Why AI Image Prompts Need a Style Reference

Why most teams overcomplicate lead scoring

Lead scoring fails when it tries to predict revenue before understanding behavior. I would start with a model that flags readiness, not lifetime value, because readiness is observable and revenue is speculative. The goal is to give sales a shorter list of people who have actually shown up, not a ranked list of everyone who might buy.

A simple model also gets used. Sales teams ignore a score they cannot explain, and a score nobody reads is just a number in a database. If a rep asks why a lead is on the list, you should be able to answer in one sentence.

The three signal types worth tracking

Fit signals tell you if the person matches your ideal customer profile: company size, role, industry and technology stack. Intent signals show active research: pricing page visits, demo requests, competitor comparisons and content downloads. Engagement signals measure responsiveness: email opens, click-throughs, reply rates and meeting attendance.

I would weight intent highest, fit second, and engagement third, because intent correlates most directly with near-term action. Engagement alone can mislead. Someone can open every email you send and never intend to buy.

Building the model in a spreadsheet first

Create four columns: lead identifier, fit score (0 to 10), intent score (0 to 20) and engagement score (0 to 10). Assign points using rules you can explain in one sentence each. For example, "VP title at a 50 to 500 employee SaaS company = 8 fit points" or "visited the pricing page twice in seven days = 15 intent points."

Sum the rows. Anything above 25 goes to sales, 15 to 25 stays in nurture, and below 15 gets archived. Then test the thresholds against your last 50 closed-won and closed-lost deals to see whether the line feels right. If most of your won deals score below 25, the thresholds are too strict, or a signal is missing.

Keep the first version rough. A model that is roughly right and easy to explain beats a perfect one nobody trusts.

How often should you review the scores?

Review the rules on a fixed schedule, such as once a month, and change one thing at a time. If you change three rules at once, you cannot tell which change helped. Ask sales which leads they enjoyed working and which felt like a waste, then check how those leads scored.

When to graduate to automation

Move to a CRM or marketing automation tool only after the spreadsheet model has run for two full sales cycles without major rule changes. At that point you have proven the logic, identified the data gaps, and earned the right to spend budget on implementation. Until then, manual scoring forces you to confront every assumption and keeps the feedback loop tight between marketing and sales.