Customer Choice Modeling with R

Predict what your customer(s) / segment(s) of customers will choose in their next purchase, statistically!
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  • Lectures 10
  • Length 2.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 8/2013 English

Course Description

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.

What are the requirements?

  • Basics of Regression
  • R (software)

What am I going to get from this course?

  • 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.

What is the target audience?

  • Product Managers
  • Brand Managers
  • Marketing Personnel
  • Market Researchers
  • Academic Researchers
  • Students
  • Customer Relationship Managers
  • Data Miners
  • Business Analytics Users

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction of the Trainer & Agenda of the course
5 pages

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

Section 2: Introduction to Choice Modeling
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.

Section 3: Basics of Customer Choice Modeling
14:56

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

10 questions

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

Section 4: Quantitative Techniques for Choice Modeling: Fundamentals
Fundamentals of Conjoint Analysis with example
21:08
Fundamentals of Multinomial Logit with example
19:21
Section 5: Quantitative Techniques for Choice Modeling: Executing on R
Conducting Conjoint Analysis on R & making predictions of customer choices
21:45
16:08

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.

10 questions

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 6: Facets of Customer Choice & Smart Marketing Strategies
38 pages

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.

10 questions

This quiz will tests your understanding gained in Lecture 8.

Section 7: Quick Recap & Conclusion
Revision of the course with important points to remember and thank you note.
06:11
Section 8: Bibliography & Resources for Further Research
References and resources for future study
4 pages

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Instructor Biography

Decision Quotient, 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 www.de-quo.com

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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