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Sales & Revenue

Lead scoring models built on your CRM data

Lead scoring models built on your CRM data, so sales calls the right leads first and marketing knows which programs produce pipeline.

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Leads scoring 70 or more convert to opportunity at 26%, three times the 9% rate of the restScore distribution histogram with the threshold at 70.Leads above 70 convert at three times the restLeads by score this quarter; conversion is lead to opportunityDemo data01002000102030405060708090100Lead scoreThreshold 70Below 70: 9% convert70 and above:26%convertLeads scoring 70 or more convert to opportunity at 26%, three times the 9% rate of the restHistogram of leads by score with a threshold at 70: below 9% convert, at or above 26% convert.Leads above 70 convert at threetimes the restLeads by score this quarter; conversion is lead toopportunityDemo dataBelow 70: 9% convert70 and above:26%convert01002000102030405060708090100Threshold 70Lead score

Lead scoring often fails when the points are guessed, not measured. We build a lead scoring model from your own conversion history. We agree thresholds with sales and marketing, set it up in HubSpot, Salesforce or Zoho, and track whether it lifts conversion. The result is B2B lead scoring your sales team trusts and acts on, and a clear answer to the daily question: which lead do I call first? Our focus is B2B teams in SaaS, professional services and other long-cycle markets.

What it is

What is lead scoring? A ranking that tells sales who to call first

Lead scoring ranks leads by how likely they are to buy. Each lead earns points for fit, such as industry, company size and role. It earns more for engagement, such as a pricing page visit, a demo request or an email reply. It loses points for negative signals, such as a student email or long inactivity. When a lead passes an agreed threshold, it moves from marketing to sales. A good lead scoring model makes that handoff a rule, not a judgment call. Think of it as a lead scoring engine: criteria, implementation in the CRM and regular tests.

One lead's score: fit plus engagement minus negativesThree point groups add up to a score of 70 that reaches the sales threshold on a 0 to 100 scale.One lead's score: fit plus engagement minus negativesDemo dataHow a lead earns points and when it moves to salesFit+30Industry match +10Company size +10Decision-maker role +10Engagement+45Pricing page visit +15Demo request +25Email reply +5Negative signals-5Inactive 30 days -5Score 30 + 45 - 5 = 700100Sales threshold 70At 70 the lead passes the threshold and moves from marketing to sales.One lead's score: fit plus engagement minus negativesFit, engagement and negative points add up to 70, the sales threshold.One lead's score: fit plusengagement minus negativesHow a lead earns points and when it moves to salesDemo dataFit+30Industry match +10Company size +10Decision-maker role +10Engagement+45Pricing page visit +15Demo request +25Email reply +5Negative signals-5Inactive 30 days -5Score 30 + 45 - 5 = 700100Sales threshold 70At 70 the lead passes the thresholdand moves from marketing to sales.
sales and revenue analytics
Challenges we solve

Untested lead scoring models get ignored by sales

The pattern is common. A lead scoring model is built once, from assumptions, and never checked. Scores creep up because engagement points never expire. Fit criteria are missing because CRM fields are empty. Sales gets leads it does not think are ready and stops looking at the score. Without a test against closed-won deals, nobody can prove the model works, so nobody defends it. The fix is not a new tool. It is a model built from evidence and tested regularly.

Unexpired points inflate scores while sales acceptance fallsLine chart over 12 months: leads above threshold rise from 14% to 41%, sales acceptance falls from 62% to 23%.Unexpired points inflate scores while sales acceptancefallsDemo dataUntested model, months 1 to 12: % of leads above the threshold, and % of those accepted by sales0%20%40%60%M1M2M3M4M5M6M7M8M9M10M11M12Above threshold41% of leadsAccepted by salesfalls to 23%Unexpired points inflate scores while sales acceptance fallsLine chart: scores inflate while sales acceptance falls to 23%.Unexpired points inflate scoreswhile sales acceptance fallsUntested model, months 1 to 12: % of leads above thethreshold, and % of those accepted by salesDemo data0%20%40%60%M1M4M7M10Sales accepts 23%Dark line: share accepted by sales.Light line: leads above the threshold.
Comparison

Rule-based vs CRM-native lead scoring: start with what you can explain

Generic default criteria switched on in a CRM scoring tool are quick to set up but untested. A rule-based model calibrated on your conversion history is easier to explain, and it can run inside the native features. Each row reads rule-based | CRM-native default.

