CRM and ERP News

Two more book reviews, "Good to Great" and "Creative Destruction"

Good to Great first. This book will (probably) terrify you if you work. Clearly outlining critical characteristics of super-successful companies it show over and over again why most companies are not. Think about where you work, and how you invest, in the light of what the authors discuss. Like Execution, another book I really liked (and reviewed here), it analyzes how companies that sustain a high level of effectiveness might do that and gives some practical advice you can follow, whether or not you are the CEO. In addition this book is highly recommended for anyone thinking about becoming a CEO or engaged in a CEO search.

Unfortunately the same cannot be said of Foster and Kaplan's book "Creative Destruction: Why Companies That Are Built to Last Underperform the Market - and How to Successfully Transform Them". The book starts of reasonably well. Its general themes explaining why large companies tend to behave in ways that make them less effective at responding to change than the market are well described. As the book tries to show examples of companies that did or did not respond well to the forces of change in business they lose their way. Not only do they extol a number of companies seemingly purely because they were founded by friends from McKinsey, they also use Enron as a successful example! Too many of their examples have not done well since the book was published and that undermines their message. The book also lacks concrete advice, though I must confess to skimming towards the end. My takeaway? The market as a whole will ALWAYS innovate more effectively than any company so get over it and be prepared for companies to come and go and change constantly. There's not much, if anything, you can do about it. The book should have been subtitled "Why Companies Underperform the Market in the Long Run" as that's really what it comes down to.

You can buy "Good to Great" here and "Creative Destruction" here.

I am getting caught up on book reviews over the break. Today's is Data Mining Techniques by Berry and Linoff. This is one of the classic works on data mining and well worth the read.I really liked the book both because it is well written and because, although it drilled into a fair amount of detail about some of the techniques, it started each new section off at a high level. This allows someone without a statistical background, such as me, to read as far as I can in each section and then skip ahead to the next technique. This is a nice change from books that simply get more and more detailed as page follows page, preventing you from gaining an overview of the subject. The book introduces data mining and a methodology for applying it, talks about some of the applications in "Marketing, Sales, and Customer Relationship Management" (as the subtitle puts it), walks through some statistical techniques and then spends the bulk of the book on various data mining techniques. It wraps up with a nice summary of how data mining plays with other technologies and with some practical advice on getting started.

One of the best summaries of where data mining, and indeed EDM, fits is given early in the book where an enterprise is encouraged to:

• Notice what its customers are doing
• Remember what it and its customers have done over time
• Learn from what it has remembered
• Act on what if has learned to make customers more profitable

The authors point out that Data Mining is focused on the "Learn" stage or, as they put it data mining suggests but businesses decide. EDM, of course, is concerned not only with learning but also with acting, most particularly acting by automating decisions in front-line systems. Merely finding patterns is not enough - you must respond to the patterns and act on them, ultimately turning data into information, information into action and action into value.

The methodology section, and the subsequent notes that relate to applying these techniques in real life, talked about the feedback loops between steps in data mining - there is not a linear "waterfall" sequence of steps but constant iteration and learning. They also emphasized the importance of finding the right business problem at the beginning - start as someone once said, with the end in mind. This was reiterated when they quote Voltaire who said "Le mieux est l'ennemi du bien" ("The best is the enemy of good"). In other words, don't get hung up on trying to find the perfect algorithm, perfect answer. Instead build something that is good, that works, and learn and improve over time.

The authors made a big point out of the value of data mining for "mass intimacy", where you want to treat customers differently and there is a business reason to do so but where customers are too numerous to be assigned to staff. One of the issues they pointed out was that staff must be trained in customer interaction skills while also using all the data you have. This can be a real challenge and is one of the reasons I prefer an EDM approach, where the decisions those staff need to make are automated, to other approaches. By giving them the decisions they need you free them to work on the relationship (as I have discussed before). The value of data mining, and EDM, in building a customer-centric organization cannot be overestimated.

Some random snippets of useful stuff from the book:

