What's in this article?
Lead scoring is meant to simplify the sales process, and yet doing it well has felt increasingly complicated and out of reach.
We’re here to explain how lead scoring can finally be made manageable, efficient, automated, and accurate in our ultimate guide to lead scoring.
Beyond a basic understanding of what is lead scoring and your lead scoring tools and options, we’ll also give you the concrete information you need to know to implement lead scoring successfully.
Use it to actually increase conversions and boost lead scoring ROI within your industry in a way that best fits the needs of your organization. Let’s dig in.
What is lead scoring?
Let’s start with the basics. Lead scoring is an important part of any good lead management system, helping marketing and sales teams quantify how qualified leads are.
This ideally informs good decisions about how to prioritize, distribute and work each lead to focus on sales-ready leads that will increase conversions.
Lead scoring involves ranking leads as they come in, whether they’re generated or purchased, based on the probability of each one turning into a sale.
Through lead scoring, values or points are assigned to each lead, showing how qualified they are, based on various criteria.
To score leads, lead data needs to be collected, and, here comes the tricky part, it needs to be interpreted accurately to assign value to each lead as leads are generated and as they continue to engage with your organization.
Read on to learn the different ways to manage and optimize lead data and lead scores.
Why is lead scoring important for optimizing sales?
Having leads is only valuable to your business if you can convert many of those leads into customers.
So whether you buy thousands of leads, invest in marketing to generate them or a combination of both, you’re paying for sales opportunities and receiving thousands of rich data points. Lead scoring is a concrete way to make sense of these data points by combining them into one model, which you’ll use strategically to assess the data and rank each lead.
These scores then translate to the next steps, like pushing a lead further down the sales funnel with a new email campaign or distributing the lead to a salesperson to reach them with a phone call.
However, if that lead data is not evaluated, and instead left static, it’s going to underperform. Manually setting and updating lead scoring rules can be difficult to manage accurately as the market, your campaigns and products or services evolve. Either way, you’ll close far fewer sales than expected.
This is why CRM and lead management systems, driven by static rules and reports, are quickly becoming obsolete and creating challenges for organizations that need improved lead scoring and the lead scoring ROI that comes from quality lead management.
On the other hand, if you optimize lead scoring with lead management innovations, you can increase conversion rates. Integrated, intelligent and predictive AI lead scoring optimizes the performance of each lead and each salesperson. We’ll cover more about this below.
Lead scoring alignment between sales and marketing
To make the most of lead scoring, agreements need to be made between the marketing and sales teams within an organization. This includes clearly defining concepts like what makes a lead qualified, how to identify qualified leads and what to do next with those leads.
What is an MQL compared to an SQL?
To help organizations prioritize which leads are most likely to convert, leads are first qualified as a Marketing Qualified Lead and then ideally move on to become a Sales Qualified Lead.
- A Marketing Qualified Lead (MQL) is someone who has shown interest in an organization’s product or service as seen through their engagement with various marketing efforts. The marketing team considers leads based on certain criteria. Once a lead is qualified, it gets passed along to the sales team to engage further.
- A Sales Qualified Lead (SQL) is a lead that is a prospective customer who has shown they’re ready to buy or close a sale. Sales Qualified Leads typically have gone through a few levels of qualification before reaching the sales team and being contacted by a salesperson. They’re likely first deemed Marketing Qualified Leads (MQLs) after various engagements with marketing efforts.
So an SQL vs an MQL can show the difference between how engaged a lead is and how sales-ready they are.
How to qualify leads with lead scoring
Lead scoring is used to help sales and marketing teams determine a value or threshold that would deem a lead qualified.
The challenge is that there are many factors that impact how someone might qualify a lead. To do this well — without bias, assumption, or human error — requires the use of deeper analytics.
Beyond the lead data itself, you also need to have an understanding of the marketing and sales team’s goals, capabilities, and capacity.
When it comes to lead scoring, organizations will have the most success if they support both their marketing and sales teams, allowing them to be aligned in their goals and have access to tools that will set them up for success.
Accurate lead scoring capabilities will ultimately help to measure the overall performance of the sales cycle for customers from start to finish.
Optimize your sales and marketing efforts. Check out our Guide to Lead Management: How to Convert More Leads into Deals.
Establish a lead scoring model
Let’s get more tactical now. Beyond the basics of lead scoring, a lead scoring model is a structure that is used to establish and maintain a lead scoring strategy.
With the best lead scoring models, organizations are able to quickly identify qualified leads and prioritize how to work them as they come into the organization’s CRM system.
How to use a lead scoring model to determine the value of leads
Within a lead scoring model, different criteria are established for assigning values or points to each lead to determine how qualified it is. These different criteria can clue sales teams into how ready-to-buy a lead may be.
