Snowplow Behavioral Data: Personalizing with Propensity Scoring

**Overview**
Learn how to leverage Snowplow's rich behavioral data to achieve our goal of converting website visitors into engaged prospects by applying ML and remarketing them through various channels.

We will utilize sample data from Snowplow's website to convert prospects from a top of funnel awareness stage to engaged users (e.g. request for demo). To unlock the value of the underlying behavioral data, we will focus remarketing efforts on prospects with highest propensity to convert. You will learn how to build a rules-based audience segment and ML-based audience segment to utilize in your activation platform.

**Use cases**
- Create compliant behavioral data using Snowplow
- Load data into DeltaLake
- Create silver tables using Snowplow's DBT package
- Perform exploratory data analysis using Databricks SQL
- Create gold table for audience segmentation
- Use Reverse ETL or Orchestration CDP for rules-based remarketing
- Train propensity to convert model using XGBoost and Mlflow
- Use model to predict propensity to convert
- Use Reverse ETL or Orchestration CDP for ML-based remarketing
- Monitor campaign performance and optimize efforts

**Product details**
-snowplow_web_page_views
-snowplow_web_sessions
-snowplow_web_users
-converted_users
-campaigns
-snowplow_user_propensity_scores
For more details, refer to the embedded notebook.

**Additional Insights**
To learn about additional use cases for Snowplow behavioral data in Databricks, see our blog post [here.](https://www.databricks.com/blog/empowering-data-teams-snowplow-first-party-digital-event-data-collection)

搜集汇总

背景与挑战

背景概述

该数据集提供Snowplow网站行为数据样本，用于构建基于规则和机器学习的用户分群模型，预测高转化倾向用户并实施多渠道精准营销。包含网页浏览、会话、用户属性及转化数据等核心表，配套完整的数据处理、建模分析和营销激活工作流。