I used to believe that more sales reps meant more revenue. I was wrong. So, so wrong.
Back in the early days of RemoteTeam, we hit a growth spurt. We’d just closed a solid seed round, and the board was breathing down my neck about scaling up. The obvious move? Hire more salespeople. We went from a scrappy team of three to a dozen reps in what felt like overnight. Our payroll ballooned. Our office suddenly felt cramped. The one thing that didn’t grow? Revenue.
It was one of the most frustrating periods of my career. We were burning through cash, our cost of customer acquisition was skyrocketing, and I was spending my days in pipeline review meetings that went nowhere. The team was busy, no doubt. They were making calls, sending emails, doing demos. But the deals weren't closing. It felt like we were just spinning our wheels in the mud.
It wasn't until we had a brutally honest conversation with our head of sales that the penny dropped. He told me, “Sahin, we’re chasing ghosts. The team is spending 80% of their time on leads that will never, ever convert.”
He was right. We had a lead generation engine, but we had no qualification engine. Every name that trickled in was treated the same. A student doing a research project got the same white-glove treatment as a Fortune 500 CIO who had explicitly requested a demo. This was my biggest mistake with deal scoring: we didn’t have any.
That realization led to a complete overhaul of our sales process. It wasn't about hiring more people; it was about making the people we had more effective. It was about focusing our energy on the deals that actually mattered. The result? We tripled our qualified pipeline within two quarters—without adding a single new sales rep to the team. Here’s how we did it, and how you can avoid the same costly mistake.
Stop Treating All Leads Equally
The first and most fundamental shift was psychological. We had to abandon the idea that every lead is a good lead. In B2B sales, this couldn't be further from the truth. The vast majority of your potential customers are not a good fit for your product, and that's okay. Your job is not to sell to everyone. It's to find the right customers and sell to them brilliantly.
This means you need a system to separate the wheat from the chaff. You need a way to score your deals so your team can focus their precious time and energy on the opportunities that are most likely to close. This is where a deal scoring system comes in.
Building Your Deal Scoring Machine
Creating a deal scoring system isn't about buying some fancy AI tool (though those can help, and we'll get to that). It's about first principles. It's about deeply understanding your business and your customers. Here are the steps we took:
1. Define Your Ideal Customer Profile (ICP)
If you don't know who you're selling to, you can't score your leads effectively. Your ICP is a living document, not a one-and-done exercise. It should be a hyper-specific description of the perfect customer for your product. We got our sales, marketing, and product teams in a room for a full-day workshop. We argued, we debated, and we came out with a crystal-clear picture of who we were targeting. We looked at our best customers—the ones who renewed, expanded, and told their friends about us. What did they have in common?
We broke it down into firmographics and psychographics:
- Firmographics: Company size, industry, geography, annual revenue, technology stack. For RemoteTeam, our sweet spot was tech companies with 50-500 employees, headquartered in North America, using Gusto for payroll.
- Psychographics: Company culture, growth stage, pain points, strategic priorities. We found our best customers were fast-growing companies that valued remote work and were struggling with managing a distributed team.
This exercise was invaluable. It gave us a shared language and a common understanding of who we were going after. It also gave us the foundation for our deal scoring model.
2. Identify Key Buying Signals
Next, we looked at the behaviors that indicated a lead was actively in a buying cycle. We analyzed our closed-won deals and looked for patterns. What actions did they take on our website? What questions did they ask in demos? What content did they engage with?
We came up with a list of positive and negative signals:
Positive Signals:
- Visited our pricing page more than twice in a week (+10 points)
- Requested a demo (+15 points)
- Downloaded our “Ultimate Guide to Remote Team Management” ebook (+5 points)
- Engaged with a case study of a similar company (+10 points)
- Asked about specific integrations during a demo (+20 points)
Negative Signals:
- Using a free email provider (e.g., gmail.com) (-10 points)
- Job title is “student” or “intern” (-20 points)
- Company size is outside our ICP (e.g., <10 employees) (-15 points)
These are just examples, of course. Your signals will be unique to your business. The key is to be specific and to base your scoring on data, not just intuition.
