A common position in which an early-stage consumer company might find itself is having reached some considerable but relatively small level of DAU solely through organic discovery. The product’s early traction is promising, and the company’s leadership must decide whether it should focus its efforts on product optimizations (eg. retention) or on growing the product’s user base through paid marketing. This decision is more challenging than it may seem: resources are usually constrained at early-stage companies, and selecting the wrong initiative can cost a company critical months of progress.
Facing this dilemma, many companies choose to focus on product optimization (which is sometimes called product growth, or, regrettably, growth hacking). Superficially, the logic behind this decision is sound: exposing users to an unfinished product presents a “leaky bucket,” and money is wasted in paying for marketing when product engagement hasn’t been optimally tuned. Assuming reliable product enhancements that yield improvements to retention over time, a given user is worth more when acquired in the future than if they are acquired now.
This situation can be paralyzing. Spending money on marketing to promote an immature product means that users are being exposed to a sub-optimal experience and will not retain or monetize to the extent that they otherwise might, given some additional amount of product optimization. At the same time: there is only one growth metric that matters, and at some point, a product must grow. There’s always another experiment that can be run in optimizing the product. By definition, for a product to grow, new people must be exposed to it.
And at some relatively low level of DAU, especially if that DAU has been exclusively accumulated through organic discovery, the product team is seeing a very limited sample of user behaviors. These people are fanatical early adopters: people for whom the product is so relevant that discovery is facilitated by active search. The ambition of most consumer products is to serve a very large total addressable market: if the product is specifically and relentlessly optimized for the small, highly qualified subset of that audience that discovered it early and organically, the product may ultimately fail to resonate with the larger portion of the total addressable market. Put another way: by investing its time exclusively into product optimization at low scale, the team risks hyper-serving a niche audience at the expense of the total addressable market.
Assuming the product retains well enough to deliver DAU growth through cohort compounding, the product team should oscillate between product optimization and deliberate (paid) marketing in order to ensure that broader audiences are being accommodated for in product updates as the user base grows. And most teams should preference scaling before they likely feel comfortable. It’s easy to retreat into endless product optimizations because iterative A/B testing provides comfortable, unambiguous guidance around what to do next. But as I detail in this article, A/B testing can actually kill product growth when outdated experiment outcomes are applied to new audiences.
I call this oscillation between paid marketing and product optimization the “growth sandwich”: growing the audience through broadened marketing reach, optimizing the product for that new composition of user profiles, and then repeating the process. This obviously has more relevance for early-stage products that have yet to reach an appreciable commercial scale, and certainly paid marketing can be pursued prematurely, which is why retention is such an important measure of readiness to scale.
And it is certainly possible to face the growth trap from the opposite direction: a company with a product experiencing enormous viral growth fails to recognize that low retention will erode its product’s cohorts in short order and that it will have overexposed the product to viable users in its immature state. The users that were exposed to the undeveloped product, before it had been optimized for retention in an avalanche of viral expansion, will be hard to recruit again once churned. Examples of products that suffered this fate abound.
In Monthly churn is a terrible metric, I wrote:
That’s a very vague clue to pursue. Growth models should be forward-looking on the basis of projected retention, not on top-level DAU changes that don’t consider the makeup of the user base over time. If the product team isn’t aware of what user retention profiles look like — broken out by geography, acquisition source, platform, and over time — then it can’t understand why slowdowns and accelerations of growth happen. And if growth isn’t understood, it can’t be managed.
Avoiding the growth trap requires understanding when product retention allows for the product to be scaled and its audience broadened with the purpose of resetting assumptions and testing retention again.