More Relevance in Content Marketing Through AI

It would be conceivable, among others, for AI to adapt texts that have

already been created to the linguistic habits of different target groups, so that

a medical text, for example, could be understood by both doctors and ordinary people by having medical terms explained.

It is merely a question of time until algorithms are able to write texts for

any target group whatsoever. In the future, AI will presumably even be able

to produce excellent content at an enormous speed. This way, texts can be

individualised and personalised more easily so that all essential information

is included via a reader and which affects the written and adapted text.

AI becomes very familiar with the readership in this process and can uti-

lise all information about the recipient in such a way that every single piece

of content is unique. Just imagine the content that would be produced if AI

could read out your entire (public) Facebook profile and were able to use

this information for matching content.

In principle, it would suffice if retargeting were not used for advertising

but used for the targeted addressing of content. In content marketing, algorithms are more and more frequently taking over this task, which is neces-

sary for the targeted play out of the content as well. In addition, contents are

played out in an appropriate context (content recommendations). Instead of

one article for all, personalised content will be possible on the basis of AI,

and which are closely based on the reader’s respective range of interests. The

result of this is unique contents in the logic of mass customisation because

the AI knows their readers and responds in a personalised way. Everyone

receives their own personal content.

Comments

What is Machine Learning

The term machine learning (ML) as a part of artificial intelligence is ubiq- uitous nowadays. The term is used for a wide number of various appli- cations and methods that deal with the “generation of knowledge from experience”. The well-known US computer scientist Tom Mitchell defines machine learning as follows: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Mitchell 1997). An illustrative example of this would be a chess computer program that improves its performance (P) in playing chess (the task T) by experience (E), by playing as many games as possible (even against itself ) and analysing them (Mitchell 1997). Machine learning is not a fundamentally new approach for machines to generate “knowledge” from experience. Machine learning technology was used to filter out junk e-mails a long time ago. Whilst spam filters that tack- ...

Possible Limitations of AI-Based Bots

The examples above already show the present-day potential of AI-based bots. At present, these systems are still in an early stage and still have certain limitations and potentials for optimisation. Twitter Bot Tay by Microsoft Most bots at present are reactive service bots. Engagement bots that actively interact with the users as market and brand ambassadors go one step further. The most famous example here is the chatbot Tay by Microsoft. Microsoft removed Tay from the web apologetically within one day. The example shows that the uncontrolled training of bots by the community can lead to fatal consequences. AI systems still have to learn ethical standards. It thus becomes apparent that even bots require a kind of guideline. Like a journalist has to observe editorial guidelines, bots have to observe certain standards. The next generation of AI-based bots must control and create the possible room for communication. IBM Watson has been able to celebrate quite a few respectable resul...

Random Forest Classifier

The algorithm gradient boosted regression trees, also called random forests, belong to the ensemble learning methods This classifier uses an ensemble of weak regression trees that have a low hit quota when considered in isola- tion. The quality of the prediction can be improved significantly when vari- ous trees are trained with different parameters or samples. The results of the individual trees are aggregated to a total result which then enables a more balanced and high-quality prediction. The so-called bagging triggered a boom of the traditional regression trees. As aggregation, either a majority vote or a probability function is chosen (Fig. 5.5). The lead prediction generates high-conversion leads because • The entire spectrum of information available about a company is inte- grated into the decision-making; • The data is highly topical and without bias; • The random forest is capable of abstracting complex correlations in the data; and • The method learns iteratively from t...

Tech & Fun

Search This Blog