Also read: 12 simple machine learning tools for web shops
Posted: Sun Dec 15, 2024 6:58 am
Supervised Learning
This way of learning is called supervised learning . This is comparable to a baby showing you pictures. If you tell them often enough that a picture of a cow is a cow, a baby will eventually recognize it themselves.
But how do you use supervised learning for marketing campaigns? Using 4 example cases, I will show you how to enrich your dataset with machine learning. The results can provide valuable insights other than target groups.
The four practical examples that follow concern the following topics:
Predict conversion probability based on e-commerce data
Cluster customers based on e-commerce and CRM data
Predicting churn based on app data (SaaS company)
Predicting Customer Lifetime Value Based on E-commerce Data
1. Predict conversion probability based on e-commerce data with machine learning
Required: e-commerce dataset including unique visitor IDs.
If you have your e-commerce data well iran telegram data organized, it is possible to make predictions based on this. If your visitors are individually identifiable with a unique code, you can see which actions have been taken on your website by users. You can then also see which users have made a transaction on the website in the past period.
When you use this data to train a model, it will identify the most valuable actions (input variables) to achieve a transaction (desired output).
Examples of such an action:
Time on site;
The number of pages a visitor has viewed;
Or the number of products placed in a shopping cart.
Target groups you can create:
Google Analytics: Target audience of website users who have placed a certain amount of products in their shopping cart.
Google Ads: A similar audience of this target group.
These audiences can then be used for dynamic retargeting campaigns , where you show them the latest products from their shopping cart via Display. You can also give the audience a higher bid within Google Ads.
This way of learning is called supervised learning . This is comparable to a baby showing you pictures. If you tell them often enough that a picture of a cow is a cow, a baby will eventually recognize it themselves.
But how do you use supervised learning for marketing campaigns? Using 4 example cases, I will show you how to enrich your dataset with machine learning. The results can provide valuable insights other than target groups.
The four practical examples that follow concern the following topics:
Predict conversion probability based on e-commerce data
Cluster customers based on e-commerce and CRM data
Predicting churn based on app data (SaaS company)
Predicting Customer Lifetime Value Based on E-commerce Data
1. Predict conversion probability based on e-commerce data with machine learning
Required: e-commerce dataset including unique visitor IDs.
If you have your e-commerce data well iran telegram data organized, it is possible to make predictions based on this. If your visitors are individually identifiable with a unique code, you can see which actions have been taken on your website by users. You can then also see which users have made a transaction on the website in the past period.
When you use this data to train a model, it will identify the most valuable actions (input variables) to achieve a transaction (desired output).
Examples of such an action:
Time on site;
The number of pages a visitor has viewed;
Or the number of products placed in a shopping cart.
Target groups you can create:
Google Analytics: Target audience of website users who have placed a certain amount of products in their shopping cart.
Google Ads: A similar audience of this target group.
These audiences can then be used for dynamic retargeting campaigns , where you show them the latest products from their shopping cart via Display. You can also give the audience a higher bid within Google Ads.