This could include:
- Lead behavior: Assess how leads engage with you through website visits, downloads, forms filled, phone calls, emails opened, etc.
- Lead demographics: Assess who leads are by evaluating how qualified they are based on their information including their location, job title, age, industry they work in, income, etc.
Beyond information from the leads, assigning value to leads could also be based on thresholds that are set to measure leads by. As we mentioned, this could include goals and metrics set by marketing and sales teams.
For example, the marketing team might launch a new campaign to attract leads for a specific product. Leads of certain demographics, showing certain behaviors within that campaign, may hit a point that the marketing and sales teams deem more ready-to-convert than other leads. Those leads would then be prioritized and contacted by salespeople.
Lead scoring models can measure and assign value to leads using different methods and criteria. Different lead scoring models will get you different results.
Each model relates to specific criteria chosen to reach certain sales and marketing objectives.
Learn more about lead scoring models: How to Develop a Lead Scoring Model, and Why it Matters
The challenge with traditional lead scoring models
There are various tools available to set the foundation for a lead scoring model.
For example, many lead scoring tools are built on the concept of assigning value to leads based on various behaviors. With this, the goal is to prioritize the leads with higher scores or that meet other thresholds such as being sales or marketing qualified. This assumes that these values are what make a qualified lead.
Tools like this do a great job of helping sales teams make sense of lead data that flows into their systems. But how do we know the criteria they use to evaluate leads, the basis of the model, will best determine what makes a lead convert to a customer?
This is where predictive lead scoring improves these models and ensures that the criteria being measured are analyzed and acted on in the most accurate way possible, improving performance across the board.
Beyond tracking and assigning value to lead data, predictive lead scoring tools establish a lead scoring model based on criteria that are established using artificial intelligence.
Machine learning continuously learns and updates its models over time, making the criteria by which leads are scored more accurate than was ever possible before.
Get ahead of the competition. Read: Why AI Lead Scoring is Essential for Raising Conversion Rates.
Predictive lead scoring innovations
To help organizations not only make sense of their leads but actually optimize them, technology has advanced to offer innovative AI lead scoring and lead management tools.
Managing thousands of data points by hand has never been realistic. Although some tools have come along to make it easier, nothing compares to the advances that have come with the application of artificial intelligence and machine learning.
Artificial intelligence and machine learning
When you hear about AI, you might associate the term with technology that mimics human behavior. Have you thought about how this could positively impact operations at your organization?
For businesses, AI software supports tasks like automation, engagement, and quick data analysis. All of these simplify many repeatable processes we use, making them more efficient than ever.
Machine learning is one of the many subsets of AI. It’s also one of the most practical applications of AI in business because it’s fairly straightforward in how it performs. It uses algorithms that allow for input of data (like analysis of leads) and output of data (like predictions and decision-making to help you prioritize and take action with those leads).
So you can see how these would present an innovative solution for your ongoing lead scoring needs.
Learn more with our Simple Guide to Optimizing AI/ML for Business Operations.
Lead management automations
You’ve also likely heard about, and may be using, various types of automations within your business. This alone is a helpful evolution to manage repeatable sales and marketing tasks like data entry and activity logging. But it gets even better with AI/ML applications.
With basic automations, as leads begin to engage, marketing teams automate steps like sending emails and tracking lead behaviors. And as leads become SQLs, sales teams automate scheduling calls, leaving voicemails, and sending emails to each of them.
AI/ML takes automation a step further, helping marketing and sales teams avoid inefficiencies and conflicts between the two teams.
With AI/ML software, sales and marketing teams get predictive insights to inform their decisions and next steps. As these predictions are based on the accuracy and intelligence of machine learning models, miscommunications, assumptions, and human error are removed from the equation.
Intelligent and predictive tools automatically score, distribute, prioritize, and communicate with leads as they flow in as MQLs and move to SQLs. And they do it with intelligent accuracy that isn’t possible when done manually.
Sales Automations backed by AI/ML software are changing the game for sales ops. Learn more: Automate Moving MQLs to Sales Qualified Leads with AI/ML Solutions.
Lead scoring tools
To optimize lead scoring in action there are many robust, production-ready lead scoring tools available.
They differ in various ways, including how they integrate with your CRM, what lead scoring model or models they use, how customizable the models are and whether lead scoring is predictive or not.
These different factors can impact which platform will help your sales team increase conversions. We’ve summarized a few of the popular tools to give you an idea of the options available.
ProPair’s AI/ML software offers predictive lead scoring with decision support through its products RANK, MATCH and MIX.
Not only does ProPair use machine learning to analyze your historic lead management data and immediately begin scoring leads, but it is also customized to your current system, your goals and your business. This allows ProPair’s clients to lift conversion rates by 10-15% without changing anything in their current system.
