Jessica Toh co-founded Huckleberry Labs after many sleepless nights. It wasn’t worry about starting a company that kept her awake.
Rather, her baby son would not sleep through the night. She tried everything, from pediatricians’ advice to baby books, but nothing helped. A data scientist, she decided to get to the bottom of it herself, and began meticulously recording the conditions under which her son fell asleep and stayed asleep.
The result was an algorithm that can tell parents exactly when to put their baby to bed to optimize sleep time. The app is free to download; premium packages start at $5 a month — a much friendlier price than expensive sleep consultants.
Now, the Irvine, California-based company has raised a $12.5 million Series A round, led by Morningside Ventures, Spero Ventures and Tamarisc Ventures — bringing its total funding raised to $16 million. More than 1.2 million people have downloaded the app and the company has grown to 12 employees and a team of 20 pediatric advisors.
Times of E reporter Skyler Rossi interviewed Toh on a Zoom call about her journey building Huckleberry Labs and becoming the entrepreneur she is today. Entrepreneurs seeking funding should focus on the big picture when pitching to investors, Toh said.
While women tend to be honest about the obstacles, but you can assume that investors already know the likelihood you’ll fail. They want to hear you sell the best-case scenario. “You don’t necessarily win points for being cash efficient,” she said.
Interview has been edited for length and clarity.
Tell me about Huckleberry labs. What’s your elevator pitch?
Yeah, at Huckleberry labs, currently, we make a good night’s sleep accessible to every family. It started from personal experience. So I have three kids now, but my son woke up every two to three hours for 20 months, which is a kind of embarrassingly long time. But it was not for lack of trying things. I did everything like read multiple books, a pediatrician, nurse practitioner, everything and it just wasn’t happening. So with my background in the data side — I was at Berkeley, I did a triple major in electrical engineering, computer science, math and statistics and was working in software. So that was the lens and approach I took. And so I started tracking everything, maybe went a little overboard with it. But in the data, there was some interesting stuff coming out. What I also realized was the data told one part of the story, it was really when pairing that with those who had pediatric sleep expertise, that the magic really started to happen. We started to try this with other families. And notice how it was amazing what a transformation it made, to not not just a child’s sleep, but also to the parents. People, you know, said that it helped their marriage. I mean, it’s just wide ramifications.
The thing is, you know, working one on one with a sleep consultant is pretty expensive, and just realistically out of reach for most families. And so I wanted to create something that would be able to help as many families as possible. And so we’ve built a lot of things that are completely free. And the things that are not free, are very affordable, starting at like $5 a month really. And, now fast forward to today, we have over 1.2 million families on the platform 4.9 out of 5 stars on the App Store. And just people saying things like, ‘life changing,’ ‘it was like black magic,’ ‘like voodoo.’ And I think what they really mean by those terms is that they don’t necessarily know or care how it works. They know that there’s a lot going on behind the scenes, a lot of data science going on behind the scenes, but it just works and their child’s now sleeping really well.
And so, with this Series A fund raise, we’d like to, of course, continue to develop the sleep solutions, but also to expand out into other common challenges for parents. Because when you become a parent, I mean, there’s no school, you just are thrown into it, and you’re supposed to figure it out somehow. And another thing that we’re trying to counteract is there’s research, especially women, actually, feel like they’re supposed to have all the answers and do it all. Who says? Your instincts, they can keep your child from getting eaten by a tiger or to know something might be happening, going on. But it doesn’t necessarily tell you what to then do, what exact steps to take based off of your instinct that something might be wrong,
Kind of going back a little bit. Can you walk me through how the product works? What did you find in the data that was so interesting, and maybe talk a little bit about what you found along the way and how it’s made the product now?
So how it works is there’s an app. Most families actually start using it when their child is born first as a baby tracker, just to remember when you last fed your child, when you last changed a diaper, because the beginning months are such a haze, and you just like need a second brain to help remember all these things.
But then starting from two months, we have something that kicks in called Sweet Spot. And this will algorithmically predict this window of opportunity when your child will be tired, but not overtired. And it gets customized to your child. And it also adjusts based on how your child actually slept so far that day. But then in addition to that, we also provide these full custom sleep analysis and plans for people. And so this part mimics working with one on one consultants so it’ll ask you questions and then based off of that, then our combination of our AI plus our human experts go through each situation and then provide a fully customized analysis and plan for people.
I was just going to ask, how does the app learn from each individual child?
We want to be able to provide guidance immediately. So first, it just will start off knowing the averages for like, ‘oh, that’s the first nap of the day for a four month old, so this is probably what you’re going to need.’ But then it can tell that compared to your child’s actual data. Then if it’s longer or shorter, and then can adjust based on that.
And that was something that I saw with my son. So when he was awake, longer than a certain cutoff, like clockwork he would wake up at night. But if I got him to sleep before then, and he would sleep through. So it was one of these things where it’s like, even if I was out, I wasn’t home in time to get him down before that cut off. Even then it was still very reassuring, because then when the awakening happened, it was more like, ‘oh, there it is,’ versus ‘Oh no.’ And so it’s just a different kind of mindset shift when you know what to expect. And then you see it happen versus the not knowing.
I think like in many things, often than not, not knowing is worse than the actual thing happening. It’s kind of like when you get an Uber or Lyft, you can see how far away your driver is and then you can plan accordingly. Even though the drivers’ gonna come when they’re gonna come like, ‘Oh, they’re 10 minutes away, okay, I can go use the bathroom, I don’t need to be a Hawkeye and see if every car coming by is my driver.’ And so kind of similarly, with Sweet Spot. It’s like, ‘oh, okay, the next time is gonna be in 15 minutes.’ You’re like, ‘oh, okay, now I know, I can start getting ready.’ And so it both improves sleep, but then also just makes things easier for the parent or caregiver to plan the day, as well.
