BigMLSchool: Customer Segmentation

Arturo Moreno #BigMLSchool ICADE
February 17, 2021
Customer Segmentation: Understanding Your
Customers to Better Develop Personalized
Relationships
BigML Machine Learning School

Welcome
print ("Welcome to BigML Machine
Learning School!")
Output:
Welcome to BigML
Machine Learning School!

About me
Associate Professor Business Analytics at ICADE Business School
Twitter:
@r2_moreno
Clubhouse:
@r2moreno

About me
Disclaimer
I think the BigML team are great experts and that they have built a top-class
machine learning platform.
I have worked with BigML in the past and love them.
Now, everything that I share with you is my honest assessment of the value of
BigML as a tool to teach machine learning at business schools.

Agenda
My perspective on teaching ML
• Setting (the right) expectations for the course
• Theoretical background + hands-on experience (for managers)
• BigML at the core of my teaching experience
• Case Study – Clustering for better understanding your customers
• Questions? Google Meet and Slack channel

1. Approach business problems data-analytically
• Think carefully & systematically about whether & how data can improve
decision performance
2. Be able to interact competently on the topic of data mining for business
analytics
• Know the basics of data mining processes, techniques, & concepts well
enough
3. Live hands-on experience applying ML models
• You should be able to follow up on ideas or opportunities that present
themselves
6
Setting (the right) expectations for the course
The goal for the class is three-fold

7
Data-related jobs are in explosive demand
Source: Indeed

8
And Schools have already / are in the process of adapting
Source: Indeed

Importance of Business Analytics
Source: McKinsey&Co.

Importance of Business Analytics
Assortment
optimization
Next-product-
to-buy mailing
Promotion
optimization
Inventory
management
Real-time
advertising
Shopper
segmentation
Shopper
loyalty/
Churn
Source: McKinsey&Co.

1. Not a computer science class. I don’t expect students to program, so
implementation won’t be covered in the class.
2. Not a Math class either. We explore just enough in order for you to
understand the concept. (i.e.: entropy of a dataset or “distance” between two
elements,…)
3. Not a course on big data technologies (data engineering and data
processing)
But also, ML education must stay away from an existing ML-charlatan trend.
Above all, it’s our duty to teach to use data with responsibility. (i.e.: stay away
from low-quality processes around data, models or evaluation)
11
The NO-goal for this class is three-fold

2
What should be the goal for business students?
• Improve decision making?
• Build well “performant” models?
• Deploy models of all kinds supervised or unsupervised?
• Make sure there is as much data as possible?
• Ok… Just throw millions and hire thousands of developers and let data for
the data scientists!?
• … Other?

Cross Industry Standard Process for Data Mining (CRISP-DM; Shearer, 2000)
Industry average is that ~
80% of the time of any data
science project is spent
here.
There is a lot of domain
expertise required but
basic data transformation
skills.
This is the ideal territory for
business students to
participate
Source: Data Science for Business – Foster, Provost

Develop the habit of data understanding

Theoretical background + hands-on experience
• Gather data. Plot data. Hypothesize with data. Infer (if possible) from data.
REPEAT!
• Introduce the data mining process
o The concept
o The role
o The process
o The techniques
• Do that through business examples (real problems, real datasets) to
illustrate type of algorithms and the data mining process
Class structure

Class project (30%): your first data-mining project
• Groups of 2
• Pick a theme that you are interested in or CHOOSE out of my suggestions
• Final Report delivery and class presentation following the CRISP-DM
methodology
o Business Understanding
o Data Understanding (& Preparation)
o Modeling
o Evaluation
o Deployment
How?: My first data science project

• Select a problem that you want to work on (your business objective) and
explain why this is relevant for you and who else is interested
• Find the data to work on that problem from the data repositories
• Presentation 1: Evaluation and inspection of the data using BigML, Tableau
and/or Jupyter Notebooks.
• Presentation 2: Determining the objective function. Feature generation,
feature selection.
• Presentation 3: Choosing and building the model(s)
• Presentation 4: Evaluation of the model(s). REPEAT!
• Final Presentation: Data Science Project Presentation
My first data science project

• “If you can’t explain it simply, you don’t understand it well enough” — Albert
Einstein
• We go at a level of detail that students can later explain and question.
• We don't get into technical details, yet deep enough so that students
understand the main questions to be asked related to the why of the
outcomes of models.
1
Focus on understanding what we are doing

Using tangible projects

Making them look accessible

Through tools they control…

5
Look for living examples of data science put to work

28
28
Source: https://www.kdnuggets.com/2016/09/poll-algorithms-used-data-scientists.html

29
29
Source: https://www.kdnuggets.com/2016/09/poll-algorithms-used-data-scientists.html

The Data Mining Process
Cross Industry Standard Process for Data Mining (CRISP-DM; Shearer, 2000)

BigML at the core of my teaching experience
Sound theoretical background with business practice

BigML at the core of my teaching experience
33
33
1
ACCESS TO DATA: READY-TO-USE DATASETS, SIMPLICITY TO
CONNECT TO EXTERNAL FILES OR DATABASES
2
MODEL CATALOGUE: SUPERVISED AND UNSUPERVISED
MODELS AVAILABLE BOTH 1-CLICK AND CUSTOMIZABLE
3
KEY ML ELEMENTS AT THE CORE: TRAINING-TEST SPLIT,
EXPLAINABILITY FEATURES, CONFUSSION MATRIX
BigML allows me to focus on what matters most for Business students

/Case Study – Clustering & Understand customers
34
34
Why are we doing this? Setting the business objective
BUSINESS CASE:
CAN MY CUSTOMER DATA BE USED TO
DEVELOP PERSONALIZED
RELATIONSHIPS WITH MY CUSTOMERS?

35
35
Let’s see the data. Does it make sense from a business perspective?

36
36
Analysis of correlations

37
37
On to clustering… [REPEAT]

38
38
Applications and Conclusions

39
39
Applications and Conclusions
ANY BUSINESS OUTCOME?
DO NOT ACCEPT THE FIRST NICE
SOLUTION, KEEP ITERATING AND
QUESTIONING WHETHER THIS MAKES
ANY SENSE FOR THE BUSINESS.
TEST.

BigMLSchool: Customer Segmentation

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