It runs in the background to analyze leads as they come in. With ProPair RANK you’ll see exactly which leads to focus on and when, using an intelligent lead value ranking system designed to help sales teams prioritize leads, guide follow-up activities, and revisit abandoned opportunities.
In addition to lead scoring, ProPair also supports other aspects of lead management to increase conversions with AI/ML tools for sales agent scoring and lead distribution.
ProPair MATCH shows exactly what sales agents to assign leads to. This turnkey solution leverages historical sales team performance data and machine learning technology to equitably get the right leads to the right sales agents and make the most of your current team.
ProPair MIX maximizes the potential of every lead and every sales agent, bringing together the best of predictive lead scoring and predictive sales agent grading.
It allows you to optimize your entire sales operation and equitably distribute leads from top to bottom performers, maximize sales production, and reduce the need and expense of churning your salesforce.
Hubspot Marketing Hub offers predictive lead scoring software that works with your CRM to apply machine learning, which allows for reviewing thousands of data points across your contacts to identify your best leads.
Hubspot’s lead scoring model evaluates leads based on their behavior and engagement, demographic information, relationship within Hubspot and logged interactions within the CRM. From there it uses artificial intelligence to optimize lead scoring over time.
As you collect more data, Hubspot’s model improves itself providing better-informed predictions. It categorizes leads using “Likelihood to close,” which shows the probability of a contact closing within the next 90 days. The “Contact priority” feature uses “Likelihood to close” to filter segments of your best and worst leads.
To collect enough significant data to see Hubspot’s “Contact priority” assigned to leads, Hubspot says you’ll first need to reach 100 contacts.
It includes features like creating different score sheets for different audiences or leads for sales and marketing teams. It allows for up to 25 unique lead scoring models to help manage various products, locations, industries, etc. And there are options to customize how leads are scored to make it work for your team.
Marketo Engage offers its Lead Scoring Model as part of its marketing automation platform.
Its model categorizes users as leads, which have the potential to become customers, or prospects, which are customers who have engaged and showed interest in products.
With Marketo’s model, leads scored above 65 points are considered sales-ready. Leads below that point are either unqualified or may need to be engaged and analyzed differently to increase their lead score. This threshold can also be customized for your organization so sales and marketing will need to align on what that threshold should be.
Marketo combines explicit (demographics, basic lead information) and implicit (behavior and actions leads take to engage with your marketing) information to score leads. This data needs to be kept up to date for accurate lead scoring.
Salesforce offers various ways to integrate lead scoring into your lead management. As a CRM it integrates with other tools like Hubspot and Marketo to take full advantage of their lead scoring capabilities within Salesforce.
There are also extensive options to customize your use of Salesforce using workflow rules to establish lead scoring methods within the platform. Because this takes time to develop and advanced functionality to execute well, Salesforce also offers a more ready-made solution for lead scoring — Pardot, now called Account Engagement, which is a marketing automation platform that allows for lead scoring based on actions leads take on your website and through your email campaigns.
Between Salesforce and Pardot, it’s easy to attach lead scoring actions and data to each lead within Salesforce. In addition, Salesforce offers Einstein to take lead scoring further by making it predictive.
Through Salesforce Marketing Cloud, Account Engagement (formerly Pardot) is available as a lead scoring model that allows you to define the value or weight of various lead data and actions within the sales funnel.
Based on these chosen values, when a lead reaches a certain threshold, sales and marketing teams then define the lead as qualified. Once a lead is deemed qualified, the sales team can prioritize working the lead.
Pardot’s lead grading system compares how well each lead matches to your Ideal Customer Profile (ICP). It automatically analyzes lead’s basic data to assign a letter grade of A through F to each lead.
To determine if a lead has a high grade, and is, therefore, worth prioritizing, Pardot assesses demographic information, such as the lead’s location, revenue, job title, industry and company size. In addition, it can also track technographics, including CRM usage, marketing automations, etc.
Pardot also makes it possible to assign value to various actions, including web page visits, links clicked, downloads, email open rates and clicks, searches and other engagement with marketing campaigns and materials.
To take lead scoring further, Salesforce offers Einstein as an add-on to offer a predictive lead scoring approach.
Using existing lead data, Einstein’s AI software finds data points that have pointed to a lead’s successful conversion. With this information, it automatically shapes the model that leads should be scored by. The higher it scores a lead, the more likely that lead is to convert.
Einstein scores each lead by using a correlation between new lead attributes and those of historical leads.
It offers tools including Discovery to show relevant patterns in your data and AI insights, Prediction Builder to predict business outcomes and create custom AI models using fields or objects, and Next Best Action to deliver recommendations and action strategies.
