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Customer Choice Modeling with R

Predict what your customer(s) / segment(s) of customers will choose in their next purchase, statistically!
3.4 (17 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
268 students enrolled
Last updated 8/2013
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  • 2 hours on-demand video
  • 3 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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The course is about understanding fundamentals of customer choice and enable participants to use R for customer choice modeling.

The course contains video lectures, power-point slides and handouts in PDF format.

The course will take approximately 4 hours to complete including study material provided.

The course starts with a basic introduction (video lecture) to customer choice. Section 2 covers basics of customer choice (powerpoint slides and audio instruction). Section 3 explains fundamentals of Conjoint Analysis (powerpoint slides and audio instruction) with worked example. Section 4 is a hands on session on conducting Conjoint Analysis on R (video). Session 5 explains fundamentals of Multinomial Logit (powerpoint slides and audio instruction) with worked example. Section 6 is a hands on session on conducting Multinomial Logit on R (video). Section 7 is a quick recap of the entire course with key points (powerpoint slides and audio instruction). Additional information and Bibliography is provided along with the course (PDF).

Product Managers, Customer Relationship Managers, Marketers, Students, Researchers etc. need to understand their customers' choices better to provide them products and services which they prefer. This course sensitizes them to understand and statistically model customer choices and discover insights for better strategic decision making.

Who is the target audience?
  • Product Managers
  • Brand Managers
  • Marketing Personnel
  • Market Researchers
  • Academic Researchers
  • Students
  • Customer Relationship Managers
  • Data Miners
  • Business Analytics Users
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What Will I Learn?
By the end of the course you will be able to conduct Conjoint Analysis on R.
You will be able to conduct Multinomial Logit on R
You will be able to make predictions about customer choices.
You will be able to conduct market research on product / service attribute choices.
You will be able to identify "Key Drivers" that drive your brand / product / service in the market.
You will be able to better appreciate the customer perspective and hence take successful decisions.
View Curriculum
  • Basics of Regression
  • R (software)
Curriculum For This Course
Expand All 10 Lectures Collapse All 10 Lectures 02:35:09
Introduction of the Trainer & Agenda of the course
1 Lecture 00:00

The PDF contains the profile of the trainer for this course along with the course outline / agenda.

Preview 5 pages
Introduction to Choice Modeling
1 Lecture 08:40

This video lecture will walk you through the need of choice modeling in today's business environment and how the field of 'choice modeling' has evolved from seminal researches.

Preview 08:40
Basics of Customer Choice Modeling
1 Lecture 14:56

This video lecture walks you through the fundamental principles of choice modeling.

Preview 14:56

The quiz is a MCQS and tests the concepts that you have understood in Lecture 2 & 3.

Section Quiz 1
10 questions
Quantitative Techniques for Choice Modeling: Fundamentals
2 Lectures 40:29
Fundamentals of Conjoint Analysis with example

Fundamentals of Multinomial Logit with example
Quantitative Techniques for Choice Modeling: Executing on R
2 Lectures 37:53
Conducting Conjoint Analysis on R & making predictions of customer choices

This video lecture gives a hands on workshop on how to execute Multinomial Logit on R for choice modeling and how to interpret the results.

Conducting Multinomial Logit on R

This quiz will test your understanding about two statistical techniques - Conjoint Analysis & Multinomial Logit that have been explained with a working example on R.

Section Quiz 2
10 questions
Facets of Customer Choice & Smart Marketing Strategies
1 Lecture 00:00

This PDF will help you understand various applications of choice modeling and some smart marketing strategies that is aimed at igniting the thought process of individuals to think out of the box and create effective marketing strategies.

Facets of Customer Choice & Smart Marketing Strategies
38 pages

This quiz will tests your understanding gained in Lecture 8.

Section Quiz 3
10 questions
Quick Recap & Conclusion
1 Lecture 06:11
Revision of the course with important points to remember and thank you note.
Bibliography & Resources for Further Research
1 Lecture 00:00
References and resources for future study
4 pages
About the Instructor
3.4 Average rating
17 Reviews
268 Students
1 Course
Data Mining and Decision Analytics

Companies are experiencing exponential growth in the data volumes. This data is the key to make critical and accurate decisions.Exponentially changing global market dynamics have posed challenges before the businesses to refine and exploit data to make decision via intelligent prediction and forecasting.

Decision Quotient (DQ) works with companies mine their data to exclude any noise and to allow for more efficient analysis and further assist develop in-house analytical capabilities.

DQ is skillset expand across most popular analytical tools in the market. e.g.: R, SAS, SPSS, etc. Our skillset with various softwares allows us to accommodate budget at all levels and deliver balance of quality and quantity.

DQ is serving clientele across the globe and has experience with various industry sectors. We have executed analytical tool development for risk and fraud analysis, supply chain automation for retail chain and consulting projects for manufacturing, restaurants etc. Refer to few published papers on our website

Few examples of solution we have extended to our clients are:

  1. Customer Choice Modelling
  2. Customer Churn and Retention
  3. Customer Loyalty
  4. Sentiment Analysis
  5. Text Analytics
  6. Behavioral & Application Scoring
  7. Credit and Operational Risk
  8. Fraud Analytics
  9. Key Driver Analysis
  10. Brand Perceptual Mapping and Management
  11. Catchment Area Analysis for Retail Location
  12. Dynamic Price Modeling
  13. Real Time Strategy
  14. Deception Dectection
  15. Linguistic Inquiries
  16. Sustainability Analytics
  17. Workforce Analytics
  18. Statistical Process Control

Our principals have experience with operating family owned business, working in small to large companies across the globe.

We look forward to discuss with you, your opportunity to grow your business with more intelligence and less risk.

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