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Marketers are judged by how effectively they can source qualified leads that convert into sales. While creating a continuous stream of high quality leads can be challenging at any stage of your growth journey, this problem often becomes acute when your business reaches its Go-to-Market stage.
As a company leans into its go-to-market efforts, lead volume tends to increase substantially. Increased online presence, ad spend, and brand awareness all yield higher lead volume. And as the scale of leads increases, so does the number of poor fit leads. Where before the marketing team could review and vet leads manually, that's not possible as the company continues to scale. Companies that fail to scale their lead vetting processes often experience decreased lead to deal conversion rates and increased sales cycle time – not to mention the time wasted by the SDR and sales team members who engaged with these leads.
Growth companies can solve this problem by deploying an effective lead scoring framework.
Guide to this blog post: We reference Hubspot properties by surrounding them in backticks (e.g. `State/Region`).
A Lead Scoring Framework helps marketers efficiently identify and prioritize their best leads. Lead scoring provides an automated, quantitative assessment of how likely a new lead will convert to a qualified opportunity and (hopefully) a customer. With an effective lead scoring framework deployed in your Marketing Automation Platform and/or CRM, marketers can prioritize creating engaging content and generating leads, while relying on their Lead Scoring Framework to streamline lead vetting and assignment to keep up with lead volume.
A lead score is a numeric or letter grade value applied to a lead based on a number of automated rules. The higher the score, the better the lead.
Pro Tip: Hubspot has a Property type called Score which allows you to build up to 100 "positive" and 100 "negative" rules. Each record will be given a score based on which rules they meet / don't meet. You will use Score properties as the foundation of our Lead Scoring Framework.
What makes an effective lead score is a little more nuanced. At Traction, we develop lead scores for our customers using two primary "axes" of distinct, automated rules: one axis measures the "fit" of a lead, the other axis measures a lead's "engagement".
Pro Tip: At Traction AI, we often use Calculation Properties to combine multiple Scoring Axes into a single Lead Score value. This allows you to assess each Scoring Axis independently or in aggregate. This gives you more flexibility as a marketer.
Please Note: The number of Score Properties you can create are limited by your Hubspot license. Review this page for more information and adjust the number of "scoring axes" based on the number of Scores you can have.
Additionally, each "scoring axis" of a Lead Scoring Framework should include positive traits, which indicate a potentially good lead, and negative ones. Prospects with a high score across both axes resemble your best customers and are engaged your marketing content. Poor fit leads are often spam submissions, irrelevant contacts, or other non-buyers like students.
When you build a Lead Scoring Framework using two distinct axes, marketers have more flexibility in how they action their leads. For example, high-scoring Fit leads may look like your best customers, but if they have never engaged with your brand, sales will need to do more upfront work (i.e. introduce the brand, share the value prop, etc.). High-scoring Engagement leads may be fans of your brand who lack the decision-making power to convert to a sale (e.g. students seeking an internship, end-users of your product, etc.) By separating your Lead Scoring rules into separate axes, you can develop Lead Nurturing and Lead Assignment strategies for your company's unique requirements while creating a positive experience for both your prospects and sales team.
To build an effective lead scoring framework for your business' GTM efforts, Traction AI recommends this step-by-step process:
Work with your Customer Success and/or sales team to identify your best customers. For each customer, gather data about their company and the key people who made the decision to purchase your product(s).
Note: This process is much easier if you've used the `Buying Role` Contact property to identify each stakeholder's role in your sales cycle. If you are not using that field, work with your Customer Success team to identify the key POCs for each of the customers in your dataset.
Optional: You can pull the deal records associated customers to explore if there are trends between higher value customers and specific products.
Once you have gathered this data, you want to search for the "identifying traits" between your best customers' decision makers and their associated companies. You should analyze specific Hubspot Standard Properties, as well as the relevant Standard & Custom Properties that contain critical data for your industry. Below are the Hubspot Properties we recommend you always review in your analysis. (We've separated these properties by their associated Record Type.)
Standard Properties to analyze:
Note: Most of these Company properties are filled in automatically via Hubspot Insights if you've added a `Company Domain` to the Company Record. You want to analyze Properties with a high "fill rate", so including auto-populated Properties in your analysis can be extremely helpful.
Standard Properties to analyze:
Custom Properties to consider analyzing if you are tracking them:
Note on `Department` and `Job Level`: Although these two fields have similar Standard Property equivalents (`Job Function` and `Seniority`), we recommend creating Custom Properties. Both Standard properties are used within the Facebook Ads integration and may be overwritten if you're using that integration. Also, the data populated by the integration may not match your company's internal data structure.
Once you've determined the "identifying traits" of your best customers, you want to pull the same datapoints for all Companies and Contacts who aren't within your Best Customer cohort. The end result should be two data cohorts that you can compare with one another. You want to compare the "prevalence" of each "identifying trait" between your Best Customer cohort and your "General Population". This goal of this step is threefold:
You'll want to perform a similar process when developing your Engagement Score prototype, with a few notable differences:
When it comes to engagement with your marketing content, your customers usually don't engage at significantly higher rates than your other contacts. Instead of looking to engagement trends within your customers, we recommend assessing the "standard" level of engagement with your content, and building your rules to reward leads that engage at or above average levels.
