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11 Lead Scoring Best Practices to Get Better Sales Results

Implementing good lead scoring best practices can significantly improve lead quality.

Recent studies reveal that companies with well-defined lead scoring systems witness a 192% higher average lead qualification rate than those without. However, crafting a robust lead scoring model isn’t just assigning arbitrary points to lead interactions.

It’s about developing a nuanced understanding of your potential customers through a blend of demographic insights, behavioral analysis, and continuous optimization.

With 68% of successful marketers citing lead scoring based on content and engagement as the most effective strategy for revenue growth, it’s clear that a dynamic and well-structured lead scoring system is not just an option but a necessity.

In this blog post, we’ll dig a bit deeper into the lead scoring best practices and models to help you get hotter leads.


11 Useful Lead Scoring Best Practices to Help You Prioritize Your Leads

Let’s get started!

1. Understanding buyer personas

Creating buyer personas is a strategic step in lead scoring, focusing on understanding potential customers through real data. 

Sample buyer persona

This lead scoring process should be grounded in real data to ensure accuracy and relevance. This approach ensures accuracy and relevance in identifying the most promising leads.

Create data-driven personas

  • Step 1 — Gathering data: Start by collecting information from your CRM system and conducting market research. This includes purchase history, interactions with marketing campaigns, and customer feedback.
  • Step 2 — Building profiles: Develop detailed profiles encompassing demographic information like age, gender, and location. Delve deeper into professional attributes such as industry, job role, and company size.
  • Step 3 — Analyzing needs and challenges: Understand the unique needs and challenges of different personas. For example, a marketing manager in a startup may have different priorities compared to a C-level executive in a large corporation.

Assess persona attributes and the impact of lead scoring

  • Aligning with ideal customer profile: Score leads based on how closely their attributes match your ideal customer profile. This makes sure a focused approach in targeting the right audience.
  • Industry and company size: If your product is designed for mid-sized technology companies, leads from this sector should receive higher scores.
  • Job titles and decision-making power: Consider the influence of job titles in decision-making. Leads with titles that align with your product’s target decision-makers should be prioritized in scoring.

By integrating these detailed personas into your lead scoring model, you can prioritize leads more effectively. 

This method enhances the efficiency of your sales team by focusing their efforts on leads with the highest conversion potential.

2. Mapping the customer journey

Understanding the customer journey is pivotal in lead scoring. This journey typically consists of several key stages, each offering unique insights into a lead’s readiness to engage or buy. 

By mapping these stages and aligning them with your lead scoring system, you can more accurately assess where a lead stands in their decision-making process.

Customer journey stages

Identify key stages

  1. Awareness stage: This is where potential customers first become aware of your product or service. They might have encountered blog posts, social media ads, news articles, or any hook. In lead scoring, actions in this stage might include visiting your website or following your social media pages. These actions are important but generally score lower as the lead is still in the early stages of discovery.
  2. Consideration stage: At this point, leads actively consider your product or service as a solution to their problem. They are seeking more information and comparing options. Actions in this stage include downloading whitepapers, signing up for newsletters, or attending webinars. These actions indicate a higher level of interest and should be scored more significantly than those in the awareness stage.
  3. Decision stage: This is the critical stage where a lead is close to making a purchase decision. Actions here are the most telling about a lead’s intent. For example, requesting a product demo, filling out a contact form, or engaging in a sales conversation are all strong indicators of interest. These actions warrant the highest scores in your lead scoring model, as they demonstrate a clear intent to engage further or buy.

Implement action-based scoring at each stage

  • Awareness actions: Scoring might include 5 points for visiting a blog post or 10 points for following on social media. These are initial touchpoints indicating interest.
  • Consideration actions: More engaged actions like downloading a detailed guide or attending a webinar could score 20-30 points. These leads are investing time to understand your offering better.
  • Decision actions: The most indicative actions, such as requesting a demo or filling out a detailed inquiry form, could score 50 points or more. These leads are showing a clear readiness to engage in a sales conversation.

Read also: A Guide To Lead Scoring For Your 2024 Sales Goals

3. Behavioral data scoring

In lead scoring, behavioral data offers a window into a lead’s level of engagement and interest.

This section explores how to effectively score leads based on their interactions with your business, ensuring a more dynamic and responsive lead management process.

Behavioral data encompasses a range of actions that leads take about your business. These actions include website visits, email interactions, content downloads, and social media engagement. Each behavior provides insights into the lead’s interest level and position in the buying journey.

