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Lead Scoring Unveiled: A Roadmap to High-Quality Leads

showing the cold , warm and hot types of lead on an repression and how it's a roadmap to high quality leads
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    From mere leads to potential customers, your audience goes through many stages in the customer journey. Lead qualification is a huge aspect to consider on this journey. While your marketing strategies aim at attracting the largest number of leads possible, it’s not all about quantity; quality matters.

    Which leads should your sales team pay more attention to? Who is more likely to convert? How do you determine when a lead is ready to move up to a marketing-qualified lead (MQL)? When are they considered sales-qualified leads (SQL)?

    Lead scoring is your answer to all of these questions. So, let’s dive right in!

    What Is Lead Scoring?

    Running marketing campaigns typically results in your business having various leads from numerous backgrounds and with different intentions. However, only 10% of your initial leads end up converting to customers. So, how do you know which lead is willing to move down your sales funnel to the next stage? How to determine the readiness of each lead to make a purchase?

    You need a scale to rank those leads accordingly. This is when lead scoring comes to the rescue. Lead scoring is a way to rank the different leads your business attracts based on how likely they are to convert into customers, enabling you to prioritize outreach for your sales team. This is done by assigning a value to each lead. This “value” is often in the form of numerical points.

    Why Use Lead Scoring?

    In the grand scheme of things, organizations that utilize the lead scoring system often see a significant increase of 77% in lead generation ROI. Furthermore, Lead scoring helps you accomplish your milestones smoothly. Here are some of the ways lead scoring serves your business:

    • Save Time: Sales teams are busy. Their time is the most valuable currency for your business. They need a straightforward way to know which leads to spend their time on. Lead scoring give them a scalable and tangible way to see which leads are more likely to convert.
    • Avoid Miscommunication: Without lead scoring, you don’t have a tangible way to determine lead qualification, meaning some leads might be mistakenly considered sales-qualified. They are handed off to your sales team too early in their customer journey. This leads to frustration on both ends and might prevent your customers from making future purchases.
    • Smooth Handoff to Sales: With lead scoring, your marketing team will know exactly when a lead should be upgraded from an MQL to an SQL, making the process of handoff to sales much smoother for all sides.

    When to Set Up Lead Scoring?

    Although many companies achieve extraordinary results with lead scoring, not all businesses will have the same results. So, when is the right time for you to set up a lead scoring system? Here are some signs that you are ready to implement lead scoring:

    • Your sales team often speaks to unqualified leads: This is a huge sign to set up lead scoring for prioritizing sales outreach. This way, you can make sure that your sales team only speaks to SQLs instead of wasting their time on unfruitful calls.
    • Your sales team is understaffed or short on time: Another sign of your need to set up lead scoring is the size of your sales team. If your sales team is often stressed, buzzing around, trying to answer as many customers as possible, but they still can’t make it, then you need to start cutting down the number of leads you direct to your sales team and utilize lead scoring.

    Lead Scoring vs. Lead Qualification

    Lead qualification involves assessing leads to determine their suitability as potential customers based on factors like needs and budget. The aim is to identify promising leads and enhance sales efficiency. This process relies on human judgment and categorizes leads as hot, warm, or cold.

    Lead scoring, on the other hand, assigns numeric values to leads based on behaviors and interactions, ranking them by perceived value to the organization. This approach is data-driven and prioritizes high-potential leads for engagement. Lead scoring is often viewed either as a subset of lead qualification or a distinct method, but a combination of both can optimize sales efforts and conversion rates.

    How Does Lead Scoring Work?

    We established that we need a scaling system to measure the priority of sales outreach for each lead. However, how to set up such a scale? What are our criteria for measuring the value of each lead?
    There is no one-size-fits-all when it comes to lead scoring. It all comes down to your business needs. Nonetheless, we have some ground rules for you to work around and tailor according to what suits your business. 

    Determine Attributes

    The main thing you need to focus on is figuring out your most valuable attributes. The ones that set apart a converting customer from a churning one. To give you some ideas, the attributes you use to rank your leads can include some of the following:

    1. Demographic Information

    • Age: Targeting a specific age group that aligns with the product or service means that you might end up giving higher weights to a certain group of people based on their age.
    • Gender: If the product or service is gender-specific, you should give a higher weight to the customers from the target gender.
    • Location: Targeting specific regions or countries means that leads from said countries get higher points.
    • Job Title: Especially for B2B, targeting specific roles within an organization.
    • Industry: Targeting specific industries or sectors.

