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The Ultimate Guide to Hubspot Lead Scoring

Learn how you can set up manual and predictive lead scoring in Hubspot.

Luke Kline.

Luke Kline

December 2, 2022

10 minutes

Hubspot Lead Scoring

If you're like most companies, chances are your marketing team is actively working to generate and nurture quality leads for your business before handing them off to your sales team. These top-of-funnel leads are often referred to as marketing qualified leads (MQLs) or leads who have shown intent in your business. The problem is only a tiny percentage of your leads end up converting to customers, and it's challenging for your sales team to know which leads to prioritize when they all look identical.

What is Lead Scoring?

Lead scoring is the process of assigning a value or numerical score to every lead you generate within your business. Lead scoring focuses on allocating a negative or positive score to a specific prospect based on key attributes you define (e.g., contact X has looked at the pricing page and opened two marketing emails.)

What is Account Scoring?

Account scoring is slightly different from lead scoring because, whereas lead scoring focuses on individual user attributes, account scoring focuses on mapping user actions and user-generated data to specific accounts. With account scoring, you're summarizing user behavior in the context of an entire account rather than looking at it individually (e.g., account X has opened 20 marketing emails in the last week.)

Lead Scoring Models

A lead scoring model represents the attributes you define and the points you assign to reflect the prospect's compatibility with your given product or service. Most of the time, lead scoring models are based on a point range (e.g., 0-100 or 0-10), but this can vary based on your specific requirements. Generally, a higher score translates into a more qualified lead or account, but you don't need to have a maximum score in place. Ultimately, the range doesn't matter as much as your measured attributes. All lead-scoring models are based on either demographic data or behavioral data.

  • Demographic data includes data about and around your customer. This can include anything from the job title, industry, company size, location, email, phone number, annual revenue, etc.
  • Behavioral data represents a prospect's critical interactions with your brand or product. Often this includes product usage data (e.g., last login date, signups, workspaces, etc.) or events (e.g., page views, form submissions, shopping carts, etc.)

The goal of lead scoring is to help your sales team identify which leads are ready to be targeted and entered into the sales funnel.

How to Create a Lead Scoring Model

To create a lead scoring model, you must first calculate your lead-to-customer conversion rate. You can do this by taking the total number of new customers you've acquired over a given time frame and dividing that number by the total number of leads you generated in that same time frame. This metric will act as a fantastic benchmark as you define precisely what scoring attributes and data points you want to measure. The best way to determine your scoring rules is to examine your current and potential customers and see what they have in common.

  • Who are your core customers? Do you sell to B2B companies or B2C companies?
  • What is your target audience or buyer persona?
  • What are the most common traits that your paying customers have in common?
  • What marketing actions did company X take to become a paying customer?

For example, if you identified that leads who requested a demo are two times more likely to close than leads who don't, you could award five points to all prospects with those attributes. Alternatively, if 75% of your buyers are at the director level, you can assign positive points to all prospects who meet this criteria. This same principle can also be applied to negative attributes. You could use a negative score for competitors and job seekers based on specific email domains.

If you know that users who visit your pricing page are 20 times more likely to convert than those who don't, you can apply a heavier grading scale to that specific metric since it closely correlates to your bottom line.

Account scoring models work very similarly, with the only difference being that you're aggregating these same metrics and attributes about your individual leads and tying them together at the account level (e.g., account ABC created a workspace one day ago.)

How to Setup Lead Scoring in Hubspot

When it comes to lead scoring features in Hubspot, the platform offers two scoring tools: predictive scoring and manual scoring. You can access both of these features in the settings underneath the properties tab in the left sidebar menu.

Predictive Lead Scoring

With predictive lead scoring, Hubspot uses machine learning to review your data points, whether it's data your sales team has manually input, email interactions, form submissions, page views, etc. Hubspot analyzes all of this information across your entire contact base to identify trends and associates this information to a property called "Likelihood to Close."

Hubspot Likelihood to close property

This predictive score is calculated by analyzing similarities across your customers and cross-referencing that information against leads that failed to close. Hubspot uses this information to give each contact record in your database a probability of closing within the next 90 days.

The downside to this approach is that this predictive scoring is based solely on how Hubspot interprets your data. You can access this feature by heading to the navigation bar and clicking on contact properties underneath settings. From there you just need to search for the "Likelihood to Close" field. This feature is only available at the enterprise level.

Hubspot’s predictive lead scoring tool also uses a feature called Hubspot Insights, which automatically populates default company properties like annual revenue, city, country, industry, state/region, address, phone number, website URL, etc.) It analyzes what information your customers have in common and cross-references it against your leads who failed to close.

Manual Lead Scoring

For more granularity, Hubspot has a feature for manual lead scoring. This contact property is known as "Hubspot Score," and unlike the Likelihood to Close" field, it's much more configurable.

