Let's read the text
Required reading: 8 minutes
An intelligent content strategy specifically includes data and AI-based systems. The automation of certain tasks is desirable in order to relieve the editorial team of repetitive work. Employees can use the time gained in this way to pursue creative approaches or invest more time in comprehensive research. In the ideal case this leads to a higher content quality.
The initial implementation requires clean data, time and a certain budget. If these basics are not available, an intelligent content strategy can only be implemented to a limited extent.
What may sound theoretical and complex is relatively simple with the help of the right tools. We explain six basic building blocks and offer the possibility to check how realistic a smart process is in your own company.
table of contents
- Data first: Clean data is the key to success
- New technologies require new functions & roles
- The intelligent content team: Looking for thought leaders
- Intelligent team leadership: championship success through further training
- Automation of repetitive tasks: How does it work?
- Intelligent content strategies: The existing remains relevant
1. data first: Clean data is the key to success
A content audit is the first step in creating a content strategy. Analyzing the performance of existing content, recognizing patterns and drawing conclusions for future content approaches is a fundamental component of content management. Every published article, video or podcast represents and provides information. The number of calls, the length of stay, the visitor source or the text length and tonality can provide valuable insights into user needs or the brand profile presented. Not only our own publications provide insights. Competitor data is also of great interest for a content strategy.
Anyone who has ever carried out a manual content audit for an average website knows how complex this procedure is. Even more time is needed for agencies that support several customers and have to present analyses at short intervals. If the five most important competitors are also included, large amounts of data can easily be collected that are difficult for humans to handle. The risk increases that important insights are not recognized or overlooked. The visualization and interpretation of the information gained is also a challenge in itself.
Content Audits are therefore a classic use case for machine learningA smart algorithm continuously (or even in real time) analyzes the performance of your own content and suggests optimization measures. Tools that take this step include MarketMuse, Concured and Acrolinx. When selecting such a tool, it should be clarified in advance whether individual reports can be created that fit your application. The dependencies - for example on a content management system - can also influence the purchase decision. And of course the budget plays a role, because some providers do not even respond to requests from smaller companies.
2. new technologies require new functions & roles
Technical support is always based on clean tracking and maintained data and therefore requires human support. One of the fundamental pillars of intelligent content strategies is a content analyst or data scientist who deals exclusively with data and its reliability. This ensures that the database for potential automation is available and clean. Or you can design and program the required analyses yourself.
Data maintenance is not a part-time job.
Besides the quantitative insights, user feedback can provide essential hints for content optimization. Regular reader interviews are therefore part of the basis of intelligent strategies. The generated learnings and experiences are priceless and can influence the choice of topics, the relationship between text and image content or design in general. A user experience expert is therefore a useful addition to the content team. If you don't want to hire such specialists directly and prefer a test phase, you can fall back on freelancers or specialized agencies.
Further article: How to identify a use case for artificial intelligence.
3. the intelligent content team: looking for pioneers
This should make it clear that automation alone does not constitute a smart strategy. The employees are an essential success factor. As part of an intelligent strategy, this is a group of specialised professionals who are dedicated to one area each. What these are exactly for experts depends on the content focus and business model. Besides the already mentioned analysts and UX professionals, there are classic fields that belong to every editorial office: Graphics, content creation, localization, outreach, (tech) project management and strategy.
The employees are an essential success factor of an intelligent content strategy.
|content creation||Text or |
|graphic||automated image |
or logo creation,
In addition to their technical expertise, all team members should have a basic understanding of artificial intelligence and possible automation in their field. Even if the current state of the art does not yet permit full automation of operational activities, this is highly likely to be part of everyday working life in the long term. This could lead to department heads being more concerned with the implementation and monitoring of the technology than with writing itself, for example. Whether an algorithm produces good or bad results can only be judged by a person with appropriate professional experience.
Further article: What content experts should know about artificial intelligence.
4. intelligent team leadership: championship is achieved through further training
Anyone who leads a team of specialised thought leaders should make further training a part of their daily work. In many companies, training courses or attending conferences are a special feature. An investment in the knowledge of employees is (unfortunately) seen as not very relevant. This attitude can become a challenge because intelligent technologies develop exponentially and are therefore in a constant process of change. If you want to ensure that your own team has a (knowledge) advantage over the competition, you should keep yourself and your employees constantly up to date and promote an active debate on innovations. This also includes the time luxury of being able to deal with new studies or external research results.
In addition to a continuous learning process, ideally there is room for ideas and innovations. Those who regularly receive new input naturally develop unusual perspectives and working approaches. This creativity needs a platform to be transformed into concrete research projects or user tests. Content hackathons or regular design sprints in cooperation with other departments can be suitable formats, for example.
5. automation of repetitive tasks: How does it work?
Automation is the right partner to create the necessary time for creative work or learning processes. It can be checked in just a few steps whether and where machines can take over part of the processes.
- Which tasks are repeated?
- Are these tasks based on data?
- If so, is the database accessible and clean?
- Which tool can support us in automation?
- How much budget can we spare for this?
- How much time can be saved and how does this relate to the investment?
An example: An affiliate company publishes vouchers on its own website and employs editors to post the vouchers and write a short description. These short descriptions always record the same information: Value of the voucher, provider, conditions, limitations. The task is repetitive and based on data from affiliate networks. It is possible to pull this data directly from the network (API). A short search in Google, keyword "automated text creation", shows a selection of suitable tools. Algorithms of these providers can learn the content structure and from now on write the texts. Those who are concerned about the uniqueness of the content (SEOs of this world) can be reassured. A variance of the used words and sentence combinations can be included from the beginning and taken into account by the algorithm. (I speak from personal experience.)
Other types of content that are highly likely to be created automatically: Sports, weather or financial news, product and category texts, but also certain contracts or legal documents.
Intelligent systems require continuous monitoring at the beginning. They often learn based on (human) corrections. Over time, they become smarter and smarter and can take over the task completely at some point. However, a transitional phase should be planned.
In some cases, companies can develop intelligent systems independently of external solutions. Since this is rarely the case and also requires considerably more time for implementation, I will not go into detail here.
6. intelligent content strategies: The existing remains relevant
Now intelligent content teams have gained time, continue to educate themselves and place innovation and iteration at the centre of their considerations. In the next step, all the knowledge must be taken into account in a strategy and then applied. For this it is not absolutely necessary to throw existing guidelines, procedures or classic tasks completely overboard. Often it is enough to adapt or expand these. Established formats, topics and channels still exist and remain relevant. Only new aspects are added. This can be, for example, a first chatbot or the content focus on investigative search queries. Both measures support, for example, the growing importance of language search (based on machine learning).
Further article: How artificial intelligence changes our search behavior.
The rapid change of technologies leads long-term planning ad absurdum. Regular adjustments based on observation of the market, competitors and consumer behaviour should therefore be part of a smart approach.
There are many other aspects that influence an intelligent content strategy and have not been addressed here. This includes the personalization of content, individual feeds or user-generated formats. But they all benefit from a solid database, human expertise and iterative work processes. If you take the basics into account and build the right team with the right mindset, you can exploit the full potential of human-machine cooperation in the long term.
Cover picture: Photo by Maskot, EyeEm