It’s your marketing team’s job to attract leads so that your sales team can convert them into customers. But how does your sales team sort the good leads from the bad?

Lead scoring allows you to rate the quality and sales readiness of your leads so that you know which ones need to be prioritised.


Click here for the ultimate guide to B2B sales and marketing alignment

What is lead scoring?

Not all leads are created equal, and there are certain attributes and behaviours that will make some leads more likely to convert than others.

By utilising a points system based on these more desirable attributes, your sales team can rank leads so that they know which ones to prioritise in their sales outreach, while continuing to nurture those leads that aren’t yet sales-ready.

How you score leads is up to your sales and marketing teams, but commonly involves explicit data such as company size, industry and geographic location, and implicit data such as past interactions with your business.

By determining which attributes make your prospect more likely to convert, you can assign a certain number of points to each attribute. This will allow you to ‘score’ each lead based on how many of these attributes they have.

Use the following process to help score your leads:

  1. Determining the lead to customer conversion rate for all your leads

    (this is equal to the number of new customers you acquire, divided by the number of leads you generate in a set amount of time)

  2. Decide which attributes make your customers more likely to convert.
  3. Compare the close rates of each attribute with your overall close rate. Based on this comparison you can determine how many points each attribute is worth. For example, if 20% of people who downloaded a document went on to become customers, perhaps you assign 20 points to that attribute. But if only 1% of people who work in a certain industry convert, perhaps you only assign 1 point to that attribute.

How do you create a lead score model?

Your sales and marketing teams need to collaborate when developing a lead scoring model, as what your sales team define as a quality lead may not necessarily line up with what your marketing team considers to be a quality lead.

By aligning your teams on what makes a good lead, you can increase your close rates while spending less time marketing to the wrong people and nurturing low quality leads.

Keep your lead score model simple. They’re generally based upon a 0 - 100 points system with each attribute receiving a certain number of points based on how likely a prospect with that attribute is to convert. If a certain attribute makes a lead less likely to convert, it should be assigned a negative score.

Here’s a list of attributes that you might wish to include in your own lead score model.

Criteria icons_Improve Sales and Marketing effectiveness with Lead Scoring 3 copy Demographics
Who are your customers?

  • Age
  • Gender
  • Background
  • Education level
  • Marital status
Criteria icons_Improve Sales and Marketing effectiveness with Lead Scoring 3 copy 2_Improve Sales and Marketing effectiveness with Lead Scoring 3 copy 2


Company details
If you sell B2B products or services, company details will be more important than demographics.

  • Company size
  • Number of employees
  • Industry
  • Location
  • Budget
Criteria icons-38

Online interactions
Interactions with your business online will help you move your customers through to sales readiness. Sales readiness can often be determined by what interactions your customer has had with your website.

  • Downloaded documents
  • Filled out questionnaire
  • Submitted a query
  • Requested a trial
Criteria icons-39 Email and social media content engagement

How your leads interact with your business outside of your website may also give you an idea of how sales ready they are. Engagement with different types of content may attract a different score. For example, engagement with sales-enablement content may receive a higher score than engaging with lead generation content.

  • Email subscriptions
  • Follows
  • Likes
  • Polls
  • Post sharing
  • Commenting

How lead scoring can improve sales and marketing effectiveness

By working together to define what a qualified lead looks like for your marketing and sales teams, you can make sure that everyone agrees on who their ideal customer is.

Teams should consider filling out a smarketing SLA to make sure their goals, KPIs and accountabilities all work together. Having a clear idea of who your target audience is, is a great place to start.

Lead scoring will save time, money and resources, by ensuring that only qualified and high quality leads make it through to your sales team.

In order to achieve this, your teams need to discuss the following:

  • What scores should be assigned to what actions and attributes?
  • What scores needs to be achieved to determine whether your marketing team should continue nurturing a lead or not?
  • What score do leads have to achieve before being handed over to sales?
  • Is the current criteria improving results?
  • Does the criteria need to be tweaked to achieve better results?
  • Based on lead scoring performance, do your sales or marketing teams need to adapt their actions to achieve better results?

Explicit and implicit data for lead scoring

Rather than just taking a stab in the dark as to which attributes make a lead more sales-ready, you should refer to the data from your marketing analytics. The insights that you can draw from these analytics will help you determine how you should be allocating your lead scoring points.

There are two types of data: explicit and implicit:

Explicit data  

This is the data that can be gained from directly measurable actions such as when a customer fills out a form.

Implicit data

This form of data isn’t directly measurable, but it can offer implied insights from online behaviour such as page activity, email interaction and social media engagement.

Negative lead scores

While lead scoring can help you recognise qualified leads, it can also help you discount unqualified leads. Negative scoring can shine a light on what makes a lead less likely to convert into a customer.

Both implicit and explicit data can help you determine when it’s time to stop putting resources towards a lead who is unlikely to convert.

For example, a lead that unsubscribes from your email or stops interacting with your brand on social media, implies that they’ve lost interest. This should attract a negative lead score as the lead becomes less and less likely to convert

Examining implicit and explicit data can boost your sales and marketing effectiveness


results icons-40 Improved productivity
Your data can help you identify high quality leads. This will help eliminate the waste of time, effort and resources on leads that aren’t a good fit for your business.
results icons-41 Improved conversion rates

Once your sales and marketing teams can recognise high quality leads, they can ensure that only qualified and sales ready leads make it through to the sales team. This means that your sales team can focus their efforts on nurturing and engaging with the right leads for an increased conversion rate.

results icons-42 Better ROI
With less time spent on unqualified leads, all your time and effort can go towards qualified leads, which means higher conversion rates, less waste, and more revenue for your business.
results icons-43 No more lost opportunities
Warm leads won’t be missed by your sales team now that they’re not getting distracted by unqualified leads.



Automate the lead scoring process with Hubspot


Predictive lead scoring uses an algorithm to predict which contacts in your CRM software are qualified or unqualified. Using machine learning and artificial intelligence, you can create a scoring system that automatically predicts how likely a lead is to close.

Hubspot’s marketing and sales platform allow you to easily scale your lead qualification processes so that you can quickly and easily identify your most qualified leads.

You can even set up an automated workflow that triggers a series of actions once your prospect attains a certain lead score.

HubSpot’s predictive lead score uses the following data points:

  • Demographic information
  • HubSpot insights
  • Business information
  • Interactions logged in Hubspot CRM (such as web analytics, email interactions and form submission events)

A HubSpot Enterprise account allows you to customise your HubSpot score property so that you can qualify leads based on up to 25 personalised criteria.

HubSpot Academy also offers a free lesson on lead scoring.

Key takeaways

  • Lead scoring allows you to determine how likely a lead is to convert.
  • You can establish different points for different attributes based on how likely those attributes are to make your lead convert.
  • These attributes can be based on demographics, company information, online interactions with your business, and email and social media engagement.
  • Sales and marketing teams should align their understanding of what a quality lead looks like.
  • Examining implicit and explicit data will help you make decisions for a higher conversion rate, better ROI and improved productivity.

The best lead scoring models are a collaborative effort

For the best results, your sales and marketing teams need to agree on what a high quality lead looks like. If they don’t agree on who their ideal customers are, your business will waste a lot of resources attracting leads that are never going to convert.

Sales and Marketing SLA Template