Category Archives: Teaching

Student cases of digitalization and disruption

I teach a M.Sc. class called “business development and innovation management”, and challenge students to write Harvard-style cases about real companies experiencing issues within these areas. The results are always fun and provide learning opportunities for the students: You may not provide the answer for the company, but you get a chance to really learn about one company or one industry and dive into the complexities and intricacies of their situation. That knowledge, in itself is valuable when you are hitting the job market.

Here is a (fairly anonymized) list of this year’s papers:

  • disruption in the analytics industry: One group is studying SAS Institute and how their closed software and architecture model is being challenged by open-source developments
  • disruption in the consulting industry: One group wants to study a small consulting company and how they should market some newly developed software that allows for automated, low-cost analysis
  • establishing a crypto-currency exchange: One group wants to study strategies for establishing a payment and exchange service for crypto-currencies
  • marketing RPA through a law firm: One group wants to study how a large law firm can market their internal capabilities for RPA (robotics process automation) in an external context
  • fast access to emergency services: One group wants to write a case on Smarthelp and how that service can be spread and marketed in a wider context
  • using technology to manage sports club sponsorship: One group wants to study how to develop strategy for a startup company that helps participation sports clubs with gain corporate sponsorships
  • electronic commerce and innovation in the agricultural equipment sector: One group wants to study how a vendor of farm equipment and supplies can extend their market and increase their innovative capability through ecommerce and other digital initiatives
  • machine learning in Indian banking: One group wants to study how machine learning could be used to detect money laundering in a large Indian bank
  • social media analysis in consumer lending: One group wants to study an Indian startup company that uses digital indicators from users’ online behavior to facilitate consumer financing for online purchases

Al in all, a fairly diverse set of papers – I am looking forward to reading them.

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.

How to write a teaching case

Since I sometimes give classes on how to do case teaching and have written a book on the subject with Bill Schiano, I am sometimes asked how to write a teaching case. The following blog post is a quick tips & tricks collection (essentially an update of this post.)

Why are you writing this case?
First and foremost: Cases are written for a teaching purpose – and to write a teaching case, you need to have a teaching objective in mind. It is not enough to have an interesting company. Even the best company story needs to have a pedagogical point, a theory or dilemma to illustrate. So don’t write a teaching case just because you happen to know someone in a really interesting company – it does need to be a good story, but it also need to have a purpose.

The standard outline
Cases – particularly the standard HBS case – follow an outline that can seem rather trite, but which is very effective. It is something like this:

  • 0.5 page: Intro: The protagonist is introduced, typically pondering a question of some importance. The idea is to tell the students from which perspective the case is written, to set the scene – and that is all there is to it.
  • 1 – 1.5 pages: Description of the company – not the whole history, but the relevant details, explaining what the company is doing, how they make their money. Most companies are to a very large degree formed by their history, so the relevant parts need to be told.
  • 1 page: Industry. Companies exist within a context, and you need to set it. Explain the industry, its evolution, and the company’s position within it. Do it succinctly, but leave more detail in than what is strictly necessary.
  • 1 – 5 pages: Specific issue. This is the meat of the case, the issue at hand, the story to ponder. Make sure you tell it logically and cooly, not leaving anything out, but also conveying the complexity of the situation.
  • 0.5 page: Conclusion, typically with the protagonist wondering what to do, often with some sort of event (board meeting, etc.) where he or she has to present a solution to the problem.

Most cases are just that – one case. You can have a B case and even a C case, but keep them short, since they have to be handed out and read in class. The B case should explain what the company did and perhaps introduce a new problem, the C case, if necessary, should bring some sort of closure, explaining what eventually happened. In my experience, it is very hard to get discussion after a C case – the students become exhausted. As a novice case writer, especially if you are writing about a company with a long history, it can be tempting to create a long string of small cases, but in practice this seldom works well – for one thing, it forces the discussion into a very predictable path.

