Analytics - Boon for banks in credit card/ debit card industry

A top brand such as Nike wants to start a campaign - whom should it go to? - SBI in India

Banks have a upper hand as they have info of all the customers who bought from Nike, Reebok and Puma showrooms using their credit cards. Simple - send the new offer to all these customers and see the magic!

All the banks/credit card issuers are setting up/have setup their Analytics centers.
American express, Barclays, RBS, Bank of America, HSBC, Citibank to name the few.

What about the supermarket chains like Walmart, Target, Woodman do they have an edge?

Oh yes, product sold of which brand was bought by which customer, and when, along with which other products.. all this info stays with the supermaket.
Time for the supermarkets to build strategies for FMCG companies!!

Aren't online stores like Amazon, ebay, bestbuy.. doing the same thing.. Yes they are!!

How about consolidation - Banks, supermarkets, bank and supermarket, e-store and supermarket -- Nice try!!

And now where is dearest Facebook..  It knows everything where have you been and where you would like to go.. So what are you looking for and when Facebook knows all :)

Logistic Regression

Hi Folks!

This is the most common type of modeling technique in which dependent variable is predicted in terms of independent variable(s). Since, this is a logistic regression model output variable should be a binary 0 or 1. However the output is in the form of probabilites varying from 0-1. This is used to predict the event such as customers churn, claim re-opening, employees attriting, etc. This is different from a linear regression model in which the output is a continuous variable such as predicting the salary of employees, cost of ticket, price of a product, etc.

Analytics companies in India

Analytics Industry in India is booming. All the big companies have already jumped in the race or jumping on the ship. IBM, Accenture, TCS are already giants along with the major banks like American Express, HSBC, Barclays, RBS which have their own captive centres along with outsourced work to companies named before or companies like Genpact, Inductis, Mu Sigma, etc. The major consulting companies like McKinsey were there long ago whereas companies like KPMG, Capgemini have felt the heat and setting up their centres.

Major work in Analytics Industry is in the form of Banking, Insurance/Actuarial and Credit Card industry for major banks; Retail analytics for Walmart, Amazon, etc; Marketing analytics for pharmaceutical industry in which companies like MarketRx, Novartis etc. operate. There are a plethora of startups in analytcis industry in the area of loylty and reward programs which is still in the nascent stage as compared to Western world since this can encompass all the industries.

Now coming to salary offered by these companies. I would distribute these companies into three categories A category companies paying the most which include the consulting companies like KPMG, etc and banks like Capital One. These companies have both higher salary along with onshore travel (generally for mid level to higher level management). Second category companies pay descent salaries but less than category A but generally have more onshore travel even for people with 2-3 years of experience. These companies include Mu Sigma, Inductis, etc. along with IBM, Accenture etc. C category compnaies which pay lower salaries than B category companies, have volume of people working for them, have much lesser onshore travel. I would include Genpact, TCS, AON Hewitt in this category. Startups should be in B category as they pay lesser than A category companies, have lesser onshore travel however along with stock options can be a gem.

Attrition is high in this industry as competitors are always ready to easily give 20% to 30% hike. In case the categories of companies get changed at junior level it can lead to easy 50% to 80% hike. The type of work done in these companies can be misleading as it might comprise of refreshing the same code/models with new data to good amount of modeling.

An advice to freshers jumping in this industry would be to be choosy in the type of work you are doing and don't stick to a company if not given good salary and work as there are may options available. Work on your own and learn good amount of modeling independently.

My next blog would be on different type of analytics in different industries.

Hope you enjoyed reading it. Send me your queries at rishigyljain@gmail.com.