3 MINUTES BULLSHIT WITH GEORGE #41: AI-Powered Loyalty Programs with Alexander Guerra             

Episode #41 of 3 MINUTES BULLSHIT WITH GEORGE featured Alexander Guerra, Senior Director, CRO at Baz Súperapp. We discussed AI-driven Loyalty Programs.    

You can listen to the full episode here: 

In this episode of 3 MINUTES BULLSHIT WITH GEORGE, George Natsvlishvili is joined by Alexander Guerra, Senior Director, CRO at Baz Súperapp. Baz Súperapp is an information technology company that provides a mobile application with multiple features of digital transactions.


Alexander shares his experience as a Mobile Growth Expert in AI Loyalty Programs. 


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Connect with Alexander and Baz Súperapp here:



Timestamps:

00:00           Dance

00:14           Alexander’s Introduction

00:29           AI for Loyalty Progam

02:18           Bloopers



Transcript & Key takeaways from the episode:

In recent years, data science and machine learning teams have advanced personalized marketing efforts aiming to maximize customer lifetime value.



George: A question is about artificial intelligence and loyalty programs. What is your experience on that and can you share with us? 


Alexander: I think over the past 2-3 years, it's been growing super fast with how the data science teams, machine learning, ops teams made this more personalized. So every time you have an offer from different points: on a credit card, on my airline or a personalized offer, sometimes there's a machine learning wall behind that gives you insights into which customers need to have that promotion in order to maximize lifetime value. So that's super exciting and working alongside with a lot of product teams, UX teams, growth marketing teams. Super exciting. Yeah…


George: So, which tools you are using for AI? 


Alexander: Yeah, it's powered by different models. One specific, one that we use is called an exploration and exploitation phase mode, which is definitely a way in which we train the model, for a couple of weeks. Then model learns how the customers react to different offers. And then based on different attributes, is if it's because the customer has iOS or Android, a female or a male, or something… It can be demographic, it can be functionality-based, it can be based on location. It gives you different kinds of attributes when you map this out you Identify different patterns. So when you send these offers and then you see how customers react over time, you can use your budget more precisely, more accurately, in terms of like I can spend x amount of money and then I'm gonna have a specific amount of return over a specific period of time. So these models are working that way.   


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