Master customer choice modeling using R. Learn Conjoint Analysis and Multinomial Logit to predict customer decisions effectively in this comprehensive 4-hour course.
This course is all about figuring out what your customers might buy next using cool statistical tools like R. You’ll dive into Conjoint Analysis and Multinomial Logit, with videos and handouts that help make everything super clear and easy to understand.
Predict what your customer(s) / segment(s) of customers will choose in their next purchase, statistically!
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.
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.
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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...
Too basic and too few examples of how things are calculated. Overly focused on theoretical points, and no example of a discrete choice scenario or how level importance is calculated in that scenario.
I am very interested in the topic but it's so far been hard to follow the lecturer due to the poor quality of the video/audio - especially in the introductory chapter.
Very practical and clear
I think the quality of the video could have been better. But the materials are well presented.
The information in the slides is not provided through PDF. I have to write it down everything plus the slide use animation which means I can't pause the video in the beginning and write down but have to wait in between which is too annoying. Please it is getting difficult to concentrate till now.
There is great information, but OMG it is hard to understand him.
This course should have more hands-on, exercises and sample data.
This course isn't just about learning syntax in R. It actually teaches you about customer choice modeling and the techniques. I like the fact that the course offers a lecture on both analytical techniques followed by a hands-on example in R. The lectures give you a good understanding of some of the theory and calculations behind the techniques so that you can better understand what's happening in R. A lot of other online courses just teach you syntax without much explanation of what's going on.
I subtracted one star because there were some points where more information would have been useful. For instance, in one of the quizzes, a lot of the questions dealt with topics that weren't covered. Also, some of the technical details of Multinomial Logit weren't explained well, so I was left trying to connect the dots. The instructor mentioned things but didn't explain how they fit into the big picture. Hopefully, the instructor will fix this for future students.
If you already have at least a basic understanding of R and data modeling, then you'll appreciate the course more, but it's probably not a requirement.
The organisation of the material was good, but the presentation was extremely difficult to follow due to the heavily accented Speech. I really wish there were a transcript provided.
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Adrian T.
The course gave a good overview. I was hoping to get some further insights into R.