By The PhantomBuster TeamWant a high-impact, low-effort way to predict lead behavior to get more from your marketing and sales efforts? You need AI lead scoring!
Supercharge your lead generation tasks with some of the latest tools.
TL;DR
AI lead scoring uses a numeric system to score leads, with the highest scores indicating the most qualified leads most likely to convert.
AI lead scores can be created using social media engagement, website use, and demographic information.
The old manual lead scoring methods used essential behavioral and demographic criteria.
AI predictive lead scoring provides scores automatically, and users can enrich the data for better predictions.
AI lead scoring is potent with minimal downsides, but versus traditional lead scoring, predictive lead scoring requires a lot of data. Many businesses choose to use automatic profile finder for social media with a CRM integration.
The best tools to start predictive lead scoring include Salesforce, PhantomBuster, ChatGPT, or Forwrd with HubSpot.
What is AI lead scoring?
AI lead scoring is an automated process that tracks and evaluates customer data when interacting with a business to create a score indicating their likelihood of converting.
AI lead scoring is achieved using implicit data like site activity, purchase history, CRM data, social media activity, engagement with content, online activity, lead source and explicit data like contact info, location, purchase history, or previous interactions.
Traditional lead scoring vs predictive AI-powered lead scoring
Historically, lead scoring used to be a manual or automated process done by a marketing team, who had to use their gut feeling to determine who would be the most promising leads.
Lead generation activities happened, and marketing teams assigned scores to predict which inbound leads sales reps should follow up on.
Traditional methods of lead scoring worked in two ways:
Manual scoring should be applied based on the lead's likelihood of conversion, generated from actions and behaviors, such as how many articles they read or links they clicked.
Score leads based on comparisons with similar demographic data.
Later, automated lead scoring became prevalent via major CRM systems, which used marketing automation to provide information like browsing data.
Yet traditional lead scoring is prone to human error.
While it prioritizes leads based on a basic lead scoring model, activities that suggest purchase intent or interest don't always indicate a qualified lead.
Enter predictive lead scoring!
Predictive lead scoring offers a system led by data science and machine learning, giving teams confidence to identify the leads to prioritize.
Elements of a good lead scoring system
Want to start lead scoring? Implementing predictive lead scoring begins with the correct lead data.
To identify high-quality leads, consider these sources:
Demographic Information:
Job Title: Indicates decision-making power.
Company Size: Larger companies might have bigger budgets.
Industry: Determines the relevance of your product or service.
Geographical Location: Regional targeting and market segmentation.
Firmographic Data:
Company Revenue: Potential budget for your product or service.
Years in Business: Stability and potential for a long-term relationship.
Behavioral Data:
Website Pages Interaction (Content Engagement): Measures interest and engagement.
Email Engagement: Open rates, click-through rates, and responses.
Social Media Engagement: Likes, shares, comments, and follows.
Event Participation: Webinar attendance, trade show visits, etc.
Download History: Whitepapers, e-books, or case studies.
Why sales and marketing teams should use AI lead scoring: Top benefits
There are some best practices to follow when getting started with lead scoring:
Accurately predict good leads and boost conversion rates.
Predictive lead scoring eliminates guesswork, allowing sales teams to spend less time chasing uninterested leads and more time on those with high lead scores.
Plus, the more data you generate, the more accurate your predictive lead scoring will become.
Ensure marketing strategies are working.
By scoring leads, marketing and sales can see an accurate sales pipeline.
Automatically updated lead scoring means data confidence.
An AI-powered score for each lead isn't a one-off bit of information.
AI lead scoring systems will automatically update the score as new information is gathered.
In short, your lead scoring model self-learns from new data and adjusts scoring criteria accordingly, ensuring they remain effective.
AI lead scoring has minimal downsides, but predictive lead scoring requires much more data than traditional lead scoring.
Artificial intelligence works best when it can predict future behavior and fill in the gaps to understand your entire customer journey and who's most likely to become paying customers through learning.
Let's take LinkedIn. It's great for finding potential customers, but your data needs to be enriched for a robust lead-scoring process.
Enrichment is labor-intensive, so most businesses leverage anonymized data like PhantomBuster to extract what people like, comment on, their connections, and their profile data on social platforms.
Best AI lead scoring software for marketing and sales teams
Here are some tools to help support your sales process. They can help score your leads, but there are differences, and you may also need to combine them with a lead nurturing process based on enriched lead information!
PhantomBuster's AI lead scoring enrichment phantom
PhantomBuster is primarily a lead prospecting and generation tool that offers pre-built automation for data scraping from social media interactions and AI automation to help enrich lead lists.
It's a great all-rounder to get better insights into customers and craft messages that improve your sales efforts.
When it comes to lead scoring, PhantomBuster can help you enrich your CRM lead lists with fresh data from LinkedIn.
This helps create better lead scores as AI and ML functionalities rely on data,
You can also use PhantomBuster's AI automations to leverage GPT 3.5 models to create and enrich your lead lists with scores based on different criteria.
You can customize the prompt based on what matters most to your business and then prioritize outreach based on the scores.
PhantomBuster pricing
PhantomBuster pricing is simple to understand.
