The WordPress.com stats helper monkeys prepared a 2013 annual report for this blog.
Here’s an excerpt:
The concert hall at the Sydney Opera House holds 2,700 people. This blog was viewed about 57,000 times in 2013. If it were a concert at Sydney Opera House, it would take about 21 sold-out performances for that many people to see it.
Click here to see the complete report.
When I go to a web site for for downloading white paper or product data sheet, I often hit the back button if presented with a form asking for lots of personal data. Any user that bounces out, is a potential loss of a lead. Perhaps a redesigned page, asking for fewer personal details, would have circumvented the problem.
In this post we will work on a solution using reinforcement learning algorithms. We will have multiple candidate page designs and have a reinforcement learning algorithm find the optimum page with highest click through rate. The algorithm will run real time deployed on Storm Continue reading
My earlier post was about storing nested objects modeled with composite key in Cassandra. Well, we need to be able to read the data back as objects and that’s the topic for this post. This post will focus on rest of the object story. This is part of my open source project agiato.
Mapping Object to Composite Columns
As described in the earlier post, here are some of the salient features of mapping between an object and Cassandra column family. The mapping logic does not use column family meta data. Instead it relies on introspection of the object passed. Continue reading
Research has shown that customers who have abandoned shopping carts, when subjected to retargeting email campaign, often come back and in many cases end up buying more than what was originally in the shopping cart.
There are many attributes of such email campaigns. In this post, we will find the attribute values that produce the maximum effectiveness for such retargeting campaigns, by including some of those attributes. A Hadoop based decision tree algorithm will be used to mine existing retargeting campaign data. Continue reading
Real time fraud detection is one of the use cases, where multiple components of the Big Data eco system come into play in a significant way, Hadoop batch processing for building the predictive model and Storm for predicting fraud from real time transaction stream using the predictive model. Additionally, Redis is used as the glue between the different sub systems.
In this post I will go through the end to end solution for real time fraud detection, using credit card transactions as an example, although the same solution can be used for any kind of sequence based outlier detection. I will be building a Markov chain model using the Hadoop based implementation in my open source project avenir. The prediction algorithm implementation Continue reading
Customer loyalty is the strength of the relationship a customer has with a business as manifested by customer purchasing more and at high frequency. There are various signal or events related to a customer’s engagement with a business. Some examples are transactions, customer service calls and social media comments. These events are indicative of a customer’s loyalty to a business. Loyalty is an internal state, that can not be directly observed and measured, but can be inferred probabilistically.
The customer events over a time window reflect a corresponding sequence of internal states of loyalty. The theme of this post is to predict the sequence of internal loyalty states by using Hidden Markov Model (HMM). Continue reading