Quick note: This post is a bit different than what we typically write on our blogs. We’re trying to be more “actionably transparent” by providing more context and insight into what we’re seeing and experiencing rather than just pure data nerd posts. If you like this, leave a comment. That’s how we’ll know to keep writing both types of posts. :)
This is probably going to sound incredibly sad to some of you, but: I’m in love with SaaS. We’re not getting married or anything, but there’s something elegant about subscription pricing and continuous deployment that keeps me excited every day and the nerd cogs turning.
That’s why I love my job, because through building the revenue and pricing engines that hold some of the biggest and best SaaS companies together (New Relic, Bigcommerce, Wistia, etc.), we get a front row seat to the cutting edge (and the unfortunate missed opportunities). In fact, at this point I’ve personally been in 147 different SaaS board rooms listening like a fly on the wall to the arguments and reactions to data, as well as arguing with execs on why a particular direction is good or bad for their company overall. Other than a few VCs, I think the PI team and I have seen more than anyone else.
In that spirit though, it’s time to start revealing all the secrets and trends we’re seeing to better help the SaaS community grow and prosper. Here are three big things we’re seeing that will hopefully get you ahead of the curve.
We don’t intimately know our businesses and buyer personas enough
The absolute beauty of SaaS is that it’s a giant, repeatable formula. If you walk into any SaaS company and know yourCACby channel, yourLTV, and your expansion plans, you can essentially predict your future.
To do this though, you need an absolute, cold understanding of your buyers and your business. Yet, I bet the entire Price Intelligently company that if you were to go ask your CMO or CEO right now who the formal buyer personas were for your product, you’d either get a high level, “uhhh marketers”, or you wouldn’t get much of an answer at all.
Not having quantified buyer personas for your business is like going to the bank, taking out your runway, and setting it on fire. Best case scenario you’re growing through brute force marketing and sales dollars, which will bite you when the money starts to dry up. Worst case scenario, you’re banging your head against the wall as to why the business isn’t working. No matter the path, you’re setting yourself up for failure.
“Not having quantified buyer personas is like taking your runway and setting it on fire."
This is because of the SaaS formula. Buyer personas act as a fulcrum for your entire product and marketing funnels. For instance, if you know that Startup Susie cares about API integrations and frequents major tech publications, then you can align your product roadmap appropriately and make sure you’re spending your marketing budget wisely. Plus, the positive externality is you settle any internal arguments or at least make them more productive since everyone isn’t politicking or talking from their opinions about direction.
The bottom Line:Quantify your buyer personas. Don’t just give them cute names and fun pictures, but actually get into the nitty, gritty of who they are, what they value, what they don’t value, and what their price sensitivity looks like.
We’re uncomfortable with data at its most basic level and use it politically
Data is beautiful, because for the most part, data is impassionate. Data doesn’t care who’s right and who’s wrong. Data just reports and answers. Yet, as a whole, we don’t understand data at it’s most basic level, and worse - we use it politically way too much.
Whenever you find yourself asking or hear someone ask, “well how many respondents or users did we get for that finding”, stop and realize this isn’t how statistical significance is measured. You’d be surprised how many times I’ve had to explain why 50 respondents to a survey is actually super significant and why 500 respondents is actually very unstable. This is because you should be constantly evaluating the margin of error of a study, which points to the variability that the sample will have relative to the population. The lower the MoE, the better.
You need to bone up on your stat knowledge, even if you’re in a part of the business that doesn’t deal with statistics and data that often, because if you don’t you’re not being a good partner. Data will constantly be used to justify decisions, and we find that without the right checks and balances, people will use data selectively for their own gains.
That sounds nefarious, but what we’ve found is that a lot of execs will use data that supports their argument, defending it to the grave, or attack the statistical methodology of the data that doesn’t support their argument. This is the opposite of data driven anything.
Six months ago, this sentence literally came out of a VP of Product’s mouth: “Well the survey was a bit off brand, so I don’t think this data is credible”. The results included 80% of their customer population, which means it’s as if we didn’t even have to extrapolate. The worst part of this story: No one in the room of executives had a statistical background enough to challenge him.
The bottom line:Create a truly data driven culture by making sure everyone’s aware of what constitutes good and bad data. You’ll rarely have perfect data, but if enough team members understand how statistics work, hopefully you’ll be able to at least scrutinize results appropriately and not get bullied around in different conversations.
We’re eating the world with capital efficiency and product focus
Not everything is doom and gloom in SaaS. We're absolutely amazed by where software is ending up in our world and how efficient we’re getting compared to stagnant on-premise incumbents or inaction. This is because the best SaaS companies out there are incredibly capital efficient and exceptionally nimble in how product is produced.
If you want to learn how to build a SaaS business, venture backed or otherwise, don’t look at giant SaaS companies that have raised millions of dollars. As mentioned above, a lot of those companies grow through pumping sales and marketing dollars into the system. Instead, look to the bootstrapped success stories. The little cluster I like to follow is comprised of
The two biggest factors that we’ve noticed that make them so capital efficient are: 1. they aren’t afraid to change directions extremely quickly, and similarly 2. everything is a test that’s evaluated constantly.
“Great SaaS companies check the ego at the door and constantly re-evaluate"
For instance, if you ask any of their leadership, “why did you do X”, you’ll always receive an answer along the lines of, “well we noticed our high end buyers were doing X, so we wanted to test Y.” There are no politics. No ego. Just beautiful, data driven SaaS.
What’s great is we’re seeing these two factors pop up more and more in the fastest growing companies we’ve worked with. Even if they don’t have quantified buyer personas or understand statistics, the success stories are constantly re-evaluating everything, testing, and re-testing.
The bottom line: Have a framework for dispassionately running your business in a capital efficient manner. Evaluate, test, re-evaluate, and test again.
Cloud rules everything around me (C.R.E.A.M.)
As SaaS continues to march as the fastest growing vertical in the history of mankind, there are plenty more lessons to share. We’ll put together more of those lessons for everyone as we continue to march against our own goals, but perhaps the biggest piece of advice we can give is this: Never do things “just because.” Justifying and being data driven ensures your SaaS machine keeps cooking and is constantly putting out more dollars than you’re putting in.
By Patrick Campbell
Founder & CEO of ProfitWell, the software for helping subscription companies with their monetization and retention strategies, as well as providing free turnkey subscription financial metrics for over 20,000 companies. Prior to ProfitWell Patrick led Strategic Initiatives for Boston-based Gemvara and was an Economist at Google and the US Intelligence community.