Markopolo AI - Reach 30–40% more customers with personal outreach at scale

@dennishlewis Hi Dennis, Thank you so much for the appreciation, currently the flow built is more suitable for consumer businesses, we will release for B2B usecases in January!

@siam_al_ahad Hi Siam, thank you so much for the comment! We had to do extensive prompt engineering, and for some cases model fine tuning to provide this with 20 languages (20 different cultures) worldwide now. We are adding up more and more contextualization everyday.

@harkirat_singh3777 Thank you so much for the warm wish! Means a lot! Glad that you have asked this question, in terms of behavior signal, we have intentionally kept it weightage dependent and the key behavioral patterns that emerged as strongest predictors were:

1. Deep engagement duration - Time spent studying product details, reading philosophy/about pages, checking specifications

2. Research intensity - Multiple tab behavior, external validation searches, fact-checking patterns

3. Friction points that didn't cause exit - When someone encountered barriers (missing certifications, price concerns, size issues) but stayed engaged rather than bouncing
   
   P.S. Just checked out Zivy, loved it!

@the_esc Hi Evan, Thank you so much for the question! We infer it in real-time from behavioral patterns, then our AI creates a communication strategy that matches their decision-making psychology.

Here's the general approach:

Pattern Recognition Phase: Early behaviors reveal decision-making style - someone zooming 5x on product details for 3 minutes is fundamentally different from someone rapid-scrolling through 6 items in 2 minutes. Night browsing vs. comparison shopping across tabs. PDF downloads vs. social sharing. Each pattern cluster maps to a psychological archetype.

Digital Twin Analysis: We build a "customer agent" that analyzes their complete journey and outputs both a behavioral profile AND a communication strategy.

@saurabh_khebade1 Lets goooo!

@musharofchy Thank you so much! Means a lot! 🙏

@mahin_aleem Thank you so much for your comment! Every event gets converted into a 384-dimensional vector stored in our vectorized_events, These vectors capture nuanced behavioral patterns like:

• 3-minute product view with 5x zoom on details = design-sensitive buyer

• Rapid scrolling + immediate wishlist add = impulse decision maker
  
  Then we do multi factor scoring. And attribution is our strong suite, our earlier product was all about attribution analytics, from Multi Touch to incrementality - also we give our users option to use other analytics like Triple-whale to attribute our recovered revenue so that it works like Triple-whale certified to them!

@prionto Hi Prionto, Thank you so much for the comment! We identify behavior signals across a wide spectrum, do predictive analysis and communication strategy analysis, best thing is - all of these happens in real time! Looking forward to the call!

@sumaiya_shuma Thank you so much Sumaiya for the appreciation! Please give it a try, would love to help you with your usecase!

@cruise_chen Hi Cruise, Thank you so much for the appreciation! We cover a range of these behaviors, categories including -

Browsing & Engagement, Shopping Intent Signals, Abandonment Triggers, Cross-Channel Behavior and more!