Lead qualification is a critical component of the modern sales process, enabling businesses to identify and prioritize their most promising sales opportunities. However, manually qualifying numerous leads is tremendously time-consuming. This article will demonstrate how implementing AI-powered lead qualification solutions can help revenue teams rapidly determine their best potential customers at scale.

What is lead qualification

Lead qualification refers to the process of determining if a sales lead meets the criteria to justify further pursuit by the sales team. This involves assessing both lead quality and sales readiness through various scoring frameworks. Highly qualified leads that align with ideal customer profiles exhibit strong buying signals and a stated need that a company’s offering can fulfill.Meanwhile, unqualified leads lack budget, authority, need or readiness to buy.

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Frameworks to qualify leads

There are several popular frameworks to qualify and score leads, such as:

• BANT (Budget, Authority, Need, Timing)
• GPCTBA (Goals, Plans, Challenges, Timeline, Budget, Authority)
• CHAMP (Challenges, Authority, Money, Prioritization, Stakeholders)
• MEDDIC (Metrics, Economic Buyer, Decision Process, Decision Criteria, Identify Pain, Champion)

These frameworks help sales teams consistently evaluate leads with a standardized set of questions to determine sales readiness. Teams can also assign numeric lead scores to each criterion and weigh them to rank leads.

Implementing lead scoring

Lead scoring models quantify leads based on profile attributes and behaviors that correlate with sales conversion. Profile scores rate leads on firmographic data like industry, company size and job title. Behavior scores rate leads based on engagement metrics like email opens, content downloads and site visits. By blending both profile and behavior scores, teams can instantly see which leads exhibit the strongest buying signals.

As leads progress through sales stages, their scores can be updated to reflect the latest qualifying information gathered by reps. Automated lead scoring helps teams keep track of a high volume of leads and identify when to pass them to sales as marketing qualified leads (MQLs) or later as sales qualified leads (SQLs).

Challenges of manual lead qualification

While sound in theory, manually executing qualifying frameworks, developing lead scoring models and continually updating numerous leads is extremely arduous. This results in slipping lead response times, inconsistent qualification, and valuable leads falling through the cracks at enterprise scale. Sales reps consequently waste time chasing unqualified leads.

Leveraging AI for scalable lead qualification

Thankfully, purpose-built AI solutions now automate huge swaths of lead qualification to help revenue teams scale.Here’s how they work:

• Identify Ideal Customer Profiles: AI analyzes historical deal data to determine your best customer     profiles based on attributes like industry, size and tech stack. New leads are automatically matched to these ICPs.
• Score Leads - AI automatically runs new leads through custom lead scoring models to instantly rate them based on fit. These combine attributes and activity scores using predictive analytics.
• Route Leads - AI instantly passes marketing qualified leads to sales reps based on scores and ICP fit. This ensures reps only receive relevant, sales-ready leads.
• Update Scores - As new lead data comes in, AI automatically re-scores leads and adjusts routing if they become sales qualified. Reps get notified when a lead passes SQL thresholds.
• Recommend Actions - AI tells reps the next best actions for advancing specific leads based on previous deal patterns. This tailored guidance helps reps progress leads quicker.

By combining the power of artificial intelligence with established lead qualification frameworks, revenue teams can finally gain an automated, scalable solution purpose-built for today’s data-driven selling environment. Leads no longer slip through the cracks and reps never waste time with irrelevant leads. Ultimately this drives more efficient funnel progression and conversion of top quality leads into successful deals.

Implementing AI-powered lead qualification 

As established earlier, manually executing lead qualification frameworks and scoring models is tedious and inefficient.However, by implementing an AI-powered lead qualification solution, revenue teams can overcome these challenges to accurately evaluate and prioritize leads at scale. Here is a step-by-step guide to leveraging AI for accelerated lead qualification:

• Integrate historical deal data. The first step is consolidating all of your historical customer deal     records, known as the customer data platform (CDP), and feeding this data into the AI engine. Information on won and lost deals spanning the past 2-3 years works best. The AI analyzes attributes of your best customers across 10,000+ data points to determine ideal buyer profiles.
• Build lead scoring models next. The AI reviews all the profile attributes and behaviors that correlate with won deals in the CDP to automatically generate a custom lead scoring model. This gives appropriate weights to elements like job title, download activity, firmographic data based on predictive impact.
• Identify ideal company profiles. The AI compares new leads in real-time to the identified ideal buyer profiles to calculate lead-to-ICP fit percentage scores, making it easy to see which leads resemble your best customers.
• Score incoming leads. As new leads come in, the AI engine instantly runs them through the lead scoring model to assign profile and behavior scores. It then blends these with the ICP match scores into a single lead score.
• Set lead routing rules. You can now set rules based on lead scores to determine when they are passed to sales reps as marketing qualified leads (MQLs) vs. later down the funnel as sales qualified leads (SQLs).
• Continually update scores. The AI solution keeps assessing new lead data to update scores in real-time. Any changes to score or routing rules are automatically applied across all leads 24/7.

Benefits of AI-powered lead qualification 

Implementing an AI solution delivers numerous advantages over manual lead qualification:

• Accurately score every lead in real-time based on ideal buyer models
• Automatically identify and route sales-ready leads to reps
• Never lose track of a lead with constant automated lead monitoring
• Gain guidance on next steps for advancing specific leads
• Quickly analyze lead trends across customizable dashboards
• Continually refine scoring models and lead routing rules

By leveraging artificial intelligence to execute lead qualification frameworks at scale, revenue teams can focus on progressing only the most promising, sales-ready leads. This results in more efficient funnel management, accelerated deal cycles, and increased conversion rates.

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Leading Scoring using Relevance Ai

Watch this tutorial on how to simply score leads using Relevance AI. Try out the lead scoring template.

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