By assigning scores to these interactions, you can gauge a lead’s interest and engagement level more accurately, enabling your sales team to prioritize leads effectively and tailor their outreach strategies accordingly.

Draft a scoring system for behavioral data

Here’s a table with lead actions and the corresponding points:

Behavioral Action Scoring Criteria Points Rationale
Website Visits Frequency and depth of visits, especially to key pages like pricing or product demos 10-30 points Visits to crucial pages indicate higher purchase intent
Email Engagement Opening an email vs. clicking a link within the email 5 points for opening, 15 points for clicking Clicking a link shows a deeper level of engagement than just opening an email
Content Downloads Downloading whitepapers, e-books, case studies 20-40 points Downloads indicate a lead is seeking in-depth information, suggesting serious interest
Social Media Engagement Sharing content, commenting on posts, webinar participation 10-25 points Active participation on social media demonstrates engagement and interest in the brand.
Repeat Interactions Consistent engagement over time across various platforms Additional 5-10 points per repeated action Sustained interactions over time signify growing interest and engagement

Implement the scoring model

Ensure your CRM system can track and score these behavioral actions automatically.

This integration is crucial for maintaining an up-to-date and accurate lead-scoring system. Further, tailor the scoring model to fit your specific business context. For instance, a B2B company might score certain behaviors differently than a B2C company.

As with demographic scoring, behavioral scoring models should be reviewed and updated regularly. This ensures the scores remain aligned with evolving marketing strategies and customer behaviors.

4. Setting feasible scoring thresholds

Establishing realistic scoring thresholds is a critical aspect of an effective lead scoring system. These thresholds determine at what point a lead is considered qualified enough for further sales engagement or nurturing. 

The process involves a careful analysis of historical data and an understanding of different market segments or product lines.

Determine the right lead scoring threshold levels

Examine your past sales data. Look for patterns that indicate at what score leads are typically converted into customers.

This analysis can reveal a benchmark score for a “qualified lead.” For instance, if most leads that converted had a score of 60 or above, this could be a starting point for your threshold.

Alongside sales data, analyze your conversion rates.

High conversion rates at certain scoring levels can indicate effective thresholds. Conversely, low conversion rates might suggest the need for adjustment.

A threshold that’s too high might result in too few leads passing through, while too low a threshold could flood sales teams with unqualified leads. 

Striking a balance is key. This involves trial and error, adjusting thresholds, and observing the impact on lead quality and sales team efficiency.

Adjust the thresholds for different segments

  • Segment-specific thresholds: Different market segments or product lines may require unique thresholds. For example, a premium product line might have higher thresholds due to its longer sales cycle and higher customer value, whereas a more standard product could have a lower threshold.
  • Customizing based on buyer behavior: Different segments might exhibit unique buying behaviors. A segment that typically takes longer to make a decision might need a higher threshold to make sure leads are truly sales-ready.
  • Regular reviews and adjustments: Market conditions and consumer behaviors change over time. Regularly review and adjust your thresholds to align with current market dynamics. This could be done quarterly or bi-annually, depending on the pace of change in your industry.
  • Feedback loop: Incorporate feedback from the sales team. They can provide valuable insights into whether the leads meeting the current thresholds are ready for sales engagement or if adjustments are needed.

By setting and regularly adjusting feasible scoring thresholds, you can ensure that your lead scoring system effectively identifies the most promising leads. 

This practice enhances the efficiency of the sales process and also makes sure that marketing efforts are targeted toward nurturing leads that are more likely to convert.

Read also: Lead Scoring Model to Close More Deals

5. Demographic data scoring

Incorporating demographic data into your lead scoring model necessitates the understanding of the story behind each lead — their industry, role, company size, and location — and how these elements align with your product’s value proposition.

By doing so, you make sure that your sales team’s efforts are concentrated on leads with the highest potential for conversion, thereby optimizing your sales process and enhancing ROI.

Demographic data examples

Effective lead scoring hinges on the strategic use of demographic data. To start, here’s how you can select demographics:

  • Step 1: Identify industries that naturally align with your product or service. For a tech-based solution, IT and tech industries might be more pertinent.
  • Step 2: Focus on job roles directly involved in the purchasing process. For B2B products, roles like ‘Purchasing Manager’ or ‘IT Head’ might be more relevant.
  • Step 3: Company size matters. The size of a lead company can significantly impact its needs. Larger organizations might have more complex requirements and higher budgets.
  • Step 4: Geographical alignment — Consider where your product or service has the strongest market presence or where it’s expanding. Regions with a higher concentration of your target market should be prioritized.

