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2.1 A section
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2.2 “Week 2 MKTR Reference Readings List”
Class,
Namaste, Salaam and Satsriakal.
Additional reference readings for week 2 covering Lec04 (Factor-An), Lec05 (segmentation) and Lec06 (Survey Research 2). All optional, of course. Do note that the SEC related material for Lec06, shared via LMS - is a mandatory read (as in, withion bounds for the exam).
2.2.1 Lec04 - Factor-An for MKTR
An excellent introductory analysis of factor-An from Statswork in this 2019 Medium article titled Factor Analysis. Excerpt:
Factor analysis explains correlations among multiple outcomes as a result of one or more factors. As it attempts to represent a set of variables by a smaller number, it involves data reduction. It explores unexplained factors that represent underlying concepts that cannot be adequately measured by a single variable. It is most popular nowadays in survey research where the responses to each question represent an outcome. It is because multiple questions are often related and the underlying factor may influence the subject responses.
A lucid but somewhat technical explanation for the age-old “Factor-An or PCA” question quite a few of you asked me during and after class in this 7-min read of a medium article titled Principal Components or Factor Analysis?. Excerpts:
PCA and EFA have different goals: PCA is a technique for reducing the dimensionality of one’s data, whereas EFA is a technique for identifying and measuring variables that cannot be measured directly (i.e., latent variables or factors). Thus, in PCA all of the variance in the data — reflected by the full correlation matrix — is used to attain a solution[…] By contrast, in EFA not all of the variance in the data comes from the underlying latent variable[…]
Here’s another lazy read (with some R code thrown in too, ignoreable if not interested) in this medium article titled Feature Extraction Using Factor Analysis in R. Excerpt:
What is Feature Extraction? A process to reduce the number of features in a dataset by creating new features from the existing ones. The new reduced subset is able to summarize most of the information contained in the original set of features.
This article here is an application of PCA and Factor-An to women’s athletic field track records from 55 countries. Excerpt:
Today, we perform Factor Analysis using the principal component method which is much similar to the Principal Component Analysis. The data we use here is National Track Records for Women representing 55 countries in seven different events. You can download the dataset here. The dataset looks like as follows.
2.2.2 Lec05 - Cluster-An for Customer Segmentation
This brief 2019 medium article titled Customer Segmentation Using K Means Clustering covers cluster-an applications in MKTR to customer segmentation using k-means. The project uses Kaggle’s ‘Mall Customer Segmentation Data competition’ and has a lot of code and results. Ignore the code, browse through the step by step results however. Excerpt: > Customer Segmentation is the subdivision of a market into discrete customer groups that share similar characteristics. Customer Segmentation can be a powerful means to identify unsatisfied customer needs. Using the above data companies can then outperform the competition by developing uniquely appealing products and services.
This here is another brief article from 2018 and from the data science side detailing customer segmentation in an e-commerce context and comes with its own dataset.Excerpt: > In today’s competitive world, it is crucial to understand customer behavior and categorize customers based on their demography and buying behavior. This is a critical aspect of customer segmentation that allows marketers to better tailor their marketing efforts to various audience subsets in terms of promotional, marketing and product development strategies.
2.2.3 Lec06 - Survey Research 2
A first read to get into some terminology we saw in Lec07. This brief medium article titled Market Sizing Techniques. Excerpt: > Ever thought/heard questions like: How big is the used mobile phone market in India? What is the market opportunity if you introduce a new clothing line in India? What is the market opportunity of launching a cab aggregator in India? How did Jugnoo grow against Uber/Ola in India? Market Sizing is a method of evaluating the potential reach and revenue of your product/service.
This article here titled A Crash Course in Market Sizing is a gentle intro to some of the terms such as TAM and SAM that we saw in Lec07. It’s byline helpfully says ‘Methods for getting to TAM and SAM, on a shoe-string budget.’ 6 min read, medium says. Worthwhile, perhaps? Excerpt: > Don’t have the budget to hire a consultant or firm to conduct market research for your fledgling product? Don’t stress. Here are two helpful, low-fi approaches to sourcing intel.
This is the official website of the census commissioner of India. Lots of downloadable datasets etc to be found.
This here is the website of NSSO data downloads from the Ministry of Statistics and Plan Implementation. Lots of downloadable datasets again to discover and utilize.
Chalo, enough for now.
Ciao Sudhir Voleti