Rule-based firstCRM-native default
How scores are setPoints agreed by sales and marketing, tested on historyDefault criteria switched on as supplied
Data neededClosed-won and closed-lost history, even if limitedFields the template expects
Can sales see why?Yes, every point is documentedPartly; defaults are rarely documented
Best fitB2B teams with long sales cyclesA quick first version
How we work

How we build your lead scoring model: criteria, weights and thresholds from your data

We build transparent, rule-based models calibrated on your conversion history. Before any build, we check that your CRM data supports a model; if not, we advise on the data first. We set the model up in your CRM's native scoring features, so sales can see why every lead scored the way it did. The outcome is a model sales understands and marketing can improve every quarter.

Criteria, weights and thresholds from your data

Select a stage, use Next or the arrow keys

Compare wins and losses. Know which attributes separate wins from losses. Compare closed-won, closed-lost and unconverted leads to find the attributes that separate them.

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Frequently asked questions

Lead scoring: questions buyers ask before they change their model

What criteria should be used to score leads?

Combine demographic and behavioral data: fit criteria such as industry, company size and role, plus engagement such as demo requests. Add negative criteria for inactive leads. Set weights from your own conversion data.

How often should a lead scoring model be updated?

Review lead scoring every quarter by comparing conversion across score bands. Recalibrate when your offer or sales process changes, and keep score decay running.

Can HubSpot or Salesforce do lead scoring natively?

Yes. HubSpot has a built-in lead scoring tool, depending on your subscription. Salesforce scores are usually built with formula fields and Flow. We design the criteria first.

Platforms we use

Lead scoring implementation works best inside the CRM your team already uses

A score sales has to look up somewhere else will be ignored. We put lead scoring where your team works, using native CRM and marketing automation features before adding any new tool, and we connect the score to routing and follow-up tasks. Every lead scoring implementation, marketing automation step and CRM integration is documented.

Scores that route leads automaticallyHubSpot: lead scoring tool, fit and engagement groups, score decay and MQL workflows.
Follow-up that happens on timeSalesforce: formula-based scoring fields, Flow, assignment rules and status automation tied to tasks.
Qualified leads reach the right territoryZoho CRM: scoring rules, territory assignment and workflow triggers.
What you get

What you get: a lead scoring model you can explain, run and measure

A sample score sheet shows each lead's points by criterion; Lead 1 scores 85 and lands in Band ALead 1 scores 85 and goes to sales firstSample score sheet: points by criterion and bandDemo dataLeadIndustry fitCompany sizePricing visitDemo requestScoreBandLead 12520103085ALead 2151003055BLead 3201010040CLead 410510025DLead 50510015DBands: A 70 and above, B 50 to 69, C 30 to 49, D below 30A sample score sheet shows each lead's points by criterion; Lead 1 scores 85 and lands in Band AScore sheet for five leads: points by industry fit, company size, pricing visit and demo request, with total score and band.Lead 1 scores 85 and goes to salesfirstSample score sheet: points by criterion and bandDemo dataLeadLead 1Lead 2Lead 3Lead 4Lead 5Industry fit251520100Company size20101055Pricing visit100101010Demo request3030000Score8555402515BandABCDDBands: A 70 and above, B 50 to 69, C 30 to 49, Dbelow 30

  • Every point explained

    A scoring matrix with each criterion, point value and decay rule.

  • No more handoff disputes

    Written MQL/SQL definitions with follow-up rules.

