If I ran a commercial sales team of 20 reps and I wanted to beat my 2025 plan by $1M ARR, here’s what I’d do.
First, some math
Let's assume I have a $20k ACV and 20% win rate.
To hit $1M in new ARR, I need:
- 50 more closed/won deals ($1M / $20k)
- 250 new opportunities (50 / 20%)
- 12.5 more opportunities per rep (250 / 20 reps)
- ~1 more opportunity per rep per month (12.5 / 12 months)
That's it. Just one more opportunity per rep per month.
As a sales leader, I don’t control marketing, so I won’t hope for a breakthrough there. Instead, I’d focus on outbound. Here’s how:
1. Identify customer lookalikes
Identify the prospect accounts in my team’s segment that resemble my current successful customers. Focus on companies with similar firmographics and technographics. Don’t rely on a "black box" score alone (that means don't just rely on prepackaged scoring data). Instead tie those lookalike prospects to specific customer examples.
2. Equip the story
Give my team a 1-paragraph use case and outcome for each customer with strong lookalikes. Lean on product marketing or summarize CS notes with AI to create concise, relevant messaging.
3. Prioritize ruthlessly
Prioritize these accounts for my team. This could mean re-ranking accounts within their current territories or assigning a focused dynamic book of high-fit prospects. Be sure reps are only focused on deeply working those high-potential accounts.
4. Measure account coverage, not activity
Track reps on account coverage, not just raw activity. Use dashboards and 1:1s to track engagement depth and quality. Provide coaching on how to better work those accounts.
If a rep isn’t actively working those accounts and aligning their engagement to the right customer story, I’d need a good reason why. If not, I’d reassign those accounts to a rep who will work them thoroughly.
The outcome
If I stay disciplined with this process, I’d bet my 2025 comp on that extra 1 opportunity per rep per month. If that happens, so does my additional $1M ARR.
I’d personally do all this with Gradient Works, but I’m obviously biased.
