By Scott Dodson
Every once in a while, a new product emerges and hits the market like a thunderclap — when it’s clear a new tool or service has solved a problem better than anything ever has, when your company is able to deliver a user experience 10X better than anything else out there. As a consumer, I had that this experience the first time I used Google, Skype, Dropbox, Uber... Wow. Game changing.
I’ve never worked for a company with this kind of lightning-in-a-bottle PMF (product-market fit). But I’ve worked with, mentored, and been mentored by many people who have. By all accounts, when they had profound PMF, growth wasn’t something they had to drive, it was something they had to manage.
The challenges during the peak times of exponential growth were keeping the servers up, hiring as fast as possible, and firefighting every day—often with little time to work on processes or long-term strategy. Marketing efforts frequently made no discernible difference—just noise against the undeniable signal of market demand. The companies that experienced this, endured, and thrived figured out how to manage their growth and sustain it—not at the same exponential rates, of course, but ideally revisiting those rates periodically with savvy strategic initiatives, innovations, and acquisitions.
Grinding your way to success
And then there are the grinders—companies which succeeded despite that initial, undeniable, lightning- in-a-bottle product-market fit. They started with maybe a 1X-2X better UX, but offered a compelling set of tradeoffs or served a niche audience, and built themselves up brick by incremental brick. They usually go to market early, learn from customers, refine and expand their value propositions, make smart bets and sometimes even grind their way into periods of accelerated or exponential growth.
I offer two examples of hugely successful companies that fit this profile: Amazon and Starbucks.
These companies may look like they had the same kind of initial home-run success—and to be sure, they have had some impressive traction—but neither of them had the initial, explosive, PMF enjoyed by Google, Skype, Dropbox, Uber, etc. Especially by the degree to which they initially solved the customer problem or delivered a better UX.
When I moved to Seattle in 1991, Starbucks had 100 stores worldwide. Today, they have over 30,000. I went to Starbucks periodically. It was OK. It offered a nice set of tradeoffs from other coffee shops at the time—consistency, comfort, and friendly service, if a bit overpriced and pretentious. Maybe a 50% better experience, at most.
It wasn’t until years later, when Starbucks had really refined its customer experience, provided WiFi, expanded and diversified its offerings with hot food and iced tea, built a fabulous loyalty program etc., that the “Third Place” concept—the coffee shop as a place away from home and office for people to gather—really resonated with me. Over time, as a founder of three different startups in Seattle, Starbucks became the far superior space for investor, partner, and early (pre-office) founder meetings. I became such a regular that I found myself on a first name basis with more than a dozen Starbucks baristas around town and could barely have operated without the existence of Starbucks.
I’d lived in Seattle nearly three years when Amazon was founded. My wife and I used Amazon in the 1990s to buy books. Occasionally. It was OK, but nowhere near 10X better. We preferred Elliot Bay Books and The University Book Store for selection, recommendations, and shopping experience; Half Price books for value; our local Barnes and Noble for convenience. As Amazon carried more and more products, we used it more, but it wasn’t until 2-day/next-day/same-day Prime that it became indispensable—to the point that we keep two active Prime accounts—one in the U.S. and one in the U.K. despite the fact that we now live in Estonia, where Amazon doesn’t even operate, and Prime shipping doesn’t work.
What looks like an overnight success, rarely is. Some of us have seen the stars align, the rest of us have to grind.
This has been the story at Lingvist. I’ve learned to love to grind. It demands rigor. It forces data driven decisions. And it’s rich with learnings, many of which can be applied to your next project.
The talk I’ll give in February at MGS20 in San Francisco is for the grinders. If you’re having to manage the growth of your company, you might still find it interesting, and I expect you’ll hear a nugget or two about how to improve your ROAS, but it’s designed to address the problems of the companies on the cusp, those trying to make paid acquisition work at scale, or work for the first time.
At Lingvist, we have a systematic framework* for growth which has three core principles:
- Loop or die. Growth comes from a system where the outputs of your activities can be (or are automatically or programmatically) reinvested in your inputs.
- Word of mouth is the fundamental growth loop for many products. Someone uses the product, gets value, tells others. Some of them use the product, get value, tell others.
- Paid acquisition is a growth loop: people pay, you invest that revenue in ads, attract more users, some of them pay, and you reinvest that revenue in ads. This is always true when your ROAS is greater than one. It is also true when your LTV/CAC is greater than one, but your loop might not spin quickly if your LTV is spread out over time. But generally, the greater the value of your LTV/CAC, and the quicker the payback period, the faster this loop spins.
- Users generated content (UGC) which attracts others (on Facebook, Pinterest, Quora, etc.) by enriching the core value proposition or improving search discovery (UGC/SEO), is a loop.
- Getting a great (single) earned media article or an app store feature is not inherently a growth loop. Unless you’re in a space where people seeing that article are inspired to create articles of their own, or you have a system in place which inspires or incentivizes this (or some other output that can be reinvested), then these are one-off (beneficial), linear events.
- Loops work together and the companies with the strongest network of compounding loops win, over time.