The no-nos
No theory. A good case should be a description of an interesting situation, frequently a decision point – and nothing else. This means that there should be no theory and no discussion of the case in the case itself. Save that for the teaching note, or write a separate academic article about it. Not only does this make the case more realistic, it also means it can be used for more purposes than the one initially envisioned. This can be quite challenging for the traditional academic writer – but it is actually good practice to only present the facts (though, of course, which facts you choose to present constitute a discussion of sorts).

No hero, no villain. When teaching students how to analyze a case, I always start by saying that for most business situations, if is useful to begin the analysis with the assumption that people are not stupid and not evil. Consequently, when you write a case, make sure it has no heroes and no villains. If a case has a clear-cut hero or villain, it is a sign that you have not done enough research. Write things so that the students can see the issue from many perspectives.

No judgement. I frequently find, when reading prospective teaching cases, especially if written with the help of the communications department of a company, that judgements tend to be embedded in the text itself, though it is supposed to be as neutral and purely descriptive as possible. Do not write that a company is “leading” – instead, describe what they do, perhaps adding a bit of industry numbers for comparison, and let the decision about how leading the company is be up to the student. Do not write “Smith was a highly accomplished manager, leading a successful technology implementation.” Instead, write “Smith, at 29, was promoted to be the youngest vice president in company history after conceiving and overseeing the introduction of the first machine learning platform in the industry.”

No consultingese. Be very careful of weasel terms – is the company embarking on a strategic alliance or merely collaborating with another company for a specific purpose? (As a friend of mine said: “I buy all my socks at Marks and Spencer. That does not mean I have a strategic alliance with them.”) Avoid terms that are not well defined, and be precise in your language. Remember, a teaching case has the longest legs when it describes a human situation (since humans change slowly) and to not tie the narrative to specifically to a technology or a situation. (See the example of Fabritek below.) I quite often use old technology cases, and when the students complain, ask them to take out a pen and change the dates mentioned to current times, update the technology capabilities accordingly – and see if anything else changes.

Dramatic structure
A really well written case has dramatic structure – there is a beginning, a middle that builds up the story, and a really compelling issue at the end. The best cases are almost like a detective story, where you have to dig deep into the analysis to find surprising and sometimes counter-intuitive conclusions. One example of a “detective story” case is Fabritek 1992*, a very old (first published 1969, rewritten by Jan Hammond) case about a quality control issue in a small mechanical workshop. (Hat tip to Robert D. Austin, eminent case teacher, for making me aware of this case and showing me how to teach it.) The case is excellent because it starts with the company (strategic level), proceeds to describe a new situation and a new process (organizational or business logic level) and then introduces the problem (operational level.) Analyzing the operational details leads to one conclusion, which then can be discussed in terms of the organization and its business logic, which can then be placed into a strategic context. The case is excellent because it allows links between these levels – and also teaches the students that the devil indeed resides in the details, and that you as a manager better be very close to how the business you are leading works and makes money.

iPremier-front-pageA second case which shows quality and innovation is iPremier, written by Robert D. Austin and Jeremy C. Short, the first and only graphic novel (cartoon) case I am aware of. The story is about a small online gift company being attacked by hackers, exposing glaring gaps in their security procedures and forcing managers at various levels to make some really hard decisions. The graphic format is excellent in making the various characters real (though they, on average, tend to be way too good-looking for a normal business situation), illustrates technical issues in a way that is very understandable even by non-technology students, and has a cracking good storyline with a B and a C case. I like to introduce a few technical cases in my courses because, well, I don’t think there is enough technology in business schools, and this cases answers very well because it illustrates that certain technical decisions very much require top management attention – ignore (or mindlessly delegate) technology understanding and responsibility at your peril. The graphic format also provides a welcome break from the standard case verbiage, which can be a trifle dour on occasion.

