How do you go from zero to 12 million users in less than four years?
PLG, that’s how.
Covering lessons and failures to avoid at each stage of the growth journey, Jessica discussed what she’s learned from driving growth at different organizations and how she has applied these learnings at Linktree to fuel rapid, sustainable growth.
Of course, the PLA community were keen to learn how they can apply Jessica's methodology at their own orgs and the Q&A popped off! In case you missed it, we wanted to share some of the best questions and answers.
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Now let’s get to those Q’s...
Q: How many failures have you faced during your time at Linktree?
A: I think when you're scaling as fast as a company like us, you have learnings and failures almost every day and every week. And I think it's creating an environment for your team to feel comfortable with that, so that they can run fast and test and measure and experiment with things and get things to market really quickly. Versus planning, iterating, designing and spending a lot of time upfront.
That may mean you actually miss the market timing on particular things. So I'd say every day, there's lots of different things that happen in and around a tech product, which I'm sure everyone is familiar with.
Q: What are some metrics you measure for activation at Linktree?
A: We look at essentially churn and what do we need people to do in order to reduce churn. So you bring that kind of metric earlier in their customer lifecycle, so they don't actually churn. And what we've observed is the tree being added to profiles, and we see link trimming added now in much more than one place, as our average is generally about four.
And outside of that if someone adds an avatar, which is a profile picture, they are also far less likely to leave us so they're the two things that we nudge people to do it across all of our activation funnels.
Q: What if when hiring, someone internally is saying yes, but you're not interested?
A: We actually have a scale with which we measure and remove some of the bias of that process. We have a scale based on different attributes that we look for, including company values and culture.
And if we get a percentage metric across all the people that interview, and if there's a lack of balance in it, we then generally add another person to the interview process to confirm or validate if it’s a yes.
Q: How do you go about currently attributing success, especially once more and more features come in?
A: This is definitely a learning experience, and I'd say be ready to not always get it right. I think the key thing that we look at is being able to understand what the data means for decision making. So it's easy to have so much that you’re unable to get through the weeds of understanding which metrics are important.
I think it comes back to that Northstar and not necessarily having one, but looking at what behaviours you're seeing in your customers that are driving your growth and driving your revenue. Working that back to an earlier point so that you're able to speed up that behaviour that you're seeing in the success measures.
And I think it comes through in the flywheel. So for us, if a linktree is added to a profile, then it's highly likely that someone will sign up from that, and we have a huge volume of our referrals come from existing linktrees. So we saw that quite early on. And it means that the more that we see that speed and volume increasing, that's how we measure success. Look at your metric set and find the thing, the attribute or the behaviour, that's driving the growth.
Q: What type of experiments helped you identify your flywheel successfully?
A: I think because we were the market leader and were first to market, it was very clear that the problem to solve was ours to own and it was that you couldn't have multiple links in your Instagram at that stage. And so we tested and learned in music to begin with, and really got an understanding of what people wanted and needed there and the problems that were being solved in and across that space.
Then through doing that, and really focusing on simplicity, which is a core ethos of our product values, it then naturally translated to both global representation of the problem as well as different verticals.
So I think approaching the experiments with a global mindset, and thinking about the problem as not just being one vertical specific. It enhances your view and perspective of the success of that flywheel and how big it can be.
Q: How did you identify the behaviours? Was it using Heap? Or do you have a bi team to achieve this understanding?
A: Definitely started with Heap. And I think our growth team dug in a lot. And Alex, who's our founder, has a huge passion for data. So he spent a lot of time in Heap early on, which was right before I started at Linktree.
Then the growth team took it on as we built it out and identified some of these behaviours by looking at churn backwards and finding the behaviour that's indicating someone will be sticky. Then nudging that behaviour earlier in the customer lifecycle. But we do have a bi team now, which is great. So getting to that 12 million plus and having people to support that is wonderful.
Q: How can you identify and be data driven if you don't have data to lean on?
A: If you don't have data at all, I think you should find a way to make sure that you have some form of data. But speaking to customers directly at that early stage is essential. Another example outside of Linktree is the way that Bumble grew really quickly and was finding the right kind of people to get it in the hands of early, so that the word of mouth spread literally. And then organically it spread virally through text.
So I'd say spend time speaking to people. And it's also important not to load your own bias in and get that kind of validation that you're looking for from the product at that stage. When we talk about building the right thing, you need to make sure you remove your ego from the product. Sometimes what happens is you get that confirmation bias from talking to people, because that's what you're looking for. So I'd encourage you to maybe bring in someone that has experience in customer testing at that stage, so that you're able to draw out some of those insights.
Fortunately, many of our early team members were very passionate in that space. So they spent time speaking directly in customer support and listening to those problems. Also, in my previous experience in finance, you can go from zero to 100 really quickly. So it's very clear when there’s pain points and we learnt from that and built out things to support it.
Q: Growth Product Management is new and emerging. How would you recommend getting into it? Resources, guides, tools etc?
A: I think the product-led team does an awesome job of driving a lot of this learning. And it's great that there's a whole growth stream here. Outside of that there's some pretty awesome books and hacking growth is a good community to be involved in. But I think any research relating to the flywheel is a good one to do a deep dive in.
I think another thing to think about is growth and where it sits in the business. So what we've done is put growth squarely between product and brand. And what that means is the flow of information is pretty seamless between engineers that are so close to the product, all the way through into how we build the greater market. Ensuring that articulates the problems that are being solved when features are released. And then all the way through to brand, so what's the message that we're conveying? And how is it really clear to users that are not part of Linktree yet?
Q: If you have less data science analytics capability, who would hire first? A PM with deep analytic skills, a data scientist or a data engineer?
A: I'm a growth person. So I would say higher growth person! I do actually think there’s value in bringing someone in that has a growth background, who will be able to fuel this flywheel and also speak to your product experience.
I think in a company's early stage, and generally, if you're product-led, the founder is so close to the product and the product vision that you need someone to round out that skill set. And if they don't have the analytics experience, then hire someone that can work with them to build out that customer funnel and the customer engine early on.
Maybe not necessarily a data scientist or a data engineer, unless you have data in the products then that's valuable. But a growth person that's able to think from the insights lead perspective because often that kind of blends across, where you're able to draw those insights out versus just looking at the data and structuring it in the right way.