The 2016 conference on R in Insurance will be held on Monday 11 July 2016. This year we are back at Cass Business School in London, UK. This is the fourth time the conference is held and CYBAEA is proud to have sponsored all four conferences.
How many responses do you need in order to have an accurate measure of the Net Promoter Score? What is the confidence interval on your score? Do you really know if it has changed since last measure? If you are going to use the score for anything, you need to know the answers to these questions.
For the very impatient, a good rule of thumb is that with 1000 responses you (only) know your Net Promoter Score to within 10 points (±5) and it takes fully 100,000 responses to know the score to one point (±0.5).
I am excited to be giving the Analytics for Marketing online training course on 25-28 September 2012. Sign up before 25 August 2012 for the early bird discount.
Big can be a qualitative as well as a quantitative difference. The gas in the ill-fated Hindenburg airship, the gas that formed our Sun, and the gas that formed the Milky Way galaxy were just lumps of hydrogen atoms (with varying impurities). The difference was in the number of atoms. But that difference in numbers made the three structures into different things. You simply cannot look at them in the same way. If you try to model the galaxy in the way you model a balloon you will fail.
Insurance pricing is backwards and primitive, harking back to an era before computers. One standard (and good) textbook on the topic is Non-Life Insurance Pricing with Generalized Linear Models by Esbjorn Ohlsson and Born Johansson (Amazon UK | US). We have been doing some work in this area recently. Needing a robust internal training course and documented methodology, we have been working our way through the book again and converting the examples and exercises to R, the statistical computing and analysis platform. This is part of a series of posts containing elements of the R code.
We have created and managed analytics teams in commercial organizations (mainly telecommunications) across Europe. The teams were using SAS or SPSS. Our company now has a commercial analytics as a service offering and we mainly use R.
Commercial Analytics is the kind that makes money. From data to dollars, insights to income, this is all about how to run the business better. To do it and to do it well you need certain capabilities in place. This article builds a map of those business capabilities to help you assess, understand, and plan your business.
We have 20 years of experience of big data environments within a variety of industries including Research, Banking, Insurance, and Telecommunications. We have especially worked with customer data: Marketing, Risk Management, customer segmentation and -profitability, and customer-driven product development.
John Kay muses on interpreting statistical data: