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How to Calculate Lead Score

Learn about the process of computing a Lead Score using a SQL database

The sales process is a fundamental aspect of business success, and a crucial part of this is focusing efforts on the leads with the highest potential to convert. Evaluating and categorizing each lead based on specific criteria is imperative to this process, ensuring a strategic and targeted approach to sales success.

In this article, we delve into the intricacies of lead scoring, exploring its methodologies, benefits, and the transformative impact it can have on the trajectory of a business's success.

What is Lead Scoring?

Lead scoring is a methodology used in sales and marketing to evaluate and rank potential leads based on their likelihood to become customers. It involves assigning numerical values, or scores, to leads based on various criteria, such as their interactions with a company's content, engagement level, and demographic information.

Lead scoring helps businesses streamline their focus, allocating resources where they are most likely to yield significant returns. This approach transforms the sales process into a targeted and efficient endeavor, allowing teams to tailor their efforts to the unique needs and behaviors of each prospect.

Impact of Data on Lead Scoring

In the majority of circumstances, sales reps from outbound sales teams, inbound marketing efforts, or referrals from partners are primarily responsible for lead generation. Regardless of the source, there are essentially three types of data that impact the lead scoring process; these include behavioral data, demographic data, and historical data. This trio of data types can impact both the marketing qualified lead (MQL) and sales qualified lead (SQL). Furthermore, this data can help identify high-quality leads to shorten the sales cycle and improve the efficiency of the sales funnel.

  • Behavioral data: Behavioral data encompasses all the distinctive events and product usage metrics gathered directly through your landing page, application, or even your marketing automation platform. This includes actions such as pages viewed, email engagement like emails opened, product selection, addition of an item to cart, workspace created, messages sent, and the last login date etc.
  • Demographic data: Demographic data includes details such as job title, industry, company size, and location provide insights into whether a lead fits the target customer profile.
  • Historical data: Historical data captures the lead nurturing journey leading to conversions. Analyzing similar past leads predicts success for new ones. This can include the initial contact on social media or LinkedIn, the number of follow-up phone calls made by sales reps, the email marketing engagement rate, the number of webinars or demos presented etc. In addition, historical data can also be derived from the metrics of the previous two types of data.

Predictive lead scoring leverages machine learning to evaluate multiple data points, assigning point values to your current leads based on their alignment with your ideal buyer persona. This is done by comparing their actions with those of your past customers. This intelligent marketing strategy allows your marketing teams to identify the best leads among potential customers.

How to do Lead Scoring?

As you go about implementing a lead scoring system, it's crucial to recognize that a one-size-fits-all solution doesn't exist. Before diving into scoring criteria, establishing a benchmark is key. The lead-to-customer conversion rate, calculated by dividing new customers by total number of leads, provides valuable insights into lead generation effectiveness. For instance, a 10% conversion rate indicates that one out of every ten new leads becomes a customer. This foundational step sets the stage for a nuanced lead scoring model tailored to your business dynamics.

Once you’ve defined this metric, creating your scoring system involves identifying key attributes for measurement. This may include demographic information, behavioral data, and engagement metrics. Then, assign scores or point values to each criterion based on its significance. For example, a lead's job title might have a higher score than their location. Negative scoring can also be applied to filter out leads, such as deducting points for unsubscribing users or those who haven’t engaged in the last 30 days.

Attribute measurement varies based on your specific requirements in lead scoring. The concept is to establish a grading system using well-defined criteria. The greater the number of criteria considered, the more precise your lead score becomes. The key is not how you categorize your leads but rather having a robust ranking system in place.

Although CRM platforms such as Salesforce and HubSpot feature lead scoring capabilities, they frequently present a standardized model that may not suit your business needs. Additionally, these CRMs contain only a portion of customer data and lack access to crucial behavioral information. The good news is, your data warehouse has all your customer info. So, you can use SQL to create a lead scoring model that's customized to your business goals—making it accurate and tailored just for you.

Benefits of Lead Scoring

Lead scoring transforms the way businesses approach lead management. It's a strategic tool that not only enhances efficiency but also contributes significantly to revenue growth and customer satisfaction. Let’s look at some of the benefits below:

Efficient Resource Allocation

Prioritizing leads based on their likelihood to convert allows your sales and marketing teams to allocate resources effectively. This ensures that efforts are focused on leads with the highest potential, optimizing time and budget.

Improved Conversion Rates

By concentrating on leads that exhibit behaviors indicative of interest, your teams can increase conversion rates. Targeting the right leads with personalized content and engagement strategies enhances the chances of closing deals.

