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Users need the ability to define custom weights for different data points (e.g., "40% engineers") when using AI to enrich CRM data. This weighting should be configurable per use case or "motion" (e.g., dev-first tools vs. RevOps platforms) to improve precision and build sales trust in the tiers.
12 examples of ways I've used AI to find "rich" data points to build a company fit/tiering model to enrich your CRM: 1. Privacy policy page updated in the last 30 days 2. Large number of SKUs (products) on a retail website 3. Using open-source AI tools (like Pytorch, tensorflow, etc.) 4. High-growth indicator (raised $100M+, founded < 5 years ago, employee count < 50) 5. 40%+ of employee count are engineers 6. "B2SMB" meaning a prospect has a price on their website under $50/month 7. Negative review on review site 8. Currently hiring 3+ 9. Presence of a very specific title (eg: UX Researcher, Search & Recs, RevOps) 10. Usage of certain technologies being used at a company (scraped from job descriptions) 11. Mentions of AI initiatives in the last 30 days (on social, or other public data) 12. The prospects' prospects sell to an ICP with a strong "digital presence" These are "rich data points" (Indicators of Company-Fit) These are not "signals" (Signals show timing/intent to buy) Use these to determine whether the company is a High Fit, Medium Fit, or Low Fit account to target. Then, layer on (intent) signals to determine the timing of when to reach out to each company on this list. ••• AI doesn't *replace* smart GTM strategy. But, it can help *scale it* in a way that wasn't possible three years ago. PS - Here's an example I built, showing a few "rich" data points plus "basic" data points, for a hypothetical example of an AI CodeGen Platform (think: a company like Cursor) 👇