The first part of our Marketing Analytics Using R course covers campaign analysis with test- and control groups and campaign optimisation using lift curves and predicted responses. Among the many topics covered, we discuss what is wrong with lift curves. They are a standard tool in marketing to select a target group for a campaign based on predicted response propensity, but they way they are used is wrong, or at least sub-optimal.
Consumer-facing companies in developed economies have experienced little or no growth since the global recession of 2008 and 2009. This is the damning introduction to a recent (August 2016) article from Boston Consulting Group. Consider it for a moment. It has been eight years. During that time we have seen an explosion of data becoming available about our markets and about our customers and their behaviours from a multitude of channels and devices; this includes both data internal to the organization and information from third-party providers. Cloud computing platforms are now readily available from vendors like Amazon, Microsoft, and others, making storage and analysis of this data possible for everyone. Data mining and analytics software and has progressed tremendously making everyone a potential data expert. And yet we have seen no growth? Not even from the very companies that should have benefitted the most from these changes. What has gone wrong? BCG points the finger at the immaturity of the Customer Insight function.
If you had asked me two years ago if Microsoft was a serious vendor for data science and analytics infrastructure and tools, I would have laughed. At the time their offering seemed to me to consist of Excel against SQL Server. There is nothing really wrong (or exciting) about SQL Server, but friends don’t let friends use Excel for data analysis or indeed for anything that matters at all, so that whole proposition was a non-starter. But things have moved on, so how does Microsoft stack up now (mid-2016)? (You can skip right to our conclusions if you are impatient.)
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.
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.