What is Lead Scoring?
How Does Lead Scoring Work?
Lead scoring is a methodology used to categorize incoming prospects based on their level of commitment.
With lead scoring models, companies use a points-based system that analyzes a range of data points to quantify commitment.
Those leads rated “most committed” receive a higher score than their less-interested counterparts, and as you might imagine, the top scores typically become the top priority.
Lead scoring success hinges on sales and marketing working together to define lead qualification criteria and agree on a numeric score for the many factors that make or break a deal.
In this article, we’re going to look at what it takes to create a lead scoring system that streamlines processes and delivers predictable wins.
Why Lead Scoring Matters
Research has shown that companies with effective qualification and scoring processes in place generate 70% more ROI from lead gen campaigns.
More specifically, lead scoring best practices, when done right, delivers the following benefits:
- Lead scoring models help sellers identify the sales readiness of incoming leads, allowing them to prioritize activities more effectively.
- Scoring gives marketing teams a clear picture of who to target and what those groups care about.
- Lead scoring also will enable organizations to use engagement metrics like email response rates or e-book downloads to inform what action to take.
- A lead scoring model provides insights into the content, product lines, and campaigns effectively engage audiences–or don’t.
While it may be hard to quantify, lead scoring systems are also a valuable tool for aligning marketing and sales teams.
Lead scoring is a collaborative strategy that relies on developing a standard set of qualification criteria and shared knowledge of what success looks like at each touchpoint.
Lead Scoring & Data Requirements
Before you can implement a lead scoring system, you’ll need to make sure that you have sufficient data to inform your strategy.
Beyond that, lead scoring systems are most effective when they incorporate data from multiple sources–creating a more accurate picture of the real people in your sales funnel.
Here’s a quick rundown of the data requirements you’ll need to have to get the best outcome.
Do you have detailed buyer persona for each segment?
What insights are your lead intelligence tools and conversion forms collecting? Do they provide a deep understanding of the buyer and their needs at each stage in the sales funnel?
You’ll want to make sure you have information that provides actionable insights about your ideal buyer’s needs and intent.
That data includes demographic and firmographic information and interaction histories that typically live in your CRM, as well as behavioral insights sourced from social media channels, campaign performance, and content engagement.
Are you looking at company-level data?
In today’s complex sales landscape, teams must go beyond individual personas and start targeting “buying centers.” The goal is to identify a company AND all of the different players involved in the decision-making process.
This is important because the average B2B deal involves 6-10 stakeholders, each with a slightly different set of priorities.
By targeting just one out of six or ten of these individuals, you end up scoring leads out of context. Capturing organizational intel allows you to make sure you’re going after the accounts most likely to convert.
Aim to capture data related to business size, industry, existing solutions, and a list of key players and their “sub-personas.”
Developing Your Lead Scoring System
Building an effective lead scoring system requires a ton of data, and to be honest, a lot of time.
You’ll want to make sure you build your strategy on a foundation of
accurate information, clearly defined rules, and company-wide consensus around point distribution.
Here are five steps that can help you create an accurate system that everyone can agree on.
1. Define the Lead Qualification Criteria
Aberdeen Research found that organizations with a strong lead scoring system in place have a 192% higher lead qualification rate compared to companies that don’t score leads.
Lead qualification is the process for predicting the quality and value of incoming leads, based on the data you’ve collected on them and how they engage with your brand. As such, defining your qualification (and disqualification) criteria is a critical step toward creating a system that works.
The process starts by defining the main characteristics of your ideal buyer including demographic and company information, behaviors, interests, and more.
You’ll also need to define things like:
- How much the typical buyer is willing to spend?
- Do they have the authority to make decisions?
- Does your solution actually addresses their needs?
Once the basics are out of the way, you’ll then need to define the difference between marketing qualified leads (MQLs) and sales qualified leads (SQLs).
- MQLs have already engaged with your brand on any inbound channel, like your website, social media, a PPC landing page, etc. MQLs fit your ideal customer profile but have yet to show signs that they’re ready to talk to a sales rep. Here, the primary goal is to separate the leads with real purchasing intent from those who aren’t a fit.
- From there, you’ll need to determine where qualified leads are in the sales process to start nurturing them into becoming SQLs. Sales qualified leads are further along in their journey and have indicated “sales-readiness” based on particular actions like signing up for a free trial or booking a consultation.
Those behaviors give sellers the green light to swoop in and close the deal.
2. Collect Persona & Behavioral Data
Like any well-planned marketing strategy, your persona research sets the stage for success.
First, you’ll want to make sure you’re collecting data that covers the following bases:
- Company size
- Job function
- Decision-making power
- Additional colleagues involved in the process
Additionally, you might want to consider demographics like age and experience, as these might help you determine which channels are best for reaching key groups.
Behavioral data includes things like how users interact with your website.
