Machine Learning for advertising is the newest buzzword. After the internet and targeted ads, it is the newest development that will catapult advertising into a new realm. So what is machine learning and how can advertising benefit from it?
In this new blog, we take a look at artificial intelligence and machine learning in digital advertising and how business, agencies, and advertisers can capitalize on the same. We also take a look at some of the examples of how advertisers are using machine learning in its nascent stages.
What Is Machine Learning in Advertising?
Machine learning in advertising is the name of the process by which advertising tech looks at data, analyzes it, and comes up with conclusions to improve a task. In simpler terms: it’s how advertising technology learns, improves and grows.
The technology can learn anything- mapping customer journeys, yearly buying cycle, the effectiveness of ad copy, audience targeting and segmentation. The applications are endless.
The catch with machine learning is that it gets better the more it gets used. Machine learning has self-learning capabilities, means with every iteration it becomes better at applying its knowledge as it efficiently learns from previous mistakes. It is like AB testing but taking it to another level.
Let us Take a Look at Some Real-life Examples:
1. Audience Testing Through Machine Learning:
Let’s say you are a shoe company that sells sports shoes. Now you may assume that you can target men, who are preferably young, go to the gym, or play a sport and then you target those audiences. But your assumptions and knowledge about the world limits who you choose to target.
Whereas, in machine learning, the system operates on a HUGE dataset and analyses audience patterns. Which means the machine is able to pick up on audiences you may have missed out on, or drop audiences that you may have chosen, but won’t get you any conversions.
So the machine may target young boys and girls, young women, and young and old men- based on how likely they are to convert and make a purchase, how much they are interested in your product, how quickly will they buy it after seeing the ad etc.
2. Improving Ad Copy / Creative
You may write an ad copy for the same shoe brand in the earlier example for a New Year offer. It can say “Run Faster, Towards A Better You,” with a bold creative.
Now, the problem with humans writing copy and designing creatives is that you cannot measure the effectiveness of the creative when it comes to direct translation towards sales. Yes, you may like it, your audience will appreciate it too, but maybe it could have been better? Maybe a little colour tweaking or change of a comma would have made the creative infinitely better.
Here is where machine learning has the advantage. Machine learning can track audience viewing patterns, how long they look at an ad, how often viewers click on an ad, run A/B testing for creative and copy etc. Machine learning used for advertising can track, analyse, and improve every part of an ad creative.
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The more personal a digital ad seems the more likely a person is to click on it. Which is why when an ad or an email has your name in it, you feel more enticed to read it. But what if it is not just your name, but your entire personality that can relate to the ad?
The colour, the language, the design, them knowing your name, capitalising on your interests- the more personal the ad gets the better. Which is exactly what machine learning does. It takes personalization to the next level. So the same product can be advertised to millions of users but using machine learning models for advertising means that the ad has a different version for each individual.
And that is it. These are just some of the examples taken from the vast majority of capabilities that MI and AI have in advertising. We are currently truly at the cusp of an AI revolution and soon the world of digital advertising is going to be much more dynamic, customized, and improved.
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