Master the Art of Lead Scoring: A Step-by-Step Guide

In the quest to convert leads into loyal customers, every digital marketer has a secret weapon at their disposal—lead scoring. This often-undervalued technique can pave the way for exponential business growth, driving your marketing efficiency to unparalleled heights. This comprehensive guide aims to demystify lead scoring, delving into its importance, basic concepts, and practical applications. You’ll learn how to create your ideal customer profile, assign point values, and implement lead scoring as part of your marketing strategy. We’ll also discuss advanced lead scoring techniques and how to avoid common pitfalls. So, are you ready to master the art of lead scoring? Let’s dive in!

What is Lead Scoring?

Lead scoring is a methodology for ranking leads based on how likely they are to purchase. Lead scores are often calculated based on behavioral attributes such as website visits, form submissions, and email clicks. Lead scores may also account for demographic and firmographic attributes such as job title and company size. Scores are often reduced based on negative attributes such as unsubscribing from emails or inactivity. Lead scores are used to determine which leads are ready to be assigned to sales and which require more nurturing.

The Importance of Lead Scoring

Lead scoring allows busy sales and marketing teams to allocate their resources to the prospects that are most likely to purchase. A study by MarketingSherpa found that businesses using lead scoring see a 77% increase in lead generation ROI compared to those that don’t. Similarly, 68% of marketers recognized as “highly effective and efficient” in a survey by the Lenskold Group identified lead scoring as their primary contributor to revenue. These numbers illustrate the immense potential lead scoring has to boost your business profitability and improve ROI. 

Here are some of the ways that lead scoring can benefit sales and marketing teams:

• Increase conversion rates by focusing sales efforts on prospects that are ready to buy. 
• Improve response times for leads that are ready to buy. 
• Allow marketers to nurture leads that aren’t ready to purchase.
• Align sales and marketing teams behind shared goals. 
• Provide marketers with better data to guide lead generation efforts. 
• Provide management with more accurate forecasts. 
• Make data-driven decisions rather than relying on individual intuition. 

Lead Scoring Models

Establishing a lead scoring model begins with selecting the appropriate attributes: demographic, behavioral, or a combination of the two. It’s not uncommon to create separate models for each product line and/or customer segment. 

Demographic Attributes

These attributes are used to measure how well a prospect aligns with your ideal customer profile. Demographics relate to the prospect’s individual characteristics such as job title, age, gender, and education. Firmographics relate to the prospect’s company and include attributes such as industry, company size, and annual revenue. It’s common to collect some of this data from the prospect while using data enrichment services to fill in the gaps.

Here are some examples of these attributes: 

These attributes don’t change frequently, so they can help you immediately distinguish whether a lead matches your ideal customer profile.

Behavioral Attributes

Behavioral attributes reflect a prospect’s interactions with your brand. These behaviors can give you insight into a prospect’s interest level and readiness to purchase.

Here are some examples of behavioral attributes:

For instance, if a prospect visits the pricing page of your website, it indicates they are actively seeking information about your product—a positive sign that can bump up their lead score.

Blended Scoring Models

It’s important that companies measure both demographic and behavioral attributes to gain a complete understanding of each prospect. Some companies combine demographics and behavioral attributes into a single score. However, we suggest keeping these scores separate since they measure two different things. Demographics are a measure of how interested you are in the prospect—whereas behavioral attributes are a measure of how interested they are in you. Additionally, behavioral scores change over time while demographic scores typically do not.

Negative Scoring Attributes

In lead scoring, not all attributes contribute positively to a lead’s potential value. Negative scoring is an essential component of a comprehensive lead scoring system. It’s the practice of deducting points from a lead score based on certain demographics or behaviors that indicates they may not be a good fit or might not be ready to buy.

Here are some common negative scoring attributes:

Negative scoring ensures that the overall lead score provides a more balanced view of the lead’s interest and potential, helping sales and marketing teams prioritize their efforts effectively.

A Step-by-Step Guide to Implement Lead Scoring

Let’s break down the steps to implement lead scoring at your company.

Step 1: Creating Your Ideal Customer Profile (ICP)

This involves determining which prospects are the most valuable. This is a crucial part of aligning your sales and marketing teams. It’s important that both departments collaborate on this definition and agree on what constitutes your ideal lead. Think about the behavioral and demographic attributes we discussed earlier. Which attributes are most important? Identify and rank those factors. 

