About a decade ago, if you worked in revenue operations or lead generation, every conversation somehow circled back to lead scoring. It was like the buzzword that wouldn’t quit. People were enamored with its potential for prioritizing leads, increasing sales efficiency, and unlocking revenue gold—if only the perfect scoring model could be cracked.
But we know what happened next. The gloss wore off. Lead scoring became one of those nice ideas that didn’t quite deliver the promised transformation for most organizations. Now, thanks to emerging technologies like automation and AI, we might finally be in a position to solve the problems that stalled lead scoring in the first place.
To understand where we’re heading, it’s essential to know where we stumbled before.
The Two Big Questions Nobody Had Great Answers For
Here’s where things got tricky when lead scoring was shiny and new. Two critical issues bubbled up over and over, and no one seemed to know how to tackle them effectively:
- What do reps actually expect from high-scoring leads?
- What’s supposed to happen with low-scoring leads?
Spoiler alert: without clear plans for either of these, lead scoring was bound to hit a wall.
The Problem with High Scores
When sales reps hear about “high-scoring leads,” they’re probably imagining something game-changing—a golden list of buyers practically begging to close deals. But what does "game-changing" even mean in most businesses?
If your industry’s average lead-to-customer conversion rate is, say, 5%, you’re not suddenly going to see a list of high-scoring leads converting at 50%. A practical expectation might look more like 10-15%. Don’t get me wrong, that’s progress—but does it feel meaningful to your team? Will they trust the process enough to shift their focus, or will they default to sticking with what they've already found works well for them (specific sources, tried-and-true tactics)?
The challenge is clear. High scores have to do more than just statistically improve outcomes. They have to earn the trust and buy-in of sales reps who may already feel like they’re doing a decent job of prioritizing leads without an algorithm.
The Low Score Dilemma
Low scores come with their own baggage. If you buy into lead scoring's potential, you’ll realize the logical next step for low-scoring leads is to deprioritize or outright reduce outreach to them. But for most organizations, that’s a hard pill to swallow—less outreach means accepting less revenue, no matter how minimal the difference.
Here’s where I’ll pull from personal experience. When I worked at Victoria’s Secret, we developed models to cut down physical mailings by over 10% while minimizing the impact on revenue. The results were clear and backed by data. But when the time came to hit the brakes on those mailings, leadership hesitated. The math made sense, but the fear of losing even a fraction of revenue was enough to halt the initiative. Knowing low scores doesn’t mean much if you're unwilling to act accordingly.
Why Automation and AI Are Game Changers
Now, here’s where things get interesting. Fast forward to today, and we have automation, AI, and machine learning rapidly advancing how businesses function. These technologies might just give lead scoring the refresh it desperately needs.
Empowering High Scores
One reason lead scoring didn’t always deliver before was that high-scoring leads still depended wholly on human effort to convert. But today, AI-powered tools can help sales teams prioritize and personalize outreach based on those scores—faster and more efficiently than a human sifting through CRM data. High-score leads don’t just get flagged; they get better follow-ups, smarter sequencing, and even predictive suggestions for what might close the deal.
When reps see tangible results because AI-assisted implementations actually help them close deals? That’s when trust in lead scoring starts to rebuild.
Redefining Low Scores
And what about low-scoring leads? Here’s where automation truly shines. You can now hand over repetitive or low-value outreach tasks to bots or automated sequences. Instead of dedicating expensive sales resources to leads that rarely convert, low scores can still be managed passively to some extent—just enough to keep the door cracked without draining your team.
More importantly, automated systems can create a loop of learning. Maybe today’s low score isn’t tomorrow’s, and AI can adapt, refining how it categorizes leads.
Efficiency Is Making a Comeback
If there’s one thing every revenue leader is leaning into now, it’s efficiency. Budgets are tighter, markets are competitive, and teams are expected to do more with less. Lead scoring, revamped with automation and AI, offers one of the most promising paths forward. It takes the time-worn concept and evolves it into a tool that truly works in practice, not just on paper.
Sure, it’s not a magic bullet, but it doesn’t have to be. The strength of lead scoring lies in its ability to empower teams to focus where it counts. And when paired with tools that reduce manual effort, it becomes less about trust falls into data models and more about achieving measurable outcomes.
Lead scoring might just be ready for its second act. This time, with the help of automation and AI, it might actually live up to the hype.