Try to answer questions that help you understand which actions indicate that a lead is ready for the next step in the buying process, such as:
- What pages do they visit before signing up for the free trial?
- Which lead magnets do they download most?
- Which lead generation channels lead to the highest win-rates?
- What content do people look at before reaching out to a salesperson?
- How many people that sign up for a free trial become customers?
The list could go on forever, depending on how many moving pieces make up your sales and marketing process.
When you develop the rules and scoring criteria for your system, you’ll want to make sure you use these behavioral and persona-based insights with a predictive scoring tool to ensure you’re getting an accurate measure of what “high-commitment” looks like for each lead.
It’s also worth mentioning that all data sources must be integrated into one central source of truth for this to work.
3. Create a Standardized System
Now that you’ve defined your criteria and identified your data sources, it’s time to talk about rules.
Sales and marketing must define how they’ll rank behaviors and lead profiles. You must rank activities based on their contribution to moving a deal forward and gather as much evidence as possible to link criteria to actual buying behavior.
You might also consider using negative scoring for certain “red flags” that may disqualify a lead.
According to Salesforce, your lead scoring model should include the following best practices:
At this stage, you’ll want to figure out what tools (if any) you’ll need to make this strategy a success and develop workflows that make it easy to manage leads spanning a range of personas, channels, and stage in the sales funnel.
Tools might include:
- Your CRM
- A marketing automation platform
- An AI-driven predictive scoring system
- A lead intelligence solution
- A sales engagement platform, etc.
Whatever tools you settle on, the most important thing is that they all work together seamlessly and provide all users with the same data.
Create Your Lead Scoring Points System
Once you’ve established rules that marketing, sales, and the C-suite can all agree on, you can then move into setting the point values you’ll use to indicate interest.
Ultimately, your lead scoring system should accomplish two main goals:
- Your lead scoring model should prevent sales teams from applying “bottom-of-the-funnel” tactics too soon by accurately identifying who is ready to buy. This way, sales teams can prioritize committed buyers while nurturing the leads who check all of the right boxes with valuable information.
- Lead scoring should also maximize the sales team’s productivity, helping them quickly spot sales-ready prospects rather than chasing the wrong leads just because they’re “in the system.”
To accomplish those goals, demand generation teams need to go through each lead qualification category, breaking down point distribution based on critical factors that have historically indicated a high propensity to buy.
You might run an attribution report to figure out which combination of strategies resulted in the most conversions.
Depending on your findings, you can start identifying specific actions, behaviors, and characteristics that are most likely to produce the desired outcome at every touchpoint in your customer journey.
- Company information
As far as points go, you have a few different lead scoring models to choose from. You’ll typically see companies working from a 0-100 scale, where the total number of points determines sales-readiness.
Again, it’s good to look at historical data to identify what factors are most likely to drive sales.
For example, you might look at engagement data to look at what pages people viewed before purchasing — was it a pricing page? Did they download a white paper?
Then, you might assign a larger share of points to the downloads that have historically driven conversions versus a blog post that caters to a broad, general audience. B2B companies might also rank leads based on business size.
For example, if the goal is to reach top decision-makers with 1,000+ employees, a company with 25 employees might receive a negative score in this area.
If you’re working with too small of a sample size, gut feelings end up filling in the gaps, creating scenarios where individual marketers or sales reps assign points based on what factors they “feel” essential.
Measure & Improve Your Process–On Loop
Long-term, you’ll want to review your strategy regularly to continue to refine the system.
A few things you might look at:
- Are your chatbot conversion funnels user-friendly?
- Can you remove any friction points (like unnecessary information) that may prevent leads from taking action?
- Are there any elements in your sales strategy that could be streamlined? Is it a process issue or do you need to introduce a new tool?
- Have you found any additional qualifying or disqualifying factors that need to be incorporated into your process?
- Are there any new segments that you should be targeting?
- Has your data revealed any new patterns or behaviors?
Expect that your lead scoring system won’t be perfect, especially not right away. Focus on maintaining clean, accurate data and creating a flexible system that makes it easy to change the rules as needed.
Additionally, as you discover new patterns or conversion anomalies (i.e. a buyer that converted despite a low score), make sure you figure out why.
While there are bound to be outliers, if low-scoring leads start converting in droves, something is amiss. It could mean you’ve missed a couple of rules that would have included those buyers or there’s a segment that you haven’t been focused on.
Whether you’re chasing productivity gains, more predictable wins, or better sales-marketing alignment, there are many reasons that it pays to score your leads.
However, building a lead scoring system that accurately separates hot leads from the lukewarm and the cold takes a significant amount of time and resources.
You’ll need to make sure you have enough data to make accurate predictions, and the right tools in place so that you can access and act on lead data in near-real-time. That said, if you’re up for the task—you’re bound to reap the rewards.