- A paid user acquisition loop positively impacts (increases the throughput) of a Word of Mouth loop (which can lead to earned media, etc.). Combined with additional loops, they might allow a level of scale where your brand recognition starts to improve conversion, earned media, etc.
- Find the loops that fit your company based on its specific product/service and stage
- Every company has a different core loop—viral; paid; UGC/SEO, etc. at each stage of its evolution
- Your loops need to fit with your place on the LTV & CAC spectrum and your specific value proposition (i.e., a Paid UA loop makes no sense for Facebook, a UGC/SEO makes little sense for Uber).
Starting with these principals, Lingvist has grinded into effective paid acquisition. Over time we:
- Recognized where we sat on the LTV/CAC spectrum (moderate to low CAC and LTV) these lines are really the ones that connect to the LTV/CAC spectrum image below
- Identified what would need to be our core loop post monetization (paid UA)
- Identified our core pre-monetization growth loop (word of mouth) and figured out how to measure it (critical for understanding the relationship between ROAS and LTV/CAC)
- Put attribution in place, understood our user economics, organized the growth team into functions for paid UA, organics, user journey, product features, and feedback/support, and
- Proceeded to grind
When I became Lingvist’s CGO, it was early. We were pre-monetization, with under 500K (all time) registrations, some sporadic organic growth (driven by earned media, and word of mouth)—nothing
resembling a “hockey stick.” The first order of business was to define what growth meant to us. Registrations? MAU? DAU? Revenue?
Finding Your Loop
Our DNA meant (and our user data told us) that we were not Duolingo. Our value came not from making language learning easy or fun for large numbers of people—many of whom would not have been learning a language otherwise. It was actually close to the opposite: making it possible to learn much faster, even if that made it more challenging.
Our learners were often intermediate or highly motivated beginners—with a powerful reason to learn: for business, expats living abroad, students, people for whom language learning (often English) would mean improving their financial situation, or people wanting to connect with family members or their heritage. Our audience was narrower than Duolingo’s. We would not be able to match their registrations, MAU, or DAU, nor their viral coefficient. But our learners were motivated, hungry—especially for the efficiency we offered. The hypothesis was that they would be willing to pay for the value we created.
We had some inherent virality. Even before we monetized, we did paid UA to have users with whom we could train our learning algorithm and test product features. We would see an uptick in the registrations whenever and wherever we bought. We analyzed the data and came up with what we term our “organic halo”—the additional registrations which come from our word of mouth growth loop. Knowing the value of this halo has been critical in our hunt for positive ROAS because we know that, for us, a measured ROAS of say, 0.8 means we have a “true” ROAS over 1.0. We went out with a Freemium monetization model initially because the initial goal was not to reach positive ROI on acquisition spending—we were still in the mode of acquiring learners as cheaply as possible to improve our product. Nonetheless, we couldn’t help but hope that we’d get lucky when we began monetizing in early 2018.
ROAS for most of the year hovered under 0.2. Our best campaign in October 2018 had a ROAS of 0.16. Coincidentally, this was also the point where we got serious about paid UA as the core loop we needed to make work and began to hunt for positive return on acquisition spend.
We developed an in-house creative team and the beginnings of a process, bringing a designer onto the growth team for the first time, followed by a copywriter (shared with Product). And with better creative, the needle started to move. Three months later we were at 0.5.
The talk I’ll give at MGS20 starts here and covers where we’ve gone from there. There have been three specific areas that have delivered the biggest results. I’ll go into detail on these three areas of focus:
- Activation: Bringing someone from the point where they try your product though the Ah-Ha moment—the point where they discover/experience its value for the first time—to the point where they repeat that experience enough for it to catch; for it to form a habit. (This is more than onboarding optimization. I’ll share our data-driven, quantitative, and qualitative process.)
- Creative and developing a systematic creative process: This one has been a deep rabbit hole for us, with a few different stages.
- Business model and monetization optimizations: Being willing to reexamine our needs and assumptions and install a completely different monetization model if necessary, not to mention experimenting with pricing, trials, packages, etc.
None of these things would have made enough of a difference on their own, but in combination we’ve been able to see results. Plenty of grinding remains to be done.
*There have been many sources of knowledge and inspiration that have helped me be effective and arrive at our processes. Books, websites, of course, but I’d call out one mentor and one program in particular. The mentor, Eric Seufert (mobiledevmemo.com) and the program (no affiliation with Eric) is Reforge from Brian Balfor, Andy Chen & many others). Those of you who have done Reforge can see that I’ve adopted much of their framework, as it resonated strongly with my experiences in product growth. If you haven’t checked it out, I highly recommend it. I receive no compensation whatsoever from Reforge or Eric except the satisfaction of being part of their Word of Mouth growth loops. 😉
About Scott Dodson
In addition to being CGO at Lingvist, Scott is a serial entrepreneur and mentor in entertainment, games, fin tech, and ed tech. He has a passion for applied AI, and is an expert at motivational design/gamification, engagement, and monetization.