You can start by discussing these six questions:

1. Who are my best existing customers? Look at your most loyal, profitable customers who advocate for your brand. What demographics and characteristics do they share?
2. What are their pain points and needs? Understanding the challenges your customers face, and how your product or service alleviates these pain points, can help you target similar leads.
3. What industries do they belong to? Identifying the sectors where your customers work can help you target leads in similar industries that might benefit from your products or services.
4. What is their job role or title? Are you targeting CEOs, managers, or individual contributors? Knowing the roles of your ideal customers can help you fine-tune your marketing efforts.
5. What is their buying behavior? What factors influence their purchasing decisions? How long is their buying cycle? Do they value quality over cost or vice versa?
6. Where do they consume information? Do they prefer social media, emails, webinars, blogs, or a different channel? Understanding where your ideal customers hang out online can help you target your marketing efforts effectively.
7. How can I tell when they’re disinterested? Do they unsubscribe or simply stop opening emails? Look for flags that indicate that a lead is not ready to purchase—these will become your baseline for negative scoring.

The BANT (Budget, Authority, Need, Time) framework is also essential in this process. A lead who checks all these boxes—budget to buy, authority to make decisions, a need for your solution, and a timely intent to purchase—is the lead scoring gold you’re seeking.

Here’s an example of what your ideal customer profile might look like:

Location	United States
Industry	Financial Services, Insurance, Technology
Role	Sales Operations Mgrs, RevOps Mgrs
Employee Count	1,000+ employees
Budget	$50,000/year
Technology	Salesforce
Pain Points	* Lack of sales automation * High customer churn rate * Slow lead response time

Keep in mind that you may have multiple ICPs for different customer segments and product lines. If so, you’ll want to create distinct scoring criteria for each. Once you have this information, you can assign scores based on demographics and behaviors.

Step 2: Identifying Lead Scoring Attributes

Now it’s time to decide on the attributes to include in your lead scoring model. You’ll want to compile a list of demographic attributes and a separate list of behavioral attributes. Your ideal customer profile (ICP) can be used to determine which demographic attributes to include. You’ll need to brainstorm with sales and marketing to determine which behavioral attributes make sense to include. 

You’ll also want to assign a point value to each attribute. We’ll discuss how to adjust these point values based on conversion data in the next step. Here’s an example of what this might look like:

Behavioral Attribute	Points
Pricing page view	+10
Scheduled demo request	+15
Installed free trial	+20
Watched a product tutorial video	+8
Engaged with live chat	+9
Attended a webinar	+5
Followed social media channel	+3
Participated in product survey	+5
Shared a company blog post	+2
Joined a product training session	+7
Requested quote	+25

Demographic Attribute	Points
Annual revenue greater than $500MM	+15
Employee count greater than 1,000	+12
Job title is Sales Operations Manager	+20
Job title is RevOps Manager	+18
Located in United States	+6
Located in Canada	+4
Technology industry	+10
Financial services industry	+8

Step 3: Assigning Point Values Using Conversion Rates

The most accurate way to determine point values for attributes is based on historical conversion data. Let’s walk through an example to illustrate how to calculate conversion rates and points for different industries. Here are the steps we’ll take:

1. List the different industries you service. 
2. Calculate the number of leads associated with each industry.
3. Calculate the number of leads that converted to customers for each industry. 
4. Calculate the conversion rate by dividing the number of customers by the number of leads. 
5. Determine what your average conversion rate is across all industries. 
6. Compare the conversion rate for each industry against your average conversion rate to determine how many points should be assigned.

Here’s an example of what the resulting table should like:

Industry	Leads	Customers	Conversion Rate	Points
Financial Services	1,000	200	20%	+20
Government	900	90	10%	+0
Healthcare	1,333	200	15%	+10
Manufacturing	800	80	10%	+0
Technology	2,000	500	25%	+30
Retail	1,667	100	6%	-8

In this example, we’ve calculated points based on an average conversion rate of 10 percent. We can see that conversion rates vary significantly based on the industry. We’ve assigned points to account for this variation. 

Setting Point Limits

When setting up your lead scoring model, it’s essential to avoid overinflating scores due to repetitive actions or outdated data. We can limit the points assigned to attributes in two ways:

1. Point Caps: this involves setting a maximum limit on the number of points assigned for a particular action, such as clicking on an email link. For instance, a prospect might earn +1 point for each email click but can accumulate no more than +5 points total.

2. Time Limits: this involves setting a limited time frame during which points are calculated. An example might be replacing “email clicks” with “email clicks from the last 90 days”. This ensures only recent activities impact behavioral scores.

Both approaches ensure that lead scores remain relevant, balanced, and reflective of genuine interest. 

Step 4: Testing Your Lead Scoring Model

Now it’s time to test your lead scoring model using historical data. It’s important that you test the model using different data than what was used to create the model. 

Historical Data Comparison

Begin by applying your scoring model to historical leads that have had enough time to either convert or be rejected. Compare the scores of leads that converted to customers against those that didn’t. Ensure that leads that converted score higher on average than leads that did not convert. Check for outliers, such as converted leads that have abnormally low scores, to ensure there are no serious flaws in your lead scoring model. 

