Malcolm has over 20 years experience working with organizations to help them unlock value in their customer data. He strives towards the goal of democratizing analytics, and believes that these capabilities should be open to all. He is currently responsible for applied analytics solutions in our Customer Intelligence 360 offering.
I promised in my previous post on automated segment comparisons that I would reveal more about how SAS measures differences between segment profiles. To recap, we wanted to have a method that would determine: If two segments are different in a meaningful way. By how much? What descriptive attributes best
How different is different? Descriptive analytics is powerful. You get to see your data and seeing is believing as they say. But blindly believing what you see is not always the best strategy. Asking someone else if they see what you see helps but is still subjective. Objectivity starts with
In an earlier blog post my colleague, Suneel Grover, wrote about how to take advantage of the product recommendations technology implemented in SAS Customer Intelligence 360. He noted that two methods are implemented behind the scenes, a visitor-centric and a product (or item)-centric approach. And most importantly, while some sophisticated
Recommendation systems work best when you can provide them with as many relevant examples as possible. On the other hand, increasing the number of products or offers leads to recommendation blind spots, especially early on in training the system. This cold-start problem is a challenge for most recommendation systems. SAS
My first post on customer journey optimization set the scene by introducing the concept of marketing as a game to be won. The rules to this game are complex, some are known, and others can be learned. Above all, the game is built on a shifting landscape of customer, competitor
In my previous post I wrote about the Atari video game, Breakout, and how an AI technique (reinforcement learning, or RL) outperformed a human player. I also drew an analogy between Breakout and customer journey optimization. In Breakout, the environment is what you see on the screen – the blocks,
The customer journey is at the forefront of every discussion about modern marketing. The idea that customers move in premeditated or, at the very least, marketer-meditated paths between well-defined states is alluring (and comforting) to a marketing professional. Of course, even a cursory examination of a web path analytics (Sankey)
In Part 1 of this blog posting series, we discussed our current viewpoints on marketing attribution and conversion journey analysis in 2017. We concluded on a cliffhanger, and would like to return to our question of which attribution measurement method should we ultimately focus on. As with all difficult questions
Multivariate testing (MVT) is another “decision helper” in SAS® Customer Intelligence 360 that is geared at empowering digital marketers to be smarter in their daily job. MVT is the way to go when you want to understand how multiple different web page elements interact with each other to influence goal
