Where Did All Our Math Go? The Formula of B2B SaaS Product Development

Let’s talk about something we all desperately avoid: math. Remember elementary school, when we sat through hours of addition, subtraction, and some nightmare teacher yelling about fractions like they were the secret to life? Well, guess what, they kind of are. Because now we’re in the world of product development, specifically in B2B SaaS companies, and numbers are the key to making everything make sense.

But where did all that elementary school math go? Somewhere along the way, we started speaking in metaphors, buzzwords, and endless PowerPoints. We talk about “synergy” and “alignment” like we’re in some sort of corporate therapy session. What we actually need is math — good, solid, Fight Club math — to solve our problems. And yes, I’m talking about that Fight Club formula: X = A x B x C.

The Fight Club Formula

Before we dive into B2B SaaS examples, let’s break this down. In *Fight Club*, there’s a chilling calculation:

X = Ax B x C

Where:

• A is the total number of vehicles in the field.
• B is the probability of failure.
• C is the average cost of an out-of-court settlement for each failure.

If X(the cost of doing nothing) > is greater than the cost of fixing the problem, you do the fix. If not, you shrug and move on. A pretty dark way to run a company, but it’s mathematically efficient.

Now let’s apply that to product development in B2B SaaS companies, because shockingly, the same principle holds: decisions need numbers. Let’s go.

1. Discovery Phase: The Cost of Doing Nothing

Let’s say you’re developing a new feature for your B2B SaaS platform. It could be anything — let’s call it “Magic Data Sync” because marketing said it sounded cool. Now, instead of endless brainstorming, let’s use math.

Formula: X = A x B x C Where:

• A is your total addressable market (how many companies might use this feature).
• B is the probability they will adopt it if you build it.
• C is the revenue each customer brings if they use the feature.

Now, let’s say **A** (market size) is 10,000 companies, **B** (adoption rate) is 10%, and **C** (revenue per company) is $1,000.

X = 10,000 x 0.10.x 1,000 = 1,000,000

Congratulations, that’s a cool million dollars of potential revenue. But — here’s the kicker — compare that to the cost of building the feature. If it costs $500,000 to build, do it. But if it costs $2 million? Nah, put the brakes on. There’s no “synergy” that’s going to fix those numbers.

Example

Take a look at Slack. Before they added integrations, they could have done a similar analysis. How many teams wanted seamless integration with other tools? How likely were they to adopt it? What would the additional revenue be? Turns out, enough people wanted it to make integrations a key selling point and generate serious revenue.

2. Delivery Phase: The Cost of Bugs

Now, let’s say your team delivered the Magic Data Sync feature, but — surprise! — it’s riddled with bugs. Half of the users are going to experience failures. Instead of holding endless meetings on whether to fix it now or later, here’s the Fight Club math.

Formula: X = A x B x C Where:

• A is the number of users affected by the bug.
• B is the probability the bug will cause real damage.
• C is the cost of each customer lost due to the bug.

Let’s say A is 2,000 users, B is 50% (half of them are affected), and C is $500 per user in lost revenue.

X = 2,000 x 0.50 x 500 = 500,000

So, if the bug could potentially cost you $500,000, and the cost of fixing the bug now is $300,000, then you fix the bug. But if it’s going to cost $800,000 to fix, maybe you let some users stumble and do damage control after.

This is where math saves you from paralysis by analysis. Without a number like. X , you end up in endless loops of trying to guess whether it’s “worth it” to fix the issue now. You need the math to cut through the noise and let you make decisions.

Example:

Look at Salesforce — when they experienced a major outage that affected thousands of users, they had to decide between quickly patching the issue or rebuilding the backend to prevent future problems. Using their version of the Fight Club formula, they calculated how many users were affected, the probability that those users would churn, and the potential revenue loss. In this case, the cost of doing nothing was greater than the cost of fixing it. So, they did what any responsible company does — they fixed it, fast.

3. Cost of Not Innovating: The Opportunity Loss

Let’s take this formula and apply it to innovation — specifically, the cost of not innovating. This is where product teams get bogged down by fear: fear of failure, fear of losing users, fear of stepping outside the box. But what’s the cost of staying inside the box? Let’s find out.

Formula: X = A x B x C

Where:

• A is the number of customers you could gain by innovating.

• B is the probability of success (based on market research or competitor data).

• C is the potential revenue per new customer.

Let’s say A is 50,000 potential new customers, B is 10% (the likelihood that the new feature will resonate with them), and C is $1,000 per customer.

X = 50,000 \times 0.10 \times 1,000 = 5,000,000

So, there’s potentially $5 million in new revenue on the table if you innovate. Now compare that to the cost of not building the feature, which is essentially zero in terms of hard costs, but high in terms of missed opportunity. If innovation costs $1 million, you do it — because the potential gain far outweighs the risk.

Example:

Think of HubSpot before they integrated advanced AI features for their CRM system. At some point, their product team must have calculated the cost of not adopting AI-driven sales tools, especially as competitors like Salesforce and Pipedrive were leaning into AI. They weighed the potential revenue loss against the investment in AI, and eventually made the leap. The result? They stayed competitive and captured more of the market.

4. Maintenance and Upgrades: The Fight Club Formula for Long-Term Strategy

Product development doesn’t end with the release. You’ve got bugs to fix, features to upgrade, and new challenges to tackle. So how do you apply the Fight Club formula here?

Formula: X = A x B x C

Where:

• A is the number of users who will be affected by not maintaining or upgrading a feature.

• B is the probability that neglecting these upgrades will lead to churn or dissatisfaction.

• C is the cost of losing those customers.

Say A is 15,000 users, B is 20% (based on previous upgrade neglect), and C is $300 per user.

X = 15,000 x 0.20 x 300 = 900,000

If it’s going to cost you $900,000 in churn or lost revenue, but the upgrade only costs $500,000, then the math says upgrade the product. If the upgrade costs more than $900,000, maybe you wait and revisit it later.

Example:

Let’s look at Atlassian’s Jira software. Over the years, they’ve maintained a steady stream of updates, from better UX to new integrations, because they knew the cost of neglecting their loyal user base would lead to churn. Every time Jira releases an update, they’re doing this math: How many users need this? What’s the risk of them leaving if we don’t upgrade? And how much does it cost to keep them happy? This constant balancing act is the Fight Club formula in action.

The Takeaway: Make the Math Your Friend

Look, we all want to sound smart in meetings. We want to use buzzwords like “agile” and “disruption” like they’re magic spells that make products better. But in reality, the decisions that matter in product development boil down to math. How much will it cost to do this? How much will it cost not to do this? What’s the risk? What’s the reward? And that’s exactly where the Fight Club formula shines.

X = A x B x C. forces you to stop making decisions based on vague feelings and start making them based on cold, hard numbers.

Final Thought: Don’t Let the Math Get Lost

Next time you’re in a meeting debating whether to build a feature, fix a bug, or upgrade an existing product, don’t drown in jargon. Pull out the Fight Club formula. Crunch the numbers. Use math, because when you skip the math, you’re just guessing. And as much as we all love to guess, guessing doesn’t make millions. Math does.