In the upcoming series, we’ll explore the intersection of “data is the new oil” and “marketing is the new sales” by giving simple and accessible examples of marketing analytics that will help marketers better understand what’s available or what to ask for, and for machine learning engineers to quickly put to use in any organization.
Increasingly, companies like Microsoft are democratizing AI for the masses, unlocking new power that smaller organizations could only ever dream of before. However, not all your data can be used with every public cloud API, because of data privacy concerns, network segregation, IT architecture standards, or any number of other reasons. For this reason, Microsoft has offerings such as as Azure Machine Learning Studio, Azure Machine Learning Service, Azure Notebooks, and Azure Databricks. Using these, you can reproduce many capabilities otherwise offered through the API’s (anomaly detection, logistics intelligence, optical character recognition, and more), or create your own that meets a specific need.
Each post in this series will offer a Detroit Data Lab Presents: Marketing with Machine Learning starter kit, consisting of the following:
- A specific type of problem to solve, and what it means for marketers
- One type of model that provides insight into the problem, and a brief explanation of the math behind that model
- Data specifications, for communicating with your teams on what data to collect, and how to store it
- And of course, a snippet of R code for trying it yourself!
Where it’s applicable, I’ll provide additional information around the Microsoft services used to complete the work. What this course will not be, is instructions on deploying an R Server to model data from Azure HDInsight. The focus is on improving marketing outcomes, not out-Hadoop-ing your neighbor.
Any marketer, armed with the knowledge of how and when to embed machine learning in their work, is easily worth 100 times more than their competition. Let’s make it happen!