Recently, I read a promotional email from one of my favorite market research vendors. The email’s first message was about propensity models, one of my favorite tools. I wondered if they really have the sample sizes necessary to create accurate scoring; in my experience, it requires very large samples that most firms cannot afford or execute.

Suppose you are driving your car on a dark and curvy mountain road, on a rainy night, with a very foggy front windshield, dim headlights, and a perfectly clear rear view mirror to guide your path forward? It’s not a comforting scenario. Surprisingly, this is how many companies still operate when attempting to understand and improve their customers’ experiences and the resulting customer satisfaction (or JD Power/Net Promoter) ratings. They know a great deal about the past but very little about the future; they know a lot about the average customer but very little about the individual customer.

When it comes to life insurance leads, too many companies are wasting their marketing dollars. In fact, their cost per lead is 2-3 times too high. 

  Executive Summary Today's member-based organizations (or any direct marketing company) can combine predictive analytics with research to efficiently identify which members will respond to cross-selling, which members won't, and which sales messages will be most effective.  The result is optimum sales, reduced direct marketing costs, and an ROI that is higher and demonstrable. Editor's Note: This blog topic applies to any company that relies on direct marketing via digital, social, mobile, email, direct mail and tele-marketing.

Why Some Analytics Initiatives Are Failing "Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway." — Geoffrey Moore Geoffrey Moore, author of Crossing the Chasm and Inside the Tornado, certainly got this quote right. And, he's not alone. The age of analytics is here and there's no turning back.

I recently attended the 2016 DMA MAC conference in Austin, Texas. As always, these conferences serve to reinforce existing best practices and ideas, while exposing me to new and different trends in the industry. In this post, I offer up a mix of both types of insights in the form of seven easy-to-consume slides.

Executive Summary Snapshot To maximize results, companies must fully leverage data and analytics Most companies use segmentation, consumer data, or predictive analytics For best results, companies must combine all three disciplines

I am a huge proponent of advanced analytics and predictive modeling to improve marketing results. Moreover, I embrace automated modeling, the integration of clusters (or segments) and modeling, and the use of omnichannel campaign management tools. In most cases, more automation and data and analytics mean better marketing results. Time after time, we see marketing results soar when consumer data and behavioral data are used to develop predictive models that allow for perfectly timed and customized messages. From health insurance, to life insurance, to retail and e-commerce, to utilities, and even CPG, our advanced analytical solutions can make heroes out of our marketing clients. To be clear, the power of advanced marketing analytics never ceases to amaze me. With that said, the advent of machine learning and unsupervised marketing execution has ushered in an era of danger and pitfalls.