Who are your customers? What are they like? How do they interact with your business? This Short Course was created to help analysts better understand their customer behaviour through the power of machine learning.
In this course, you will apply two different machine learning techniques to segment customers according to their purchasing behaviour and provide actionable insights for each group. Along the way, you'll also examine some other retail case studies, including web visitor analysis for marketing and store clustering for logistics.
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Who are your customers? What are they like? How do they interact with your business? This Short Course was created to help analysts better understand their customer behaviour through the power of machine learning.
In this course, you will apply two different machine learning techniques to segment customers according to their purchasing behaviour and provide actionable insights for each group. Along the way, you'll also examine some other retail case studies, including web visitor analysis for marketing and store clustering for logistics.
Who are your customers? What are they like? How do they interact with your business? This Short Course was created to help analysts better understand their customer behaviour through the power of machine learning.
In this course, you will apply two different machine learning techniques to segment customers according to their purchasing behaviour and provide actionable insights for each group. Along the way, you'll also examine some other retail case studies, including web visitor analysis for marketing and store clustering for logistics.
By the end, you'll be able to use customer purchase data to group customers using machine learning, analyse your results to extract business insights and apply multiple types of machine learning to generate business value.
This course is unique because it focuses strongly on interpreting your data within a retail context and supports you in identifying opportunities for machine learning in your workplace.
To be successful in this project, you should already be familiar with Python Pandas for data manipulation, filtering and aggregation. You should also have some experience with basic principles of data analysis, like histograms and summary statistics.
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Syllabus
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Activities
Compile a list of resources on customer analytics using machine learning
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Gather and organize relevant resources will help you stay up-to-date and expand your knowledge base.
Browse courses on
Knowledge Organization
Show steps
-
Search for articles, books, videos, and other resources related to customer analytics using machine learning.
-
Create a document or spreadsheet to organize the resources.
-
Categorize and tag the resources for easy retrieval.
Read 'Consumer Behavior: Building Marketing Strategy'
Show steps
Provides a comprehensive overview of consumer behavior, helping you understand your customers better.
Show steps
-
Read the chapters on segmentation, targeting, and positioning.
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Analyze case studies provided in the book.
-
Apply concepts to your own business or personal experiences.
Solve machine learning practice problems
Show steps
Practice solving common machine learning problems will reinforce your understanding of the concepts.
Browse courses on
Machine Learning
Show steps
-
Find practice problems online or in textbooks.
-
Attempt to solve the problems on your own.
-
Review your solutions with an expert or peer.
Five other activities
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Join a study group or online forum
Show steps
Engaging in discussions with peers can help you clarify concepts and gain new perspectives.
Show steps
-
Find a study group or online forum related to customer analytics or machine learning.
-
Participate in discussions and ask questions.
-
Share your own insights and knowledge.
Segment customer data using machine learning
Show steps
Hands-on practice with real-world data will solidify your understanding of machine learning techniques.
Browse courses on
Machine Learning
Show steps
-
Obtain customer purchase data.
-
Clean and prepare the data.
-
Apply machine learning algorithms to segment customers.
-
Interpret results and draw insights.
Complete Coursera's 'Customer Analytics Using Machine Learning' specialization
Show steps
Builds upon the course material by providing a structured learning path in customer analytics using machine learning.
Browse courses on
Machine Learning
Show steps
-
Enroll in the Coursera specialization.
-
Complete the courses in the sequence.
-
Complete the projects and assignments.
Attend industry conferences or meetups related to customer analytics or machine learning
Show steps
Connecting with professionals in the field can provide valuable insights and expand your professional network.
Browse courses on
Networking
Show steps
-
Research upcoming conferences or meetups.
-
Register and attend the events.
-
Network with other attendees and speakers.
Mentor junior colleagues or students
Show steps
Explaining concepts to others can help you solidify your own understanding and identify areas for improvement.
Browse courses on
Mentorship
Show steps
-
Identify someone who could benefit from your guidance.
-
Set up regular meetings or communication channels.
-
Provide support and guidance on customer analytics, machine learning, or related topics.
-
Encourage questions and discussions.
Compile a list of resources on customer analytics using machine learning
Show steps
Gather and organize relevant resources will help you stay up-to-date and expand your knowledge base.
Browse courses on
Knowledge Organization
Show steps
- Search for articles, books, videos, and other resources related to customer analytics using machine learning.
- Create a document or spreadsheet to organize the resources.
- Categorize and tag the resources for easy retrieval.
Read 'Consumer Behavior: Building Marketing Strategy'
Show steps
Provides a comprehensive overview of consumer behavior, helping you understand your customers better.
Show steps
- Read the chapters on segmentation, targeting, and positioning.
- Analyze case studies provided in the book.
- Apply concepts to your own business or personal experiences.
Solve machine learning practice problems
Show steps
Practice solving common machine learning problems will reinforce your understanding of the concepts.
Browse courses on
Machine Learning
Show steps
- Find practice problems online or in textbooks.
- Attempt to solve the problems on your own.
- Review your solutions with an expert or peer.
Join a study group or online forum
Show steps
Engaging in discussions with peers can help you clarify concepts and gain new perspectives.
Show steps
- Find a study group or online forum related to customer analytics or machine learning.
- Participate in discussions and ask questions.
- Share your own insights and knowledge.
Segment customer data using machine learning
Show steps
Hands-on practice with real-world data will solidify your understanding of machine learning techniques.
Browse courses on
Machine Learning
Show steps
- Obtain customer purchase data.
- Clean and prepare the data.
- Apply machine learning algorithms to segment customers.
- Interpret results and draw insights.
Complete Coursera's 'Customer Analytics Using Machine Learning' specialization
Show steps
Builds upon the course material by providing a structured learning path in customer analytics using machine learning.
Browse courses on
Machine Learning
Show steps
- Enroll in the Coursera specialization.
- Complete the courses in the sequence.
- Complete the projects and assignments.
Attend industry conferences or meetups related to customer analytics or machine learning
Show steps
Connecting with professionals in the field can provide valuable insights and expand your professional network.
Browse courses on
Networking
Show steps
- Research upcoming conferences or meetups.
- Register and attend the events.
- Network with other attendees and speakers.
Mentor junior colleagues or students
Show steps
Explaining concepts to others can help you solidify your own understanding and identify areas for improvement.
Browse courses on
Mentorship
Show steps
- Identify someone who could benefit from your guidance.
- Set up regular meetings or communication channels.
- Provide support and guidance on customer analytics, machine learning, or related topics.
- Encourage questions and discussions.
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