Alright, I need to get this off my chest after a month of testing. We've been running Gemini's lead scoring in our sales pipeline at work, and I'm convinced the "AI-powered" scoring is just putting a fancy UI on what amounts to a random number generator.
Here's my evidence. We set up a clean test: 50 inbound leads with near-identical profile data (same industry, similar company size, identical webpage interaction). According to the criteria we configured, these should have landed within a tight scoring band. Instead, we got scores ranging from 35 to 82, with no discernible pattern. The "explain score" feature gave us generic, non-actionable reasons like "high engagement" across the board, even when the engagement metrics were logged as identical.
It feels less like predictive analytics and more like:
```
score = base_score + (Math.random() * 30)
```
I'm all for AI, but this seems like a black box that adds little value over a simple, rule-based system you could build yourself. We've spent hours tuning the "importance" sliders for fields, but the output stays wildly inconsistent.
Has anyone else done a similar deep dive? I'd love to be proven wrong, but right now it's costing us more in time verifying its erratic scores than it's providing in insight.
What's your experience been?
- Did you find a config tweak that actually made it reliable?
- Are we just using it wrong?
- Is the real value somewhere else in Gemini that we're missing?
-jk
Oof, that's a frustrating test. Your controlled experiment with identical leads is exactly the kind of scrutiny these tools need.
While I don't think it's purely random, your experience highlights a massive issue with "black box" scoring models that aren't built on your own historical conversion data. If Gemini's base model is trained on a generic dataset, its weighting for things like "engagement" might be factoring in hidden variables you can't see, or even time-of-day latency in processing events, which would create that spread. The useless "explain score" output is the real red flag for me.
Have you tried scoring those same 50 leads with a simple, manual rule set in your CRM as a baseline? It might prove your point that transparency beats mystery math. I've seen similar inconsistency when the model isn't periodically recalibrated with closed-won/lost outcomes.