world, publishers as content providers need to design and maintain a content centre that can feed into the growing diversity of business opportunities. Such paradigm shifts have empowered publishers to create new content strategies built around data-driven metrics rather than intuition or guesswork.

Publishers can leverage such algorithms to give life to a content strategy that aligns with the likes of the end-user. This helps to optimize the value of the content from a data-driven perspective. Consumption analytics designed for the needs of the publishing industry delivers valuable and actionable insights that empower them to make better editorial decisions and increase their readership.

Importance of Consumption Analytics for Publishers

Consumption insight is the analysis and interpretation of consumer behavior and trends. This feature helps identify who consumes particular resources and the purpose and motives behind their choices, thereby helping improve the effectiveness of these resources.

In the form of recommendations, actions, and measurements, these insights can be leveraged by publishers and authors to gain a qualitative and holistic understanding of their audiences. It further empowers them to design results-oriented content strategies to achieve their goals more effectively.

Benefits of Consumption Analytics for Publishers

To better understand why publishers need to apply Content Intelligence or Artificial Intelligence to their content to formulate editorial strategies and refine their practices. Here is a list of its advantages:

Facilitate classification of all content: Publishers and authors deal with vast content and managing its classification with manual intervention is a tedious and time-consuming task. This also bears an additional risk of producing inconsistent results. Putting an effective and consistent automatic content classification system operated by AI ensures quick and easy retrieval of the right content through machine learning. Such a system can help the publishers create an ordered content archive where it is easy to find what you are looking for and optimize its value.

Collect actionable data and KPIs: Consumption analytics applied to the content provides more actionable metrics than traditional web analytics tools. Relevant tags can be associated to provide publishing-critical insights by tracking segmented audiences and identifying areas of potential performance increase. Further, valuable data on the interests of the consumers can be used to determine the best performing content, which in turn can be used to perfect the content strategy to optimize its results.

Track and monitor reader engagement: Consumer analytics measures consumer engagement through various measures to provide publishers with meaningful insights into factors like- what content is interesting to their audience or which content is being read or watched and shared across the reader’s networks. Such metrics can help the publishers put their finger on what content drives the most engagement to measure content success.

Allow decision-making in real-time: Analytics driven by tracking consumer usage can allow publishers to understand the dynamics of their business, anticipate market shifts, and mitigate risks. When a publisher can realise the accurate measure of how engaged their audience is, they can use that information for their future content efforts in terms of topics, channels, and authors. Such analysis can enable the publishers to make more effective strategic decisions about creating, updating, and promoting.

Optimize content distribution: Content distribution is an integral part of any content strategy. An effective content distribution strategy requires understanding what content is driving the most engagement and which segments of the audience that content is engaging. Tracking engagement segregated by audience segments allows the publishers to disseminate their content across various channels to attract more readers, boost their brand awareness, and encourage them to click, act, and become customers.

Conclusion

When an effective analytics system powers publishers and enterprises, it can help them make decisions that drive profitability, grow readers, set new trends, and futureproof their business operations.

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