Starter ($69/ month): 20hrs execution time/ 10k AI credits/ 5 Phantom slots/ Unlimited data exports/ Community access/ Priority support
Pro ($159/ month): 80hrs execution time/ 30k AI credits/ 15 Phantom slots/ Unlimited data exports/ Community access/ Priority support/ Account consultant
Team ($439/ month): 300hrs execution time/ 90k AI credits/ 50 Phantom slots/ Unlimited data exports/ Community access/ Priority support/ Account consultant
Forwrd AI lead scoring models with HubSpot data points
Forwrd AI offers lead scoring for Hubspot and leverages AI and machine learning to analyze and score leads with unprecedented accuracy.
Your lead score will adapt over the site, and the Forwrd algorithms are designed to reveal hidden relationships and factors that can influence lead conversion.
If you want to use Forwrd, make sure to add as much detail as possible to your lead lists so you leave no stone unturned.
PhantomBuster can help support this by scraping public data from LinkedIn. You can also use PhantomBuster and HubSpot integration to keep your CRM up to date.
Forwrd Pricing
Starter: $0 per month. This includes the identification of highly engaged leads and customers, up to 20,000 contacts, an AI segmentation model, results displayed in HubSpot, email support, and 1 seat.
Growth: $1299 monthly (paid annually, 14-day free trial). Includes unlimited seats, unlimited correlation analysis, up to 2,000 monthly predictions, unlimited models, real-time prediction via API & Webhook, and in-app support.
Pro: $1999 monthly (paid annually, 14-day free trial). Includes unlimited seats, 2 standard integrations, unlimited correlation analysis, up to 5,000 monthly predictions, unlimited models, real-time prediction via API & Webhook, onboarding & training, and in-app support.
Enterprise: Get a Quote. Includes unlimited seats, 2+ standard, DWH, and analytics integrations, unlimited correlation analysis, up to 5,000+ monthly predictions, unlimited models, real-time prediction via API & Webhook, bi-directional sync (unlimited runs), email & Slack alerts, and a dedicated CSM.
Einstein lead scoring from Salesforce
Einstein Lead Scoring pulls information into a dashboard, creates lead scores by source, and runs every ten days, revealing fresh insights tailored for your business.
Einstein is an assistant that automatically captures and syncs relevant customer and sales information from email and calendar. At the same time, other functions, such as call insights, can reveal intent based on live sales calls.
It's great for larger enterprises; you'll need the Professional package for forecast management.
Salesforce pricing
Starter Suite: It starts at $25 per user/month (billed monthly or annually) and includes Simplified Setup and Onboarding, Lead, Account, Contact, Opportunity Management, Email Integration, and Automated Activity Capture.
Professional: It starts at $80 per user/month (billed annually) and includes Forecast Management, Customisable Reports and Dashboards, Quoting, and Contracting.
Enterprise: It starts at $165 per user/month (billed annually) and includes everything in Professional plus Advanced Pipeline Management & Deal Insights, Territory Management, Planning, Workflow, and Approvals.
Unlimited: It starts at $330 per user/month (billed annually) and includes everything in Enterprise plus Predictive AI, Conversation Intelligence and Sales Engagement, Premier Success Plan, and Full Sandbox.
Einstein 1 Sales: Starts at $500 per user/month (billed annually). Includes everything in Unlimited plus Einstein Copilot powered by Generative AI, Performance Management, Sales Programs, Team Collaboration with Slack, connecting and unifying all Data with Data Cloud and Revenue Intelligence.
ChatGPT for AI lead scoring
Using ChatGPT for AI lead scoring can be appropriate for small lead lists. Although it is time-consuming, it can still be used for insightful scoring on a small scale.
To use Chat GPT for AI lead scoring, you need to feed a list (ideally with a mix of data points) with a prompt to evaluate the leads. For example:
"Please evaluate the following leads and score them on a scale of 1 to 10 based on their potential to convert into customers. Consider the following criteria attached and explain each score."
You will likely need to analyze and improve the output.
ChatGPT pricing
Free: $0 per month. Access to GPT-3.5, Limited access to GPT-4oLimited access to data analysis, file uploads, vision, web browsing, and custom GPTs
Plus: $20/ month. Early access to new features, access to GPT-4, GPT-4o, GPT-3.5, up to 5x more messages for GPT-4o, Access to data analysis, file uploads, vision, and web browsing, DALL·E image generation, Create and use custom GPTs
Team: $25 per user/month billed annually/ US$30 per user/month billed monthly. Everything included in Plus includes higher message limits on GPT-4 and GPT-4o, tools like DALL·E, Browsing, data analysis, and more, creating and sharing GPTs with your workspace, and an Admin console for workspace management.
Conclusion
Predictive lead scoring is only going to grow in importance, and the rise of AI means that any lead scoring process that's manual or labor-intensive, like the traditional lead scoring methods of the past, will not stand the test of time.
Make the most of machine leading to determine a lead's likelihood to instruct, guarantee the lead quality, and steer your marketing team in the right direction.
An AI predictive lead scoring model is easy to integrate, and you're spoiled for choice!
Why not take PhantomBuster for a 14-day free trial and improve your lead prospecting.
