The Best Lead Scoring Models In 2023
Establishing an effective lead generation model is a critical component of any good go-to-market plan. It's simple math that increasing the overall volume of leads for your sales team will help grow your business.
But let's face it, not all leads are created equal. And if you don’t have a systematic way to prioritize leads, it's highly likely you are driving the wrong focus and missing sales opportunities. This is where having a good lead scoring model comes into play.
I'm Mohammed Abukar, and I lead the Sales Technology strategy at TELUS, one of Canada’s flagship telecom and technology companies. In this article, I'll be covering some important principles about lead scoring models, plus the best models for 2023.
What is Lead Scoring
Lead scoring is a strategy centered around creating closer alignment between sales and marketing teams. Through this alignment, both organizations determine how likely it is for each lead to buy and assign a score or numerical value to each lead.
Better fit leads or hot leads, will be assigned a higher score as they are viewed as high-value. While lower quality leads receive a lower score.
Lead scoring helps organizations prioritize their efforts on qualified leads. It gives sales teams and sales reps the ability to focus their time on higher converting leads, therefore, impacting pipeline and revenue growth.
An Overview of Calculating Scores
There are a number of different ways to score leads. Your organization can choose to score leads based on multiple attributes with endless variations. Sales ready leads is the goal but how you get there will look different from company to company.
With any good marketing strategy, technology will play a pivotal role. When it comes to lead scoring, there are a number of different lead scoring tools on the market that can help.
Lead scoring can be as simple as setting up formulas in your CRM to look at specific fields that have inputs from your website’s forms. Based on the information contained in these fields, your formula can generate a score for each lead.
You can also make this a bit more complex by leveraging marketing automation platforms like Hubspot or Marketo to help execute on this strategy. Lead scoring software like these have evolved significantly over time and require specific skills sets and resources to maintain.
While we’ve talked a bit about score accumulation, a good lead scoring strategy should also consist of a method for negative scoring.
Decreasing lead scores is an important piece of any good strategy. After all, it’s not like every single interaction a lead takes with your company is a step forward in the buying process.
For example, you may want to reduce the score of a lead for some of the following:
- A lead has completely stopped engaging with your brand for a long period of time
- A lead has hit unsubscribe on your email marketing efforts
- A lead has visited your career page (this may indicate they are more interested in a job versus your product)
- A lead has indicated that they are a student or researcher
When looking to implement a lead scoring model ensure you factor in both score accumulation and decrease.
Steps on Setting Up a Strategy
As we just alluded to above, scoring models vary in level of complexity and will require some level of reiteration and maintenance. It is important to note that there is not a one size fits all strategy here.
This is why setting up an effective strategy will require extensive planning.
As always, and to help you get started, here is a simple three-step process you can follow to ensure you are on the right track.
Step 1: Analyze Customer Data
The first step in developing a lead scoring model involves analyzing customer data from your CRM. You will want to look at the attributes and characteristics your customers have in common.
Look to identify patterns, behaviors and the factors that have contributed to them converting at various stages and becoming a customer. Review conversion metrics and mine your sales funnel for more data.
This analysis will help provide insights into the key attributes that indicate a good fit for your business. Once you have the data compiled, work with your sales and customer success team to help verify your assumptions.
Creating a lead scoring model is a team project. It requires input and buy-in from multiple business units. Getting these teams involved early is a critical step in the process and will help drive adoption and trust in your scoring model once it goes live.
Step 2: Analyze Non-Customer Lead Data
Next, start looking at the attributes of leads that did not become customers. Interview your salespeople to back up any assumptions being made.
Look at the personal and behavioral characteristics of both leads and the organizations they represent. Comparing this group with your current customer base can help provide specific attributes that may indicate a bad fit for your organization’s products or services.
This analysis will help you determine what attributes to not give significant weight towards or even flag as having a negative impact on your scoring structure.
Step 3: Assign Point Values and Scores
Once you have analyzed historical data, it’s time to assign point values to attributes. You will want to weigh all the important attributes you have indicated as a good fit for your organization. Remember to also assign a negative weighting for negative attributes too.
