Lead Scoring

What is lead scoring?

Lead scoring gives each lead a value, usually points or a letter grade, based on two kinds of signal. Fit signals describe who the lead is: industry, company size, role, region or the tools they use. Engagement signals describe what they have done: visited the pricing page, requested a demo, replied to an email or attended a webinar. Together they turn a pile of leads into a ranked list.

Scores update as data changes. When a lead crosses a threshold, the CRM can mark it as marketing qualified, alert a rep or route it to a queue. Leads below the threshold stay in lead nurturing until they show more intent.

Fit criteria usually come from the ideal customer profile. Scoring runs inside a CRM or marketing automation tool and is most common in B2B sales, where reps have more leads than time to call them. Smaller teams with a handful of inbound leads a week often do not need it yet.

What is the difference between rule-based and predictive lead scoring?

Rule-based scoring is set by people. You assign points to attributes and actions, subtract points for poor-fit signals such as students, competitors or personal email addresses, and let engagement points fade over time so old activity does not keep a lead looking hot. It is transparent and easy to explain to sales, but it reflects your assumptions.

Predictive scoring uses a model trained on your past won and lost deals to estimate how likely each new lead is to convert. It can find patterns people miss, but it needs enough historical deals and reasonably clean data to be reliable, and its reasons are harder to explain.

Many teams start rule-based, learn which signals matter, and add a predictive score later as a second opinion rather than a replacement.

What signals should go into a lead score?

Use signals that separate buyers from browsers in your own data:

• Firmographic fit: industry, company size, region and business model.
• Role and seniority: whether the person can buy, influence or only research.
• High-intent actions: demo requests, pricing page visits, replies and return visits within a short period.
• Product usage: for free trials or freemium plans, reaching key actions in the product.
• Negative signals: unsubscribes, job seekers, competitors and repeated bounces.

Avoid giving many points for low-effort actions like email opens or a single blog visit. They are frequent, cheap to perform and weakly linked to buying. Check each signal against your closed deals: if won and lost leads show it equally often, it does not belong in the score.

Why does lead scoring often fail?

The most common failure is that sales does not trust the score. That usually happens when marketing builds the model alone, so the criteria reflect what marketing can measure rather than what sales sees in good deals. Reps then ignore the score and go back to their own judgment.

Other causes are data gaps, where company size or role is missing for most leads, and models that are never revisited after the product, pricing or target market changes. A threshold that once made sense can quietly flood sales with weak leads or starve it of good ones.

The fix is to define a qualified lead together with sales, write the definition down, and review it on a fixed rhythm with examples of leads the score got right and wrong.

How do you know if lead scoring is working?

Compare outcomes by score band. If high-scoring leads do not turn into opportunities and customers noticeably more often than low-scoring ones, the model is not separating anything, whatever its point values say.

Look at both kinds of error. False positives are high-scoring leads that sales rejects; false negatives are deals that closed even though the lead scored low. Each one points to a signal that is overweighted or missing.

Also track what happens after the score: how fast high-scoring leads are contacted, and whether reps follow up on them at all. A good score that triggers a slow or missing response produces the same result as no score. Speed and consistency of follow-up are part of the system, not a separate sales problem.

How does Vizio AI set up lead scoring?

Lead scoring is part of the marketing automation and lead routing work in our Growth Studio. We define fit criteria with you from the audience and positioning work, choose engagement signals that reflect real intent, and build the scoring and routing rules in your CRM so qualified leads reach sales and the rest move into nurturing.

Growth analytics then compare score bands with real pipeline, so the model can be tuned as evidence comes in.

The starting point is a review of your recent won and lost deals and the data your CRM already holds, before any points are assigned.