How Machine Learning Advertising Would Improve Your Ads
- What is machine learning?
- What is machine learning in advertising?
- Benefits of machine learning advertising
- To conclude:
Even just a few months ago the advertising market was somewhat like standing on a roof with a megaphone. You could reach a lot of people, but not always the right people.
Now programmatic advertising allows the advertiser to speak directly to their future customers with the help of machine learning. ML is a new trend, however, most people are too afraid to use it because it’s complicated and somewhat of a mystery. And mystery always comes with suspense and anxiety. So let’s pull back the shroud of secrecy and learn how machine learning advertising actually works.
What is machine learning?
If you are familiar with the programmatic ecosystem, you’ve probably encountered the terms like “machine learning” already. The development of this technology is expected to change the world of digital advertising dramatically. But how will it look like and what is the role of machine learning in online advertising?
Machine learning (ML) is a set of algorithms used to draw conclusions and predict results based on the input data. Some of these algorithms are even capable of improving their own performance once they get more data to learn from. In broader terms, machine learning is a subfield of artificial intelligence (AI) applied in many industries from particle physics to digital advertising.
What is machine learning in advertising?
In the industry of digital advertising, machine learning is usually used to let brands understand customers better and optimize ad campaigns accordingly. Same as for other industries, the main task of online advertising machine learning is to analyze data points and detect correlations that are not so obvious to the human brain. But even for advertising the range of opportunities is so broad that machine learning can be applied to a huge variety of problems from improving ad performance to the development of computer vision.
But there is already enough theory around the web, so it’s time to explore practical questions - what does machine learning have to do with your ad spend?
Benefits of machine learning advertising
The number of benefits provided by machine learning is steadily rising as technology develops. However, even now there are already numerous practices applied throughout different stages of creation and optimizing an advertising campaign that involve the usage of machine learning at least to some extent. Let’s explore some of these:
1. Machine learning can boost ad performance
Keeping track of big data and analytics can be quite tiresome when done manually. Machine learning has great potential to make this process much faster and more precise. There is always a huge number of factors within even one campaign that can be adjusted to improve ad performance. The machine learning algorithm can give you insights regarding what these exact factors are and how you should change them in order to build a winning campaign based on data you input, as well as your goals, budget and other variables of your choice. Furthermore, over time the system gets smarter, especially when the amount of data you gather increases and boosts the accuracy of an algorithm.
2. Machine learning helps to improve ad creatives
Different ad creatives can show different results and this has nothing to do with the overall performance of the campaign. Details such as font, formats, colors, sizing, wording - all can have a severe impact on the success of an ad creative. Machine learning algorithms can analyze creatives from your previous campaigns in order to determine what will work best in the future. With machine learning, it is now possible to predict how your target audience will react to different types of messages depending on their habits, personality types, as well as many other at times unobvious factors.
3. Machine learning & targeted advertising help to make ads personalized
Many companies already struggle with enormous volumes of user data they gather. They hire data science professionals to help them draw correlations and somehow organize all these data sets, but it still takes too much time to deal with it manually. For targeted advertising, machine learning helps to face these challenges and build campaigns where not only creatives will be designed with granular precision, but every single detail about the encounter with the user can be analyzed and improved.
Take, for example, contextual targeting. This type of targeting gets especially important after the GDPR implementation in Europe and the subsequent discussions around user privacy (which then echoed in CCPA privacy regulation). To use it efficiently, a webpage’s content has to be analyzed and understood, so that one could run ads that align with the context and match the user’s interest. With machine learning and its more complicated subset - deep learning, it is already possible to analyze nuances like authors opinions and emotional attitude to the subject. Based on this information, it is then possible to predict what kind of message would work best in this environment and for certain types of visitors.
4. Machine learning can predict your campaigns
Imagine that you could know the outcome of your campaign before it is actually finished. Well, with machine learning this is no longer a fantasy - once you start a campaign, an algorithm starts analyzing it in real-time, comparing it to your previous campaigns and adding customer behavior on the top of it. After you have a whole story it is easier to predict what a big picture might look like. This, of course, doesn’t mean that a machine could make all the decisions humans do, but at least it could save some of your time and budget.
Another important feature of algorithm-based predictions is that kind of “outside-of-the-box” thinking many humans wouldn’t dare to apply. Not limited by cultural or social assumptions that bias our strategies, machine learning can uncover lots of new potential channels we might have missed. And some of those revelations can play a defining role in your campaigns’ budget optimization.
A wise advertiser would always keep track of the latest technological developments and the best ways of their applications. The usage of machine learning in online advertising already showed impressive improvements in targeting, ad performance and campaign optimization. One can easily observe this technology in action using a demand-side platform that already employs such algorithms for campaign optimization.
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Irina Kovalenko, CMO of SmartyAds