The ability to identify and prioritize high-quality leads can make a drastic difference in your company’s revenue. Over the years, some fairly sophisticated methods of lead scoring have been developed. However, those traditional methods can’t match the accuracy of emerging AI-powered lead qualification technology. This post will dive into how AI-powered and traditional lead qualification differ. Learning how they work together is vital for businesses that want to drive the best results.
Understanding Traditional Lead Scoring
Let’s start by looking at how traditional lead scoring works. There are many formulas in use by various industries and needs. What they all have in common is that they use a predetermined point system that assigns values based on a set of criteria. These criteria and their associated scores depend on demographic information, behavioral indicators, and other data that have been identified as predictive of successful conversions.
To see this in action, let’s look at a simple lead scoring model. An e-commerce site might have the following point structure:
- 5 points for company size over 500 employees
- 10 points for visiting a pricing page
- 10 points for holding a decision-maker title
- 15 points for downloading a whitepaper
- 20 points for requesting a demo
Any lead that reaches some predetermined threshold becomes “sales qualified.” It is then passed on to the sales team to close the deal.
Why It Works
Although simple, these methods are systematic and predictable. Sales and marketing teams can easily understand why a lead was passed on to them. The two teams are able to align around a shared opinion of what makes for a qualified lead. The relatively small number of variables also makes it easy to refine the model over time using historical data to optimize point values or add variables.
Imagine a B2B software company realizes that the healthcare industry has a 40% higher conversion rate than other industries. It’s easy to add an industry type variable to the scoring formula. Companies can then adjust the values so each industry is weighted according to its likelihood to convert.
This method is perfect for situations where buying patterns remain stable and predictable. Companies with well-established customer profiles and clear conversion paths have an easy time using that data to create a reliable foundation for lead scoring with minimal ongoing maintenance.
The Evolution to AI-Powered Lead Qualification
Despite their utility under ideal circumstances, traditional lead qualification is very static. The points and predefined criteria stay the same until they are manually changed. They rely on averages rather than the individual specifics of a lead.
AI-powered lead qualification removes these limitations. They don’t rely on predefined criteria alone. Instead, they engage prospects in conversation, analyze responses, and adapt in real-time to the information gained from each individual lead. These algorithms are also able to process more variables and find more obscure patterns than humans can.
Why It Works
The prospect-facing aspect of these AI systems uses the same chatbots you may use to research topics or perform tasks. However, they are trained to extract meaningful information that can then be passed into powerful machine learning-powered lead scoring algorithms to get highly accurate results. The chatbots are intelligent enough to ask follow-up questions, identify any objections or concerns, and probe deeper into specific areas of interest. As a result, it can get a better feel for the chances that the prospect will convert.
Let’s go back to our traditional point scoring system. The model scores 15 points if a prospect downloads a whitepaper. This could be a serious prospect looking to make a purchase now, or it could be someone researching for future needs. Under the traditional system, both prospects get the same 15 points, despite one being clearly more valuable than the other.
An AI qualification system can start a conversation immediately after the whitepaper is downloaded. It can ask the prospect if they are currently evaluating solutions to gain an idea of how serious the person is about purchasing and how quickly they may be likely to do so. This allows for far more accurate lead scoring than the traditional method does.
These conversations can reveal significantly more information as well. For example, it may learn that the project is already budget-approved, signifying that a purchase is imminent. The natural language processing capabilities of modern AI solutions turn everything the prospect reveals into potentially useful information for scoring the lead. It’s like having a full-time employee pre-screening your leads for you.
Key Advantages of Each Lead Qualification Approach
Both approaches to lead scoring bring something valuable to the table. Here, we’ll break down where each excels:
Traditional Lead Scoring
In certain contexts, traditional lead scoring offers several compelling advantages. It has a systemic, rule-based nature that gives teams complete transparency and predictability. Rather than being a black box, it’s something that allows for easy troubleshooting and refinement as new data comes in or market dynamics change.
Traditional lead scoring is also a scalable and cost-effective solution. After implementation, it requires minimal ongoing resources to maintain. For organizations with large lead volumes and limited staffing, this method can process thousands of leads at once with no human or additional technological resources required.
Finally, for businesses that have established customer profiles, the variables chosen can accurately reflect an ideal customer. This makes the simple system also a very effective one for identifying high-quality leads.
AI-Powered Lead Scoring
For companies where the ideal customer is less of a cut-and-dry question, AI-powered lead scoring can probe much deeper than simple point systems can. It can ask specific questions and intelligently respond to the answers, guiding the conversation to glean as much information about conversion chances as possible.
This level of engagement improves the customer experience as well. Businesses have long known that personalization is key to successful sales. Nobody wants to be treated like a number or get the impression that they’ve been sent the same form response that everyone else has. AI-powered lead scoring actually engages in conversation that’s tailored to each prospect’s responses. The result is a significantly more engaging experience that begins building rapport with a prospect before a human ever connects with them.
Perhaps most importantly, these qualification methods identify high-intent prospects that might otherwise slip through the cracks. A small company CEO who has an urgent need and available budget should be elevated ahead of a large company employee who simply gathers enough points through low-intent interactions.
Practical Implementation Examples
As alluded to earlier, these two methods work best when they work together. To see how this might work, let’s consider how a company that develops marketing automation software might implement each. They may set up a traditional lead scoring model with the following variables:
- Company size (larger companies get more points)
- Job title (C-level executives get the most points)
- Website behavior (Pricing pages are scored higher than landing pages)
- Content engagement (Webinar attendance, whitepaper downloads, etc. are scored higher)
Under this model, a marketing director at a 200-person company who arrives at the landing page, downloads two whitepapers, attends a webinar, and visits the pricing page might get 75 points and get passed on to sales. This is a great starting point, but doesn’t say anything about whether they currently have the budget to make a purchase or the authority to do so.
Here, the AI qualification system can step in. It can engage the prospect in further conversation: “I see you’re interested in our marketing automation features. What is driving your interest in a new solution?” The response will provide valuable information about how well the company’s products fit the prospect’s needs. Follow-up questions can determine how likely they are to make a purchase soon, and when they may be able to do so. The information passed on to the sales team is now significantly more valuable, giving them a ton of background information to tailor their initial contact to the specific prospect’s situation.
The Power of Integration
It’s easy to see how these two systems can work together in a very harmonious way. AI qualification requires significantly more technological resources than traditional scoring methods do. Having a well-refined traditional lead scoring system feed prospects to an AI-powered lead qualification system for further analysis minimizes the costs of the AI analysis while still providing the benefits. This also ensures that high-potential leads get immediate personalized attention to help keep their interest.
This integrated strategy can also make it easier to refine both systems. The AI qualification steps might reveal which traditional scoring criteria are the most predictive of actual buying intent, allowing for refinement of that system. Conversely, traditional scoring can identify which types of leads benefit the most from AI engagement, allowing for even more efficient use of those extra technological resources.
Discover the Power of AI Lead Qualification
Verse’s AI-powered SMS platform takes lead qualification beyond traditional scoring by engaging and qualifying prospects in real-time, two-way text conversations on the world’s most preferred communication channel. SMS benefits from 98% open rates and Verse.ai provides 24/7 instant response capabilities. Our system automatically qualifies leads, books appointments, and delivers warm prospects directly to your sales team. To learn more, book a demo with us today.