Details, details, details!
Research cases – the kind that is published in refereed journals – tend to be written from a very specific viewpoint, and only facts pertaining to that perspective is included, often in a very abstract format. A teaching case is the direct opposite: It needs lots of details, frequently made available as exhibits (graphs, pictures, documents, tables, etc.) placed at the end, after the main text. A teaching case writer, when visiting a company to write about it, needs to notice the small details, much like a really good journalist does. I tell my students that they should prepare each case so well that they feel like they have worked in the case company – and to allow them to do that, you need to provide the operational details necessary. (Incidentally, having more details than strictly necessary has the added benefit of making the case realistic – in the real world, you have to decide what is important and what is not.)

Doing it – and reading about it.
grandongillI am not aware of many books about how to write a good teaching case, with one exception: Grandon Gill (pictured), professor at University of South Florida and an excellent case teacher, has written a book called Informing with the case method, which is available for free download in PDF, MOBI and EPUB format from his web site. It has lots of details, tips and tricks, not just about case writing, but also about case teaching and course planning. (For the latter, of course, I am duty bound to recommend Bill Schiano’s and my book Teaching with Cases: A Practical Guide.)

Last but definitely not least: Don’t underestimate how much work writing a proper business case is. Getting the details right, describing the dramatis personae, and making the storyline compelling is quite a challenge, in many dimensions different from the traditional academic article. On the other hand, should you get it right, you will have a very effective teaching tool for many years to come.

Good luck!

Analytics 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 second 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) list:

  • The Agency for Public Management and eGovernment (Difi) wants to understand and predict which citizens are likely to reserve themselves against electronic communications from the government. The presumption is that these people may be mostly old, not on electronic media, or in other ways digitally unsophisticated – but that may not be true, so they want to find out.
  • An electric power distribution company wants to investigate power imbalances in the electric grid: In the electric grid, production has to match consumption at all times, or you will get (sometimes rather large) price fluctuations. Can they predict when imbalances (more consumption that production, for instance) will occur, so that they can adjust accordingly?
  • A company in the food and beverage industry want to offer recommendations to their (business) customers: When you order products from them, how can they suggest other products that may either sell well or differentiate the customer from the competition?
  • A petroleum producing company wants to predict unintended shutdowns and slowdowns in their production infrastructure. Such problems are costly and risky, but predictions are difficult because they are rather rare – and that creates difficulties with unbalanced data sets.
  • A major bank wants to look into the security profiles of their online customers and investigate whether some customers are less likely to be exposed to security risks (and therefore may be able to use less cumbersome security procedures than others).
  • An insurance company wants to investigate which of their new customers are likely to leave them (churn analysis) – and why. They want to find them early, while there is still time to do something to make them stay.
  • A ship management company wants to investigate the use of certain types of oil and optimise the delivery and use of it. (Though the oil is rather specialised, the ships are large and the expense significant.)
  • Norsk Tipping runs a service helping people who are in danger of becoming addicted to gaming, an important part of their societal responsibility which they take very seriously. They want to identify which of their customers are most likely to benefit from intervention. This is a rather tricky and interesting problem – you need to identify not only those who are likely to become addicted, but also make a judgement as to whether the intervention (of which there is limited capacity) is likely to help.
  • A major health club chain wants to identify customers who are not happy with their services, and they want to find them early, so they can make offers to activate them and make them stay.
  • A regional bank wants to identify customers who are about to leave them, particularly those who want to move their mortgage somewhere else. (This is also a problem of unbalanced data sets, since most customers stay.)
  • A major electronic goods retailer wants to do market basket analysis to be able to recommend and stock products that customers are likely to buy together with others.

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. Now – a small matter of implementation!

Cool ad. Literally speaking.

This is our (NI Norwegian Business School) new ad for attracting international students. Which, if they do master’s courses in strategy, might mean that they will meet me. Be forewarned, I am just like this guy, with somewhat shorter hair and beard…

Looking forward to seeing you!