• A model "can result in insight" and "produce scores". The first kind is used in EDM largely to product rules while the second is often embedded directly in the decision services being built
• Analysis can be directed (find the value of something) and undirected (find structure)
• Data visualization is very useful during the initial exploration of information.
• There is some discussion of the difficulty in deploying models when the step involves"a programmer takes a printed description of the model and recodes it in another programming language so it can be run on the scoring platform". EDM's focus on automating the deployment of models into a rules-based decision service is designed to address this issues.
• Besides coding the actual model, data transformations are also a big issue and remain one even in EDM.
• Decision trees are "powerful and popular" for classification and prediction because they can be represented by, and represent, rules. Indeed decision trees are a cross-over artifact between rules and models that are critical in EDM also. One of the things that makes trees particularly useful is because they need less data preparation as they can handle all kinds of variables well.
• The authors emphasize repeatedly the importance of time series data e.g. detecting early signs of attrition by tracking all actions of checking account customers in the time up to when they leave a bank. The time-based signatures thus created are great predictors. They note also that this is one of the weaknesses of data warehouses when using them for analytics - they tend to arrange data by absolute time/date when the analytics are more useful relative to an action or event.
• The value of neural nets is noted but the problems neural nets have with respect to traceability and explicability are also noted. This makes neural nets great for things like fraud detection, where results matter and reasons matter less, and poor for things like credit assessment where regulators expect to see compliance with rules.
• The section on market basket analysis and association rules is very good and describes these forms of undirected analysis well. They point out that these can, if you are not careful, describe the history of marketing promotions rather than genuine decisions to purchase products together. They also give some good examples of using product hierarchies to generalize where some products are much lower volume than others.
• They describe a pyramid with operational data on the bottom, summary data next, the database schema on top of that followed by metadata and finally busienss rules - what's been learned from the data.
• They worry that"rules" are not actionable but I think this is because they focus on rules that describe the data not on rules that describe the actions to be taken

You can buy the book here and it should definitely be on your bookshelf.

Technorati Tags: algorithms, analytic application, business intelligence, business rules, CRM, customer insight, customer segment, decision automation, EDM, Enterprise Decision Management, marketing, predictive analytics, segmentation, statistical analysis, data mining

Over the weekend I finished "Execution. The Discipline of Getting Things Done" by Larry Bossidy and Ram Charan. This book is a succinct summary of all that is wrong in many companies. Larry and Ram analyze many of the most dysfunctional behaviors seen in large corporations and lay out some steps to address them. While many of their stories focus on senior management and execution failures, their suggestions and guidelines work just as well for all levels of management. If you are responsible for planning and getting things done, this book will give you some tips and ideas as well as codifying your "gut feel" for why some people just don't get things done. My only complaint with the book was that it did not address the problems of getting your information systems to "get things done". As businesses are increasingly embodied in their information systems I think this is going to become more and more important. Clearly this is my bias but to give you a sense of what I mean, here are some of my favorite quotes from the book with commentary.

• "when a company executes well, its people are not brought to their knees by changes in the business environment"
  But if that company has information systems that do not change easily then it will lack the agility it needs to respond to these changes. In reality most businesses now have information systems that must be changed to cope with a new business environment. If these systems are hard to change, they will be brought to their knees.
• "leaders placed too much emphasis on what some call high-level strategy,...,and not enough on implementation" and "unless you translate big thoughts into concrete steps for action, they're pointless"
  Ram and Larry are talking here mostly about the implementation in terms of making sure successive layers in the organization can deliver on the strategy - that all the pieces add up. Again, if the lowest levels of your organization are driven by information systems, or if your customers interact directly with your information systems, you need to also be concerned with the implementation of your strategy in those systems. But most information systems are impenetrable to most business people and so it can be hard to tell, let alone ensure this.
• "If your business has to survive difficult times, it if has to make an important shift in response to change - and these days just about every business does - it's far, far more likely to succeed if it's executing well"
  I have written a lot about the need to have agility in your information systems to cope with change but I thought this quote brought home how essential this is.
• "when decision-making is decentralized or highly fragmented, ..., people at many levels have to make endless trade-offs"
  In reality people at every level are making trade-offs and you need to decide how to make sure that the right trade-offs are being made even when the trade-off is being made by someone with limited business know-how or by an automated system. Using analytics to embed effective risk management and risk/reward trade-offs will help make the information systems at the bottom of your organization manage this.
• "Behaviors are beliefs turned into actions...They're where the rubber meets the road"
  The business rules embedded in your information system are where the rubber hits the road. They decide how your website treats customers, how your IVR system works and so on. Controlling them is essential for turning your beliefs into behaviors.

There were some other interesting sections from an enterprise decision management or EDM perspective. One of the building blocks identified was "insist on realism" and it struck me that this is part of what makes the use of analytics in EDM so powerful. Analytics are, because they are derived from actual data, steeped in realism. Using them to drive decisions can really improve the amount of realism in your decisions. Similarly the use of rules to define how customers are treated allows for a realistic assessment of how they were, in fact, treated in a way that interviewing people and asking them how they treat customers will never be.

Finally I thought the quote about execution below was lovely and very relevant to EDM. EDM is not tactical, it is fundamental to your strategy. If your systems don't follow the rules your strategy implies or use the data on which you based it, how likely are they to do it right?

"People think of execution as the tactical side of business. That's the first big mistake. Tactics are central to execution, but execution is not tactics. Execution is fundamental to strategy and has to shape it"

You can buy the book here.

Technorati Tags: analytics, business agility, business rules, decision automation, EDM, Enterprise Decision Management, risk management, strategy, management