Online Marketing Implementation: Owned Machine Learning in Use
Posted: Thu Jan 30, 2025 3:52 am
How can Owned Machine Learning make our everyday work easier in marketing?
We can outsource numerous tasks to our data models, which can effectively perform these tasks with some training and high-quality data.
Audience prioritization : Unfortunately, we cannot create a personalized user experience for every single target group. We always lack the time and often the money to do so. But we don't have to. Because if we use machine learning to find out which target groups are most likely to convert, we can focus our limited resources on them.
Affinity Audiences in Google Ads, which is the most valuable for me?
Customer classification : Does a user belong to our more valuable target groups? Is there a high probability of a high customer lifetime value? Or is the cost and effort of advertising to them not worth it? We can automate this classification using machine learning. Google itself provides a detailed description of the approach:
Keyword segmentation : How does our target group search? Are there search terms and keywords that we can assign to the lower funnel ? ( More on customer journey and sales funnel ) Classification algorithms malaysia phone number data can help us here. You can find one way of implementing this with the logistic regression algorithm here:
Audience Clustering : Where does it make sense to set the cut and separate two or more user groups from each other? Let the data decide. The k-Means algorithm is often used for problems of this kind:
Audience Segmentation with Machine Learning
Marketing channel attribution : If we work with multiple channels in parallel, we can use machine learning to determine the value of individual channels in the marketing mix for specific user groups. You can find more information on implementation here:
Forecasting and budget calculation : How many conversions can we expect for budget X? How many clicks will we get with budget Y? Every marketer knows this problem. A classic case for logistic regression and time series algorithms.
Anomaly detection in reports : Reports are one of the biggest time-wasters in online marketing. Let your machine learning model search through your data and effectively identify outliers or possible trends. One of the most detailed explanations of anomaly detection algorithms can be found in Andrew Ng's Machine Learning course on coursera.org:
We can outsource numerous tasks to our data models, which can effectively perform these tasks with some training and high-quality data.
Audience prioritization : Unfortunately, we cannot create a personalized user experience for every single target group. We always lack the time and often the money to do so. But we don't have to. Because if we use machine learning to find out which target groups are most likely to convert, we can focus our limited resources on them.
Affinity Audiences in Google Ads, which is the most valuable for me?
Customer classification : Does a user belong to our more valuable target groups? Is there a high probability of a high customer lifetime value? Or is the cost and effort of advertising to them not worth it? We can automate this classification using machine learning. Google itself provides a detailed description of the approach:
Keyword segmentation : How does our target group search? Are there search terms and keywords that we can assign to the lower funnel ? ( More on customer journey and sales funnel ) Classification algorithms malaysia phone number data can help us here. You can find one way of implementing this with the logistic regression algorithm here:
Audience Clustering : Where does it make sense to set the cut and separate two or more user groups from each other? Let the data decide. The k-Means algorithm is often used for problems of this kind:
Audience Segmentation with Machine Learning
Marketing channel attribution : If we work with multiple channels in parallel, we can use machine learning to determine the value of individual channels in the marketing mix for specific user groups. You can find more information on implementation here:
Forecasting and budget calculation : How many conversions can we expect for budget X? How many clicks will we get with budget Y? Every marketer knows this problem. A classic case for logistic regression and time series algorithms.
Anomaly detection in reports : Reports are one of the biggest time-wasters in online marketing. Let your machine learning model search through your data and effectively identify outliers or possible trends. One of the most detailed explanations of anomaly detection algorithms can be found in Andrew Ng's Machine Learning course on coursera.org: