Lead Scoring is a tool used by sales and marketing teams to determine the worthiness of leads. Scores are attached to each lead based on their behavior relating to their interest level in products or services. The “value” of each lead differs from company to company. Generally, their value is characterized by their engagement during the procurement cycle.

Companies assign points in qualifying leads. They categorize them as “hot,” “warm” or “cold” based on their characteristics and interactions.

Lead Scoring Types

Rule-Based Lead Scoring: Assign point values based on a lead’s demographic & behavioral attributes. Point thresholds determine whether the lead is worth pursuing. These rule-based scoring solutions are integrated and provide results in CRM systems such as C2CRM.

Predictive Lead Scoring Models: Machine learning and artificial intelligence (AI) generate a predictive model (algorithm) based on past customer data and behavior.  With predictive lead scoring, you don’t have to figure out how much weight to assign each characteristic or criteria.

Establishing a formula that works in all cases is difficult and often comes down to trial and error. However, with predictive lead scoring, the algorithm looks at the information your customers have in common. From there, it generates a formula that will automatically categorize your leads for you. Then you can identify the most qualified leads.


Customer Characteristics

The most critical part of the lead scoring process is determining your “ideal” customer. You identify the characteristics of people who are most likely to buy. These are individuals that display high-commitment behaviors vs. low-commitment behaviors.

When qualifying leads, you must look at two main categories:

  1. The general way that the person fits with your business.
  2. The person’s actual interest level and engagement with your product.

Your ideal customer will receive a high lead score in both categories.

  • Explicit criteria. Explicit scoring is based on information supplied by the prospect. This information tells you who the person is and whether they fit into your ideal customer mold. For instance, whether they have purchasing power within their own business or not. Or whether their own business is even big (or small) enough to work with the service or product you offer. Measuring explicit criteria means measuring things like:
    • Company size.
    • Geographic customer locations.
    • Degree of decision-making & purchasing power.
    • Past purchases.
    • Job title and level
  • Implicit criteria. Implicit scoring is based on the information you gather indirectly. It is behavioral data that tells you how interested someone is in your service, product, or business. It doesn’t tell you whether they are a fit for your company. It tells you whether they engage with your content, website, and advertisements. For implicit criteria, consider metrics like:
    • Number of phone calls.
    • Time-on-page.
    • Number of website visits.
    • Tradeshow/Webinar attendance.
    • Marketing email interactions.
    • Live chat interactions.
    • Social Media interactions
  • Negative criteria. Negative criteria will help identify the people who aren’t interested in your products and services. This allows you to discard them early in your sales process so you don’t waste time nurturing leads that will never convert. Here are some examples of negative criteria:
    • Low email open and click-through rate.
    • Long inactivity periods.
    • Very little engagement.
    • Invalid email addresses
    • Any unsubscribes to previous campaigns.

Quantifying the Criteria

Once the criteria for your “ideal” customer has been identified, it’s time to assign quantitative values or points. The values represent their importance to the lead scoring model.

Below is an example of assigning points to criteria (the total score for the example is 78):

Point System Example

Lead Temperature

Once the quantification has been activated, the point totals can be categorized using a threshold system. This system easily identifies which leads to focus on and assign to the sales team.

Below is an example of a lead temperature threshold assignment.

Hot: 80-100

Warm: 50-79

Cool: 25-49

Cold: 0-24

In the “Point System Example” above, the total score of 78 will fall into the category of a “warm” lead.


Lead prioritization:

Lead scoring helps sales to segment and focus on leads based on their anticipated desire to buy. Follow-up is more effective when engaging prospects with higher interest.

Coordination of Marketing and Sales efforts:

Strengthens the relationship between marketing and sale. It helps clear a path for a smoother hand-off of leads from marketing to the sales team, to close the deal faster.

Increased marketing effectiveness:

It quantifies types of leads or lead characteristics that are most important. This allows marketing to better target their programs and deliver more high-quality leads.

Increase in Sales Revenue:

Information obtained during the lead scoring process, allows your teams to tailor their sales pitch to prospects.  Which will, in turn, increase the conversion rate. Also, leads with higher scores having a higher close rate than a lower score. This all results in an increase in sales revenue.


Check out the advanced lead scoring system in the C2CRM system. It is completely integrated with the marketing automation module. So, opens & clicks on emails and lead generation from your website will all factor into determining the priority of your leads.

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