The "Hubspot Score" property enables you to award or deduct points for specific fields that you define in a few clicks. You just have to create a list of positive and negative attributes that you want to measure. From there you can add or remove points as necessary. If you don't want to use the "Hubspot Score" field you can even build your own custom score properties to further optimize your sales and marketing efforts.

Hubspot Properties

Hubspot scoring.png

Types of Lead Scores

In an ideal world, you should implement multiple lead scores so your business teams can prioritize leads by quality. The three categories and questions below will give you a good starting point.

  • Interest: How much has a contact engaged with your brand or product in the last 30 days?
  • Personas: Does this buyer align with your ideal customer profile?
  • Up-sell Opportunities: Are your existing customers in overage or exceeding their contract/product limits? Have they submitted more support tickets lately?

These are just a few examples, but ultimately, you could build even more granular lead scores based on your specific use case. The advantage of a multi-lead score approach is that your business teams can apply more robust segmentation. This is especially helpful if you use Hubspot Lists to build to manage contacts and accounts.

Although implementing multiple lead scores inevitably creates more work, it establishes a more accurate view of your leads. If you really wanted to get advanced, you could create a holistic lead score that summarizes and aggregates all your data into a single score to create the most accurate representation of your prospects.

Best Practices for Lead and Account Scoring

Lead scoring should not be a cookie-cutter, one-size fits all approach. What works for one company might not work for another, and the best way to build your scoring criteria is to look at the analytics and do some detective work.

Building an attribution report to identify which marketing and sales channels are yielding the most conversions can help you understand which channels are working so you can develop your lead-scoring model around them. Once you've identified the core attributes you want to track, the next step is assigning a value for negative and positive attributes.

Your sales team is on the frontline selling to your customers daily, so nobody has a better understanding of your ideal customer profile than them. They know better than anyone what marketing materials or specific actions lead to a conversion, so you should pick their brain to see which marketing/sales collateral is most impactful in moving customers from the top-of-funnel to the bottom-of-funnel.

Your sales team only shows one side of the story, though. Talking to customers can also provide unique insight because they typically have different perspectives and often share other unique insights into their buying process and what led to their decision. What your sales team perceives as the primary catalyst in the buying process might be completely different in your customer's eyes.

Advantages to Hubspot Lead Scoring

One of the major advantages of Hubspot lead scoring is that you can segment your contacts and accounts into Hubspot lists for marketing automation and streamlined workflows. For example, you could build a workflow to route leads and accounts to the appropriate sales reps so they can take action in real time.

With a relevant lead scoring model, you could also build customized email campaigns to qualify your prospects further and raise their lead score. For example, run an email marketing campaign focused on raising the interest or awareness of a specific group of leads to increase potential conversions.

Downsides to Hubspot Lead Scoring

One of Hubspot's core problems with lead scoring is that the data housed within the platform is relatively limited. Hubspot doesn't store your important behavioral data. Most of the data is sales-related, so you don't have a full 360-degree view of your customer.

Unless you're using Hubspot-specific landing pages, you can't actually track the user journey through your website. If you're not using Hubspot for email marketing, you don't have access to relevant campaign data either.

In reality, you probably have many more data sources in addition to Hubspot, whether it's product usage data (e.g., last login date, signup, new user-added, workspace created, etc.) or key events happening on your website (e.g., pricing page view or item added to cart.)

Without access to all of your customer data, it's impossible to build a fully comprehensive lead-scoring model in Hubspot.

Building Your Lead Score in the Data Warehouse

It won't matter how many fields you create in Hubspot because the platform cannot capture certain data types. The only complete 360-degree view of your customer lives in your data warehouse. Getting access to this data in Hubspot is difficult because your data team either has to download/upload manual CSVs or build and maintain another data pipeline.

In reality, you need a way to sync the data in your warehouse to Hubspot, which is the exact problem Hightouch solves with Reverse ETL. Hightouch is a Data Activation platform that queries against your existing data warehouse and syncs to over 125 destinations (including Hubspot.)

Instead of building and maintaining your lead scoring models in Hubspot, Hightouch enables your data team to leverage their existing data models and sync that data directly to fields in Hubspot–thus giving you a more up-to-date and accurate view of your customer. The first integration with Hightouch is free, so you can start syncing data to Hubspot immediately.

More on the blog

  • How to Calculate a PQL (Product Qualified Lead) in SQL.

    How to Calculate a PQL (Product Qualified Lead) in SQL

    Learn how you can calculate product qualified lead in SQL.

  • How to Calculate a (MQL) Marketing Qualified Lead in SQL.

    How to Calculate a (MQL) Marketing Qualified Lead in SQL

    Learn how you can calculate a marketing qualified lead in SQL.

  • How to Calculate a SQL (Sales Qualified Lead) in SQL.

    How to Calculate a SQL (Sales Qualified Lead) in SQL

    Learn how you can calculate a sales qualified lead score in SQL.


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