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Mastering Predictive Modeling: Insights and Techniques

Join Eran Friendinger, CTO of Voyantis, in a webinar hosted by Meta on Mastering Predictive Modeling: Insights and Techniques. Discover how predictive and prescriptive AI are driving growth in the fintech space through real-world case studies from companies like MoneyLion in the consumer sector and HoneyBook in SaaS. In addition, Eran will provide a step-by-step roadmap to help you understand how to organize your AI journey and implement these insights for optimized growth strategies and smarter decision-making

09
Sep
2024
12:00
PM
EST
I want to join

Highlights

01

When control is taken, signals still matter

Ad networks are introducing more automation, reducing manual settings in favor of AI-driven optimization for targeting and bidding. While it may seem like you have less control over optimization, two critical levers remain at your disposal: Generative AI to scale your creative optimization, and Predictive AI to send valuable signals to the ad network. These signals help train their AI to acquire high-value customers for you.

02

pLTV to the rescue

Users all look the same on day one, making it tough to optimize acquisition when true value can take weeks or months to emerge. Who will request a loan? Who will activate that investment plan? To help ad networks perform better, sending user value signals early—with day one being crucial—is key. After that, signals lose their impact, forcing your team to rely on tCPA campaigns and upper-funnel events with limited long-term value. But with predictive LTV, you can change all that.

03

So predictions are enough?

Well, there’s a bit more to it. You need more than one model to start with—considering factors like timing, accuracy, and value adjustments throughout the user lifecycle and the ad network’s algorithm life cycle. You also need to account for data changes and potential losses, which is why you need three AI layers: one for predictions, one for communicating with the ad network, and one to maintain quality and retrain models as needed

"In fintech, there’s a wealth of extra data compared to other industries—KYC, deposits, bank account connections, and risk qualification. It’s incredibly valuable. Plus, you have data like visits, email sign-ups, and session length, all of which help create better predictions."
Eran Friendinger
Eran Friendinger
CTO @

Mastering Predictive Modeling: Insights and Techniques

"As the customer journey progresses, you gather more data, allowing you to continuously refine predictions. In the crucial first seven days, especially within the first 24 hours, it’s essential to frequently update user predictions as new data flows in."
Eran Friendinger
Eran Friendinger
CTO @

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