Why do marketers still build models when we have ample amounts of data everywhere?
Stephen H. Yu
Marketing professionals mess up good models “after” they’re built. (That happens a lot.)
Many organizations put unreasonable expectations on data scientists. Their job descriptions require a superhuman.
The first step in analytics should be “formulating a question,” not data-crunching.
There are so many ways to mess up data or analytics projects, may they be CDP or whatever sounds cool these days.
Users are quickly realizing that investing in AI is not the end of the road.
I find more and more people use the term “machine learning” when they really mean to say “modeling.”
Yes, data is an asset. But not if the data doesn’t generate any value.
They say data is an asset. I say it, too. If collected data are wielded properly, they can definitely lead to financial gains.
It’s the junk food of marketing. We all know that the email batch and blast practice really isn’t good for anyone.
Some even claim that human behaviors are just algorithmic responses developed over past 70,000 years or so.
Last month, I talked about factors marketers should consider for attribution rules. Here, I would like to get a little deeper.
After each campaign effort, a good database marketer is supposed to study “what worked, and what didn’t,” using attribution rules.
We are obviously living in a multichannel marketing environment, whether we are marketers or consumers.
Lester Wunderman is called “the Father of Direct Marketing” — not because he was the first one to put marketing offers in the mail.