For 70% of companies, ‘Big Data’ fail to generate returns above the cost of capital. This is bad, doubly so because the data is from the telecommunications industry which started exploiting big data early and where the scope for exploiting data is perhaps greater (and surely more obvious) than in most other established sectors. Indeed, by 2014 leading telcos were reporting fully 20% of their profits came from big data, clearly demonstrating the opportunities for getting it right. Let’s examine this in more detail.
Many organizations worry
Do we have enough data to gain insights and take meaningful commercial action? The short answer is ‘yes’. The long answer is ‘almost certainly yes’. In this case study, a Nordic giant in the automotive import and servicing business was facing a real crisis in their service centres where low utilisation was driving them to be unprofitable. They were initially sceptical that they would have enough quality data to gain practical insights, but at the end of this project they were astounded by the depths of insights and commercial actions delivered from the analysis, not just to reduce churn but across the business, their customers, markets, and competitors.
Classes 3-7 July 2017: We have updated our very popular training course to take advantage of the latest Microsoft Advanced Analytics technology and re-launched it as Marketing Analytics using Microsoft R and Azure. We have just announced public training dates and are looking forward to giving the course in the week 3-7 July 2017. This is a live, instructor-led course in an online classroom environment. There are class times to suit everyone; participants in Middle East and Asia register here for live classes 08:00-12:00 London time while participants in North and South America register here for classes 10:00-14:00 New York time. Please see the listings for additional time zones. This course has consistently achieved 100% positive promoter scores both on recommending the course and recommending the instructor.
What is the value of a good customer experience? From decades of experience, we usually quote around 20% EBITDA growth for companies who have a strategic focus and strong execution. However, almost everyone has low-hanging fruit in this area which can be harvested easily, as in this case study which delivered 5.5% growth. Nothing wrong with taking an initial tactical approach, prove the benefits, pocket the money, and then decide how to move forward.
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.