1 Customer Dimension Table Maintenance Program for Insurance Released 28/11/ Today I am pleased to announce that the availability of a suite of C code to maintain large customer dimension tables in the Insurance Industry. Earlier this month I published a suit of 'Newsletters' which describe many of the issues and solutions to issues in large scale data warehousing. I have had numerous comments back saying that these 'Newsletters' are great value in learning about data warehousing. For those providing feedback, thank you. For those of you who browsed though them all you will have noticed that Newsletter #9 was all about building multi-level customer dimension tables for large insurance companies. It talks about the concepts of having multi-level dimension tables keying back to multi-level fact tables.Newsletter #9 Most people I have spoken to have difficulty understanding this concept, why it is important, and the value of it in demographic analysis (which may be expanded to customer attribute analysis) of large customer bases. The importance of being able to do this can be measured in the tens of thousands or hundreds of thousands of $US/€ a large company has to pay for hardware and software to be able to perform similar analytical processing on detailed level customer data or the constant re-building of summaries to perform such analysis. Or, worse still, doing business without this kind of customer attribute analysis because it's 'too hard' and 'too expensive'. Press Release From the desk of Peter Nolan Insurance companies, telcos, banks and retailers have great need for customer attribute analysis for functions as diverse as sales and marketing to fraud detection. It is no exaggeration to say that customer attribute analysis in insurance companies and banks provides the single biggest opportunity to improve the profitability of the company over the longer term. Especially in cross selling and up selling products and services. I have worked in company after company where response rates to marketing campaigns are lifted as by as many as 5 times and, on those rare and wonderful occasions, 10 times over the traditional 'scatter gun advertising' approach so common today. You may find it hard to believe, but I have worked on projects where we have sold life insurance products via direct mail and managed 20-30% purchase rates. Yes, that's 20-30% purchase rates for selling low end insurance products via direct mail. Most people don't believe it. The newsletter on customer demographic analysis was my effort at sharing with you that there is a better way. Today, I'm pleased to announce I am able to show you how to do customer attribute analysis in a better, more effective way. Today I am publishing a highly sophisticated customer dimension table maintenance program for the Insurance Industry. The actual source code you need to do this is being published here, for free, in C, for you to learn from and to use as you wish…

2 Customer Dimension Table Maintenance Program for Insurance Released In the Customer Dimension Maintenance Program for Insurance package you will find:  The source code for the program.  The DDL to create the input and output tables and views.  A small access database that shows you the input and output for the program.  The newsletter number 9 again so that you don't have to go hunting on my web site for it. I have published detailed comments about the customer dimension maintenance program for insurance on my Downloads page. Feel free to go to my Downloads to read more about it. It is my hope is that you will recognise this as a good idea. If it is better than what you have at the moment perhaps you will move towards more sophisticated customer attribute analysis capability in your company. The result being we all move the data warehousing industry that little bit further forward. If you think you have something better and want to share, by all means do so. I'd be happy to publish other peoples ideas on my web site with appropriate acknowledgement of course. Press Release From the desk of Peter Nolan Have fun reading. I hope it makes a difference to your data warehousing efforts. I am looking forward to being able to assist many of you in your data warehousing efforts. Best Regards Peter Nolan