When assessing "engagement norms" for your company, you should include engagement on your Marketing Emails (open rates, click rates etc.), the "registration rate" of CTAs to webinars or other events, and website visits/sessions.
Pro Tip: you can use the standard Hubspot Contact Properties `Number of Pageviews`, `Time Last Seen` and similar fields to assess site traffic norms without needing to explore site analytics with other tools.
Engagement rules are different than Fit ones because prospects can engage more than once. If one webinar registration in a quarter is a good signal of engagement, then two registrations is even better! We recommend building out 2-3 "tiers" of Engagement rules based around the same activity, which progressively higher scoring values for more engagement.
Important! When building an Engagement score that includes tiering, make sure each tier's rules are mutually exclusive. Counting a lead in two tiers will artificially inflate their scores.
Example of a simple "tiering strategy" for page views with three mutually exclusive rules:
Standard Properties to consider including:
Pro Tip: One point of nuance to keep in mind during this step. Contacts who've never had a chance to engage with your content should be treated differently than contacts who have received your content and chosen to ignore it. You should generally score the former with an Engagement Score of 0, and score the latter negatively.
Now that you have your list of "identifying traits" across both Axes, you're ready to weight each of them based on their importance. You should apply the highest positive and negative score to the most predictive traits within your list of Properties.
Don't worry too much about the precise value of each Rule at this point in time. We'll be reviewing the results and adjusting at a later step.
Pro Tip: We recommend using a "standard increment" when defining your scores (e.g. only use scores that are multiples of 5 or 10). Scores done this way are often easier to explain to other stakeholders who weren't involved in developing the score. A common multiple makes it easier to compare the relative importance of each rule vs each other (e.g. two rules with a score of "+5" are similarly predictive, while a score of "+25" should be five times more predictive.)
Now that you've performed your Best Customer analysis, built your two Scoring Axes, and applied weights to each rule, it's time to see it in action!
You should return to your two original cohorts and re-export their data again, only this time you should include your new Lead Scoring Properties (both Axes Scores and the combined score if you opted to create one). This time around, you want to vet if your Best Customers on average score higher than leads within the General Population. Ideally your best customers should have an average Lead Score that is significantly higher than the average Lead Score of contacts among your General Population.
Pro Tip: If you've set up your Lifecycle Stages and they accurately represent where each contact is on their Buyer Journey, you can check to see if later-stage contacts (e.g. SQLs and Opportunities) Score higher than earlier ones (e.g. MQLs or Leads). Ideally your average Lead Score should be higher among Contacts with a later Lifecycle Stage.
If you're not satisfied with the distribution of your lead score, you can go back to your Score Properties and test the score with some of the Contacts who scored significantly lower or higher than you expected. Testing a contact will show you each rule they meet/don't meet.
From there, adjust the weighting of each rule that yielded unexpected and/or undesirable results, then re-pull and reassess the distribution of your Lead Score. Once you're satisfied with your results, you're ready to put your lead score into action!
You may be thinking "I know I need to build a Lead Scoring Framework but I'm not an analyst... Just how rigorous do I need to be when developing my Score?". Don't worry! Building a Lead Scoring Framework is part art and part science. While it's certainly important to employ a thoughtful methodology when analyzing this data, you won't fail if you don't have a statistics degree. You should use your knowledge of the industry and your common sense in this process to make judgement call on what to include/exclude.
Another thing to consider is an effective Lead Scoring Framework evolves over time. They can, and should, be continuously reviewed and updated. This means you always have the chance to fine-tune specific rules and weightings to be more effective at predicting high fit leads as you gather more data. Observe your Lead Score in action and see how it performs. Theory will only get you so far.
Now that you have a fancy new Lead Scoring Framework, you'll want to put it into action right away! While there are ways you can use your new lead score, we recommend you consider starting with these three use cases:
Not only can you trigger assignment of leads based on a minimum scoring threshold, but you can also assign leads of certain scores to different teams (e.g. assign low scoring leads to the SDR team for additional vetting).
Many of our customers have high Fit contacts sitting in their CRM who have extremely low levels of historical engagement. Employing a two-axis lead scoring framework will help you identify these "sleeper leads" and enable you to test and learn what content actually gets them to engage with your company.
Leads captured from marketing campaigns usually take some time before they mature into deals and customers. A well-designed Lead Score can give you a rapid preliminary read of the effectiveness of your marketing campaigns without needing to wait for sales to convert the leads you assign them into deals.
Pro Tip: this process is especially helpful to prioritize leads captured from Event sponsorships or other traditionally expensive marketing investments. You can prioritize and distribute the best scoring leads across the sales team, and get a quick read on the effectiveness of a sponsorship without needing to wait until a deal closes.
Do you have a great use case for a Lead Scoring Framework we didn't list here? We'd love to hear all about it!
