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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Customer Choice Modeling with R

What You Will 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.

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.

Who Should Attend!

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

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Tags

  • Decision Making
  • Financial Modeling
  • R (programming language)

Subscribers

369

Lectures

10

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