Assign scores to each demographic

  • Benchmarking against industry standards: Look at industry benchmarks to understand what demographic factors are most predictive of lead quality in your sector. 
  • Using internal data for customization: Analyze your sales and customer data to identify which demographic factors have historically correlated with high-quality leads. Customize your scoring model based on these insights.
  • Scoring scale adaptation: Adapt your scoring scale to reflect the relative importance of different demographics. For example, if industry alignment is crucial for your product, it might carry a higher lead score than other factors like company size.
  • Dynamic scoring adjustments: Be prepared to adjust your demographic scoring as market conditions change. Regularly review and update your scoring criteria to ensure they align with current market trends and internal sales strategies.

6. Negative scoring and decay rates

Not all actions should positively influence a lead’s score.

Negative scoring and decay rates are essential components, making sure that your scoring system accurately reflects a lead’s current interest and engagement levels.

Negative scoring and decay rates add a layer of sophistication to your lead scoring system. They ensure that your marketing team focuses on leads that are not only high-scoring but also currently engaged and interested, thereby increasing the efficiency and effectiveness of your sales process.

Implement negative scoring to get more accurate results

Negative scoring involves deducting points for certain actions or inactions that indicate a decrease in a lead’s interest or a mismatch with your target audience.

This approach helps in maintaining the integrity of the lead scoring system by preventing score inflation.

  • Unsubscribing from emails: Deduct points when a lead unsubscribes from your mailing list. This action clearly indicates a loss of interest.
  • Inactivity: If a lead has not engaged with your emails, website, or other communication channels for a set period, this should trigger a point deduction. For example, ‘no interaction’ for over 60 days might result in a score reduction.
  • Irrelevant interactions: Visiting non-sales-oriented pages like careers or investor relations sections might lead to a deduction in points, as these visits do not align with buying intent.
  • Incorrect demographics: If further information reveals that a lead’s demographics do not match your target audience (e.g., student status in a B2B context), this should also result in a score reduction.

Manage decay rates

This table explains various decay criteria and the corresponding rates:

Decay Criteria Decay Rate Implementation Rationale
Inactivity Period 10 points reduction for every 30 days of no engagement Automated via CRM/lead scoring software Reflects diminishing interest over time without interaction
Email Non-Engagement 5 points reduction for every email not opened over 60 days Tracked and updated in the email marketing system Indicates declining interest in communications
Website Non-Visits 15 points reduction for not visiting the website in 45 days Monitored through website analytics integration Absence of website visits suggests reduced interest in products/services
Social Media Inactivity 8 points reduction for no social media interaction in 30 days Linked with social media monitoring tools Lack of social media engagement can signal a drop in interest or relevance
Product-Specific Decay Varies based on product/service (e.g., higher decay for fast-moving products) Customized in lead scoring settings Different products/services have varying sales cycles and engagement patterns.

Read also: Maximize Your Sales: Top 12 Lead Scoring Tools Reviewed

7. Sales and marketing alignment

The alignment between sales and marketing teams is a critical factor in the success of lead scoring. Businesses can make sure that leads are scored, nurtured, and followed up on in the most efficient way. 

This alignment not only improves the quality of leads passed to sales but also enhances the overall effectiveness of the marketing and sales funnel.

Here’s how you can develop collaborative criteria:

  • Joint workshops: Regular workshops where sales and marketing teams come together to define and agree on lead scoring criteria. These sessions should focus on understanding each team’s perspective on what constitutes a qualified lead.
  • Shared definitions: Develop a shared language and definitions for lead scoring. For instance, what marketing considers a ‘hot lead’ should align with the sales team’s understanding of the term.
  • Feedback loop: Establish a continuous feedback loop where sales provide insights back to marketing on the quality of leads. This feedback is crucial for refining lead scoring criteria.

Integrate sales and marketing insights

Utilize sales data to understand which leads are converting and why.

This analysis can provide valuable insights into which behaviors and characteristics should score higher. Other than that, regularly involve the sales team in scoring model reviews. Their firsthand experience with leads can provide practical insights that no amount of data can fully capture.

For the marketing aspect, analyze the outcomes of various marketing campaigns to see which types of leads they are attracting. This helps in adjusting scoring models to align with the most effective campaigns.

