One of the core differences between outbound and inbound marketing is that in outbound marketing, you’re actively reaching for the sale rather than attracting people into doing business with you.
Instead, when companies go inbound, people come to them through whatever channel they’ve chosen — social media, blog posts, and more — and actively engage before deciding to do business. For many teams, this shift means that the sense of “control” is lost — how do we know who’s going to buy if the salesperson isn’t leading the conversation?
That’s where lead scoring comes in.
Lead scoring is the process of using data to qualify a potential lead depending on how they interact with our business in order to prioritize an outreach strategy. A few examples of interactions that would make a lead’s score change include:
We want to qualify — or score — incoming leads because not everyone who engages with inbound marketing will be a potential buyer. The competitor who’s checking out a PDF would fall into a different bucket than the reader who’s using tutorials to set up a process.
A HubSpot score is simply a score assigned to your contacts based on their interaction and other “ranking” factors such as demographics and company details within the HubSpot CRM to measure how likely each contact is to become a customer.
You could create a manual system for lead scoring. But HubSpot has a tool called Predictive Lead Scoring that takes different attributes into account to qualify leads and let us know how likely they are to buy within a certain period of time. A couple of key terms you need to understand:
To implement a lead scoring model in HubSpot, the first thing you need to do is analyze past data to determine what common characteristics you can find among your deals won and your deals lost. It’s these characteristics that will help you define the attributes that will be used to add or subtract points from a contact.
Look for these attributes to start your lead scoring model:
Factors like age, location, and whether or not the person has children, for example, can add or subtract “points” from your contact’s lead score depending on who your target audience is.
Filter contacts by job title, seniority, decision-making, or what department they belong to.
Similarly, if your company sells primarily to other businesses, you can filter leads based on how closely a contact’s company matches the profiles of the businesses you typically work with. What industry do they belong to? Do they have a minimum yearly revenue? A specific number of employees? Where are they based?
The lead’s initial point of contact can also make a significant difference in their lead score. Did they request a demo or consultation — or did they download a workbook? Additionally, a prospect that includes non-required information in a form (like a phone number or a message explaining their needs) would probably get a higher score.
Once you have the hard data, it’s time to find trends. We want to use both positive and negative attributes to create a more accurate system. A positive attribute is a box your contact checks, like having the job title you’re most likely to engage with or being a decision-maker within their organization. On the flip side, a negative attribute could be repeated visits to the Jobs page, which could signal a candidate rather than a prospective customer.
The key here is to assign a value to each characteristic that will add up to give you the lead score. The value for each characteristic is a comparison of how likely a contact with this characteristic is to close vs. your overall conversion rate.
Once you identified the positive and negative attributes for your lead scoring model and assigned values to them, it’s time to create the score properties in HubSpot.
Watch the video below to find a step-by-step play on how to create each attribute within the HubSpot CRM.
Once you’ve created the attributes in HubSpot, your contacts’ score will show up as a property, like this:
Manually scoring leads can cost your team endless hours and energy they could be using to engage qualified prospects instead of sorting through data.
To help you develop a lead scoring model, we created this Lead Scoring Cheat Sheet which will give you an accurate representation of how much each attribute increases a lead’s likelihood to become a customer.
Build a Lead Score with our Cheat Sheet