    2. Behavioral Criteria

    • Website Visits: Frequent visits to the site or specific pages mean more interest and thus more points.
    • Content Engagement: Downloading e-books, or attending webinars indicates interest in your product or services.
    • Email Engagement: Opening emails and clicking links are also huge signs of interest.
    • Social Media Engagement: Following, sharing, or engaging with posts. What kind of posts is a certain lead interacting with the most? And through which channel? You can use UTM to track this information.

    3. Company Information (B2B Specific)

    • Company Size: Targeting businesses of a certain size.
    • Revenue: Aligning with companies within certain revenue brackets.
    • Industry Type: Targeting specific business sectors or niches.

    4. Lead Source

    • Referral Source: Where the lead came from (e.g., social media, organic search, referral).
    • Campaign Source: Specific marketing campaigns that attracted the lead.

    5. Engagement Level

    • Product Interaction: Trials, demos, or product inquiries.
    • Sales Interaction: Responses to sales outreach or inquiries.
    • Customer Support Interaction: Engagements with customer support or help resources.

    6. Purchase Intent

    • Buying Stage: Where the lead is in the buying cycle (e.g., awareness, consideration, decision).
    • Budget: Whether the lead has the budget for the product or service.

    7. Negative Scoring Criteria

    • Unsubscribing from Emails: A sign of disinterest that leads to eliminating points from this certain lead.
    • Lack of Engagement: No interaction over a specified period should also mean subtracting points from the lead’s overall score.

    Remember, despite all the mentioned attributes, you are the one who knows your business the best. That’s why, you must tailor your lead-scoring attributes and their assigned weights accordingly.

    Assign Weights and Score Values

    Now that we have determined the most valuable attributes of the business, we need to assign weights to the chosen attributes. How do we do that?

    You need to ensure that you and your sales team are on the same page. They are the ones who interact daily with customers. Take advantage of that. Ask for their opinions on the weights assigned per attribute and the score values for each action. You can use a color-coded system to indicate weights.

    Set A Threshold

    You need to determine a threshold point for your SQLs. Say your lead scoring system ranges from 0 to 100. Setting a threshold of 80 means that any lead that reaches this point is ready to move down the sales funnel to the sales-qualified lead stage, making them ready to speak to your sales team.

    Rinse and Repeat

    You have to remember that lead scoring is a continuous process. You need to continuously monitor the scores and weights based on the changing variables. Go back from time to time and make sure that your lead scoring system still represents your customer’s journey in the best way possible.

    Examples for Setting Up Lead Scoring

    Remember how we established that the most important thing when it comes to lead scoring implementation is determining attributes? How do you do so? Well, you have many options. Here are some ways to select the most valuable attributes of your lead scoring system:

    • Buyer Persona: The first and foremost thing to look into is the attributes of your target audience. Which segment do they belong to? What age are they? What gender? Which country do they come from?
    • Historical Data: You can figure out favorable attributes by looking at your historical data. Which attributes drove more customers towards conversion in the past?
    • Sales Team: You can also talk to your sales department since they are the ones closing the deals and talking to customers daily.
    • Customer Interviews: You can gain more insights into the attributes driving customers toward conversion by directly speaking to your customers.

    Here’s an example that brings to life the concept of lead scoring as explained above, using a fictional tech company that sells software solutions.

    Example #1: TechSoft Solutions

    Objective: TechSoft Solutions wants to implement a lead scoring system to prioritize leads for its new project management software.

    Steps Taken

    1. Buyer Persona Creation:
      • Segment: Small and Medium-sized Enterprises (SMEs).
      • Age: Decision-makers aged 30-50.
      • Country: Primarily targeting the United States and Canada.
    2. Analyzing Historical Data:
      • Success Factors: Past conversions were driven by leads who showed interest through multiple website visits, downloaded white papers, or attended webinars.
      • Failing Factors: Leads that only signed up for newsletters but didn’t engage further had a low conversion rate.
    3. Consulting the Sales Team:
      • The sales team identified that leads who asked for a product demo were more likely to convert.
      • They also found that engaging with leads on LinkedIn increased the likelihood of conversion.
    4. Conducting Customer Interviews:
      • Feedback from existing customers revealed that case studies and testimonials greatly influenced their buying decisions.