3. Build a Simple Scoring Model
Once we had our ICP and buying signals, we built a simple scoring model in a spreadsheet. We assigned points to each attribute and signal. We didn't get too fancy. We just wanted a way to rank our leads so our sales team could prioritize their outreach.
Here’s a simplified version of what it looked like:
| Category | Attribute/Signal | Points |
|---|---|---|
| Firmographic | Company Size (50-500 employees) | +15 |
| Industry (Tech) | +10 | |
| Using Gusto for payroll | +20 | |
| Behavioral | Requested a demo | +15 |
| Visited pricing page (3+ times) | +10 | |
| Downloaded “Remote Work” ebook | +5 | |
| Negative | Using a free email provider | -10 |
We set a threshold of 40 points. Any lead with a score of 40 or higher was considered a “hot lead” and was immediately routed to a sales rep for follow-up. Leads with a score between 20 and 39 were put into a nurture sequence. Leads with a score below 20 were disqualified.
4. Integrate and Automate
A spreadsheet is a good start, but it doesn't scale. The real magic happened when we integrated our scoring model into our CRM. We used a combination of off-the-shelf tools and some custom code to automate the process. Every new lead that came in was automatically enriched with firmographic data, tracked for behavioral signals, and assigned a score in real-time.
This was the game-changer. Our sales reps no longer had to waste time researching leads or trying to figure out who to call next. They could just log into the CRM and see a prioritized list of hot leads waiting for them. It was like giving them a superpower.
The Impact: More Than Just Revenue
The impact of our deal scoring system was immediate and profound. As I mentioned, our qualified pipeline tripled in just two quarters. But the benefits went far beyond that.
- Sales team morale skyrocketed. Reps were closing more deals, making more money, and feeling more successful. The frantic, wheel-spinning energy was replaced by a focused, confident hum.
- Our sales cycle shortened. By focusing on the right leads, we were able to move them through the funnel much faster.
- Our marketing became more effective. The deal scoring data gave us a feedback loop. We could see which channels and campaigns were generating the highest-quality leads, and we could double down on what was working.
- I could finally sleep at night. I was no longer worried about our burn rate or our anemic pipeline. I had a predictable, scalable sales engine that I could trust.
Don't Make the Same Mistake I Did
Looking back, it's almost embarrassing how long it took me to realize the importance of deal scoring. I was so focused on the vanity metric of hiring more reps that I completely missed the foundational issue. Don't be like me.
If you're feeling stuck, if your revenue isn't growing despite your best efforts, take a hard look at your sales process. Are you treating all leads equally? Are you chasing ghosts? If so, it's time to build your own deal scoring machine. It's not easy, but it's one of the highest-leverage things you can do for your business. It was for mine.
Beyond the Basics: The Future is AI-Powered
What I've outlined here is the foundational, manual process we went through. It’s the 101-level course, and it works. But the world has moved on since then, and so have I. Today, you can and should go further by incorporating AI into your deal scoring.
Modern AI sales tools can automate and enhance every step of this process. Instead of manually defining your ICP, AI can analyze your entire customer history and identify the subtle, non-obvious characteristics of your best customers. It can track thousands of buying signals in real-time, from social media activity to intent data from across the web. The scoring models are no longer simple, linear point systems; they are sophisticated machine learning models that can predict a lead's likelihood to convert with stunning accuracy.
At my current ventures and in the companies I invest in, like Scale AI and Hugging Face, we are constantly pushing the envelope on this. We use AI not just to score deals, but to understand them. Conversational sales AI can analyze call transcripts to identify the exact language that top reps use to close deals. Sales forecasting AI can predict our quarterly revenue with a margin of error that would have been unthinkable a few years ago.
If you're just starting out, begin with the manual process. It will force you to understand the fundamentals of your business. But don't stop there. The future of sales is intelligent, and the sooner you embrace it, the faster you'll scale. The mistake of not having a deal scoring system is a big one, but the mistake of not evolving it with the power of AI is even bigger.
Frequently Asked Questions
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
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.
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.
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.