Update date : 28 Jul 2023 | 6 Min Read
Whether you are a novice in the sale & marketing team or head of the group, you must know that lead generation is an unavoidable factor for growth. The lead generation process involves multiple aspects and subdomains; one is lead scoring. Your website is getting good traffic, but only some visitors are potential buyers, so how do you qualify your leads for the maximum conversions?
Okay, let's understand the lead-scoring phenomenon with an example:
How does a teacher evaluate the exam paper and identify the top-performing students?
Simply by following a set of rules to check their exam sheets, give numbers accordingly, and filter out the top performers based on their numbers.
Lead scorings work the same way. Let's uncover the basics of lead scoring, what is lead scoring, and what are the benefits of lead scoring.
Before we proceed with lead scoring, it is vital to understand lead generation, as it is part of the strategy.
Lead generation identifies potential leads and fetches the lead data for the conversion follow-up.
Suppose a website gets 500 visitors daily; 100 visitors explore the brand's services and fill in the contact details. Not all 100 leads need to be highly prominent, with a 100% chance of conversion.
So, lead scoring is the method to rank the leads depending on their actions to identify the most and least potential leads and prioritize your sales efforts to maximize the conversions.
Definition of Lead Scoring:
Lead scoring ranks the leads by assigning them numerical values based on multiple attributes like submitting the contact form, visiting service pages, and interacting with the website, reflecting their interest in the conversions.
This methodology helps marketers prioritize their efforts and focus on the high-value leads, who have more chances of conversions than throwing their efforts in the air.
As a marketing & sales head, I know the nuts and bolts of the lead generation process. Regarding the traditional practice of identifying quality leads, most sales & marketing people use the BANT strategy: Budget, Authority, Need, and Timeline.
Because that's how the buyers used to proceed with their purchases, but now the time has changed, and hence the purchasing approach of buyers as well. Before defining the budget and timeline, people search for the product and services, research, and follow and download case studies.
The sales reps have no visibility into all these processes, and without the lead scoring approach, the sales reps may miss reaching the most potential leads. In contrast, the advanced lead-scoring process uses multiple data points to get to the most-qualified leads. Explicit details about the buyer, like job title, company revenue, and designation, may help. Still, the primary key factors for qualifying leads are the implicit information about the buyer persona, including how they interact with the website, what they search for, where they focus their attention, and whether they try to interact with the team.
There are multiple different methods to rank the leads and calculate the lead score. Here are some of the most used lead-scoring models:
Following are the steps of the manual lead scoring method:
Lead-to-Customer Conversion Rate Calculation
This calculation gives an approx idea about how many of your leads are converting into customers. It can be calculated using the number of new customers gained divided by the total number of leads generated. This conversion rate will help you identify the right criteria to score leads. Mark this conversion rate as the benchmark for the process.
Pick Attributes of the Higher Quality Lead
Your current customers were also leads at some point of time in the process. Choose the customers who you once considered high-quality leads and check their attributes.
Potential attributes could include:
Selecting which attributes to incorporate into your model requires a certain level of artistic judgment. Conversations with your sales team, analytics, and other factors will inform decision-making. It's important to note that this process will differ from person to person, but any approach is valid if the scoring is based on the data above.
Define the Close Rate for Each Attribute
Calculate the close rate for each attribute you have identified in the above step for every action taken by individuals on your website and the type of individuals who take these actions. This information will determine the appropriate response measures the sales reps should take. To do this, determine the number of people who become qualified leads and eventually convert them into customers based on their actions or how they fit into your core customer group. In the following step, you will use these close rates to "score" them accurately.
Compare the Close Rates of Each Attribute
Now we have data to assign values for lead scoring. Compare the close rates of each attribute and identify the attributes with higher close rates. Assign values or points to the attributes based on their close rates. The actual point values will be arbitrary but try to be as consistent as possible.
For example, if your total close rate is 1% and your "Contact Us" close rate is 20%, then the close rate of the "Contact Us" attribute is 20X your overall close rate -- so you could assign 20 points to leads with those attributes.
In the above segment, we have now understood the basic and simple method to calculate the lead score. Now we are coming to the mathematical data mining technique, such as logistic regression. However, such data mining techniques are complex and more intuitive to calculate close rates. Logistic regression involves creating a formula in Excel to predict the probability of a lead becoming a customer. This approach considers all customer attributes, such as industry, company size, trial requests, and how they interact. It is a more accurate method than previously outlined, as it considers a broader perspective.
The significance of lead scoring is very evident in improving the lead-handoff process, lead conversion rate, team productivity, and more. But, as you can see from the two methods above, creating a scoring system can be time-consuming when done manually. Also, scoring criteria isn't about "set it and forget it" but resetting your lead points based on the team feedback and results. Tweaking the lead scoring patterns during a specific time interval is essential for accurate results.
There is no second thought that manual calculation takes much more time than using technology to manage manual setup and continuous tweaking. This will give your team the bandwidth to manage customer relationships.
The predictive scoring method involves machine learning to parse thousands of data points to identify your best leads, so you don't have to. Predictive scoring creates a formula to rank your contacts by their likelihood of becoming customers by analyzing common characteristics among your customers and the leads that didn't convert. This helps you and your sales team prioritize leads, avoiding unnecessary contact with those who aren't interested and focusing on engaging those who are.
The best part about predictive scoring? As with any application of machine learning, your predictive score gets smarter over time, so your lead follow-up strategy will optimize itself.
Leads are the primary factor for any business; lead scoring is critical for identifying quality leads. More is always less when talking about sales efforts. Lead generation requires meticulous efforts to improve the conversion rate, and lead scoring is one of the tactics to get the most accurate results for lead generation.
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