High-propensity leads are most likely to take the desired action and are more likely to convert. They are often in the advanced stage of the sales funnel. Marketers can reduce costs by using lead scoring to identify their common traits or behaviors with maximum sales closing. However, traditional lead scoring does have its challenges, which led to LEadNEuron AI solutions for quicker and more accurate lead scores to identify high-propensity leads.
What is AI Lead Scoring
AI lead scoring, simply put, is automated lead scoring. It uses algorithms to process lead data and automate each lead’s propensity level. Many database platforms use their own lead-scoring models.
AI Lead Scoring of Your Database
CRM’s lead scoring model processes your existing database of leads. It determines where they are in the sales funnel and automates the nurturing process through personalized content.
AI Lead Scoring of Third-Party Databases
These third-party databases adapt their own model of automated lead-scoring. A user can acquire leads and their contact information using different search filters. For example, LinkedIn has a search function to look for professionals via job titles and industries.
How Traditional Lead Scoring Works
Traditional lead scoring, often called manual lead scoring, has long been a fundamental practice in B2B marketing. This approach involves assigning scores to leads based on predefined criteria such as demographic information and behavioral signals.
Traditional lead scoring for personal databases includes:
- Response rate to marketing emails
- Downloaded giveaways
- Frequency of social media engagement
Manual lead-scoring for third-party databases:
- Reviewing professional profiles and including people who fit in their Ideal Client Profile (ICP)
Challenges in Traditional Lead Scoring
Traditional lead scoring is prone to subjectivity due to its reliance on manual evaluation and judgment rather than data-driven algorithms. Here’s a more detailed explanation of how subjectivity can affect traditional lead scoring:
Prone to subjectivity
Human evaluators may unintentionally introduce bias into the scoring process. They may also have varied interpretations that can lead to inconsistencies in scoring.
Time-consuming
Manual lead scoring can be time-consuming, especially for businesses with a high volume of leads. Evaluators may struggle to keep up with the pace of incoming leads, potentially delaying lead follow-up and reducing the efficiency of the sales process.
Miss valuable insights
Traditional lead scoring may overlook nuanced data points and intricate patterns, limiting its accuracy in identifying high-propensity leads.
Additionally, In today’s fast-paced business environment, many organizations are turning to automated lead scoring, harnessing the capabilities of artificial intelligence to overcome these limitations and enhance lead prioritization for improved conversion rates.
How LeadNeuron AI Lead Scoring Works
LeadNeuron AI or predictive lead scoring is a data-driven model based on people’s skills signals. LeadNeuron detects signals in a professional’s profile such as technical skills, technologies, certifications, membership to relevant groups and associations, etc. The model detects these signals in Rhetorik’s database of 800M+ professional profiles and builds your ideal audience following a 3-step process:
1) Defining target persona through keywords, job titles, certifications, , skills
(e.g., Python programming, seniority
level, etc.), industries, company sizes, locations, etc.
2) Running LeadNeuron AI to identify relevant
signals across Rhetorik’s database of 800M+ professional profiles.
3) Scoring and identifying thousands of individuals
as high propensity leads, understanding your sales pitch and product offering
at scale.
Here is a real-life example of a LeadNeuron AI customer in the Cybersecurity industry where the usual job title approach did not provide a large enough qualified audience for this campaign.
LeadNeuron AI detected client’s desired keywords in cybersecurity like CISSP, IT, Compliance, Network security, Hitrust, Security policies, Security management systems, etc. across all the professional profiles. Each of those expressions are signals that LeadNeuron AI compiles and weights in order to confirm the relevancy of identified profiles. Usually, no less than 7 signals are required. The lead is categorized as a high propensity lead if the score is higher than 100. Subsequently, by using the power of LeadNeuron, you can access thousands of leads aligning with your ideal customer profiles.
The detection scores’ outcomes are presented in the graph below. Inside the green rectangle, you’ll observe that thousands of leads have received a high propensity score, surpassing 100. The efficacy of LeadNeuron’s AI skills-based targeting is remarkable, swiftly pinpointing high-propensity buyers for the Rhetorik customer in a matter of minutes.
This dynamic and automated approach offers enhanced precision, scalability, and consistency, allowing B2B marketers to prioritize high propensity leads with a higher likelihood of conversion. The resulting list of prospects, accompanied by contact data (email, last name, first name phone number and much more), is regularly refreshed and provided to our customers.
The Benefits of AI Lead Scoring
Adopting AI lead scoring in B2B marketing has led to many compelling AI lead scoring benefits, transforming the landscape of lead evaluation. Here are the advantages AI lead scoring can bring to a business.
Accuracy
AI’s ability to analyze vast datasets and identify subtle patterns translates into a more precise evaluation of leads, reducing the risk of misjudging their conversion potential.
Efficiency
Automating the process allows marketing teams to focus on high-propensity leads, ultimately maximizing the return on investment and fostering a more strategic and data-driven approach to B2B marketing.
Scalability
AI-powered lead scoring can efficiently process and evaluate a large volume of leads in real time. This scalability is essential for businesses with extensive lead databases or high inbound lead generation, ensuring no potential opportunities are overlooked.
Consistency
Unlike manual lead scoring, AI systems maintain consistent scoring criteria and objectivity, regardless of the volume of leads. As your lead database grows, your scoring remains reliable and uniform.
Rapid Adaptation
AI lead scoring models can swiftly adapt to changes in your lead generation strategies or ideal customer profiles. As your business scales and evolves, AI algorithms can be updated to reflect new market dynamics and criteria, ensuring that lead scoring remains relevant and effective.
Integration with Marketing Automation
AI lead scoring seamlessly integrates with marketing automation platforms. This integration streamlines lead nurturing and engagement processes, enabling personalized and timely interactions with leads, even as lead volumes increase.
Enhanced Lead Prioritization
Scalable AI lead scoring prioritizes high-propensity leads, enabling sales teams to allocate their resources efficiently and focus on prospects with the highest conversion possibility. This targeted approach can significantly boost conversion rates.
Global Reach
AI lead scoring can scale to handle leads from a global audience, accommodating businesses with diverse customer bases and varying market conditions worldwide.
LeadNeuron AI Lead Scoring Success Story
In a Rhetoric Customer Case Study, an AI lead scoring software called LeadNeuron™ uncovered ten times more qualified leads for a cloud-based cybersecurity provider. LeadNeuron™ utilized its skills-targeting system and was able to accurately pinpoint the desired tech personas by analyzing numerous signals such as: certifications, people skills, experience, and more!
If you are looking for high-propensity leads that understand your product or service, look for Skills-Based Targeting in AI lead scoring!
