Is intelligent content on the web the answer to mass content blocking?

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As the internet grew in popularity, the marketing industry was quick to see that it had become an important channel for reaching out to potential customers. Websites increasingly began to host ads that were often unconnected with the site content. Advertising content became more widespread and unfortunately, also often obtrusive. The audience began to wonder how to counter ads, and mechanisms called ad-blockers were developed.

This started a kind of arms race between Internet users, who did not want to see ads, and advertising networks and publishing portals, who tried to counter the growing interest in blocking ads. It is estimated that in the US alone, content blocking solutions have been installed on 17% of devices. Perhaps more importantly, data from 2015 show that there was a very large year-on-year increase in the use of ad-blockers, around 48% in the USA and 82% in the UK. What’s more, this issue is no longer restricted to browsers on personal computers: increased use of mobile browsers has resulted in dedicated solutions for these too.

How can marketers now reach their customers? In other words, how can we break through this security ‘jungle’ to reach our intended audience? Only one thing is clear: there are no obvious answers. Each publisher is working on the problem in their own way. Some display information about the importance of ads to their finances, to encourage users to turn off ad-blockers. Others block part or the whole of the site until any ad-blocker is disabled. Each of these methods, however, has one major drawback: it assumes the end user's acceptance of advertising content. Some visitors may temporarily disable their ad-blocking mechanisms, but the effectiveness of the advertising content delivered is still very low. Each of us subconsciously defends ourselves against imposed content.

Alternative ways to meet the customer online

This means we need to look for another way to deliver advertising content. We cannot interest customers in our offerings by forcing them to view content they do not want. Instead, we need to link our offer with the content that our customers are already looking for and reading. This means taking into account preferences for the broad content area, and also the deals and offers that particular customers may want. Until recently, this level of personalized retail and automatic content generation was not possible. Nowadays, however, increases in computing power have made it possible to use deep or machine learning systems to automatically generate content on a massive scale.

These algorithms themselves are not new. The first was added to SAS’ solutions in 1979, but current computing power allows these options to be used on a mass scale. More and more publishers are beginning to use mechanisms like this to generate articles automatically. Now that the popularity of news turns on its speed, this was only a matter of time. Some press releases are generated automatically by the Associated Press. Both the Fox Network and Yahoo use this type of solution to generate summaries of some sports events. Some options use both the transmission data on which the article is based, and the personal preferences of the future reader. Knowing the preferences of the recipient, it is possible to extend the article with offers that may be of interest. These extensions are much more subtle than normal advertising and therefore more difficult to block.

Personalized approach with machine learning algorithms

Imagine a press release about the launch of a new car. In the classic version, we might read about the wide range of engines available to suit everyone, that the interior and trunk are spacious and that there is an impressive range of accessories available. But what if the article is read by a man who has just become a father? Of course, he could select out the advantages that he finds interesting. But with new algorithms, he could also see a completely different description of the same car, something like this:

The new version of our car is designed especially for families. Economical engines will enable you to travel to holidays or to visit family without worrying about cost. The interior has been designed to maximize space. The shape of the rear doors and the head room in the back make it easier to transport young children, and the luggage space will satisfy even the most demanding of families.

This technology could therefore open up completely new areas for the advertising industry. Its effectiveness, however, will depend on how the industry approaches the opportunity. The development of online advertising has often been chaotic, resulting in growing user frustration. Will it be the same this time? Will the emergence of new ways of reaching customers just fuel the ‘war’ by generating increasingly sophisticated content blocking mechanisms? Machine learning algorithms could also be used in these. It is worth keeping this in mind when planning new, innovative campaigns.

Original post in Polish has been published on the Bright Data blog.

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About Author

Dariusz Jańczuk

Dariusz Jańczuk is a Senior Business Solution Manager at SAS Poland. The Master of Science obtained in Computer Science allows him to play a role of a bridge between typically separated worlds of business users and IT guys. For more than ten years he's been responsible for promoting and developing Customer Intelligence solutions. An active promoter of the latest trends in marketing communications including rapidly evolving digital space. Supporting customers from many different industries but his main area of focus are communications and banking.

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