Customer Reviews

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management

5 star:		(15)
4 star:		(7)
3 star:		(4)
2 star:		(1)
1 star:		(6)

 

 

It covers almost every aspect of data mining. It is clear, precise, goes to the point, and sometimes goes into some depth. If you are a marketing person, it will give you a very refined idea of what can be done with data mining, and what does it involves for your company ( remember the first thing you need is DATA!!). If you are an academic person, you will get a general idea of the different kinds of data analysis. You won't see any formulae or algorithms, after reading this book look for details somewhere else.

Although this book was not written as a text book, we have noticed that it is frequently used that way. For the benefit of instructors, we have collected some exercises and datasets that can be used in the classroom or for self study. These can be found at www.data-miners.com/companion.

The authors explain in a detailed way the most popular Data Mining techniques. The topics about Neuronal Networks, Decision Trees, Market-Basket Analysis and Memory-Based Reasoning are excellent. I think the topic Genetic Algorithms could be a bit more developed, but for the beginner is a good first overview. I have missed a topic about fuzzy logic. Given that the 90% of Data Mining projects are based on Marketing (1:1), the book is absolutely suitable for starting with these concepts, although I feel the book can be used in any other field (Just-In-Time Inventory, Demand Forecasting, Supply Value Chain, etc.) In my opinion, it was very useful for my work and I considered it as a reference book.

This book gives an overview of what data mining is and the tools available to perform it; Market Basket Analysis, Memory Based Reasoning, Automatic Cluster Detection, Link Analysis, Decision Trees, Artificial Neural Networks. Genetic Algorithms are also included, which, while not a data mining tool, are being used to train neural nets.

In each case the authors describe the principles behind the tool, its strengths and weaknesses and applications were it is applicable. The authors give tips on what data preparation is required for the tool, both in terms of data "massaging", (which is required for neural nets) and indicate were it is important to select training sets that have approximately equal proportions of "good" & "bad" outcomes, in order for the tool to predict correctly.

The descriptions include simple examples of the tool to give an overview of how the tool works. But as the title indicates, this book is for users who are considering using data mining tools. It does not describe how to use particular applications, neither does it include code examples (pseudo or actual) if you are interesting in developing your own tools.

The book is easy to read and includes many examples from their experience of data mining in the real world.

When I started data mining (over 25 years ago)after graduating from MIT, neither the name nor the techniques existed. I had to develop both concepts and approaches that were then applied to situations as varied as evaulating the US SBA for the Federal Government to mapping the market for and product requirements of ATM machines for a Fortune 500 to developing a strategy to export shoes (leading to a tripling of industry exports) for the Commerce Dept. to predicting the lodging demand in Orlando, FL during the gas crisis for a series of majorinvestors to making sense of survey data for a major credit card company. The approach accurately described the situation in all of these cases and led to major management decisions.

The authors go beyond what I used in those days, describing a series of techniques and their applicability - through easily understood case studies.

This is not only an intelligent book, it is a very easy read. It doesn't go into mathemetical complexities because that is unnecessary. There are many treatises available on those subjects. Just surf the web to find some of them.

This book provides the information required by managers, leaders, and entrepreneurs to understand their marketplaces and customers.

Having run many companies and turned around a lot more, it is clear that this understanding is often sorely missing. Once gained, sharp increases in profitability usually result. The trick is something like an "aha" effect, an invention. The data is stored in a person's unconscious and, through predictable processes, pops out somewhat on demand.

Data mining is the process of causing similar "aha's" to pop out of the unconscious, the collective memory, of a company.

I recommend this book highly as a way to gain an effective understanding of how to do this.

It looks to me as a good book, because it mixes the strategic approach with data mining techniques. It begins defining what Data Mining is, main tasks, Data Mining stages and then it follows with techniques.The best is that there is some chapters that covers the same topics but one with theoretical approach and the next with examples for those previous topics. You really understand what this is about.This is specially great for non native english speakers as me. (spanish) I guess it requires some statistical knowledge and some systems modeling too. If readers forgot something about probabilities, hypothesis tests, etc... i recomend having some statistical book next to you. I guess is a good book for business college students as me, getting involved in this matter.

Be careful, the first edition is MUCH older. Make sure you get the current 2004 edition.

There are most recent books, but this one is still worth reading first. This is especially true is you are an analyst. Managers of analysts might enjoy Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart or Competing on Analytics: The New Science of Winning, but analysts will need much more detail.

This the best single volume on Data Mining you can buy. As one who mostly teaches methodologies, I like that all the major topics are here: neural nets, market basket, cluster, and trees. But there are also techniques that SPSS and Clementine (the software packages I use) can not do like "link analysis". Also, unlike Larose Discovering Knowledge in Data: An Introduction to Data Mining, the data preparation reads like preparing data for data mining, not a carbon copy of preparing data for statistics. Regarding this issue see the excellent Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems). I have pretty much concluded that a data mining book that does not make clear that data mining and OLAP are not the same is not a great book. This book has an extended section on just that. It is highly readable and comprehensive.

Once in a while, you run into a book that sheds new light into a subject that you thought you knew. This book redefined what data mining is for me. It also showed me how it fits into the bigger picture of enterprise business intelligence.

I come from data warehousing background. I studied statistics and familiar with the techniques described. Until now, I regarded each topic as separate with its own functional applications.

Now I realize that all these pieces come together in a single solution that maps to all business processes. Also the examples of easy to understand marketing applications got me started in identifying various processes that can benefit from it.

Now I am only left with the details of implementation that I am eager to get started on.

This book labors to explain that Data Mining is possible. It spends 3 chapters explaining the business process flow of data mining as: 1. Figure out your business problem 2. data mine to figure out what to do 3. do it 4. measure to see if you did anything.

It's nicely written, but VERY BASIC

Data mining is such a simple thing that you wonder why more companies don't do a better job of mining their own data sitting on their own hard disks.

If a customer buys the first in a series of mystery novels, who better to send a note telling him that the second book is now available. That's the essence of data mining. This would allow you to get a much higher return on your mailing, saving money and increasing return on your marketing.

This is one of those books that you need to read every few months. Each time you go through it you will find some idea that will enable you to get more out of your data. It isn't a book heavy on programming, but on the concepts that have worked for others.

Highly recommended.