Blog posts from CYBAEA

Net Promoter Score confidence intervals 2: maths and stats

14 January 2016

We looked at a rule of thumb for confidence intervals and what that means for a business manager in our previous post. Now we do the maths, stats, and R code for the practitioner.

Read more (~1670 words)

R in Insurance 2016

6 January 2016

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.

Read more (~110 words)

Net Promoter Score confidence intervals

4 January 2016

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).

Read more (~1190 words)

Analytics for Marketing online training 25 - 28 September 2012

1 August 2012

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.

Read more (~210 words)

When Big Data matters

21 March 2012

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.

[Hindenburg, Sun, galaxy]
Just a bunch of hydrogen atoms: when “big” makes a qualitative difference.
Read more (~2170 words)

R code for Chapter 2 of Non-Life Insurance Pricing with GLM

13 March 2012

We continue working our way through the examples, case studies, and exercises of what is affectionately known here as “the two bears book” (Swedish björn = bear) and more formally as Non-Life Insurance Pricing with Generalized Linear Models by Esbjörn Ohlsson and Börn Johansson (Amazon UK | US).

Read more (~3720 words)

R code for Chapter 1 of Non-Life Insurance Pricing with GLM

1 March 2012

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.

Read more (~1690 words)

R versus SAS/SPSS in corporations

28 October 2011


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.

Read more (~810 words)

Commercial Analytics: The Capabilities

5 October 2011

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.

Read more (~2340 words)

5 common pitfalls of commercial analytics projects

5 September 2011

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.

Read more (~2140 words)