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If you’ve been with us for a while, you might notice how different Slidebean is today compared to what it was a year ago. Probably the most noticeable change is a big user interface revamp, but the truth of the matter is that our startup is a completely different business compared to what it used to be. And our numbers are proof.
During our time at 500 Startups we went from an average transaction size of $5 to $159, that’s 30x!! By changing and re-focusing our target audience, we found a new set of users that was willing to pay a premium price for our product. But more on that later.
Related read: What is the right accelerator for your startup’s stage?
The first problem we had before we came into 500 was that we didn’t even know we had a problem. As everyone, we wanted our company to grow faster… but we were focusing on the wrong metrics to do so.
After we launched the platform in July 2014, our absolute #1 focus was to increase our active user base. We had a lot of organic growth (sign ups coming from word of mouth or other user’s presentations) and on paid advertising, we were able to sign up new users for about $1!
We figured that we were able to convert about 5% of our active users into paying customers; our pricing back then was around $5.99/mo.
1,000 Sign Ups >400 Active Users > 20 Paying Customers > $120 MRR
Since most of those signups were coming in organically, we didn’t have to spend much to bring them in. As long as we could keep the organic engine growing, we would do fine, right?
FUCK NO! We completely overlooked our churn, and that nearly killed us. You see, our churn rate then was around 25-30%, which means that an average user would stick around for 4 to 5 months. The revenue we got from an average customer (Customer Lifetime Value) was $22!
This meant that in order to scale the company, we had to spend less than $7 to acquire each paying user ($22/3 = 7, since the LTV should always be 3x the Customer Acquisition Cost).
Welcome to the real world. It’s nearly impossible to sign a paying user for less than $7. The cost for a single click on Google Search Ads is usually over $2, and getting one paying customer per 4 clicks is absurdly impossible.
There was no business model and no product market fit, but the revenue growth we were getting from organic channels made us feel like we were doing something right.
Putting the right price on a product is REALLY hard, especially if you take the wrong variables into account.
We had priced Slidebean at $5.99/mo because we wanted to be cheaper than most of our competitors, and because we wanted to be affordable enough for students. We even started a $25/yr academic deal which meant less than $2/mo.
The team at 500 Startups and particularly our POC Poornima Vijayashanker brought this issue up and pushed us to try to charge A LOT more for our product. The team and our Board agreed that this was a very bad idea because it was going to scare our customers away, so we pushed back, hard!
After a month of sticking to our model and seeing no way to finding sustainable growth, we agreed to A/B test our current pricing versus a different, higher alternative. To be totally honest, I was positive our old model was going to win and I wanted to shove it in their face with real data. Oh boy was I wrong!
So we built the following A/B/C/D pricing alternatives and directed users to the different pages through Optimizely:
As you can see, the transaction difference between pricing A monthly and pricing D yearly was over 30x. So we sat down and waited. About a couple weeks later, the answer spat in our faces:
Even if we subscribed a few less users in Pricing D, we were making 18x more money on the first transaction and almost 2x MRR. If the original LTV was $22, our new LTV was at least $159 and $348 if they renewed next year.
You could argue that the sample was small and perhaps not statistically correct, but it was immediately obvious that the higher pricing wasn’t a barrier: people were willing to pay $159 for our product!!!
If we made $159 on a single account, we could increase our marketing budget and spend up to $55 to acquire a single user, while still maintaining the golden LTV > 3xCAC formula.
We also started looking closely at the type of users that we were subscribing. Our previous audience consisted more of students or single users within larger companies, that would choose to pay the tool to impress their bosses. In both cases, they churned out quickly.
With the new pricing, almost every user we attracted was coming from small businesses or from another startup. It was obvious really, a student is not going to want to pay $159 up front for a tool, but it's not a steep price for a business, especially if they need to build presentations regularly.
The reason we originally focused on a B2C model was because it was the path that other competitors have followed, and it seemed to be successful for them. However, going B2C with a SaaS product requires close to viral growth in order to keep sustainable unit economics, and the competitive landscape makes that very hard at this point.
The team had little experience in B2B and this was the main reason why we stayed away from this model at first: we wanted to raise money first to attract some talent to be able to close B2B deals.
It turned out we had to become a B2B company earlier than we thought and figure out a way to do it ourselves. We started targeting small businesses and startups because we figured we could onboard them automatically, and Intercom was a key variable in the success of this plan .
Related read: 5 startup tools that have been key to our success
Completely refocusing a company was not easy, but having some proof about the potential success of the new focus was definitely easier. It taught us, once again, that the answer was in the data.
Another big influence in making this decision was this article by Josh Pigford from Baremetrics. Quoting him...
“Would you rather support 1,000 customers paying $3/mo or 30 customers paying $100/mo?
— Josh Pigford, Baremetrics
Reducing churn is key to increasing the LTV of your customers, but an often overlooked multiplier is the ARPU. In many cases, changes in pricing have little to no effect on the % of users that covert. The real trick is gathering enough data of these variables to decide which plan works best.
Finding that balance between conversion rate and churn is truly the sweet spot of any subscription business. Don't underestimate your product! Your price loses relevance to your customers if your product solves a real need for them.
After a few months of running with yearly-plans only, we decided to bring our monthly alternative back on a new $29/mo pricing. It turned out that the new users we were targeting would actually stick for much longer, so our average plan has about 6.5% churn! Once again:
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This is a functional model you can use to create your own formulas and project your potential business growth. Instructions on how to use it are on the front page.