  • A live model in your CRM

    Scoring set up, tested and running.

  • Proof it is working

    A dashboard of conversion by score band over time.

KPIs we track

How we measure lift: MQL-to-SQL, win rate and speed to lead

GoalMore revenue from the same lead volume
  1. Handoff quality
    • MQL-to-SQL rate
    • Share of MQLs rejected by sales
  2. PrioritizationReview first
    • Win rate by score band
    • Conversion by score band
  3. Speed
    • Speed to lead
    • Follow-ups completed on time
Goal, drivers, metrics. The highlighted driver is the one to review first.

Sales accepts more of what marketing sends

Demo

MQL-to-SQL rate: share of scored MQLs that sales qualifies.

Higher scores win more deals

Demo

Win rate by score band: closed-won divided by opportunities in each band.

Hot leads get called sooner

Demo

Speed to lead: time from crossing the threshold to first sales contact.

Sample dashboards

Sample lead scoring dashboards show whether the model earns its place

Built on demo data, these dashboards show score distribution, conversion by score band, MQL volume by source and speed to lead by owner. They are designed for weekly use by marketing and sales leaders, to decide where to adjust the lead scoring model and which reps need support. Each chart title states the insight, so the decision is clear at a glance.

Is the lead scoring model earning its place?

Demo data
MQL-to-SQL rate42%▲ +9 pts
Band A lead-to-opp26%▲ +17 pts
MQLs this quarter470▼ -8%
Band A share12%▲ +2 pts

Band A leads convert at 26%, Band D at 5%: move Band D to nurture

Lead-to-opportunity conversion by score band, this quarter, %

Band A26%Band B14%Band C8%Band D5%5% in Band D: Move to nurtureBand A26%Band B14%Band C8%Band D5%5% in Band D: Move to nurture

So what: Send Band D to nurture so reps spend time on Bands A and B.

BandScore rangeLeadsConversion
A70+14826%
B50-6932214%
C30-494108%
DUnder 303605%
sales dashboards
Benefits

Lead scoring puts sales time where revenue is most likely

Good lead scoring helps sales prioritize leads most likely to buy and moves the rest to lead nurturing.

Marketing and sales share one definition of a qualified lead, and the conversion rate at each stage of the sales funnel shows which programs produce sales-ready leads.

Half of sales time now goes to the likeliest buyersTwo 100% bars: sales hours by band before scoring are even; after scoring Band A gets 52% and Band D 5%.Half of sales time now goes to the likeliest buyersDemo dataShare of sales hours by lead score band, before and after scoringBefore scoring24%26%25%25%After scoring52%30%13%Band A: 52% of sales hours, up from 24%Band ABand BBand CBand D (nurture)Half of sales time now goes to the likeliest buyersTwo 100% bars of sales hours by band; Band A rises from 24% to 52%.Half of sales time now goes to thelikeliest buyersShare of sales hours by lead score band, before andafter scoringDemo dataBefore scoring24%26%25%25%After scoring52%30%13%Band A: 52% of sales hours, up from 24%Band ABand BBand CBand D
Related resources

Next: build lead scoring criteria and a forecast on clean data

Further reading for revenue leaders.

Guides 1

Solutions 2

Book a lead scoring audit and find out which points match revenue

Already scoring leads? We test your current lead scoring model against your conversion data. We show which criteria line up with won revenue and which add noise, then recommend changes to weights, thresholds and handoff rules.

Starting from scratch? We begin with advice on your data and criteria, train your team where needed, and build the model once your CRM is ready. Work runs as a fixed project or as part of a fractional RevOps and data team, with SaaS and professional services firms among our focus sectors.

The audit is remote and results come back as a written report with a scored summary of each criterion. The timeline is set in writing before the audit starts.

SaaSprofessional servicesanalytics as a servicedata modernization projectsAI and data readiness assessmentstrategic planning advisory
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