Determining Your Lead Scoring Threshold

A lead score threshold is the minimum score a lead must reach before they’re considered sales ready. Your goal should be that at least 90% of the leads that historically converted score above this threshold.

Step 5: Implementing Your Lead Scoring Model

Now it’s time to put your lead scoring model into action. Lead scoring is usually done using a CRM or marketing automation platform. Most platforms have built in features to support lead scoring. You’ll simply need to enter the attributes and point values you determined above.

You’ll also want to automate certain processes based on your lead scores. Here are some common examples of how automation can be used to leverage lead scores:

• Prioritizing leads: you might use lead scores to prioritize the order in which leads are assigned and/or worked. 
• Display lead scores: you might display lead scores to your sales team so they can self-prioritize their work. You may choose to simplify the lead score down to a star rating, hot/warm/cold rating, etc. 
• Lead scoring thresholds: you might set a minimum lead score threshold that must be reached before leads can be assigned to the sales team. 

Step 6: Monitoring and Adjusting Your Lead Scoring Model

Just like any other strategy, your lead scoring model needs to be regularly reviewed and adjusted to ensure its effectiveness. It’s crucial to remember that your customer base, product line, market trends, and business objectives can change over time. As a result, a lead scoring model that worked perfectly a year ago might not be as effective today. Periodically monitor your model’s performance, comparing actual sales results with your lead scoring predictions.

Additionally, keep an open mind for opportunities to refine and enhance your model. Experiment with new scoring attributes, tweak point values, or explore more advanced techniques to enhance your model’s accuracy. The world of lead scoring is not static but evolves with your business and the broader market environment. Staying proactive and flexible in your approach to lead scoring will ensure your model remains a powerful tool for prioritizing leads and driving sales growth.

Advanced Lead Scoring Techniques

As you get comfortable with lead scoring, you can explore advanced techniques such as predictive lead scoring and multivariate techniques. 

Predictive Lead Scoring

Predictive lead scoring takes the traditional lead scoring model a step further by leveraging machine learning. Machine learning can be used to identify patterns that are extremely difficult to spot. This automated, data-driven method offers a more precise scoring model that continually refines itself as more data is collected.

Multivariate Lead Scoring

Multivariate lead scoring models evaluate several attributes simultaneously to calculate a lead’s score. This approach acknowledges that leads often interact with businesses in complex ways. For example, while most lead scoring models assign points to leads that visit a pricing page–a clear indication of buying intent–a multivariate model delves deeper. It considers more nuanced factors such as whether the lead is a newcomer or an existing customer already on the premium plan, potentially indicating a desire to downgrade. 

Common Pitfalls to Avoid in Lead Scoring

The principle of “garbage in, garbage out” applies to lead scoring. Lead scoring models based on flawed data can lead to misguided decisions. The following are some common pitfalls you’ll want to avoid. 

Pitfall #1: Neglecting Negative Lead Scoring

Negative scoring and score degradation ensure that your data remains fresh and accurate. Negative scoring involves subtracting points for (a) demographics that indicate a prospect is a poor fit, or (b) behaviors that indicate a prospect is not ready to purchase. Score degradation, on the other hand, automatically reduces a lead’s score over time if no new interactions occur. By incorporating these methods, you ensure that high scores are indicative of current and sustained interest, not past engagements that may no longer be valid.

Pitfall #2: Overcomplicating Your Lead Scoring Model

Another common pitfall is overcomplicating your scoring model. While it might be tempting to account for every minute interaction, this can result in a convoluted model that’s difficult to manage and interpret. Instead, focus on high-impact behaviors and signals that truly indicate a lead’s readiness to convert. Simplicity is key to a manageable and effective lead scoring model.

Pitfall #3: Failing to Update or Adjust Lead Scoring Criteria

Remember that lead scoring isn’t a ‘set it and forget it’ activity. Your lead scoring model must evolve as your business, customers, and market dynamics change. Regularly reviewing your model’s effectiveness, updating scoring criteria as needed, and continuously testing and optimizing are vital to maintaining a lead scoring model that continues to deliver results.

Pitfall #4: Out-of-Sync Sales & Marketing Teams

Finally, don’t forget that lead scoring is a collaborative process that should involve both sales and marketing teams. When marketing and sales are out of sync, the lead scoring system can become skewed, undervaluing some leads and overvaluing others. To prevent this, establish open communication and regular meetings between the two teams. This ensures everyone is on the same page regarding what constitutes a high-quality lead and aligns your scoring system with the actual sales process. Remember, the purpose of lead scoring is not just to generate leads, but to generate leads that are likely to result in sales. And this can only be achieved when your sales and marketing teams are in harmony.

Conclusion

Lead scoring will empower your sales and marketing teams to focus their efforts on leads that are ready to purchase. This is one of the most effective ways to increase sales. And this is something you can start doing today!