Tyson P.

Yeah, I have made custom event reg microsites and always think, "we should have just used Eventbrite." It's easy to overthink this one. I still don't love the app but I still pick it.

Pure gold man! I love it.

Harsh R. Man.... I love hearing this stuff and I feel it gets overlooked with all the noise these days. What stood out most wasn’t the Loom (though smart move)… it was that you chose to be thoughtful. That mindset — noticing something real and taking the time to say it — is a rare advantage. Keep that up and you’ll never run out of meetings.

Yeah. It always is man! It's such an expensive mess to fix, we have to get creative with how we work around that enormous elephant in the room.

Yeah, that totally makes sense, Nuno P., and you're definitely not alone in that thinking. I dig the ambition. I think like this, too, but the reality of most clients' CRMs usually forces me to take a simpler approach! haha That trial conversion model is honestly how more scoring setups should start. It's focused, property-driven, and tied to a specific action, like converting in 30 days. What you're exploring now — extending that logic to a full company becoming a customer — is definitely doable. It just takes more orchestration. My only concern would be around the AI model acting like a bit of a black box, but it sounds like you and your dev team handled that well. You're right that the key is defining the right trigger events and making sure your model is powered by clean, consistent data. Both HubSpot and Marketing Cloud offer out-of-the-box scoring and prediction models, but they tend to be more manual and less flexible than a proper predictive approach. That's why I always recommend starting with the simplest possible prototype, proving the signal is there, and then scaling the complexity. Also, here are some tried-and-true CRM triggers that work well for Account-level scoring, especially if you're using enrichment services and have them fully integrated:

Since you're using HubSpot as the hub for the predictive model, but most of the raw data lives in Salesforce, you'll likely need to calculate rollups and custom metrics in Salesforce first (rollup fields, rollup helper, or similar), then sync them over to HubSpot. The native integration handles a lot, but scoring across multiple objects usually takes some setup to get clean, reliable data into the Company object where it can be used effectively. Building a plugin to automate this logic could be a great move. You've clearly got the context and experience to bring it to market. Good luck, and happy to keep the convo going anytime.

I have used Fyxer AI and I personally think it's one of the best out there. It can parse Zoom meetings and integrates with Salesforce. Depending on how many users you have the $22.50 tier is pretty solid.

Nice thinking, Nuno P. — I love this thread and how you're framing scoring. You're talking about building a global scoring model derived from everything in the CRM, then layering on enrichment from other tools. Salesforce Data Cloud, anyone? There are really two solid ways to approach this, and I totally see where Pat H. and Dan Rényi are coming from. Salesforce and HubSpot both offer out-of-the-box solutions for lead and account scoring. Personally, I like Marketing Cloud Account Engagement (formerly Pardot) for this — though it's often underutilized or not set up correctly. You're basically describing the two ends of a spectrum I see play out a lot: On one end, you've got the full-stack enrichment and scoring beast — Clay + Clearbit + Apollo + behavioral tracking + firmographic enrichment — all piped into a custom ML model. It's amazing if you've got the data, budget, and a dev team to keep it all running. It sounds like you had that previously. But it's not just about building the system — it's about maintaining trust in it and refining it over time. That can easily turn into a nightmare, and most teams fall short with the upkeep. Doesn't Chris Walker's Passetto aim to do something like this? On the other end, you've got a Salesforce Flow that updates a score or flag based on a few key fields (email type, job title, company size, country, intent signal). It's simple but effective — and more importantly, it gives teams a way to test hypotheses and start having better conversations about lead quality. That said, what you're describing is bigger than just lead scoring. You're talking about a predictive model that listens across the entire CRM — Leads, Contacts, Accounts, Opportunities, and even Custom Objects. That's a much more complex architecture. A Flow can definitely prototype this — it's quick, has no code, and is great for iterating — but once you're triggering logic across many objects and records, you'll likely hit DML limits or run into orchestration complexity. That's where Salesforce Data Cloud starts to make sense. It's designed to unify data across Salesforce (and beyond), consolidate it into a single profile, and apply segmentation, calculated insights, or even AI-driven predictions in real-time. If you're already deep in the Salesforce ecosystem, it's a powerful way to score based on everything without building brittle automation chains. So, wherever you start — I'd recommend this general path: