What is lead scoring? Why should B2B businesses or the service industry build a lead scoring model to optimize conversion efficiency, consult and make the most out of the company?
Before learning what a lead scoring model is, let’s start with Weapon Agency with the basic concept first: What is ‘Lead’?!
What is Lead? Distinguish 2 concepts MQL and SQL
A lead is the information of a potential customer who “appears” to be interested in the company’s product or service. In addition, these people are potential buyers whom a company has a good chance of influencing to purchase at the proper time.
It’s important to note that not every lead has the same potential for success. For instance, the same lead may have a greater conversion rate if it is submitted by someone interested in receiving restricted offers instead of attending the free event. Because according to common psychology, after ‘joking’ incentives, the next step in everyone’s mind is usually how to use them effectively!
In addition, the individual who provided their information (lead) is not always a qualified sales prospect. It’s important to remember that the odds of effectively converting things like emails and phone numbers left to download papers and reports, for example, are often as low as zero. It’s easy to see why, given that the business’s product or service isn’t really what people need.
Therefore, besides the concept of lead, businesses also need to distinguish the following two concepts:
- Marketing Qualified Lead (MQL) : Leads earned from marketing activities and are considered to have a higher conversion ability than the rest, but still need more time to take care of before they are ready to trade – In a nutshell. , these are potential customers!
- Sales Qualified Lead (SQL) : Leads who are ready to move to the next stage in the sales funnel – usually leads who can call a consultant to close the sale right away!
And among the list of hundreds and thousands of leads collected from many channels, how to distinguish leads, marketing qualified leads and sales qualified leads is the function of the lead scoring model!
What is lead scoring?
Through the use of a customer relationship management (CRM) system and a study of data ranging from demographics to customer interactions, an algorithm calculates a score for each lead based on how likely it is to be converted into a qualified sales lead (SQL).
A lead’s potential for becoming a customer is often ranked on a scale from zero to one hundred. The CRM system will uncover common variables that, when qualified, will reward a lead based on data gleaned from the past. + or minus a specified fraction of a point.
Visualize the CRM system beginning to keep tabs on the target customer’s interactions like this once they have left their information:
- Go to the website and go through the entire quote page – If, according to historical data, this is the ‘signal’ of most successful deals, that lead will be ‘scored’.
- Do not open emails for more than 3 months – If according to the data, this is the behavior that signals the target audience’s interest in the product/service has cooled down, the lead will be ‘pointed’.
And the above process will continue with many other factors until the lead’s quality score reaches ‘potential’ (MQL, SQL) or is ‘rejected’ when it is deducted to the minimum threshold!
Why is it important to build a lead scoring model?
Businesses may address the issue of “effective optimization” with the use of the lead score model. When resources are limited and timely switching cannot be accomplished, companies may now filter out the SQL list to focus their efforts and ensure they don’t lose out on any potential switchers. Always follow up on your leads.
In other words, lead scoring functions as a “filter funnel” for salespeople, allowing them to zero in on the best chances while spending less time on those that are quickly disqualified.
Using the lead scoring model is a great way to improve the customer experience, which is important from a marketing standpoint. Using the quality scale, organizations may automatically send emails/remarketing based on the available sizes, as well as filter the list of MQLs to be cared for based on the previously defined “customer picture.”
Businesses may boost their ‘favorite points’ from consumers when it’s absolutely required, such as when sales KPIs are enhanced, by focusing on nurturing leads whose scores are near to the MQL limit.
The core principle behind the lead scoring model
In reality, lead scoring is an algorithm that operates by compiling a list of criteria shared by prior transactions that were either successful or unsuccessful, and then “assigning points” to each item.
One of the most basic principles for building a lead scoring model is:
- Identify common elements of successful transactions
- Determine the ‘presence’ of each element in successful transactions – for example ‘see quote page’ is the behavior that 9 out of 10 successful cases do
- Compare the ratios found in step 2, the higher the ratio, the more important the factor. Usually when scoring, these criteria will have a higher value!
It sounds simple, but in reality, when building a lead scoring model, there are countless criteria to consider. Especially without a CRM system that helps classify or filter a variety of information fields, it will be difficult for businesses to build an effective lead scoring model!
Scoring criteria of the lead scoring model
Depending on the characteristics of each field, the criteria for evaluating the quality of leads will be different, but basically, businesses can be divided into three main groups:
- Customer information – the main information they provide to the business, such as: company, business area, position title, revenue, budget, number of employees, source of leads
- Authoritative behavior – their behaviors on digital platforms, such as how they interact with your website, content, emails or registrations, calls, and even offline activities like attending events!
- Negative factors – signs that they are no longer potential customers such as less responsiveness to advertising such as emails, messages, and social media content; unsubscribe, the phone number is unreachable; Website visits decrease….
Businesses will easily find standard rules with these three criteria groups with the existing data system!
5 steps to build and deploy a lead scoring model
Step 1: Create an MQL list
To begin, decide what factors will be used to “judge” whether a lead is MQL (review the suggestions mentioned above). The next step is to use a customer relationship management system like Hubspot to whittle down the list of contacts to those who meet your established criteria.
Step 2: Determine the value system for the selected criteria
Find out what the possible range of scores is for each criterion. Most companies use a scale from 1-10 to establish their priorities. However, experts suggest that the greater the precision of an evaluation, the lower the maximum value for a component. Therefore, Companies should give the best possible elements a rating between 1 and 5. Correspondingly:
- Factors that have little influence on the buying decision are usually worth 1-2 points
- Factors that have a moderate effect on the final decision usually have an average value – of 3 points
- The factors that have a significant impact on the purchase decision will be worth 4-5 points
Step 3: When dividing points for each criterion, businesses need to carefully consider the correlation relationship
It’s only fair to provide the same points to similarly consequential actions. In the same way that 5 points would be awarded automatically for completing a consultation form, 5 points would also be awarded for acts like opening an email several times or reading many “essential” items on the website. That’s the purpose!
Step 4: The maximum score is the sum of the maximum scores of the selected criteria
For example, you have 10 criteria to evaluate potential 1 lead, each criterion is maximum 5 points, so the maximum scale is now 50 instead of 100 as in the original example.
Step 5: Divide the quality scale into 3 main ranges
Corresponding to these three points is the priority of each lead: SQLs that need consulting priority, MQL leads that need extra care, leads that need to continue to interact… At this point, businesses will need to experiment and coordinate Work closely with the sales team to find out where the ideal boundary is between the three groups of points mentioned above!
Lead scoring is a model that requires digging into data analysis and programming thinking to create systemic formulas. Although there are difficulties in the process of trial, error-correction constantly, once put into operation, businesses can apply long-term applications to continuously optimize costs – effectively for advertising campaigns.