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In an era of digital transformation, where the vast majority of leads flood in from online sources, businesses are turning to lead scoring to handle the chaos of trying to prioritize them.
A lead scoring mechanism helps you to qualify leads based on:
As leads move through the buyer's journey, your lead scoring method helps to distinguish hot prospects from time-wasters.
Hot prospects should be prioritized for instant sales outreach, while unqualified leads typically should be routed to nurture flows.
Powerhouses like Salesforce offer turnkey scoring, but their solutions lean on either rigid rule-based formulas, or one-dimensional predictive scoring, which considers just data that lives in Salesforce.
And while Salesforce core business doesn’t focus on more advanced lead scoring mechanisms, alternative solutions are rising in popularity.
Legacy players like Salesforce offer rigid rule-based scoring, baked into its email marketing solution, Pardot.
However, this method has many blindspots and is considered outdated in light of the latest innovation in AI lead scoring.
And while Salesforce went ahead and acquired a predictive analytics company Einstein, this solution proves to be limited.
The problem with Einstein is its inability to consider cross-cloud data.
Cross-cloud data simply means data coming from a variety of sources, and we all know that in today’s environment, companies collect data from a variety of sources and SaaS products.
So if your prospects' interactions are stored in your website analytics, CRM, marketing automation, product analytics, support ticketing, and more, then Einstein is not going to produce scores that reflect the real quality of your leads.
In 2024 and beyond, Einstein is not enough for modern companies, as modern buyer journeys are rarely documented in a single data source – Salesforce CRM in this case.
As companies collect more data than ever before, and AI advances, a superior method for scoring emerges as the new standard for lead qualification that drives real efficiencies.
AI lead scoring, that considers your Salesforce CRM data, as well as other sources offers the ultimate flexibility to incorporate signals like intent, engagement, demographic and firmographic data not found in conventional prospect profiles.
Tools like Forwrd.ai offer a streamlined method for producing highly accurate lead scores, and a simple way for feeding scores back into the business process of sales reps.
To put it simply, Forwrd pushes AI-generated scores and their underlying explanations directly into the CRM view of your marketing managers and sales reps.
In the process, they unlock higher efficiency, improved marketing and sales alignment, and higher conversion rates.
The market shows which way the wind blows. As business cycles accelerate, only surgical AI lead scoring keeps pace.
Those refusing to explore the new opportunities AI offers in prospect scoring, may soon find themselves without a chair when the music stops.
