The practice of marketing analytics is not new. For years, organizations have been applying analytics to optimally price products and services, strategically promote to target customers and gain customer share. However, traditional marketing analytics may no longer be enough to remain competitive and retain market share. Many companies are moving to the next generation of marketing analytics by applying the latest techniques in machine learning to optimize their models and automate critical business actions and decisions.

At our next ALN Executive Forum breakfast, analytics leaders from two companies will share real world examples of how they are applying machine learning and predictive analytics to move their organizations to the next generation of marketing analytics.

Marketing Analytics & Machine Learning at Redbox

Redbox has built a machine learning model to optimize promotions issued through email marketing which has led to significant top line and bottom line value for the company. During this presentation we will explore the core business problem and the machine learning solution leveraged. We will also discuss our test & learn strategy to rapidly iterate on the model while driving revenue during market testing. Finally we conclude with challenges we encountered and key learnings for future analyses.

Marketing Analytics & Machine Learning at HAVI

In this presentation, we will share our experience of designing, developing, and deploying HAVI’s first predictive analytics solution. As part of our discussion, we will touch on the challenges and key learnings associated with migrating to the Cloud, implementing machine learning, leveraging open source tools, and adopting the Agile methodology to develop predictive analytics software. In addition, we will provide an overview of the why (the strategy), what (the product), and how (the methodology) as it relates to our decisioning around our predictive analytics solution.