We can predict anything!

July 12, 2011

conversion, marketing, underwriting model, powerful analytics API, grow your thin file customersWhat?

We’re opening some private beta trials to the DeMyst API, a tool that leverages rich public data sources; think digital footprint, telecommunications usage, and much more, to predict risk or conversion when there is minimal consumer information available.  Initially targeted at lenders, a few client meetings quickly revealed that our tool had broad applications to anyone working with ‘thin file’ customers.  So, like any good group of bootstrapping engineers, we iterated a bit, and hence, the latest version of the DeMyst API, a tool capable of predicting anything, was born.

How does it work? 

The tool excels at predicting a ‘target’ with minimal inputs/identifiers. Of course, the more identifiers the better, but the reality is, there is a bunch of rich data out there in the public domain that with a bit of aggregation, some pretty geeky analytics, and kickass technology, allows us to produce either a standalone prediction or complementary attributes for your own scorecards.    The UX is painless; just upload a decent sized sample containing whatever identifiers are available (e.g. it even works with just email!), we’ll append nifty third party data, exert our ‘muscle’, and within minutes, produce an API with a custom prediction. Yes, minutes.

Sounds cool, prove it: 

From a proud founder’s perspective, we’ve been pretty blown away by how much lift is being created by the toolkit.   We can boast a  >90% hit rate and significant growth improvements.  To prove it, we’re offering a few private beta spots so you can test for yourself.

What’s the catch? 

There are two:

  • You must be brutally honest and willing to provide us with feedback to help us refine the product.
  • You must have a genuine interest in commercially using the product if you like it (at a preferred rate of course).

To show our appreciation, we promise to provide attentive support and assistance, some free consulting help, and a certain level of exclusivity as a lighthouse customer.  This of course all comes free with your early access to a slick new tool that could massively increase your distribution without impacting your current risk level.

Early Adopter?

To reserve your spot, click here .

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Unbiased estimator variable selection

July 16, 2010

We’re continuing to test the tendency of modeling processes to overfit, per an earlier post.

The issue with this approach is that, in most practical settings, variables are either in or out, based on some variable selection process.

varselectionsilne.jpg

When a variable is “in”, typically the parameter is considered a best unbiased estimator … in normal speak this means that if the average in the sample data is .3, the the parameter will be such that the model predicts .3.

OLS.jpg

This is why, with sparsely populated variables, there is such a risk of overfitting when including too many variables – the model will fit to the sample noise.