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Mark Sturdevant edited this page May 26, 2018 · 5 revisions

Short Name

Understand customer interests with clickstream analysis

Short Description

Ingest clickstream data and analyze your website customer activity with compelling interactive visualizations

Offering Type

Data Analytics

Introduction

If you are interested in clickstream analysis with compelling interactive visualization, this code pattern is for you. This code pattern uses Scala in a Jupyter Notebook to ingest and analyze clickstream data. The data is fed into IBM Db2 Event Store which is optimized for event-driven data processing and analytics. Brunel is used to create interactive charts to visualize customer behavior.

Author

By Mark Sturdevant and Siva Anne

Code

Demo

Video

Overview

In this Code Pattern, we will see how a retail business uses IBM Db2 Event Store to capture and analyze clickstream data from its web channels. The clickstream analysis helps the business to closely track customer browsing patterns and better understand their changing interests. Acting on these insights, the business offers a personalized experience for every customer with targeted offers to drive sales.

Sample notebooks demonstrate the use case of clickstream analysis with IBM Db2 Event Store using Scala APIs to ingest and analyze web event data. IBM Db2 Event Store offers high-speed ingestion and real-time analytics for large volumes of streaming data. The platform enables event-driven applications to persist event data at scale and powers high performance Spark analytics on all data for quick insights.

Using Spark SQL and Brunel visualizations, interactive charts show the popularity of product lines, products, and features -- based on page hits and time spent on web pages. A view of a specific customer's most recent interests and activity over time is shown by drilling down to that customer's activity.

When the reader has completed this code pattern, they will understand how to:

  • Install IBM Db2 Event Store developer edition
  • Ingest data into Event Store using Scala in a Jupyter Notebook
  • Query the Event Store using Scala and Spark SQL in a Jupyter Notebook
  • Use Brunel to visualize the data with interactive charts

Flow

architecture

  1. Add a CSV file as a data asset
  2. Run a Jupyter Notebook using Scala to ingest data from the CSV file into Event Store
  3. Run a Jupyter Notebook using Scala and the Brunel visualization language to analyze the data from Event Store

Included components

  • IBM Db2 Event Store: In-memory database optimized for event-driven data processing and analysis.
  • Jupyter Notebook: An open source web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text.
  • Scala: Scala combines object-oriented and functional programming in one concise, high-level language.
  • Brunel: Brunel defines a highly succinct and novel language that defines interactive data visualizations based on tabular data.

Featured technologies

  • Databases: Repository for storing and managing collections of data.
  • Analytics: Analytics delivers the value of data for the enterprise.
  • Data Science: Systems and scientific methods to analyze structured and unstructured data in order to extract knowledge and insights.

Blog

Links

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