The success stories of the FAZ and DAZ show how it can be done: AI-based automation leads to sustainable gains in visibility. For publishers, top search engine rankings are crucial for online reach and business success. Their digital strategies are increasingly moving away from financing through advertisements and are concentrating on the monetisation of their own content: paid content and subscription models should turn readers into paying customers. Content automation plays a decisive role in achieving this goal.
The digital product of media professionals is no longer based on journalistic skills alone; editorial departments should instead incorporate data-based work into their practice. This is where Retresco’s automation tools come into play by combining natural language processing and machine learning algorithms: the Topic Management System (TMS) allows online editors and SEO experts to group relevant articles into an independent topic page with just a few clicks by semantically analysing the entire content inventory. Adding new, relevant articles to existing ones or creating additional topic pages can be done both manually and automatically. An integrated trend analysis guarantees that these are created for specific target groups according to current demand. In addition, demand-oriented presentation strengthens the digital reach of the online offering and meets the needs of the target group. The visibility of the relevant content increases – not only with the user, but also with Google and similar companies.
The TMS has the potential to significantly advance the monetisation goals of publishers. The following functions and ideas demonstrate how to get even more out of the intelligent content delivery tool:
Customers instead of readers – that is the goal. However, much to the chagrin of publishers, most consumers currently limit themselves to taking out one or just a few digital subscriptions – entertainment takes precedence over news. Fortunately, podcasts not only constitute a meaningful connection between the two worlds, but are also experiencing a consistently increasing demand. So far, however, the coveted audio content has escaped semantic automation possibilities. This need not be the case: just as for articles, semantic enrichment also works for transcripts of podcasts or videos. The TMS can also semantically classify podcasts and other audio media and use them in a more targeted manner.
Identifying the current demand, adapting a company’s offer and communicating it to the readership in a targeted manner – these are the classic challenges of the media world. The title line thus becomes a decisive criterion for success: it must not only be attractively formulated, but also match the searcher’s input. Depending on the department and interest, however, the expectations – and thus the input – of the reader can vary widely. In order to always strike a chord with the reader, templates for meta-titles and descriptions of the identified target groups can be stored in the TMS and applied automatically.
Personalised reading recommendations optimise the user experience and extend the length of time spent reading. Based on the semantic analysis, the TMS can not only bundle articles on relevant topic pages, but also provide the reader with precisely tailored recommendations for related articles. In addition to topic proximity, the TMS also takes into account other parameters to enhance the positive impact, depending on the strategic orientation:
Instead of only recommending content reactively, TMS allows users to be notified by automated push messages or newsletters. The TMS compares the latest articles with the interests of the respective user and informs the reader about new relevant content without manual effort.
Media companies that can provide their readers with the best all-round experience convert them into long-term customers. Content automation makes an important contribution to this process.