What is Lead Scoring?
Lead scoring is one of the key marketing automation processes intended to target and prioritize the right customers and prospects to improve productivity, efficiency, and efficacy of marketing and sales functions within an organization. Lead scoring is the process of identifying Leads or Accounts who are most likely to buy your product or service while allowing you to weed out unqualified leads. Lead scoring supports sales and marketing teams to focus on the most important opportunities, close more deals, and better understand who they're attracting and the types of leads they should be attracting more of.
Why Lead Scoring?
Lead scoring is more than just a sales or marketing strategy—the impact can make your entire business more effective and can help to align the many different teams within your organization. 69% of top-performing companies say that excellent cooperation between sales and marketing teams is the most important factor to maximize marketing automation ROI. When the lead scoring model is based on insights from both teams, the teams are on the same page when it comes to what qualifies a lead and no leads fall through the cracks. When engineered and implemented correctly, Lead Scoring can impact your business in some of the following ways:
- More Effective Marketing Campaigns
- Lead Scoring—predictive Lead Scoring specifically, allows the marketing teams to run more focused campaigns and investments for maximizing ROI from the marketing spends. Lead scoring can also enable you to identify the campaigns and channels that result in high-quality leads. Then, you can tailor your marketing strategy to generate more qualified leads.
- Prioritization for Sales Effectiveness
- Lead Scoring improves the efficiency and productivity of sales teams by optimizing their time and effort spent on working with high-quality customers and prospects. Reducing the time you spend on leads and accounts unlikely to convert reduces your opportunity cost and focuses your efforts where revenue can be maximized.
- Sales and Marketing Alignment
- The absence of a Lead Scoring model can contribute to misalignments across the organization, particularly the alignment between sales and marketing teams. When your organization has an aligned-upon, distinct rule set in place for scoring leads, you can ensure that every lead you pass on to sales should meet their criteria for qualification—thereby strengthening the alignment between the two functions, facilitating coordination and reducing friction.
- Improve Revenue and Conversion
- Last but not least, it improves the overall conversion as well as purchase rate by aligning acquisition and sales objectives.
Lead scoring models
There are multiple types of Lead Scoring models in existence today, and due to the complex nature of B2B marketing, there is no one-size-fits-all approach to scoring your leads and accounts. Buyer behavior is constantly evolving and you should ensure your Lead Scoring strategy evolves and eschews stagnation.
Traditional Lead Scoring
Traditional lead scoring models generally take into account two types of data sets about a potential buyer: Implicit and Explicit - also known as Demographic and Behavioral, respectively. Examples of each are below:
- Explicit: Known factors such as firmographic or demographic details. Examples include Job title, Role, Level of seniority, Industry, Company size, Company revenue Geographic location
- Implicit: Factors based on a lead’s behavior with your brand: Website visits, Email opens/clicks, Opt-Ins, Newsletter Subscriptions, Contact form submissions, and Content downloads
Once you identify these characteristics and behaviors, you then assign numerical values to important and revealing data points. Once that’s complete, you combine these variables into a formula that produces a score that you can then track within your marketing automation
Predictive Lead Scoring
The nature of B2B marketing and sales is that potential buyers are complex organisms who need to take into account several diverse factors when making a decision. According to McKinsey, sales and marketing teams need to factor in a new “response to changing buying preferences, sales organizations have started moving to become more agile.” Predictive Lead Scoring can support Sales and Marketing teams respond to those changing buying preferences and reducing ambiguity in buyer behavior and consolidating insights into easy-to-use data points defined by Machine Learning.
Predictive lead scoring takes the traditional lead scoring model and optimizes it, taking it to the next level through the application of big data and machine learning algorithms to evaluate the key facets of existing customers, prospects, and opportunities—ranking them against a scale that can distinguish customers and prospects who are more likely to convert, retain, or buy from the company’s products and services.
Leadspace Predictive Scoring
At Leadspace, our ethos is to uncover meaningful insight within your data where that you’d not be able to do otherwise. Essentially, we look to identify and evaluate relationships between various attributes associated with your customers, prospects, opportunities, and leads and the identified behavior in your funnel (such as closed/won opportunities) and subsequently score them based on the propensity to achieve that desired behavior. What we’re left with is the Leadspace FIT Profile.
We feed the model with positive and negative examples relative to the use case, and the 0-100 score represents the propensity of that action to happen. The higher the score, the more likely this action will indeed occur.
What are some of the key attributes we include in the predictive lead scoring model?
- Customer profile data capture and gauge the fundamental attributes of your customers and prospects. These properties may include demographics such as job function, title, skills, and expertise captured from social profiles
- Account profile data reflect important firmographic attributes, such as company size, industry, account type, etc.
- Technographic features include the various technologies that exist as part of a customer’s ecosystem, including website technologies and Installed Base Technologies which may impact and highlight if your product or service is complementary to the inbound contact or account
- Industry features highlighting the customer or prospects industry, NAICS, or subindustry information
- Customer funnel data captures the purchase activities of existing customers, including the quality and amount spent on buying your products and services, as well as the time and frequency of their purchases.
- Interaction Features uncovering the relationship between the features above, often deriving insights from how they interact with each other
Here’s how we do it:
We can’t reveal all our secrets but below is a general overview of the Leadspace approach to predictive scoring and analytics. It’s a combination of Machine Learning, AI, and Data Science to ensure we’re optimizing a model against your unique business and tailor to your desired business outcomes:
- We join, enrich, and deduplicate disparate data objects to build a unified representation of your historical sales funnel
- Records are methodically filtered and tagged to tailor the data to your specific use case
- Custom feature engineering and proprietary text analytics
- The model is selected, trained, validated, and tested.
- Learning the feature interactions that best explain the boundary of positive tagged records from rest-of-funnel records to uncover those meaningful insights
- Activation monitoring for constant optimization and custom tweaks.
Putting it all together
Once the model is built, it can be used to score your inbound leads, your existing accounts, and any lead or contact you might want to identify as a good potential fit for your products or services.
The output of our models is intended to be actionable in your systems and your enrichments. Once the model is created and implemented, we’ll score your leads based on their propensity to meet the desired action, whether that’s converting to an MQL, MQO, or even closed-won.
Those scores will range from 0 - 100, where the values represent the following on a scale of 1 - 100:
- 0 - represents a low propensity to meet your desired outcome, KPI, or goal
- 100 - represent that the account, contact, or lead has a high propensity to convert against your targeted KPI.
In your enrichments, you can expect to see your enriched leads, contacts, or accounts with a numerical score, like below, representing their propensity.
Person First Name |
Person Last Name |
Company Name |
Person Title |
LS Predictive Score |
Jenny |
Mcintyre |
Marketing Needs, Inc |
Creative Director |
90 |
Dan |
Cruz |
Marketing Solutions 101 |
Product Manager |
65 |
Joe |
Warner |
20/10 Solutions |
Director, Marketing |
90 |
Rich |
Swenson |
23andMe |
Manager, Online Marketing |
90 |
Susan |
Simmons |
2U |
Senior Digital Marketing Manager |
90 |
Will |
Mcguinness |
Omnicom |
Director, Social Media |
90 |
Dan |
Spellman |
322 Media |
Co-Founder / Chief Operating Officer |
30 |
Ernesto |
Gonzalez |
360 Technologies USA, LLC |
CEO & Owner |
55 |
Mohamed |
Waheed |
360Imaging |
Performance Marketing Manager |
75 |
To learn more:
To learn more about Predictive Lead Scoring with Leadspace, contact your CSM.