Personalization is a team sport, and it is only as good as the weakest link.
Stephen H. Yu
Model-based targeting is a long series of iterative adjustments, and there are many things to be checked and tweaked.
Before you get started, you must recognize that there are multiple steps in predictive modeling.
Beware of technology providers who insist on a “one-size-fits-all” customer data solution.
Here are the essentials of what a CDP needs to be and do, and what the common elements of useful marketing databases are.
In late 2019, Gartner predicted “80% of marketers who have invested in personalization efforts will abandon them by 2025…”
A recent report shared that only about 20% of all analytics projects work turns out to be beneficial to businesses.
If a marketer sends you 20 promotional emails in a month, is that too much? You may say “yes” without even thinking about it.
What will be so different in this ever-changing world, and how can marketers better prepare ourselves for the new world?
Why do marketers still build models when we have ample amounts of data everywhere?
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.