If you're in paid search marketing, machine learning is one of the hottest topics around. Many people wonder how machine learning works, but there's a lot of misinformation out there that can lead to confusion. So how does machine learning work in paid search marketing?
Machine Learning Requires Supervised and Unsupervised Learning
To understand machine learning techniques, it helps to know some machine learning basics. There are two primary types of machine learning: supervised and unsupervised. Both have their place when thinking about machine-driven PPC optimization strategies.
Supervised Machine Learning
When most people think about machine learning, they're probably thinking about supervised machine learning. With this technique, an advertiser trains algorithms with specific data points by labeling each end with the desired output. For example, machine learning for paid search means choosing a keyword set and manually tagging each ad with the conversion event you want it to drive.
In supervised machine learning, machine learning techniques are used to make predictions based on labeled training data. Within machine learning for paid search, these labels typically come from the historical performance of keywords or ads. For machine learning to work in your account, you have to have a solid history of information available. If you only have a few months of data, machine-driven optimizations probably won't be able to produce meaningful results for you yet. You'll also need at least several hundred conversions per month for positive results that beat an optimized control group.
Unsupervised Machine Learning
You can use unsupervised machine learning in addition to supervised machine learning. Unsupervised machine learning can reveal hidden patterns within your data that you couldn't see before. It's generally used for exploratory analysis, such as identifying clusters of keywords or ads that could be optimized together. This is where machine-driven optimizations will shine the most in paid search marketing -- when you have enough clicks and conversions across a large set of keywords.
How Does Machine Learning Work with Paid Search Marketing?
The key to successful machine learning has high-quality training data with which to train models. Machine learning won't produce better results than what an experienced marketer could do without machine assistance. To make machine learning work, you'll need structured (keyword, ad group) and unstructured (keyword text, landing page URLs) training data.
The machine learning process in paid search marketing isn't any different than machine learning for other fields. The difference is that machine learning works best when you have high-quality training data free of human bias.
If your keywords and ads are structured well enough to be machine-readable, machine-driven optimizations can produce better results than if you leave optimization up to humans alone. Of course, it takes time to build machine-readable keyword structures, but it's worth it in the long run because machine learning will be more accessible with good raw material to work with. In addition, you can use unsupervised machine learning techniques during this initial stage of building a machine-readable account structure and once machine learning has been enabled.
Why Can't You Use Automated Optimization?
Many people wonder why machine learning is necessary if paid search marketers can already use automated optimization. The key difference between automated and machine-driven optimizations is how machine learning uses training data to make predictions about the future. With machine learning, you're not limited by rules that need to be changed manually; machine learning algorithms can also help you create new rules that you didn't think of before.
Machine Learning vs. Automated Optimization: Which Is Better?
The answer for which approach performs better depends on your type of business and what problems you're trying to solve with machine-driven optimizations. If you have a large enough account history with machine-readable data, machine learning may outperform automated optimization. Overall, machine learning should provide better long-term results than automatic optimizations alone because machine learning can adapt to changing conditions more quickly and efficiently than automated rules.
However, machine learning won't always produce better results. If your account history isn't large enough or you have structured keywords and ads, machine learning may not offer substantial benefits. In these cases, automated optimizations might be a good place to start instead of machine learning (especially if you're new to paid search marketing). With limited training data, machine-driven optimization can be like trying to drive based on limited views of the road ahead.
Machine Learning for Advertisers:
Implementing A Strategy
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The machine learning process for paid search marketing isn't any different than machine learning in other areas. Once machine-driven optimizations are enabled, machine learning algorithms will make predictions based on the keywords and ads that are already present.
This information can then be fed into automated actions, such as creating new keyword groups or adjusting bids. Machine-driven optimizations are great at optimizing ROI because they can handle unexpected changes more efficiently than automated rules alone. Machine learning is especially helpful for advertisers who want to quickly optimize effective keywords with little manual work. While machine learning doesn't have to replace your existing automation, it is helpful if you'd rather let machine-learning algorithms find patterns within your data instead of manually tagging every campaign and ad group.
Machine Learning for Publishers: How It Works?
With machine learning, machine-readable training data makes machine learning easier. Without machine-readable data, you'll have to rely on human expertise to generate predictions. Machine-readable data is much more precise than relying on human intuition alone because it leaves out people's subjective opinions in paid search marketing. In addition to being machine-readable, training data needs to be large enough for machine learning algorithms so they can produce accurate predictions from your keywords and ads.
If you use machine-driven optimizations, the process will get even more accessible. Instead of relying on specific rules, machine learning can use machine-readable training data to predict new keywords and ads. Machine learning is helpful because it can adapt quickly to changing conditions without requiring human intervention. In many cases, machine learning will produce better results than automated rules by themselves.
