The Stacked S-Curve Playbook for Sustained Growth with Tatiana Birgisson

Stacked S-curves describe how companies sustain growth by developing the next product or channel before the current one matures. Tatiana Birgisson explains that an initial growth engine will eventually level off, so teams need another one reaching product-market fit and accelerating before that slowdown becomes a business problem. The same principle applies to marketing: adding and improving channels over time can extend a growth curve or make it steeper. Rather than relying on one durable tactic, the goal is to build several engines at different stages of development, each capable of contributing as another reaches diminishing returns.

First, confirm that the channel is being run well. Birgisson cautions that weak performance may reflect poor execution rather than a mature channel: teams should inspect targeting, creative, historical performance, audience-message fit, and the conversion path before declaring a channel exhausted. Once execution is strong, softening input metrics can be meaningful warning signs. For paid social, that might mean rising CPMs, declining engagement, limited remaining audience, or an inability to add budget without overexposing the same people. By the time those signals are obvious, the next growth curve may already be overdue.

A new channel needs enough investment to produce a real signal. Birgisson’s example is out-of-home advertising: buying a single billboard and then judging the entire channel on its results is unlikely to be informative because the investment is too small to affect meaningful metrics. Teams should choose experiments carefully, then resource them at a level that gives the approach a genuine chance to work. Small, fractional tests can help establish whether inputs affect outcomes, but a company may need to increase budget, staffing, or specialist support once it sees evidence of traction. A limited experimental budget can protect downside, but it should not make every test too small to learn from.

Serval began by determining whether it had a visibility problem. After measuring its presence for relevant terms in LLM search, the team found it was below 1%. Birgisson then assigned a product marketer and a growth marketer to run focused experiments, treating the work as a small “five percent hustle.” When visibility rose to 4% and then 6%, the team had evidence that the right inputs could move the outcome. Serval expanded the investment by bringing in a specialist agency and hiring for dedicated ownership. The broader lesson is to establish a baseline, test whether the metric is responsive, and then scale resources when the experiment earns confidence.

Trust comes from showing credible, observable customer value—not simply adding AI to the message. Birgisson notes that enterprise buyers are surrounded by AI claims and may be especially skeptical when they understand the technology well. Her approach is to demonstrate the product in action and let authentic customer stories, testimonials, and case studies carry the proof. For Serval, that included making customer storytelling an early marketing priority. Strong positioning also depends on a product that delivers materially better results; messaging can make the value easier to understand, but it cannot substitute for it. Companies must keep innovating because competitors can copy visible elements of their go-to-market approach.