Data and analysis of data have, in some form, been used to aid decision making since ancient times. So why, after all these centuries are data and analytics not more embedded in corporate decision making?
A lot of marketing research is aimed at uncovering why consumers do what they do and not just predicting what they'll do next. Marketing scientist Kevin Gray asks Harvard Professor Tyler VanderWeele about causal analysis, arguably the next frontier in analytics.
Though it doesn’t get a lot of buzz, sampling is fundamental to any field of science. Marketing scientist Kevin Gray asks Dr. Stas Kolenikov, Senior Scientist at Abt Associates, what marketing researchers and data scientists most need to know about it.
A lot is changing in the world of marketing analytics. Marketing scientist Kevin Gray asks Professor Michel Wedel, a leading authority on this topic from the Robert H. Smith School of Business at the University of Maryland, what marketing researchers and data scientists most need to know about it.
Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering. The word learning in the term stems from the ability to learn from data.
Propensity scores are used in quasi-experimental and non-experimental research when the researcher must make causal inferences, for example, that exposure to a chemical increases the risk of cancer.
Kevin Gray is president of Cannon Gray, a marketing science and analytics consultancy.
Making Sense of Machine Learning