Ad tech selling has a sort of rhythm about it.
A rep spends a minimum of six months working on a holding company deal. The CMO changes. The deal evaporates. The rep misses their number. Everyone acts surprised.
I've sat through more third-party sales methodology classes than I can count. For me, the common takeaway was that none of them hit the mark for ad tech SaaS sales. Not with agencies and certainly not with brands directly. They each had some great ideas, but none of them landed the plane well for my use case.
This guide offers a different approach. I've created a mashup. Taking the best elements of ideas from other sales methodologies and adapting them to the ad tech sales realities.
The result is what I call the Fibonacci Velocity Framework or FvF.
Uhh what?
I know, the name is...a lot. Honestly, that's why I stuck with it.
In short, it takes models like the 2-4-6-8 deal velocity framework, popularized by sales leaders like Andrew Johnston, and combines them with the mathematical principles behind agile software estimation.
I think it is the perfect marriage of the two "lanes" of my career. This model joins my ad tech sales and my product management experience to produce a much more manageable (predictable) model.
In ad tech sales, deals scale unpredictably, with the difference ranging between $30K in platform fees to $1M+ enterprise licenses.
I hope you'll join me for this journey. Here is what we have in store:
- Linear Quotas: The Core Problem
- A Better Way: Why Linear Doesn’t Work for Ad Tech
- The Fibonacci Velocity Framework (FvF)
- Timing and Pacing: The "Rule of 68"
- Annual Planning Calendar
- Implementing FvF for Your Business Model
- The 3Ps: Tactical Execution
Linear quotas: The core problem
Most quota planning treats deals as interchangeable units of revenue when reality rarely bears that out. Hit $1M however you want, right? Close one whale or twenty minnows. Cash is cash, pal.
Lies!
A rep who's solely chasing a $1M deal lives in feast-or-famine mode. They have a fundamentally different risk level than a rep slinging $50K opportunities.
But why is this a problem either way? Traditional quota planning focuses too much on the outcomes rather than the probability distributions.
The 2-4-6-8 framework addresses this way of thinking for SaaS sales, especially in product-led growth organizations. However, my adaptation adds a layer of psychological realism, heavily borrowed from agile estimation.
My goal, as a sales leader, was always to create a predictable pipeline first. Even if the grand total was grim, I needed to know it was reliable and likely.
The original 2-4-6-8 model
The original 2-4-6-8 model is a great system. It is classically "bottoms up" in style. Instead of treating quotas as a single number to be achieved however you can, it mandates a specific distribution of deal flow across four tiers.
For example, let's say we have a rep carrying a $250,000 quaterly target.
| Tier | Divisor | Target value | Purpose |
|---|---|---|---|
| Tier 1 | ÷ 2 | $125,000 | Whale deals, enterprise expansions |
| Tier 2 | ÷ 4 | $62,500 | Mid-market accounts, significant upsells |
| Tier 3 | ÷ 6 | $41,667 | Standard platform deals |
| Tier 4 | ÷ 8 | $31,250 | Test deals, pilot conversions |
This model is great because it creates multiple paths to the target. Any single tier, if 100% fulfiled, hits the quota on the nose. But the real genius is in the combinations.
If a rep closes one Tier 1 deal and four Tier 4 deals, they are at 62% of their target with a significant pipeline still remaining. They are flush with options.
This is more important in ad tech sales because we need more flexibility than in general SaaS sales. Agency holding company deals move on... "political"... timelines. Deals get paused when CMOs rotate.
This way of thinking means that a single delayed deal does not crater the entire quarter.
But there is a better way
My only criticism of the 2-4-6-8 model, in our ad tech context, is that it assumes the effort required to close deals scales linearly with the deal size. In other words, a $125K deal requires roughly four times the effort of a $31K deal. That's great for PLG SaaS sales organizations where revenue maps cleanly to "seat count."
Ad tech does not work that way.
The jump from a $30K measurment platform deal to a $300K agreement is not a 10x increase in effort.
The smaller deal example involves one buyer, one budget, and probably one use case. The larger deal involves procurement, legal, IT, and a half dozen other stakeholders with differing agendas. And it will take 6 months to get across the line, assuming no delays.
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Enter Mr. Fibonacci
The Fibonacci sequence in agile product management exists because people are bad at perceiving the linear differences in complex tasks. Really, really bad.
In agile, story points of 1, 2, 3, 5, 8, 13 reflect a person's ability to distinguish effort levels as the tasks get bigger and more complicated. This is actually called Weber's Law.
Talk to me like I'm a salesperson, not an engineer. Roger that.
Weber's law basically says that if I handed you a 1-pound bag of flour and a 2-pound bag of flour, you could pretty easily tell me which one was heavier. However, if I gave you a 20-pound bag and a 21-pound bag and asked you the same question...you are very unlikely to be able to feel which is heavier. You would basically be guessing.