Marketing should track which content is resonating with leads. High engagement with certain types of content can be a strong indicator of lead quality.

8. Customized scoring for different products andservices

Customized scoring for different products or services ensures leads are evaluated in the context of their specific interest and potential value. 

This practice allows for more targeted marketing efforts and more efficient sales processes, as leads are scored and nurtured in a way that aligns with their specific interests and the unique selling points of each product or service.

Tailor your scoring to product characteristics

Products with higher complexity often require a longer decision-making process. Leads interested in such products might need higher engagement scores before they are considered sales-ready.

For services, especially consultative or customizable, scoring should emphasize interactions like service inquiries or consultations.

High-value products or premium services might attract a different demographic. Scoring for these products should reflect the unique characteristics of their potential buyers.

Implement segment-specific scoring models

  • Different market segments: Different market segments may have varying online behaviors and needs. For instance, enterprise-level leads might be scored differently than small business leads.
  • Industry-specific interactions: Certain industries might value specific interactions more highly. For example, in the tech industry, downloading a technical whitepaper might score higher than in other industries.

Utilize dynamic scoring adjustments

  • Regular reviews: Regularly review and adjust each product or service’s scoring models to ensure they remain relevant and effective.
  • Adaptation to market changes: Be prepared to adjust scoring models in response to market changes, new product features, or shifts in customer preferences.
  • Data-driven approach: Utilize data analytics to continuously refine and optimize the scoring models for each product or service.

Read also: Lead Scoring Strategy to Discover & Prioritize High-Value Prospects

9. Automated lead scoring systems

Incorporating automated lead scoring systems into your marketing and sales strategy can significantly enhance the efficiency and accuracy of your lead management process. 

Automated lead scoring systems represent a significant advancement in lead management.

By leveraging technology to score leads accurately and efficiently, businesses can make sure that their sales and marketing efforts are focused on the most promising prospects, ultimately driving better conversion rates and sales success.

A sample lead scoring workflow using EngageBay
A sample lead scoring workflow using EngageBay

Know the benefits of automation in lead scoring

  • Consistency and objectivity: Automated systems score leads based on set criteria, eliminating subjective biases that might occur in manual scoring. This consistency ensures that all leads are evaluated equally based on their actions and characteristics.
  • Efficiency and time-saving: Automation platforms speed up the lead-scoring process, freeing up valuable time for sales and marketing teams to focus on strategy and engagement with high-potential leads.
  • Scalability: As your business grows, the volume of leads can increase significantly. Automated systems can handle large volumes of leads without compromising the scoring quality.
  • Real-time scoring: Automated systems update lead scores in real-time as new data comes in, ensuring sales teams have the most current information on lead engagement.

Automate lead scoring using marketing tools

  • Integration with CRM and marketing tools: For effective automation, integrate your lead scoring system with your CRM and marketing automation tools. This integration allows for seamless data flow and scoring based on real-time interactions.
  • Setting Up scoring rules: Define clear rules for scoring based on both demographic and behavioral data. These rules should reflect the actions and characteristics most indicative of a lead’s potential to convert.
  • Training and understanding: Ensure your sales and marketing teams understand how the automated system works. This understanding is crucial for effectively using the system and making informed decisions based on the scores.

Important: While automated systems handle the day-to-day scoring, it’s important to regularly review and adjust the scoring rules to align with evolving marketing strategies and market trends.

10. Continuous optimization of lead scoring

Continuous optimization of your lead scoring model is crucial for maintaining its relevance and effectiveness over time. 

By regularly updating your model based on data, feedback, and market trends, you can make sure that your sales and marketing efforts are always aligned with the most current and actionable insights.

Understand the key aspects of continuous optimization

Regularly analyze your sales and marketing data to identify trends and patterns.

Use these insights to refine your scoring criteria. For instance, if certain behaviors or demographics are consistently leading to conversions, they should be weighted more heavily in your scoring model.

Apart from that, stay attuned to changes in your industry. Shifts in market dynamics, such as new technologies or emerging competitors, can influence buyer behavior and necessitate adjustments in your scoring model.

Monitor how customer interactions with your brand evolve. Changes in the way customers engage with your content, website, or sales team can signal a need to adjust your scoring criteria.

