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Recommendation Engines in Marketing Analysis

Recommendation engines, the concept of RFM and market basket analysis are covered in this free online marketing course.

Business
Free Course
Recommendation Engines in Marketing Analysis is a free online course that offers you an in-depth understanding of recommendation engines and retail market analytics. You will learn about RFM (recency, frequency, monetary) research, market basket analysis for retailers, and some problems faced in retail analysis. Register for this course today and take your marketing analytics skills to the next level.
  • Duration

    4-5 Hours
  • Certification

    Yes
  • Mobile Friendly

    Yes
  • Publisher

    NPTEL
  • Accreditation

    CPD

Description

Modules

Outcome

Certification

View course modules

Description

This comprehensive guide will introduce you to understanding the functions of different recommendation engines and teach you how to use the recency, frequency, monetary (RFM) technique as a tool to segment customers in marketing analysis. It begins with an analysis of collaborative filtering in marketing analytics, looks at the problems faced in retail analysis and sees how it can be used to solve real-life challenges in marketing analytics.

Next, you will learn about recommendation engines for ecommerce firms, as well as how to monetize a long-tail search strategy with surprising recommendations. You will also study how to use RFM analysis in R (the language and environment for statistical computing and graphics) with a real data set in marketing. Then discuss the analysis in R for segmentation through purchasing behaviour. You will also study the concept of market basket analysis, as well as how it can be used in retail and ecommerce ventures. Finally, you will grapple with the data mining technique, how an advanced version of the algorithm can be used for large data sets, and the features of collaborative filtering in marketing analytics.

This free online course provides practical knowledge and numerous applications of RFM and market basket analysis, as well as collaborative filtering and cluster recommendation engine approach to ensure a great learning experience. IT will be of use to digital marketers, programmers and anyone interested in using marketing analytics for ecommerce.

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