Predicting conversion probability based on E-commerce data Clustering. 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. Predicting conversion probability based on e-commerce data with machine learning Required: e-commerce dataset including unique visitor IDs.
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If you have properly organized your e-commerce data, it is possible to make predictions based on this. If your visitors are individually identifiable with a unique code. You can then also see which General Manager Email List users have made a transaction on the website in the past period. If you use this data to train a model, it will identify the most valuable actions (input variables) for achieving a transaction (desired output).
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Examples of such an action: Time on site; The number of pages a visitor has viewed; Target groups you can create: Google Analytics: Target audience of website users who have added a certain amount of products to their shopping cart. Google Ads: A similar audience of this target group. Where you show them the latest products from their shopping cart via Display.