How to Create a Lead Scoring System That Works
Updated: Jan 24

[Warning: Your email platform may not have a lead scoring functionality so before you spend any time on this, first check on whether you can implement it or not.]
Nearly every organization I speak with wants to setup lead scoring. However, many don't know where to start or their scoring setup is just not working.
So let's get into it.
Step 1: Build Your Demographic Profile
There are a couple ways to start here. The first being to look at your defined ICP (Ideal Customer Profile) and add those attributes. This may include,
Company size (revenue or employees)
Geographic area
Industry
Installed software
Now let's dive a level deeper and ask sales to validate. Who do they want to talk to? Here you may find answers like,
Job role
Job function
Department
Job title
Now put everything you learned into a table that will look like this,
Category | Details | Impact | Score Change |
Demographic | Industry | | |
Demographic | Company Size | | |
Demographic | Job Function | | |
Once you have this, we now need to assign the Impact. I like to use a rating of 1-3. The Impact is your ranking of importance. For example, if it is more important that the industry is an exact match vs. the company size, you would rank that as such.
Here is what your table now looks like where the most important characteristic is an Industry match,
Category | Details | Impact | Score Change |
Demographic | Industry | 1 | |
Demographic | Company Size | 2 | |
Demographic | Job Function | 3 | |
Note that your list will be much bigger than the three I am using in my example and there can be multiple one's and multiple two's etc.
We have one more column to complete and that is the Score Change but we need to look at that later when we start building out our scenarios.
Step 2: Build Your Behavioral Profile
Now that we know what our demographic profile looks like, let's identify behaviors a person can take. This could be a form fill, attendance at a webinar, a request for demo or a high-value webpage visit like your pricing page.
Let's get this into a table like we did above.
Category | Detail | Impact | Score Change |
Behavioral | Request a Demo Form | | |
Behavioral | Whitepaper Download | | |
Behavioral | Tradeshow Booth Scan | | |
Now we want to assign an Impact score to each - same as above.
Category | Detail | Impact Score | Score Change |
Behavioral | Request a Demo Form | 1 | |
Behavioral | Whitepaper Download | 3 | |
Behavioral | Tradeshow Booth Scan | 1 | |
Step 3: Assign Score Changes
Now let's look at how we want to assign a score to each of the categories we have identified. I like to use a model of 0-100 with 100 being the best lead to give to sales.
Before we do that, we need to consider the weight of demographic vs. behavioral. Do we want 30% of the score to be demographic based and 70% to be behavioral based?
To make this decision, weigh in on how much a demographic fit vs. an action is more important. For instance, is it more important that a CEO at a company in the Transportation Industry with 10,000 employees did something but it doesn't really matter what they did? Or is it more important that someone at a company in the Transportation Industry visited your tradeshow booth?
Take a moment to consider both and also ask your sales team for their input.
Now that we have that, we can start to assign a score to each attribute. I am going to use the 30/70 rule for weighting in this example. That means our Demographic score can be no more than 30 and our Behavioral score can be no more than 100.
We now have a table that looks like this,
Category | Detail | Impact | Score Change |
Demographic | Industry | 1 | 15 |
Behavioral | Request a Demo Form | 1 | 30 |
Demographic | Job Function | 3 | 5 |
Behavioral | Visited Tradeshow Booth | 1 | 30 |
Behavioral | Whitepaper Download | 3 | 10 |
Demographic | Company Size | 2 | 10 |
So now I need to look at what is the ideal lead I can deliver to sales? This will be my bar for setting a MQL status which will trigger the sales team to take over (more on that in another post).
This is a bit tricky because there is a piece of information you need to gather here and that is what volume of leads are you acquiring and what can sales actually handle?
If you have a sales team of 2 people and are acquiring 100 leads a day. We need to set the bar for reaching MQL pretty high to control the lead flow and allow for proper follow-up.
Using this scenario, I will start to build a few mocks to see where I get.
Mock 1:
Score Change | |
Request a Demo | 30 |
Industry Fit | 15 |
TOTAL | 45 |
Mock 2:
| Score Change |
Industry Fit | 15 |
Job Function Fit | 5 |
Visited Tradeshow Booth | 30 |
TOTAL | 50 |
Mock 3:
| ScoreChange |
Industry Fit |