Network Analysis for Customer Community Detection [Learning Lab 19]

As marketers, we often fall into the trap of trying to please everyone. This is a massive mistake that can end in disaster. What if there was a way to customize products to a specific group of people and have them go viral?

There is! Customer Community Network Detection.

In Learning Lab 20, you learn how to use Network Analysis to identify the most influential customer networks within your customer-base. It's then easy to cater products to them. The result is that products are much more likely to go viral!

Transcript

1. For Customer Community Detection Matt Dancho & David Curry Business Science Learning Lab Difficulty: Intermediate Network Analysis
2. Learning Lab Structure • Presentation (20 min) • Demo’s (30 min) • Pro-Tips (15 mins)
3. Success Story Josh Nelson - Friday - Began Jumpstart - Sunday - Began 101 - Monday - Analyzing Google Analytics API & Plotting “I’ve been [messing] around with Python for 4 months. I learned more in the 2 days I did Jumpstart with R.” #Business Science Success
4. Agenda • Business Case Study ◦ Customer History • Network Analysis ◦ 2 Types ◦ Key Concepts • R Packages ◦ tidygraph ◦ ggraph • 30-Min Demo ◦ Bank Customers ◦ Account History ◦ EDA ◦ Network Analysis ◦ Machine Learning • Pro-Tips: ◦ Tactics to Explain Why Customers belong to Communities
5. Learning Labs PRO Every 2-Weeks 1-Hour Course Recordings + Code + Slack $19/month university.business-science.io Lab 18 Time Series Anomaly Detection with anomalize [HOT - 300+ Data Scientists Attended Live!] Lab 17 Anomaly Detection with H2O Machine Learning Lab 16 R’s Optimization Toolchain, Part 2 - Nonlinear Programming Lab 15 R’s Optimization Toolchain, Part 1 - Linear Programming Lab 14 Customer Churn Survival Analysis Continuous Learning Jet Fuel for your Brain
6. Customer Communities Business Case
7. Customers Naturally Form Communities Can Be Profitable to Detect Communities Customer Communities are Natural Phenomenon Detection is really important: 1. Avoid Trap of Focusing on EVERYONE 2. Customize Products & Services for Key Groups
8. Customer History Customers Descriptive Features
9. Network Analysis Basics 80/20 Concepts
10. Undirected Strength of Relationship Types of Network Analysis Directed Hierarchical Structure 1 2 Clustering Composition of Groups
11. Undirected Strength of Relationship Types of Network Analysis Directed Hierarchical Structure 1 2 Clustering Composition of Groups
12. Core Concepts 80/20
13. Adjacency Matrix Key Concept N x N matrix of Relationship Strength (Measure)
14. Nodes & Edges Nodes • Customers Edges • Relationship Strength Key Concept Groups (Clusters) have more edges connecting more nodes at a given relationship threshold. Clusters (Groups) • Densely Connected Web
15. Pruning & Threshold Key Concept Data mining is subjective. Where do we cut off? Pruning • Filtering to reduce to the most “influential” nodes • We use a threshold to find an optimal visualization that explains the groups Threshold = 0.9999 Too High Threshold = 0.99 Too Low Threshold = 0.996 Just Right!
16. Network Analysis Software
17. tidygraph https://github.com/thomasp85/tidygraph Tidy Network Data • Combines Node and Edge Data inside 1 tidygraph object • Makes it super easy to work with network data • Can activate() inner node and edge tbls to manipulate them • Can apply special network analysis functions like group_components() & centrality_degree()
18. ggraph Visualization Package for tidygraph data https://ggraph.data-imaginist.com/
19. Customer Segmentation Workflow Step-By-Step Start Finish 1 2 3 Data Clean & Transform Exploratory Data Analysis Adjacency Matrix, tidygraph, & ggraph Visualizations Develop Segments H2O & LIME Predict & Explain Customer Segments
20. 30-Min Demo Analyze Customer Networks
21. Network Analysis Secret Tactics for Use these tips to increase your customer segmentation explainability
22. Pro Tip Use H2O & LIME to EXPLAIN WHY
23. Data Science Transformation Skills that are needed to do what we just did
24. Customer Segmentation Workflow Step-By-Step Start Finish 1 2 3 Data Clean & Transform Exploratory Data Analysis Adjacency Matrix, tidygraph, & ggraph Visualizations Develop Segments Data Cleaning H2O & LIME Predict & Explain Customer Segments 101 & 201 201 Lab 19
25. dplyr, ggplot2 101 & 201
26. Adjacency Matrix, tidygraph, & ggraph Lab 19
27. ggplot2 & purrr 201
28. Business Science University Our program that will TRANSFORM YOU in weeks, not years.
29. The program that will deliver YOUR Transformation
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31. Key Benefits - Fundamentals - Weeks 1-5 (25 hours of Video Lessons) - Data Manipulation (dplyr) - Time series (lubridate) - Text (stringr) - Categorical (forcats) - Visualization (ggplot2) - Programming & Iteration (purrr) - 3 Challenges - Machine Learning - Week 6 (8 hours of Video Lessons) - Clustering (3 hours) - Regression (5 hours) - 2 Challenges - Learn Business Reporting - Week 7 - RMarkdown & plotly - 2 Project Reports: 1. Product Pricing Algo 2. Customer Segmentation Visualization Data Cleaning & Manipulation Functional Programming & Modeling Business Reporting Business Analysis with R (DS4B 101-R) Data Science Foundations 7 Weeks
32. Key Benefits Understanding the Problem & Preparing Data - Weeks 1-4 - Project Setup & Framework - Business Understanding / Sizing Problem - Tidy Evaluation - rlang - EDA - Exploring Data -GGally, skimr - Data Preparation - recipes - Correlation Analysis - 3 Challenges Machine Learning - Weeks 5, 6, 7 - H2O AutoML - Modeling Churn - ML Performance - LIME Feature Explanation Return-On-Investment - Weeks 7, 8, 9 - Expected Value Framework - Threshold Optimization - Sensitivity Analysis - Recommendation Algorithm Data Science For Business (DS4B 201-R) Machine Learning & Business Consulting 10 Weeks Advanced Visualization Advanced Data Wrangling Advanced Functional Programming & Modeling Advanced Data Science End-to-End Churn Project
33. Key Benefits Learn Shiny & Flexdashboard - Build Applications - Learn Reactive Programming - Integrate Machine Learning App #1: Predictive Pricing App - Model Product Portfolio - XGBoost Pricing Prediction - Generate new products instantly App #2: Sales Dashboard with Demand Forecasting - Model Demand History - Segment Forecasts by Product & Customer - XGBoost Time Series Forecast - Generate new forecasts instantly Shiny Apps for Business (DS4B 102-R) Web Application Development 4 Weeks Web Apps Machine Learning
34. Success Story Masatake Hirono - Took DS4B 201-R - Completed the 10-Week Course - Landed a Job at one of the most Prestigious Management Consulting Firms “This course showed me how to place data analytics in real business settings.” #Business Science Success
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