So tell me more about your experience building the company. What did that process look like for you?
So I always knew from a young age that I wanted to start something. Even as a child, I would play business owner and have my Lego briefcase and go around selling things. Even in high school, this one summer I did this entrepreneurship program. So I always knew that I would do something one day. But until Huckleberry it was never the thing that I felt like I could risk it all for. I could have literally sat and done nothing and maybe come out ahead, but you just know, with this certain idea that you have to give it a try. And you would really regret not giving it a try. And that’s what it was with Huckleberry for me. And I felt like it was just the combination and culmination of the academic, professional and personal experiences as well.
There were definitely different twists and turns. Actually, when we first started we were working on a hardware device, thinking that was the answer. And it actually worked very well. But then we noticed that parents actually had no problem logging sleep. And then also, our mission was very much to help as many families as possible. And if it was behind having to buy a device that was hundreds of dollars, it wasn’t going to help as many families as possible. So we put that on the backburner.
What was the process for developing the product? How many years did it take and what were the steps along the way?
Yeah, it was definitely gradual, and it wasn’t the kind of thing where I knew immediately from day one what it was. There was a lot of iteration and learning along the way. And so yet people always asked like, especially those starting out, like how did you get your first 100 customers and things. And so we had it completely free. And then a friend in Toronto posted it on her Facebook Toronto moms group. And then it just spread from there. And then people in that Facebook mom group were part of other groups, and they posted it in those other groups. And so fortunately, we had something that people found really helpful and valuable. And so the word of mouth was really great. And so that’s how we snowballed from there. Even today, most of our users come through word of mouth.
That’s cool. So then the app’s free. So where does most of your revenue come from?.
So it’s premium. We have over 1.2 million families on the platform. And then we have a lot that is available for free and we’ll always keep it that way. There are memberships that people can subscribe to.
So how much revenue do you typically bring in annually?
Yeah, we don’t publish that now as a private company, but we did grow 4x in revenue over the last year.
So you just raised the $12.5 million round Series A. What was your experience raising funding? Was it pretty straightforward? Or what challenges did you run?
I will say that raising this series A was actually easier than raising our seed round, which I think is not necessarily always the case. But just because at the seed round, it’s still early, you know. For the stage, we still had really great metrics. But there was still more proof points to be made at this point. We continue to have really great metrics. And so the interest here was preemptive. We were actually planning to raise in January, or to start the fundraising process in January. But we knew this firm, and they knew us and the investor there, became a Huckleberry user himself with his new baby. And he was like, ‘You guys, this is amazing.’ And actually, Spero, who led our seed round, she also was a Huckleberry user as well. So that’s always nice when your investors are also your users. So they know what’s going on.
Absolutely. And I wanted to ask you, we know that women, especially diverse women, struggle to raise venture capital, I think the number is like less than 3% of venture capital went to women last year. I’m curious, what’s worked for you to secure funding? What is your advice for women who are building their companies and looking to raise venture capital?
Yeah, maybe, especially for women, you know, stereotypically, don’t shy away from the big picture. Also, I will say in fundraising, you don’t necessarily win points for having been really cash efficient. It’s more about the big picture, and the speed. So, I think for myself, and many women, and obviously, men too, you want to do what you say, which is a good thing, obviously. But I think investors just assume that they need to discount a little bit of whatever you’re going to actually say. So if you’re not giving the confident projection, you’re best case scenario, then they’re assuming that you are giving the best case scenario, and then they’re already discounting it even more.
Then outside of raising funding, what’s your advice for entrepreneurs in the medtech space who are building their company?
Yeah, don’t just look around at what everyone else is doing. And always get back to the first principles of why somebody thinks they need something — see if there’s actually something else that they really need. So yeah, I mean, I just see a lot of people rehashing stuff that’s already out there. It’s like, everybody got the memo to do X, and then everybody comes out with the same thing. And I don’t think that’s the best way to stand out.
What do you suggest people do to find inspiration, to innovate a brand new idea? Do you have any advice there?
Yeah, I think it’s to keep asking deeper levels of why. I think there’s like six levels of why to ask. Even for ourselves, we have to catch ourselves. You know, when we talk to users, and we uncover some kind of need, and it can be easy to jump to, like, ‘oh, they’re asking for this or this is happening, so we need to do this.’ When you really have to ask the layers deep like, ‘okay, but are they asking for that because they actually have this, or is this other thing happening?’ And so we’re going to just assume everyone needs them. So it’s like, it seems like everybody’s coming out with their own community group at the same time. It’s like everyone said, ‘Oh, community is really important.’ So everybody’s like, ‘Okay, we’re gonna slap on this forums-style group with our company.’ But Facebook already has that, so I don’t know if it’s necessarily the thing that’s going to suddenly set you apart. So if you’re trying to ask many layers deep like, what are the important parts? Why is it not working? What is it actually we’re going to help and be different? And to take out the components and break it down versus assuming that they said they want this, so we’re gonna make it.
Is there anything else I didn’t ask that you think is important or anything else you want to add?
I mean, just what’s very important for us is the accessibility and making something that is currently just something that people with the means have access to that we’re trying to make possible for every family and also something that uses data to make it effective and tailored to each family.
Well, thank you so much. It was so nice to meet you.
Great meeting you, Skyler.