Lead Scoring 101: What is lead scoring?

Published by Nilangan Ray on

This post is part of a series called Lead Scoring

With the rise of digital selling, a growing need for qualifying leads has come forward for B2B businesses. There are two primary reasons for this:


  • With accessibility being at the peak, more people now interact with your business and not all of them are your potential customers.
  • B2B prospects go through an extended buying process and selling to them before they are sales-ready or serving them untimely content does not work well.


While a segmentation method or a profile based automatic lead grading strategy can help you weed out unqualified leads through preliminary vetting, it can’t help you align buyer stage with customer engagement and determine sales-readiness. This is where lead scoring comes into play.


Lead Scoring Infographic – Salespanel


What is lead scoring?

To quote Wikipedia directly, “Lead scoring is a methodology used to rank prospects against a scale that represents the perceived value each lead represents to the organization.” Lead scoring is also used to determine sales readiness of leads and move them through stages of the sales process. Leads can either be given positive points or negative points. Positive points are awarded for positive characteristics or behaviors (eg: lead is from targeted business demographic). Similarly, points are deducted for negative behavior (eg: unsubscribing from important mailing subscriptions or removing payment information).

Lead scoring is done based on marketing and sales datasets like a lead’s individual profile data, company data, behavioral data, and sales/CRM data. Let’s have a look at these data points.

Individual Data: This data consists of information about the person who is evaluating the product on behalf of their company. The most commonly used individual data for lead qualification are job role and seniority. This helps in identifying leads who are decision-makers.

Company Data: Again, another crucial data point for initial vetting, this data consists of firmographic information of your lead like company size, industry, revenue, etc. The data helps you filter out leads who match your target business profile and also separate leads based ticket size.

Behavioral Data: This data point is where gradual lead scoring’s leverage comes from. Leads perform different activities while interacting with your content and touchpoints. As leads perform desired or undesired activities, their score keeps changing dynamically. Apart from helping understand a lead’s intent for initial qualification, behavioral data also helps in moving leads through different stages of the buying process and serving personalized content.

Sales/CRM Data: This dataset brings information from sales to marketing for sales and marketing alignment, lead qualification and targeted nurturing. For lead scoring, the dataset can help update lead score based on a lead’s interaction with sales and CRM data.

Businesses also use other datasets like technographic data and demographic data in accordance to their requirements. Lead scoring is heavily reliant on data and data enrichment is needed for a proper lead scoring strategy.


Setting up lead scoring


Types of Lead Scoring

There are two prominent methods to score leads: predictive and rule-based. Let’s have a look at them and find out how they differ from each other.


Predictive Lead Scoring:

A predictive lead scoring software uses algorithms to automatically score your leads based on your marketing or sales data. Some lead scoring software would also use external data or combine with a third-party data enrichment software to score your leads.

Predictive lead scoring is completely automatic and will work based on the data it is fed and how the software has been designed.


Source: https://untitled-research.com/blog/lead-prioritization-with-predictive-analytics-in-recruitment


Rule-based Lead Scoring

Rule-based lead scoring is a simpler lead scoring method where you set up rules to score your leads. Rules can be set up based on the data available to you and the functionality provided by the lead scoring software.

When a lead triggers a rule set by you, the lead score is updated. You can set up rules based on desired or undesired characteristics and behaviors like we mentioned before and the score will keep changing. You can then trigger workflows based on lead score or set up a minimum scoring threshold to mark a lead qualified. For example, you can create a system where when a lead accumulates a minimum score of 100, they are sent to sales or served pre-sales content.


Rule-based lead scoring


The key difference between predictive lead scoring and rule-based lead scoring approach

The main difference in these two approaches is that with predictive lead scoring, the software does the work for you and with manual lead scoring, you set up your lead scoring workflow based on how you intend it to work. Rule-based lead scoring offers you the flexibility to set up your rules how you want to and assign weightage to each characteristic or behavior.

Let’s say, based on previous won deals, you have found out that a key product interaction (eg: trial extension) is a very strong indicator of purchase intent. You can simply create a lead scoring rule for this but can a predictive lead scoring software find this correlation? Maybe it can, but this entirely depends on how the machine learning and the algorithm works. So, the results might vary from software to software. Also, it would be harder to use predictive lead scoring to move deals across different stages of the buying process through stage based nurturing.

The advantage predictive lead scoring has over rule-based lead scoring is that it can pull data from a lot of different sources and you essentially don’t have to work on setting anything up yourself or analyze data to find key indicators. Depending on which software you use, you might also need to subscribe to data vendors for setup. This applies to both predictive and rule-based solutions but not all vendors need you to purchase 3rd-party data. Salespanel, for example, comes with data out of the box.



Some lead scoring tools in the market and their prices


Rule Lead Scoring:

LeadSquared: Lead scoring is available on plans starting at $1200/month
Salespanel: Lead scoring is available on plans starting at $249/month
Hubspot (rule-based): Customizable rule-based lead scoring is available on plans starting at $800/month.


Predictive Lead Scoring:

Infer: Price not disclosed
Madkudu: Starts from $999/month
Hubspot (predictive): Predictive lead scoring on Hubspot is available on Enterprise plans. Price is not disclosed.



Setting Up Lead Scoring

For predictive lead scoring, setup is fairly simple. You integrate the software with your CRM, marketing tools, data enrichment service or your website and you are good to go. The software will start scoring your leads automatically. For rule-based lead scoring tools, on top of connecting your lead scoring software to your sales and marketing workspace, you would also need to set up your rules. The flexibility that rule-based lead scoring provides needs you to put a bit more effort in setting up your workflow.

In this article, we will provide a quick brief of how you can set up rule-based lead scoring with Salespanel. The fundamentals should be the same for other tools as well. So, on Salespanel, we provide you a person’s individual, company, and activity data right from the start. Salespanel just needs to be connected to your website (and also to your CRM if you want to use sales data). Once set up, you can create different rules based on the data that is provided to you.

Before you set up your rules, you should research your previous deals and find out strong indicators. Some data filters that will be helpful for this are job role, company size, page visits, email engagement, etc. Behavioral data will let you progressively score leads and also move them through the sales process like we mentioned before. If you need some information to find out strong indicators, check out reports of customers on Salespanel and also discuss with your sales team. They often know which indicators are stronger.



Syncing data and triggering marketing automation

The lead scoring data can stay on your lead scoring software but it makes more sense if you leverage the data to trigger certain workflows or send the data to sales to help them prioritize hot leads. Salespanel can sync lead score to other software like CRM or Slack instantly. This will help your sales You can also create a system to notify sales when leads cross a certain minimum score.

Lead score can also be used to trigger nurturing campaigns or optimize bidding value for your retargeting and nurturing campaigns. We will talk more about it in the future. Please leave us a message if you are interested in knowing more.


To conclude, lead scoring is a crucial asset in modern B2B sales and marketing. It can be beneficial for all digital businesses irrespective of size. While predictive lead scoring is more geared towards enterprise businesses with high volume, rule-based lead scoring can be helpful for all.

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Nilangan Ray

Nilangan runs marketing operations for Salespanel. Join him on LinkedIn: https://www.linkedin.com/in/nilanganray

Nilangan Ray, Marketing Head Nilangan from Salespanel

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