Dave Kellogg Profile picture
Jun 19 8 tweets 2 min read Read on X
Large quota over-assignment, e.g., where the sum of ramped, rep-level quotas >= 150% of plan causes some major problems. A short 🧵
The company can consistently make plan when most sellers are not making good money and most bonus-driven staff are coming up short.

In short, a morale problem. As I once said when working at a large public company,

The only people drinking champagne at the end of the quarter are the CEO and the CFO.

That's bad for culture, morale, and turnover.
It makes marketing planning hard. Is marketing supposed to generate enough pipeline to cover plan or cover quota?

Say you need to generate 100 oppties at a per-oppty cost of $4K to covert plan.

To cover quota you'll need to generate 150 oppties, so you'll need $200K more in demandgen budget to do it.

This applies to other horsemen (pipeline generation sources) as well.
So everyone ends up confused. If you cover plan, then all the reps don't have 3x coverage at an individual level -- on average they have 2x -- so the sales team feels like marketing is failing.

If you cover quota, you're potentially spending 50% more than you need to cover plan. So the board thinks you're inefficient.
In general, I say cover plan, but plan conservatively so you can beat it. That works, mostly, if your over-allocation is 20%.

But at 40%, 50% and 75% it just plain doesn't work. You can't fudge over it.
That's why I think smart companies drive their annual planning off productivity models (not quota models) that model what you actually think you will sell and then uplight to quota and do so by around 20%.

That means you're covering plan, not quota, but the gap isn't that big and if you're low-balled the pipegen plan a bit you can beat it
The keeps costs in line, a larger percentage of the sales team hitting their number, and pretty much everyone but the CEO and CFO happier.

They're less happy because they're taking more risk on their numbers (and maybe should plan more conservatively as a result) -- but they'll start to realize the true cost of buying all that insurance on the number and the damage it does to the company.

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More from @Kellblog

May 18
Ut oh. I've said the quiet part out loud again.

European founders lose their bearings when they expand to the US and hire their first few employees, typically salespeople.

"These people are gods," is how one founder told me he felt early in the expansion process.
But what's going on is simple.

Americans are taught to inflate, hype, take credit, embellish, real push their career claims to the limit.

Europeans are taught the opposite.

So when a European might be inflating an American CV as they woulud a European one, they should be discounting it
It's a perfect storm.

What happens when a culture of understated achievement meets one of overstated accomplishment?

"These people are gods" is what happens
Read 8 tweets
May 10
Many wish to be in a large, greenfield, forming market. And it's a great thing and a HUGE opportunity. But the rules are different. You can't be mild mannered. You can't sandbag growth plans. You need a barbarian running your sales team. The discounting guide gets thrown out the window and you need to win, win, win every deal you find.

Why?
1. CAC is the wrong measure (bet you never thought you'd hear me say that). If you're locking someone into a platform with high switching costs for decades, you shouldn't care whether your CAC ratio is 0.8, 1.2, 1.8, 2.5, or even 3+. (That said you better be darn sure about that lock.)

2. CAC's also the wrong measure because it's only looking at the initial deal. If you're really selling a platform with N use-cases, the customer could expand 5x to 10x in the first decade.

3. Network effects. Many platform markets have network effects where the more people use it, the more value it has. AI training data is a subtle form of network effect -- the more data I have, the better my solution and the more people should want to use it.
4. Increasing returns of market leadership (aka, the rich get richer). The more you're a market leader, the more you're the default choice, the more the other guys have to prove differentiation from you, the more you can run them ragged on experimental / PR initiatves, the more consultants want to implement your stuff, the more other vendors want to integrate with it, the more the big SIs want to build practices around it. Network effects are arguably a sub-case of this.
Read 6 tweets
Jul 19, 2023
Reasons that, despite its popularity, I dislike CAC Payback Period as a SaaS metric. A short 🧵
It's a compound metric that includes S&M cost, new ARR, and subscription gross margin. That makes it hard to figure out how to fix it when it's broken.

kellblog.com/2022/10/11/you…
It's a risk metric conflated with an efficiency metric.

Payback periods measure how long it takes get your money back. Given two projects with similar IRR, you pick the one with faster payback

investopedia.com/terms/p/paybac…
Read 8 tweets
Feb 19, 2023
Had a chat with a founder the other day on the difference between an ideal customer profile (ICP) and sales qualification. A kick-it-around thread 🧵
I always say, "in the early days, the ICP is aspirational" -- i.e., it reflects who we want to sell to and it's usually about companies (e.g., size, vertical), buyers/personas (e.g., VP of FP&A, VP of revops) and use-cases / problems to solve / jobs to be done.
It's aspirational because we're not sure. It reflects who we think we should be selling to. But in the early days we don't know.
Read 16 tweets
Feb 14, 2023
How much does it cost to grow from $20M to $100M. Some quick back-of-the-envelope math. A thread 🧵
Let's start with the CAC ratio, i.e., the S&M cost of acquiring a dollar of new ARR. Say your CAC ratio is 1.6.

kellblog.com/2013/12/01/the…
Growing from $20M to $100M means you need $80M in new ARR, so it takes 1.6 * 80 = $128M in S&M expense alone. SaaS companies buy customers. And that costs money. Real money.
Read 14 tweets
Jan 20, 2023
When I speak to one of an early-stage startup's first reference customers, I look for these things. A short 🧵
1. Are they actually using the product or are they a "fake customer" who's a friend of a board member or executive? Ideally, they're an arms-length customer who may have found the startup through a referral, but made an objective decision to use the product.
2. Are they well informed ? Do they understand the alternatives in the market, both direct and indirect (e.g., gluing two things together). If I were them, would I have bought it or is there a fairly obvious alternative solution? Are they, in fact, "accidental customers?"
Read 8 tweets

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