Data is the new oil, gushes The Economist in their 6 May 2017 edition. Just like oil fuelled a fast growing economy, so data is the power behind the current economy. Well, if that is the case, then we at CYBAEA are the downstream data scientists of the 21st century. We are concerned with refining, processing, and purifying the raw materials to innovate and grow in the data economy.
Google loves us 100%. At least that is what the PageSpeed Insights tool tells us for both Mobile and Desktop. And since we are currently promoting our popular Marketing Analytics using Microsoft R and Azure training course for live classes 3-7 July 2017, we thought we should try to make our digital masters happy. So we did. Even if we are not sure it is the right thing to do; it may even be evil.
Facing stricter regulation and low growth, Bupa Global, the ~£1bn revenues provider of international health insurance, looked for opportunities to acquire and serve new customer segments. Using robust data and customer insights they redesigned all their international health insurance products and service propositions and launched globally in record time.
I am not sure the term ‘Data Scientist’ means anything anymore. As often happens with new buzzwords, they take on a life of their own and the original meaning becomes diluted. But I do think the term was useful and could be useful to distinguish from other data related functions in the organization, and I feel it is worth reclaiming the term for this precise usage.
The Economist has two articles on the future of Insurance. The first article estimates that the US general (P&C) insurance industry had a combined ratio of 100.3% in 2016, ie a net loss, due to its complacent refusal to modernise and a stubborn reliance on manual processes. It proposes two ways out:
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.
How do we know that marketing works? It was National Poetry Day in Britain recently, and I do believe that poetry and imagination, and the wisdom and insights it can bring, has a place in business and corporate world. But wisdom isn’t knowledge; it does not immediately convince in the way that facts presented as a coherent story does. So when I am asked “how do we know marketing works?” the question usually means “how do we collect facts and present them in a persuasive narrative that will convince the board?”
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.)