Building Custom Lead Scoring Models with CronDB
Generic lead scores treat every business the same. CronDB's custom scoring lets you define what a qualified lead looks like for your specific product and market.
Why Custom Scoring Matters
A domain intelligence platform selling to e-commerce companies cares about different signals than one selling to cybersecurity teams. Off-the-shelf lead scores can't capture this nuance.
CronDB's custom scoring engine lets you weight the factors that matter for your business:
- Technology signals: +20 points if they use Shopify, +10 for WordPress
- Company attributes: +15 for US-based, +10 for 50-200 employees
- Behavioral signals: +25 for recent technology changes, +20 for hiring activity
- Negative signals: -30 for government domains, -20 for non-profit
Setting Up Your First Scoring Model
Step 1: Define Your ICP
Start with your best customers. What do they have in common?
- Which industries convert best?
- What technology stacks do they typically use?
- What company size closes fastest?
- What signals preceded their purchase?
Step 2: Create Rules in CronDB
Navigate to Custom Scoring and create rules for each attribute. Start simple — 5-10 rules is enough to see significant improvement over generic scores.
Step 3: Test Against Known Outcomes
Score your existing customer list. Do your best customers get the highest scores? If not, adjust the weights until the model reflects reality.
Step 4: Automate with Workflows
Once your scoring model is tuned, use CronDB workflows to automatically:
- Add high-scoring leads to priority lists
- Trigger outreach sequences when a domain crosses your score threshold
- Alert your team when a watched domain's score changes significantly
Iteration Is Key
Your scoring model should evolve with your business. Review it quarterly:
- Are high-scoring leads actually converting?
- Have your target market or ICP changed?
- Are there new signals you should incorporate?
Check the Scoring API documentation for programmatic access to custom scores.
Related
- Understanding intent signals — the behavioral data that powers scoring
- Why data freshness matters — stale data degrades score accuracy
