How many times did you lose customers because you address them with your marketing campaign?

You sent so many freebies and promotions that they actually got annoyed and leave.

And how many times did you actually waste money in freebies and promos on customers that would have bought from you anyway?

The answer usually is a lot, but there is an answer to this problem, and this answer is called uplift modeling,an innovative machine learning technique that relies heavily on causal learning and causal inference, an innovative AI concept that leverages causal relationships between causes and effects, and not just simple correlations like 99% of machine learning out there.

In this episode of the AI and Digital Transformation Podcast by G.M.S.C. Consulting, we are going to discuss the ins and out of this novel and powerful approach with Aleksander Molak, author of the book, Causal Inference and Discovery in Python.

Stay tuned, because if you are a marketer, this is a game changing technology.

Who is Aleksander Molak?

Aleksander Molak is a Machine Learning researcher, educator, consultant, and author who gained experience working with Fortune 100, Fortune 500, and Inc. 5000 companies across Europe, the USA, and Israel, designing and building large-scale machine learning systems. On a mission to democratize causality for businesses and machine learning practitioners, Aleksander is a prolific writer, creator, international speaker and the author of a best-selling book Causal Inference and Discovery in Python.

He’s a founder of Lesprie.io – a company that provides machine learning trainings for corporate teams, and the leader of CausalPython.io community.

Aleksander has provided workshops, talks, and trainings for companies across industries, including market leaders like Mercedes-Benz, innovative disruptors like TechHub, international consulting companies like Lingaro and more.

Check out our show notes to know more about Aleksander, his book, and his work.

Timestamp

(00:01:29) Who is Aleksander?

(00:05:06) Machine learning vs. causal learning

(00:12:27) Potential application of using causal learning - uplift modeling

(00:22:45) Executing a causal learning project requires meaningful data

(00:26:14) Causal learning as a low hanging fruit for businesses

(00:29:41) Evaluating causal models

(00:35:19) Balancing the cost of experimentation

(00:38:26) Other applications of uplift modeling

(00:41:13) Preparing for a causal learning project with Lespire Consulting

(00:44:03) Educating data science teams on causal learning

(00:46:53) When should someone buy uplift modeling?

(00:50:07) Concluding remarks and book recommendation

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