Alignment of Sales and Marketing

Lead scoring fosters collaboration between sales and marketing teams. It establishes clear criteria for what constitutes a qualified lead, ensuring both teams work towards common goals and objectives.

Optimized Marketing Campaigns

With insights from lead scoring, marketing campaigns can be fine-tuned to target ideal customer profiles more effectively. This leads to higher engagement, better response rates, and ultimately, increased return on investment (ROI).

Data-Driven Decision Making

Lead scoring relies on data and analytics, providing actionable insights for decision-making. It allows businesses to adapt strategies based on real-time information, improving overall efficiency and effectiveness.

How to use SQL to calculate Lead Score?

Each business is unique and therefore employs differing templates to determine their best leads. To understand the concept of lead scoring, let's look into a basic use case. Let's consider a scenario where a business in the e-commerce industry wants to score leads based on their interactions and behaviors. The criteria could include website visits, product views, email engagement, and purchase history.

Here's a simplified representation of the leads table with some sample data:

lead_idnameemailwebsite_visitsproduct_viewsemail_engagementpurchase_history
1John Doejohn.doe@rudderstack.com1053100.5
2Jane Smith jane.smith@rudderstack.com58250.25
3Bob Johnsonbob.johnson@rudderstack.com15105200.75
4Alice Brownalice.brown@rudderstack.com8124150.8
5Charlie Davischarlie.davis@rudderstack.com20157300.5
6Eva Garciaeva.garcia@rudderstack.com1285180.25
7Frank Harrisfrank.harris@rudderstack.com18106250
8Grace Miller grace.miller@rudderstack.com65280.75
9Henry Turner henry.turner@rudderstack.com22189400
10Ivy Robinson ivy.robinson@rudderstack.com14114220.3

Now, let's write SQL queries to calculate lead scores based on these sample criteria. We'll assign weights to each criterion and calculate an overall lead score.


SQL
SELECT
lead_id,
name,
email,
(website_visits * 5) +
(product_views * 7) +
(email_engagement * 10) +
(purchase_history * 15) AS lead_score
FROM leads;

Where the weights assigned to website visits is 5, for product views it is 7, and so on. The above query gives us the following lead scores:

lead_idnameemaillead_score
1John Doejohn.doe@rudderstack.com1622.5
2Jane Smith jane.smith@rudderstack.com854.75
3Bob Johnsonbob.johnson@rudderstack.com3206.25
4Alice Brownalice.brown@rudderstack.com2426
5Charlie Davischarlie.davis@rudderstack.com4782.5
6Eva Garciaeva.garcia@rudderstack.com2869.75
7Frank Harrisfrank.harris@rudderstack.com3970
8Grace Miller grace.miller@rudderstack.com1296.25
9Henry Turner henry.turner@rudderstack.com6326
10Ivy Robinson ivy.robinson@rudderstack.com3491.5


In this example, we've assigned weights to each criterion based on their perceived importance. The lead score is calculated as a weighted sum of the individual criteria. Adjusting the weights allows businesses to prioritize certain behaviors over others based on their strategic goals.


To make it simpler, you can also create a view for getting the lead scores.

SQL
CREATE VIEW lead_scores_view AS
SELECT
lead_id,
name,
email,
(website_visits * 5) +
(product_views * 7) +
(email_engagement * 10) +
(purchase_history * 15) AS lead_score
FROM leads;

You can then query this view to retrieve lead scores without having to rewrite the calculation each time.


SQL
SELECT * FROM lead_scores_view;

After you've established your lead scoring model using SQL, the subsequent phase involves integrating this data into your business tools such as Hubspot, Salesforce etc. This integration allows your sales team and marketing teams to prioritize leads with a higher score, ensuring the focus on high-quality leads.

Further Enhancements using DBT

You can further enhance the development, testing, and documentation aspects of your data transformations by incorporating DBT (Data Build Tool) into your workflow. Here's how you might structure a dbt model for the above example for calculating lead scores:

SQL
WITH lead_scores AS (
SELECT
lead_id,
name,
email,
(website_visits * 5) +
(product_views * 7) +
(email_engagement * 10) +
(purchase_history * 15) AS lead_score
FROM {{ ref('leads') }}
)
-- Output the lead_scores model
SELECT * FROM lead_scores;

Wrapping Up - Lead Scoring Using SQL

In conclusion, lead scoring stands as a pivotal strategy in the modern business landscape, leveraging the power of data to refine and enhance customer interactions. The ability to adapt and refine scoring models, foster collaboration between sales and marketing, and continually learn from lead interactions contributes to a dynamic and effective lead scoring approach. It allows businesses to not only identify high-value leads but also to optimize resource allocation, enhance customer experiences, and ultimately drive revenue growth.

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