Big Data in practice

(This is a translation of an earlier post in my Norwegian blog. This translation was done by Ragnvald Sannes using Google Translate with a few amendments. This technology malarky is getting better and better, isn’t it?).
ml_mapI have just finished teaching four days of data analytics – proper programming and data collection. We (Chandler, Alessandra and the undersigned) have managed to trick over 30 executives and middle managers in Norway to attend a programming and statistics course (more or less, this is actually what analytics basically is), while sort of wondering how we did that. The students are motivated and hard-working and have many and smart questions – in a course taught in English. It is almost enough to make me stop complaining about the state of the world and education and other things.
Anyway – what are these students going to do with this course? We are working on real projects, in the sense that we require people to come up with a problem they will find out in their own job – preferably something that is actually important and where deep data analysis can make a difference. This has worked for almost all the groups: They work on real issues in real organizations – and that is incredibly fun for the teacher. Here is a list of the projects, so judge by yourself. (I do not identify any students here, but believe me – these people face these issues every day.) Well worth spending time on:
  • What is the correct price for newly built homes? A group is working to figure out how to price homes that are not built yet, for a large residential building company.
  • What is the tax effect of the sharing economy? This group (where one student works for the Tax Administration) tries to figure out how to identify people who cheat on the tax as Uber drivers – while making suggestions on how tax rules can be adapted to make it easy to follow the law.
  • What characterizes successful consulting proposals? A major consulting firm wants to use data from their CRM system (which documents the bidding process) to understand what kind of projects they will win or lose.
  • How to recognize money laundering transactions? A bank wants to find out if any of their customers are doing money laundering through online gaming companies.
  • How to offer benefits to customers with automated analysis? A company that supplies stock trading terminals wants to use data analysis to create a competitive edge.
  • How to segment Norwegian shareholders? A company that offers online trading of shares wants to identify segments of its customers to pinpoint and improve its marketing strategy.
  • How to lower costs and reduce the risk of production stoppages in a process business? A hydropower company wants to better understand when and why your power stations need repairs or maintenance.
  • How to identify customers who are in the process of terminating? A TV company wants to understand what characterizes “churn” – how can they identify customers who are about to leave them?
  • Why are some wines more popular than others? A group will work with search data from a wine site to find out what makes some wines more sought after than others.
  • Which customers will buy a new product? A group is working on data from a large bank that wants to offer its existing customers more services.
  • How to increase the recycling rate for waste in Oslo? REN – Oslo’s municipal trash service – wants to find out if you can organize routes and routines differently to better utilize trash trucks and recycling plants.
  • How to avoid being sold out for promotional items? One of Norway’s largest grocery chains wishes to improve their ordering routines so that customers do not get to the store and find out that there is no more left of the offer they wanted.
  • How to model fraud risk in maritime insurance? An insurance company wants to build a model to understand how to find customers attempting to fraud companies or authorities.
  • Which customers are about to leave us? A large transport company wants to find out which customers are about to go to a competitor so that they can take action before it happens.
  • What characterize students who drop out? BI enters 3500 new students each year, but some of them end after the first year. How can we find evidence that a student is about to drop out?
Common to all the projects – and so it’s with all the student projects I have advised since I started in this industry – is that you start with a big question and reduce it to something that can actually be answered. Then you look for data and find that you need to reduce it even more. Then you get problems that the data is either not found, unreliable or inadequate – and one has to figure out what to do with it. Finally, after about 90% of the time and money budget is gone, one can begin to think about analysis. And then there is a risk that you find nothing…
And that is an important lesson of this course: The goal is that the student should be able to know about actual data analysis to ask the right questions and have a realistic expectation of what kind of answer you actually can get.
There is a great demand for this course – so we have set up an additional course this fall. See you there!

Made my day!

digøkskjermI just got the message that the new bachelor program Informatikk: Digital Økonomi og Ledelse (Informatics: Digital Economics and Management) is now the most sought-after study program in Norway, with 19 applicants per available place (514 first-priority applicants for 27 available places).

Since I have taken the initiative to this program and developed it with colleagues at the University of Oslo (where I have an adjunct position, this definitely made my day. Week, actually.

Just sayin’…