    Based on these insights, TechSoft Solutions crafted the following attributes for their lead-scoring model:

    • Lead Source: 10 points for leads from LinkedIn, 5 points for leads from other social media.
    • Engagement Level: 15 points for requesting a demo, and 10 points for attending a webinar.
    • Content Interaction: 5 points for downloading white papers, 3 points for reading a case study, and 1 point for every webpage he visits.
    • Company Size: 10 points if the lead is from an SME, 5 points if from a large enterprise.
    • Geography: 5 points for leads from targeted countries (U.S., Canada).

    Based on the previous example involving TechSoft Solutions, we can consider two scenarios. In one, the lead scores low, and in the other, the lead scores high.

    Scenario 1: Low Lead Scoring

    Let’s assume we have a lead from a large enterprise, not falling within the SME target segment, and only engages minimally:

    • Lead Source: Lead comes from other social media (not LinkedIn): 5 points
    • Engagement Level: No demo requested, nor attended any webinar: 0 points
    • Content Interaction: Only read a case study: 3 points
    • Company Size: From a large enterprise: 5 points
    • Geography: From a non-targeted country: 0 points

    Total Lead Scoring: 5 + 0 + 3 + 5 + 0 = 13 points

    With a score of 12, this lead would be considered low priority. The sales team might put this lead in a nurture campaign but would likely not pursue it aggressively.

    Scenario 2: High Lead Scoring

    Now, let’s consider a lead that perfectly aligns with the target segment and is actively engaged:

    • Lead Source: Lead comes from LinkedIn, indicating higher interest: 10 points
    • Engagement Level: Requested a product demo, showing strong engagement: 15 points
    • Content Interaction: Downloaded white papers, visited 9 pages on your website, and read a case study: 5 + 9 + 3 points
    • Company Size: From an SME, fitting the target audience: 10 points
    • Geography: From the United States, a targeted country: 5 points

    Total Lead Scoring: 10 + 15 + 5 + 9 + 3 + 10 + 5 = 57 points

    With a score of 56, this lead would be considered a high priority. The alignment with the target audience and engagement level indicate a strong likelihood of conversion. The sales team would likely reach out to this lead promptly, offering personalized attention and resources to move them further down the sales funnel.

    By meticulously crafting these attributes based on buyer personas, historical data, sales team inputs, and customer interviews, TechSoft Solutions was able to build a lead-scoring model that allowed them to prioritize leads effectively. The targeted and flexible nature of this model made it highly adaptable to their specific business needs.

    Example #2: Educational Institution


    • Inquiry about a Specific Course: 5 points (captured through inquiry forms).
    • Attendance at Open House Event: 15 points (registered at event sign-in).
    • Interacting with different links on Social Media: 2 points for each link interaction (tracked through Shorten URL & UTMs).
    • Completing Application Form: 25 points (tracked via the online application system).

    Example #3: B2B Tech Company


    • Downloading a Whitepaper: 10 points (collected through gated content forms).
    • Attending a Webinar: 15 points (registered via webinar platforms).
    • Engagement on LinkedIn Post: 5 points (racked through Shorten URL & specific UTMs).
    • Requesting a Product Demo: 20 points (captured via a specific demo request form).

    To wrap up, the adoption of a lead scoring system into your marketing strategy can greatly impact your sales and boost your ROI. It saves time for your sales team and provides a smooth handover from marketing to sales. However, you want to pay attention to the process of implementation. You also need to think carefully about what suits your business needs to guarantee the best results.

    Frequently Asked Questions (FAQs)

    What Is Lead Scoring?

    Lead scoring is a method used in sales and marketing to prioritize and evaluate potential customers or “leads” based on their likelihood of becoming actual customers. It involves assigning numerical values to leads based on various criteria and behaviors, such as demographics, engagement with marketing materials, website interactions, and more. The higher the score a lead receives, the more likely they are considered to be ready for sales outreach or further nurturing. 

    Why is lead scoring important?

    Lead scoring enhances efficiency, boosts conversion rates, aligns sales and marketing efforts, and optimizes resource allocation. Lead scoring is also crucial for data-driven decision-making, better customer relationships, and measuring ROI. It shortens sales cycles and focuses on leads most likely to convert, making sales and marketing efforts more effective overall.

    What is predictive lead scoring?

    Predictive lead scoring is an advanced technique that uses machine learning and data analysis to forecast the likelihood of a lead becoming a customer. It goes beyond traditional lead scoring by analyzing historical data and identifying patterns to predict which leads are most likely to convert. In predictive lead scoring, you don’t have to handcraft the attributes and extract the features on your own, you can automate the process of feature extraction and pattern recognition using machine learning models. 

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