I almost tanked my second company. My big idea? 'More.' More funding, more features, and, God help me, more sales reps. It was the single dumbest thing I've ever done.
This was back in 2018. RemoteTeam was finally getting traction. We had a product people actually wanted, a real customer base, and a pocket full of seed money. Every investor, every advisor, every article I read screamed 'SCALE!' So, like a good little founder, I went out and hired five SDRs in a quarter. The classic 'spray and pray' approach.
It was a complete and utter disaster.
Our burn rate went vertical. Revenue? Flatlined. The team looked busy. They were making calls, sending emails, booking demos—all the vanity metrics looked great. But the deals weren't closing. Our pipeline was a toxic waste dump of unqualified leads. We were burning cash to tread water.
I locked myself in a room for a week, mainlining coffee and staring at spreadsheets. Call volume, open rates, demo attendance—I looked at it all. The data was screaming at me, and I didn't like what it was saying. My expensive new sales team was spending 80% of their day chasing ghosts. Leads with a sub-10% chance of ever becoming customers. We weren't just inefficient; we were lighting money on fire.
That's when it hit me. The problem wasn't the reps. The problem was the leads. We were treating every single lead like it was the next Google. We had zero signal. We were flying blind, and the ground was coming up fast.
The Deal Scoring Revelation
I got obsessed with deal scoring. I'd heard the term thrown around at conferences, but it always sounded like some enterprise-level voodoo. Something only Salesforce or Oracle could afford to do. It felt too complex, too expensive, too... much for a startup like us.
But I was desperate. So I did what any founder does when their back is against the wall: I opened up a Google Sheet. I started tracking the basics for every lead: company size, industry, job title. It was ugly, but it was a start.
Then I got a little smarter. I started pulling in behavioral data. Where did they come from? What pages did they look at? Did they download that one whitepaper we spent a month writing? And then, the patterns started to jump out. The real buyers? They all looked alike. Same industry, same job titles, and they all hit our pricing page within a day of signing up.
This was it. This was the signal in the noise. I could finally see the difference between the tire-kickers and the actual buyers.
Building Our AI-Powered Deal Scoring Engine
I knew this was bigger than a spreadsheet. We needed a real system. Something automated, something that could give my reps a simple, real-time score for every single lead.
So, I did what any self-respecting, slightly arrogant founder would do: I decided to build it myself.
I grabbed my two best engineers, pulled them off the core product (much to their dismay), and gave them a new mission: build me a deal-scoring engine. I wanted it to be smart, automated, and predictive. I wanted our own secret weapon.
They delivered. Three months later, we had a prototype. We named it 'Cerberus,' the three-headed dog that guarded the underworld. It was our gatekeeper. Only the worthy leads would pass.
Cerberus was a beast. It plugged into our CRM and marketing automation, slurping up data. It used some basic ML and NLP to chew on 50+ data points for every lead and spit out a score from 1 to 100.
The rules were simple. 80 or above? That's a hot lead. It shot to the top of the queue, and a rep was on it in minutes. 50-79? That's a warm lead. We'd nurture it with some targeted content. Below 50? Dead to us. We didn't waste a single second on them.
The Results: Tripling Our Pipeline Without Hiring a Single Rep
The results were insane. Our qualified pipeline tripled in the first month. We weren't generating more leads, just better ones. My reps were ecstatic. They were closing more deals, making more commission, and they weren't wasting their days talking to deadbeats. And our burn rate? It dropped like a rock.
We had cracked the code. We were growing faster than ever, and we hadn't hired a single new person. We were doing more with less.
All thanks to deal scoring.
Now, you're probably thinking, 'Cool story, Sahin. But I don't have a couple of spare engineers to build me a custom AI engine.'
You're right. And the good news is, you don't need to.
How You Can Implement Deal Scoring Today
The world is different now. There are a ton of AI sales tools out there that can do this for you. MadKudu, Clearbit, even the higher tiers of HubSpot have this stuff built-in now. You can get this up and running in a weekend.
Here’s how you can do it. It's a simple four-step process:
Define your Ideal Customer Profile (ICP). And I mean really define it. Who are your absolute best customers? What's their job title? What industry are they in? What other software do they use? Get ridiculously specific.
Map the buying journey. What do your best customers do before they buy? Do they read 3 blog posts? Watch a webinar? Visit the pricing page 5 times? Figure out the digital breadcrumbs they leave behind.
Create your scoring model. This is just a simple point system. 5 points if they're in your target industry. 10 points if their title is 'VP of Marketing'. 20 points if they visited the pricing page. You get the idea. The closer they are to your ICP and the more buying signals they show, the higher the score.
Automate it. This is the most important step. Use one of the tools I mentioned to automate the whole process. The score should update in real-time, and your reps should see it right in their CRM. No more spreadsheets.
Look, this isn't a magic bullet. It'll take some work to set up. You'll have to tweak your model. But I promise you, it's worth it.
Stop hiring more reps. Start scoring your deals. It's the most powerful lever you have for efficient growth. It's the secret that nobody is talking about.
Now you know it, too.
Frequently Asked Questions
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.