Ever since the birth of print publishing, newspapers and magazines have been a primary source of information about the world around us. Keeping us in the loop about the changes that affect us, and the subjects we enjoyed learning about. Newspapers and magazines always relied on subscriptions and advertisements for revenue.
Newspaper ads of yore, no blocking here.
The two major disruptions in print publications and advertising came with television and the internet. Today, newspapers and magazines are increasingly moving to an online-only model to cut costs as circulation and ad revenue fall.
Online Publishing Today: The Pain Points
Digital publishing companies now have large audiences that are continuously consuming news and other forms of content online. This audience views ads on the internet and social media and does a large chunk of their shopping on the internet as well. The key difference is, unlike their predecessors who viewed ads in newspapers and on television, readers of online content don’t want to look at ads.
This seems natural. Who likes ads anyway? Yet in some ways this is also unfair. We all know that digital publishing companies make their revenue from ads, yet we refuse to see them. Audiences today want absolutely ‘free’ content without spending the time to view an ad (let alone actually subscribing to a paid plan to support online publishers.) Publishers now have little choice but to ask their audiences to disable ad blocker to view content online, a move that is more of a desperate plea to audiences.
Will recommendation engines change this?
Yes, they will. For customers to start paying for content, publishers need to generate value and inspire audiences to pay for the content they consume. Publishing companies not only need to show audiences the content they find engaging, they also need to encourage audiences to pay for subscriptions – or they will cease to exist. The recommendation engine approach to solving this dilemma is two pronged.
- A recommendation engine can engage audiences with the right content
An artificial Intelligence recommendation engine has the ability to understand a customer’s preferences using factors like how long they spend reading a particular piece, what topics they read most often, what their political stance is, what device they use and a whole lot more. Using artificial intelligence, a recommendation engine can connect the many patterns on user interactions, measure and analyze them and make predictions.
- A recommendations engine can customize ads or sponsored content for a user based on their preferences
Most people who use the interwebs hate looking at ads for a few key reasons. The ads are either not relevant, intrusive or just plain bad. While not much can be done about the latter, advertisers and online publishers can ask customers what kind of ads they would like to receive – if at all – and recommendation engines can work to display these ads at the right time to audiences.
What else can recommmendation engines do?
Recommendation engines aren’t limited to textual content. With the right amount of data and computation, they can recommend videos, music, images, and a whole lot more – personalized for every member of your audiences at the right time, on the right channel as attention spans for text continue to drop.
Can I use a recommendation engine for my publishing website?
Yes you can. A recommendation engine like Boomtrain can help you get started with engaging members of your audience on a personalized, 1:1 level.
This blog is the last in our series on Artificial Intelligence today and how it can change your future. Read our last post on how Artificial intelligence for ecommerce moves beyond customer segmentation here.