Who’s likely to be your valuable customer? What will their value be in next few years? How long will they continue to do business with
Stephen H. Yu
All of this hype about machine learning must be addressed somehow. This blog post is about how marketers can coexist with machines, and
Any serious Trekkie would immediately recognize this title. But I am not talking about the Borgs, who are coming to assimilate us into
Personas are like menu items, each representing key characteristics of target customers that marketers need to know to push their
Machine-learning is getting better at recognition and categorization by leaps and bounds, for sure. But do they understand the purpose?
The main job of a modern data scientist is to answer business questions for decision-makers. To do that, they have to be translators
Too many marketers are personally annoying their customers in the name of personalization. For that reason alone, I am looking for an
You get to hear “actionable insights” whenever analytics or roles of data scientists are discussed. It may reach the level of a
When faced with a large amount of unrefined, unstructured and uncategorized data, we must indeed fix the data first. Let’s not even
What do you think that ERA (Earned Run Average) stands for? If you can paint the quality of a baseball pitcher with a bunch of
I have been writing about using “model-based” personas stemming from a 360-degree
In the age of constant bombardment with marketing messages, staying relevant to prospects and customer is not just good practice in the
One-dimensional techies will be replaced by machines in the near future. So what if they’re the smartest ones in the room? If decision-
Let’s talk about why buzzwords are bad for the data and analytics business. I don’t entirely deny that there are some benefits of
The thing about predictive analytics is that the quality of a prediction is eventually exposed — clearly cut as right or wrong.