Ready to start increasing sales? Check out our Guide to Implementing AI/ML for Executives in Sales Operations.
Lead scoring examples and use cases
Lead scoring is used across industries to improve the process of selling a product or service.
Let’s dig into a few real-world examples of how lead scoring can be tailored to improve marketing and sales operations within industries that benefit most from quality lead scoring.
The universal basics that apply to industries
As we’ve mentioned, lead scoring involves collecting both implicit and explicit data on leads, from tracking their basic demographics to their behaviors in engaging with your organization.
Across industries, there are various data points you can collect to improve how you score and manage leads once they’re generated. This is often done well through a lead-capture form on your website, or other marketing tools, that asks leads to provide information about themselves.
In addition to this, lead scoring can be supported by having targeted marketing campaigns to attract and nurture leads that you can use to measure their behavior as they engage with the materials.
All that said, depending on the lead sources and lead scoring tools you use, you may not need to stress about how you’ve structured collecting lead data through these tactics, as the tools and sources might offer helpful support.
Mortgage lead scoring
Mortgage lenders will want to collect data that specifically helps loan officers gauge whether a lead is ready to close a loan. This includes data like credit score, income, debt amount, home value, etc. These data points also relate to several different loan programs the lender offers.
One aspect of scoring these leads and deciding how to prioritize and work them may be to distribute them to the loan officers who connect best with leads in that location or with certain levels of credit score, for example.
Beyond the lead data, lenders may also want to provide marketing materials and campaigns that meet a lead where they are in the process of considering a home loan and help to move them along the path to be ready to close. This could include blogs and emails aimed at educating homebuyers and homeowners about their financing options.
Financial services lead scoring
Beyond mortgage lenders specifically, various financial services organizations benefit from effective lead scoring. This includes banks and lenders providing student loans, auto loans, other personal loans, business loans and debt consolidation.
In this case, qualifying a lead may also be based on their credit score, as well as their income, debt, financial needs/goals, and where they are in the process of looking for a financing solution.
All of these points allow you to prioritize a lead based on how ready they are to move forward with the solution you have to offer.
Leads can be attracted and nurtured through various marketing efforts that show the benefits of your loans compared to other options, as well as how the financing can be simple and affordable.
Insurance lead scoring
Whether you’re selling health insurance, Medicare, life insurance, or any other form of insurance, you have certain criteria that leads need to meet before they’re ready to get started.
Depending on the type of insurance you offer, this could include collecting specific data such as details around a lead’s current state of health, their budget for insurance premiums, if they’re the decision maker, etc.
As for helping to attract and move leads through the sales funnel, educational resources that build trust and show the need for the insurance you offer can be helpful to advance their decision to commit to your offering.
See more lead scoring examples and benefits: The Ultimate Guide to Optimizing Lead Scoring and Growing Business.
How to make your lead data work for you
Depending on how hands-on you want to be and other considerations of your current lead management system, there is likely a lead scoring tool available that could help you sustainably prioritize which leads are sales-ready.
The beauty of tools based specifically in AI/ML capabilities is that lead scoring becomes completely automated while also being continuously updated so that the automations are also intelligent and more accurate than a manual system could ever be.
With machine learning, lead scoring can rely on the accuracy of data and the machine’s ability to analyze and learn from thousands of data points in an instant.
With an option like ProPair, your historic data and newly generated lead data don’t have to be perfect for you to see results. We can help you adjust our model to best fit the needs of your organization overall, your sales team and your leads.
With our customized solution, you apply predictive lead scoring to your organization’s unique sales processes, lead buying, culture, and sales agents.
AI/ML lead management support can take what your organization already has in place to transform your efforts into creating the best version of you.
Optimizing sales has a direct impact on revenue. Learn more in our Guide to Optimizing Your Revenue Operations.
Boost ROI with strategic and intelligent lead scoring
Before reading this ultimate guide, you might have thought that lead scoring was one small part of your overall lead management system.
However, we hope you’ve come to understand that qualifying leads is actually the first step that impacts how you make use of the leads you’ve spent time and resources to generate or buy, as well as how you move those leads through to become customers.
With effective lead scoring and using the right lead management tools for your organization, you can boost lead scoring ROI and increase conversion rates.
ProPair’s clients see a 10-15% lift in conversion rates without having to change anything in their current lead management systems.
How could your business grow with intelligent lead scoring? We can tell you for free: Try The ProPair Challenge for a no-cost, no-commitment 48-hour data analysis.
Using machine learning to analyze your current leads and their performance, we’ll help you see where you’re missing sales opportunities.
We’ll also share the options you have for improving lead scoring, sales agent performance and overall conversion rates with our production-ready machine learning software.