Customer Lifetime Value (CLV)

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Customer Lifetime Value (CLV)

The second very popular direction is to load first-party data to determine Customer Lifetime Value (CLV) . Many clients try to load this data correctly, but often encounter fragmentation and inconsistency of information. This makes it difficult to determine the value of each customer from unified sources.

Customer Lifetime Value (CLV)

Роль BigQuery Machine Learning
BigQuery Machine Learning helps you train your afghanistan telegram data system on a variety of data, even data you can't see directly. Loading data through BigQuery projects and Data Import has become a necessary process for most customers who have a significant amount of first-party data.

Building CLV models for scaling
Without creating CLV models, it is impossible to automate and scale advertising projects. It is important to correctly collect data from all sources, including search, affiliate CPC and others. This will not only improve CPA (cost per action) indicators, but also free up resources for other tasks.

Using Vertex AI
Learn about the capabilities of Vertex AI , a native Google Cloud ecosystem that allows you to quickly load, analyze data, and activate advertising campaigns based on the value of each customer.

Integration of offline and online activities
Combine your offline activity with online data and let machine learning identify which customers bring the most value, using all the data available. Don’t rely on standard methods alone—let the machine help you optimize your processes.