Tracking Data from an Online Shop
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In this article series, I wrote so far about Marketing Data Science ”Use Cases” and ”Customer Data Platforms”, and I described typical applications of Data Science ”Lead Prediction” and “Churn Prediction”. In this article, I bring an example of “Customer Segmentation”.
Customer segmentation is a data-driven decision technique to classify customers into homogenous groups. The data based on which segmentation is done can be structured data (e.g., demographic data such as gender, age, and income) or unstructured data (e.g., social media data). Further data can be collected to identify customer groups, such as data on customers’ behavior (e.g., which websites customers visited) or data on purchases.
In this post, I’ll show you how to group consumers using web analytics data from an online store. On-site personalization and targeted marketing campaigns may be implemented based on the findings.
On the way there, we’ll look at the data in further depth (“Explorative Data Analysis” or “EDA”), do some preliminary processing on the data, create segmentation, and then present the clusters. For the calculations, we will use Google Colab.
The data is from the Kaggle data platform and contains web tracking data for one month (Oct. 2019) from a large multi-category online shop.
Each line in the file represents an event. There are different types of events, such as page views, shopping cart actions, and purchases.
The record contains information about:
- event_time / When was the event triggered? (UTC)
- event_type / view, shopping cart, purchase
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Source: https://medium.datadriveninvestor.com/marketing-data-science-segmentation-of-customers-e02e048b4d47