Give a higher weight to factors and attributes that have a strong correlation with leads that convert. You will most likely consider factors such as behavior, demographics, engagement levels and other relevant information.
After assigning weights, go ahead and calculate a score for each lead by adding up all the weighted attributes. Make sure your sales team is hyper focused on your high-quality leads.
Pro Tip - Make sure you incorporate SLAs as part of your lead scoring strategy
This three-step process provides a starting point for developing a lead scoring system. It’s incredibly important to remember that creating a lead scoring strategy can be a complex process and will depend on your business size and model.
Make sure you are iterating your model over time. Remember to incorporate feedback from sales and customer success.
Components of a Good Lead Scoring Model
A good lead scoring model has a few different characteristics to consider. If you are looking to establish a new model or looking to reiterate a current one, include the following items in your planning:
- Aligned to Business Objectives - This is the place to start. A good lead scoring process aligns closely with your organization’s marketing and sales goals. The model should be a reflection of the unique requirements needed to execute your business goals.
- Tried and Tested - Your model should yield positive results in the way of improvement to your conversion rates. A good scoring model needs to be based on reliable data and provide consistent trends and actionable insights.
- Constant Iteration - A good lead scoring model is evolving over time and is subject to continuous refinement. These models should be reviewed regularly. You will need to update and adjust to changing customer behavior and market conditions.
- Cross-Functional Collaboration - An effective lead scoring model requires cross-functional collaboration between business units. Marketing and sales should both be at the table contributing their expertise. By doing this, you will ensure the scoring criteria is accurately reflecting the characteristics of customers you deem a good fit.
Best Lead Scoring Models in 2023
Over the past decade, a number of lead scoring models have gained popularity. As we begin to unpack some of these models, it's important to reiterate that there is not a one size fits all approach here.
Before diving head first into setting up a lead scoring model for your company, take some time to evaluate different modes to determine what would work well for you.
With that said, let’s get started in reviewing some of the best lead scoring models for 2023.
Predictive Lead Scoring
Leveraging machine learning and algorithms, this model uses historical data and patterns to forecast the probability of leads converting. With the help of AI, you are now able to analyze a large amount of data points in order to determine who are your best leads.
Instead of relying on compiling a bunch of reports or getting anecdotal information from your sales team, let the AI algorithm do all the heavy lifting for you.
This model is great for seasoned marketers. Because of its complexity, this model can be harder to implement but it can offer better accuracy and predictive power.
Implicit Lead Scoring
This model focuses on actions and engagement that leads make with your brand. For example, attributing different values to the web pages your leads will visit and accumulating that score over time.
While webpages is part of the scoring model, here some other actions that can be included:
- Email engagement
- Social media engagement
- Content download
A great example of this is applying a higher value for someone who visits your pricing page versus a score you would apply if they only visited your product page. Pricing page visits could be a sign of high intent and may indicate a lead is further along in the buying journey.
Behavioral data will be a good sign of the intent for each lead. But it is important to understand this component with the overall fit level of your leads as well.
In order to operationalize this model, you’ll need to have a good understanding of your current buyer’s journey.
Explicit Lead Scoring
When determining fit you can incorporate a mix of demographic and firmographic data that a lead will submit using a marketing form on a landing page. Assigning specific scores for certain job titles, industries, geographical locations, and company sizes.
This model assigns scores based on attributes such as:
- Industry
- Company size
- Location
- Job title
This lead scoring model is one of the simplest to implement. Depending on where your organization is today with understanding your ideal customer profile, this fit component can be an important part of your scoring.
For example, if your ideal customer is a Director of Marketing from a medium size tech company, you can make sure to collect industry, company size and title to see which leads are a good fit.
Wrapping it All Up
Again, it’s important to note that the best lead scoring model for any company depends on its specific needs, target audience and industry dynamics. Make sure to evaluate the different models and experiment with them if you have to. Use data to optimize and make sure to routinely revisit your model as your business evolves and changes.
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