Sorry guys to have been AWOL all this while. Some hectic travelling followed by some ill health and a lot of overdue work has been keeping me away. Having said that, I still should have posted at least a line to inform the regular visitors. By the way, it might be a good idea to grab the RSS feed for this blog, so that you get informed whenever its getting updated.

Now back to work.

A concern some of you have raised about the Consumer Finance data model we discussed (see Open Source Analytics in a month and subsequent posts) is that it appears far too simple to be able to deliver any analytical value to the business. Wouldn’t we be needing the behavioral, payment, response, clickstream, usage data, blah-this, blah-that, and blah-other in order to deliver any value? Isn’t a three table (Loan, Customer, and Marketing) demo too simplistic to be of any real use?

This post is really about answering this. Get out of the hype-victim mode and start thinking. If you look close enough there is enough you can do with just this much data. And in this post we explore just that.

Let us look at various categories of analytics we could do with this data.

1. Operational Sales Reports and MIS

  • Daily, Weekly, Monthly Loan Bookings
  • Disbursal Reports and Portfolio Growth
  • Analysis of No of Loans and Loan Amount by Loan Product, Interest Rate Slabs, Customer Demographics
  • Asset Reports

    • 2. Credit and Collections Reports

  • Exposure Analysis by Customer Segments and Loan Products
  • Portfolio Growth Analysis by Segments and Loan Products
  • Delinquency Analysis and Tracking
  • Roll-rate analysis (rates of improvement/worsening for delinquent loans)

    • 3. Predictive Modeling and Customer Segmentation

  • Credit Scoring
  • Marketing Promo Analysis
  • Promo and Capmpaign optimization

    • I just put this list down in 10 minutes. There’s a lot more that can be done, but this is just to give you a sense of what all is pssible even with seemingly limited data. You just need to look closer and put yourself in the other person’s shoes.

    Okay, so now we have done away with your top two excuses for not doing analytics yet.

  • Analytics is not accessible/affordable: That’s history now. This blog addresses that problem of yours.
  • You do not have enough data: Whatever you have is good enough to begin with. If you are not convinced, tell me what data you have and I’ll tell you 10 things you could do with it, for free!!

    • Any more excuses? Cool, we can now go ahead and start doing things.

    Keep watching…