Analytics III: Projects

asm_topTogether with Chandler Johnson and Alessandra Luzzi, I currently teach a course called Analytics for Strategic Management. In this course (now in its third iteration), executive students work on real projects for real companies, applying various forms of machine learning (big data, analytics, whatever you want to call it) to business problems. We have just finished the second of five modules, and the projects are now defined.

Here is a (mostly anonymised, except for publicly owned companies) list:

  • An IT service company that provides data and analytics wants to predict customer use of their online products, in order to provide better products and tailor them more to the most active customers
  • A gas station chain company wants to predict churn in their business customers, to find ways to keep them (or, if necessary, scale down some of their offerings)
  • A electricity distribution network company wants to identify which of their (recently installed) smart meters are not working properly, to reduce the cost of inspection and increase the quality of
  • A hairdressing chain wants to predict which customers will book a new appointment when they have had their hair done, in order to increase repeat business and build a group of loyal customers
  • A large financial institution wants to identify employees that misuse company information (such as looking at celebrities’ information), in order to increase privacy and data confidentiality
  • NAV IT wants to predict which employees are likely to leave the company, in order to better plan for recruitment and retraining
  • OSL Gardermoen want to find out which airline passengers are more likely to use the taxfree shop, in order to increase sales (and not bother those who will not use the taxfree shop too much)
  • a bank wants to find out which of their younger customers will need a house loan soon, to increase their market share
  • a TV media company wants to find out which customers are likely to cancel their subscription within a certain time frame, to better tailor their program offering and their marketing
  • a provider of managed data centers wants to predict their customers’ energy needs, to increase the precision of their own and their customers’ energy budgets
  • Ruter (the public transportation umbrella company for the Oslo area) wants to build a model to better predict crowding on buses, to, well, avoid overcrowding
  • Barnevernet wants to build a model to better predict which families are most likely to be approved as foster parents, in order to speed up the qualification process
  • an electrical energy production company wants to build a model to better predict electricity usage in their market, in order to plan their production process better

All in all, a fairly typical set of examples of the use of machine learning and analytics in business – and I certainly like to work with practical examples with very clearly defined benefits. Over the next three modules (to be finished in the Spring) we will take these projects closer to fruition, some to a stage of a completed proposal, some probably all the way to a finished model and perhaps even an implementation.

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