What is lead scoring?
Lead scoring is giving each prospect a number that estimates how likely they are to become a customer, so you contact the best ones first. Scores usually combine fit (do they match your ideal customer) with timing (are they showing signs of need now).
Two halves of a score
- Fit: is this the right kind of person at the right kind of company? Role, seniority, company size, industry, location. Mostly firmographics and job data.
- Timing: is there a reason to think they need it now? A post asking for help, a hiring spree, repeat visits. These are buying signals.
A perfect fit with no timing is a name for later. Strong timing with poor fit is usually a waste of a message.
A points model you can copy
A traditional rules-based model adds points per trait. A worked example for a company selling to marketing agencies:
| Trait | Points |
|---|---|
| Title contains founder, owner or managing director | +25 |
| Company is an agency of 5 to 50 people | +25 |
| Located in a country you serve | +10 |
| Posted or commented about the problem in the last 30 days | +30 |
| Works at an existing customer | Exclude |
| Student or job seeker | -40 |
Cap at 100. Contact everyone above 70 this week, 50 to 70 if time allows, and leave the rest. The weakness of points models is that someone has to guess the weights, and nobody revisits them.
How do you know scores are honest?
Group sent messages by predicted score band and compare reply rates. If leads scored 80 to 100 reply at 8% and leads scored 50 to 70 reply at 7%, the score is not separating anything. A score is honest when reply and win rates rise with it.
Common mistakes
- Scoring opens. Email opens are inflated by privacy features that load images automatically, so they say little about interest.
- Too many inputs. Twenty weighted fields feel rigorous and are impossible to debug.
- Never testing the threshold. The line you picked on day one is a guess.
- Scoring before deduplicating customers. A high-scoring lead who is already a customer is an embarrassing email.
Related terms
The score is a distance from your ideal customer profile. In many companies a score threshold is what turns a marketing lead into a sales qualified lead; see MQL vs SQL.
How Sluice scores
Sluice asks three typed questions of each person against your profile: fit from 0 to 100, whether they are worth approaching, and whether what they wrote suggests they have the problem now. 70 is the line for worth contacting, and directory contacts are only revealed (and charged) at 70 or above. Insights shows reply rate by predicted score, so you can see whether the scores hold up.
Questions people ask
- What is a good lead score threshold?
- There is no universal number. Pick a threshold, then check reply and win rates above and below it. If leads just under the line convert as well as those above, move the line.
- What is the difference between fit and engagement scoring?
- Fit scoring measures how closely someone matches your ideal customer, using traits like role, industry and size. Engagement scoring measures what they have done, such as opening emails or visiting pages. Good models use both.
- Should lead scores decay over time?
- Engagement points should. A pricing page visit from last week means more than one from six months ago. Fit points change only when the facts change, such as a new job.
Try it on your own market
Sluice quotes the worst-case price before anything runs and charges only for lookups that found something, so finding out costs close to nothing.
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