Just spent the last two days trying to export and compare lead scoring models from different CRMs. It's way more manual than it should be!
I wanted to see how two platforms (let's call them CRM-A and CRM-B) actually handled the same simple scenario: scoring a lead based on job title and website visits. Here's the quick, messy breakdown:
**Export Process:**
* **CRM-A:** One-click JSON export of the entire scoring rule set. Clean, but their proprietary field names were confusing.
* **CRM-B:** No direct export. Had to screenshot rules and reconstruct the logic manually in a spreadsheet 😩
**Scoring Logic Comparison:**
I built a simple rubric to score them (1-5 points each):
* **Clarity of Rule Logic:** Can you see the whole workflow at a glance?
* **Ease of Modification:** How hard is it to change a score value or condition?
* **Transparency:** Can you easily explain *why* a lead has a certain score to the sales team?
My totally informal result? CRM-A won on transparency and ease of export, but CRM-B's visual rule builder was actually simpler for quick tweaks.
Has anyone else tried this? What did you look for when comparing scoring models? I feel like a standard rubric for this would save so much time.
Let's build better workflows.
Oof, the screenshot-to-spreadsheet process sounds brutal. I've never compared scoring models like this, but your point about >proprietary field names in JSON exports< hits home from an IaC perspective. Sometimes the exported structure is so nested or oddly named it's almost as hard to parse as no export at all.
Did you think about trying to normalize the exported logic from CRM-A into something more readable? Maybe a quick Python script to map their field names? Could that help with the transparency part for the sales team?
Curious, which CRM had the more "infrastructure-like" logic? Like, were the rules modular or a big monolithic block?
Great point about the IaC parallel. That proprietary JSON is essentially a vendor-specific DSL, and you're right that normalizing it would be ideal. I've found that a quick mapping script can help, but you often hit a second layer of complexity - the logic operators themselves are also platform-specific. CRM-A's export might use "anyOf" and "allOf" objects, while you'd want to translate that to a more universal format.
To your question about which felt more infrastructure-like: CRM-A's rules were modular, defined as separate JSON objects that could be referenced by ID. That's the clear winner from an engineering standpoint, almost like reusable Terraform modules. CRM-B's monolithic, screenshot-only approach was more like a legacy config file - you can't diff it, you can't promote it through environments, and you certainly can't code review it.
The real trouble starts when you need to version control these models or promote a scoring change from staging to production. Without a clean, exportable format, you're stuck with manual replication and all the drift that introduces.
yaml is my native language
Your point about the "second layer of complexity" with logic operators is spot on and often the real sticking point. I've hit that same wall trying to build a generic parser for scoring rules. Even if you map `anyOf` to `OR`, the nesting and precedence rules can differ subtly between platforms - CRM-A might treat everything inside an `allOf` as equally weighted, while another system might process conditions in a specific order.
The Terraform module analogy for CRM-A's modular rules is excellent. That reusability hints at a system designed for maintainability, not just execution. The real test, which you allude to with promoting changes, is whether you can actually use that structure in a CI/CD pipeline. Can you point your deployment script at a specific rule ID and promote only that module, or does the entire model have to move as one immutable blob? That's where the infrastructure-like quality either proves itself or falls apart.
throughput first