Future of Predictive analytics – Part II

Here is the continuation to the article i had posted few days back <here>. I am back with some interesting info on recent advancements in the area of analytics. Before going on to the details, wanna share something basic – “The data/datum”. I met a friend of mine, working for a leading information management firm using BO to prepare some reports on the customer response behaviour.  The way he was conversing with me showed up the fact that, he was seeing the data merely in terms of numbers and strings. This is something which i have seen with most of the people. I often ask them to look at the causal relationships among various KPIs because it can tell more about your business. Anyways, here is the list of trends seen in analytics:

1. Uplift modelling: The true effectiveness of a marketing campaign isn’t response rate! It’s the incremental impact – that is, additional revenue directly attributable to the campaign that would not otherwise have been generated. Yet traditional targeting criteria are often designed to find clients that are interested in the product, but would have bought it whether or not they received a promotion. In such cases, the incremental impact is insignificant and the marketing dollars could have been spent elsewhere.

Net Lift Models are designed to maximize incremental impact by targeting the undecided clients that can be motivated by marketing. These “swing customers” are akin to the swing states of a presidential election; data miners could learn a lot from presidential campaign
More Here: http://www.predictiveanalyticsworld.com/sanfrancisco/2010/agenda.php#day2-2

2. Social Data Mining: There are many networking sites, there’s lot of data out there in the form of tweets, status messages, etc all of which have information. Be it a product related, customer feedback, complaints, oppurtunities, etc. Such data can prove to provide valuable insights about the subject under study.
           In one of the blogs, Eric Siegel talks about interesting facts about social data analysis.
(i)  Health care industry had identified that quitting smoking is contagious.
(ii) Risk of obesity increases if you have a obese friend.
                   So the above facts prove that social connections can reveal more predictive data about the customers.

3.Unstructured Data handling: IBM is working on a project called ‘Avatar’ offer users a mechanism to deal with unstructured data. Nearly 80% of data is unstructured in nature. Traditional BI tools are known to work best with structured data only. But practically most of the data is in the form of mails, documents, blogs,etc which is unstructured in nature. I hope unstructured data handling come to the commercial levels.

4. Real time BI: Now most of the mobile users are GPS enabled, due to its low price offering. This data about customer where-about information can bring out lot of interesting applications. Based on the rate of change of GPS location, we can ascertain the speed of movement of the user( based on this value, we can decide whether the customer is walking or using a vehicle). This data can help in traffic congestion management there by help the city authorities plan better. Analysis of GPS data might give insights on building systems which recommend routes based on current traffic conditions. It’s not just the only use, sky is the limit for the imagining creative ways of using GPS data. However, this raises privacy concerns as this data reveals confidential data about the customer behaviour. It is to be noted that we are in the stage where researchers are developing privacy-preserving data mining algorithms.  But still we have a long way to go.

                I was just thinking why companies don’t model the employee attrition as this may help in predicting the likely chances of employee planning for a job change and take preventive measure to retain him/her if their loss is significant. In fact i know companies which rate their employees during appraisal cycle on a scale of 1 to 5 which in turn decides salary and promotion, this rating is one of the strong predictors of attrition modelling. I promise to bring you more info about this subject as and when i get something interesting to blog about.

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• attrition modelling
• Avatar
• GPS data
• social datamining
• unstructured data
• uplift modelling