Read also: The A-Z of Predictive Lead Scoring: Insights and Strategies

11. Conversion tracking from MQL to SQL

Tracking the conversion rate from Marketing Qualified Leads (MQLs) to Sales Qualified Leads (SQLs) is a critical measure of the effectiveness of both your lead scoring system and the overall sales process. 

Tracking helps understand how well marketing efforts align with sales objectives and where improvements can be made.

A marketing Qualified Lead (MQL) is a lead deemed more likely to become a customer than others based on their engagement with marketing efforts. A Sales Qualified Lead (SQL) is a lead that the sales team has accepted as worthy of a direct sales follow-up.

The MQL to SQL conversion rate is a key metric that indicates how effectively marketing leads are being qualified and handed off to sales. A low conversion rate might suggest that marketing is passing over leads that are not yet sales-ready.

Implement various strategies for effective conversion tracking

  • Establish and agree upon clear criteria for an MQL and an SQL. This clarity ensures that both marketing and sales teams have a unified understanding of these terms.
  • Utilize a CRM system that can track the journey of a lead from MQL to SQL. This system should be able to record interactions and engagements, at which point a lead moves from being an MQL to an SQL.
  • Analyze the conversion data regularly to identify trends, patterns, and areas for improvement. Look for factors such as the time taken to convert an MQL to an SQL and the percentage of MQLs that become SQLs.

Utilize conversion data to improve lead quality

Use the conversion data to create a feedback loop between sales and marketing.

This loop should inform marketing about the quality of MQLs and guide adjustments in lead scoring and qualification criteria. Break down the conversion data by different segments, such as industry, product interest, or lead source. This analysis can reveal which segments have higher conversion rates and why.

Apart from that, use insights from the conversion data to refine marketing strategies, lead nurturing programs, and sales approaches.

For example, if a particular type of content is consistently associated with higher conversion rates, marketing can focus on creating more of that content.

Read also: Lead Nurturing: A Complete Guide with Strategies

Closing Thoughts

The path to a successful lead scoring system lies in its continuous evolution.

Embrace a data-driven approach, align closely with your sales and marketing teams, and regularly refine your strategies to reflect changing market dynamics.

An effective lead scoring model is not set in stone – it’s a dynamic tool that grows with your business, ensuring every lead is an opportunity waiting to be realized.

EngageBay is an all-in-one marketing, sales, and customer support software for small businesses and startups. You get predictive lead scoring, marketing automation, personalization and segmentation, landing pages, free email templates, sales pipelines, and more.

Sign up for free with EngageBay and start scoring your leads. You can also book a demo with our experts.

Frequently Asked Questions (FAQ)

1. Can lead scoring integrate with AI to predict future customer behavior?

Yes, integrating AI with lead scoring is increasingly popular.

AI algorithms can analyze vast amounts of data to predict future customer behaviors and preferences. This integration allows for more nuanced scoring, considering not just past and present interactions but also potential future engagement.

AI can identify patterns and trends that might be invisible to the human eye, making your lead-scoring system more predictive and proactive.

2. How does lead scoring change for a service-based business compared to a product-based business?

For service-based businesses, lead scoring often emphasizes the depth of engagement and specific inquiries, such as requests for consultations or detailed service questions.

In contrast, product-based businesses might focus more on actions like product demo requests or e-commerce interactions. The key difference lies in the nature of the interaction – service-based scoring might weigh personalized engagement more heavily, while product-based scoring might focus on actions indicating readiness to purchase.

3. Is it beneficial to score leads based on social media engagement?


Social media engagement can be a strong indicator of a lead’s interest and alignment with your brand.

Actions like sharing your content, commenting, or participating in social media discussions can be scored to reflect a lead’s engagement level. This is particularly relevant in today’s digital age, where much of the customer journey happens online, and social media plays a significant role in decision-making.

4. How can small businesses implement lead scoring effectively with limited resources?

Small businesses can start with a simple, manual lead scoring system based on key customer actions that indicate interest or purchase intent. As the business grows, it can gradually adopt more sophisticated systems, possibly integrating automated tools that are cost-effective and scalable. The focus should be on identifying a few critical behaviors that align with successful conversions and scoring leads accordingly.

5. How often should a company revise its lead scoring model?

The frequency of revising a lead scoring model depends on several factors, including changes in customer behavior, market trends, and the introduction of new products or services.

Generally, it’s advisable to review and potentially update the scoring model at least bi-annually.

However, if significant changes occur in the market or customer base, more frequent revisions may be necessary to ensure the model remains accurate and effective.

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