Category Archives: Big Data

Measuring Campaign Effectiveness for an Online Service on Spark

Measuring campaign effectiveness is critical for any company to justify the marketing money being spent. Consider a company providing a free online service on signup. It’s critical for the company to convert them so that they subscribe to a paid … Continue reading

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Processing Missing Values with Hadoop

Missing values are just part of life in the data processing world. In most cases you can not simply ignore the missing values as it may adversely affect whatever analytic processing you are going to do. Broadly speaking, handling missing … Continue reading

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Mining Seasonal Products from Sales Data

The other day someone asked me how to include products with seasonal demand in recommendations based on collaborative filtering or some other technique. The solution to the problem involves two steps. The first step is to identify products with seasonal … Continue reading

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Predicting Call Hangup in Customer Service Calls with Decision Tree and Random Forest

When customers hangup after a long wait in a call, it’s money wasted for the company. Moreover, it leaves the customer with a poor experience. It would have been nice, if we could predict in real time while the customer … Continue reading

Posted in Big Data, Customer Service, Hadoop and Map Reduce, Machine Learning, Predictive Analytic | Tagged , , | 1 Comment

Mobile Phone Usage Data Analytics for Effective Marketing Campaign

Insights gained from analyzing mobile phone usage data can be extremely valuable in marketing campaign and customer engagement efforts. For example, hour of the day when an user engages most with his or her mobile  device could be used to … Continue reading

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JSON to Relational Mapping with Spark

If there one data format that’s ubiquitous, it’s JSON. Whether  you are calling an API, or exporting data from some system, the format is most likely to be JSON these days. However many databases can not handle  JSON and you … Continue reading

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Gaining Insight by Mining Simple Rules from Customer Service Call Data

Although the goal for most predictive analytic problem is to make prediction, sometimes we are more interested in the model learnt by the learning algorithm. If the learnt model could be expressed as s set of rules, then those rules … Continue reading

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