How Bots Change Content Marketing

When considering the future of content marketing, one aspect is of par-

ticular significance that nobody who wishes to be successful in the long run

should neglect: AI and bots will become game changers in a few years. Many

of the former content strategies will be turned upside down by the new pos-

sibilities and thus become a greater challenge to companies. Some experts

thus speak of the death of the (former) content marketing by the AI algo-

rithms. This is certainly an exaggeration, even if provided with a spark of

truth.

Content marketing itself is regarded as one of the most cost-effective mar-

keting strategies that is asserting itself increasingly more worldwide. Even if

it is not always easy to be visible on the Internet with one’s own content,

one thing remains certain: Customers have a great need for information and

want to be entertained. Despite the content shock, the best and most unique

contents will always assert themselves somehow. If the demands on content

change, then brands and media have to react by responding to this, present-

ing their contents more visually and changing the channel they are played

out on if necessary. As long as content marketing quickly reacts to the stake-

holders’ interests, it is successful for the main part.

Due to its usership on several platforms, Facebook has sufficient data to

be able to analyse the way communication takes place on digital channels.

Anybody who best understands how their customers communicate could

use this profound knowledge for the set-up of their bots, and give them

much more AI. Whilst bot providers have to understand what the messenger

users want, people learn at the same time how best to speak with bots. The

expectations of bots, however, have quickly decreased since the beginning of

the hype around the virtual assistants. Most bots are too rudimentary. They

frequently only appear as small FAQ assistants that can only respond to a

few questions. Yet, this could change very quickly with the slowly growing

number of AI-based bots.

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...

Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1): 1. First you would look at what spam typically looks like. You might notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to come up a lot in the subject. Perhaps you would also notice a few other patterns in the sender’s name, the email’s body, and so on. 2. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. 3. You would test your program, and repeat steps 1 and 2 until it is good enough. Since the problem is not trivial, your program will likely become a long list of com‐ plex rules—pretty hard to maintain. In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually fre‐ quent patterns of words in the spam examples compared to the ham e...

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