You've built a software product. Now, growth is the mission.
You have a product that works. What you don't have is a repeatable model to find customers who'll love it - let alone find the next thousand. Designli staffs dedicated Go To Market Engineers who run experiments across every channel until we know which ones actually pay you back - then, we pour the gas on those.
Book Your Impact WeekIntroducing Hypothesis-Driven Growth.
Designli runs a methodology called Hypothesis-Driven Growth, which applies the scientific method to figuring out your best growth channels with rapid experimentation, then pouring more fuel onto the channels that prove to give a return on investment.
Our product development practice runs on Hypothesis-Driven Development, the methodology we use to make product decisions from real user data. Hypothesis-Driven Growth is that same methodology pointed at marketing, at winning new users, and at opening new revenue channels. Hypothesis-Driven Growth is the scientific method applied to digital product marketing.
We begin with a full audit of your platform in the Impact Week and of your current go-to-market activities. Out of that comes a customized go-to-market plan built for your segments and your unit economics, and dedicated GTM Engineers - Go-to-Market Engineers - staffed to execute the traction plan and run the framework.
They take full accountability for proposing the experiments, executing them across channels, and reporting back on what actually happened, as we figure out what to pour the gas on.
Most agencies sell you activity. We sell you evidence.
Most marketing agencies sell you a retainer and a calendar. Posts go out, ads run, a monthly report lands in your inbox, and a year later you still can't say which channel actually brought you customers.
We're built differently. We don't sell activity, we sell evidence. Every campaign enters as a hypothesis with a number attached and a threshold that would tell us to kill it. When the number comes back, we either scale the channel or shut it off and move the budget somewhere that's working.
What you end up with is worth more than a campaign: a short list of the channels that reliably return more than they cost, and the proof behind every one of them.
How Hypothesis-Driven Growth runs.
Four steps, on repeat, across every channel on your plan. It's the same loop our product teams run - the only thing that changes is what we're testing.
1. Write the hypothesis.
Before anything ships, we say what we expect it to do: this message, to this segment, on this channel, should move this number to here. We name the result that would tell us to stop, too.
2. Ship the experiment.
Campaigns go out in staged waves with one hypothesis per variant, so a result points at a cause instead of a guess. Pricing and copy tests run behind feature flags against real cohorts.
3. Read what happened.
Every funnel stage has one governing metric, baselined at the first review. Cost per click, cost per qualified reply, cost per first sale, and contribution against your CAC ceiling - by channel and by segment.
4. Pour the gas on.
Channels that clear the ceiling get more budget and more build. Channels that don't get shut off, and the money moves to what's working. Then the loop starts again on the next hypothesis.
It starts with an Impact Week.
Before we write a single campaign, we spend a week auditing two things: your platform, and every go-to-market activity you have running today. What each one costs, what it returns, and which questions your current setup simply can't answer.
What comes out of it is a customized go-to-market plan built around your segments, your unit economics, and the outcome that matters most to your business. Not a template, and not a channel list recycled from somebody else's account.
Everything below is the shape those plans usually take.
Baseline activities.
The foundation every experiment runs on. Built once, built inside your own accounts, and yours to keep whether we keep working together or not.
ICP & messaging.
We segment your buyer population and agree on a primary and a secondary segment, so every experiment after that points at a named target instead of at everybody. Then a message architecture for each one: the proof points behind every claim, answers ready for the objections that actually come up on calls, and an agreed brand voice and claims list.
Ecosystem mapping.
The registries, associations, chapters, training providers, and practitioner communities where your buyers already gather - ranked, with the access route for each one. Conferences and events get the same treatment: cost, audience, and lead time, so event spend has to compete on the same terms as every other channel.
Unit economics & pricing.
Cost to serve a customer, including model and compute where AI is in the loop. Contribution per sale, your CAC ceiling, and breakeven volume at every price you're considering - the number each experiment gets judged against. Price sensitivity is probed in structured user interviews, then tested for real on a cohort behind a feature flag.
KPIs & current channel analysis.
Funnel stage definitions with one governing metric each, baselined at the first review. Then an honest audit of the traffic, outreach, and referral activity you already have running today.
Audience creation & enrichment.
We build your prospect universe out of the ecosystems we mapped, then enrich it: verified work email, company and company size, sectors served, geography, and years in practice. AI fit-scoring assigns segments, dedupes, and suppresses. Matched and behavioral audiences get pushed to the ad platforms for retargeting, and the database lives in your accounts with a documented refresh process.
CRM setup.
A UTM taxonomy with codes for every channel, sequence, and asset, wired into analytics so a click can be traced to a customer. Lifecycle stages, deal pipeline, and a data model that holds the enriched attributes. Lead capture, follow-up, scoring, and internal alerting, all tested before launch.
Channel experiments.
Every one of these enters as a hypothesis with a number attached. Which ones you run, and in what order, comes out of your plan - not out of a package someone sold you.
Outbound.
Dedicated sending domains and mailboxes, kept well away from your primary domain, with authentication records configured properly. Warmup is staged across three to four weeks before any meaningful volume goes out, because burning your domain is the fastest way to lose the channel. LinkedIn connection and message sequences run in step with the email against the same enriched audience. Campaigns go out in waves, one hypothesis per variant, reported on qualified reply rate and sentiment rather than on open rates.
Paid media.
Matched, lookalike, and behavioral audiences built from the enriched database and your own first-party conversion data. LinkedIn and Google carry cold acquisition, Meta carries retargeting and lookalike expansion. Ad creative and copy are produced with AI, in variants built to isolate one message hypothesis at a time. Landing pages are built to the agreed positioning with conversion events specified and instrumented, and a dashboard tracks cost per click, cost per qualified reply, cost per first sale, and budget pacing by channel and by segment.
Social media.
Account creation and day-to-day management on the platforms where your segments actually spend time - not on all of them. Influencer marketing run like every other channel: one creator, one hypothesis, one number to beat before the spend scales. Referral programs that put acquisition in your existing users' hands, with the incentive tuned against your contribution per sale. And reporting that ties follower and engagement movement back to signups instead of leaving it as a vanity number.
SEO.
Commercial-intent keyword research mapped to pages and content briefs, with volume and difficulty attached, so you can see what a ranking is worth before we go chase it. Content gets produced against those briefs. We track AI visibility as well: whether your product shows up when a buyer asks an AI assistant the questions you want to be the answer to.
Content.
Lead magnets whose engagement scores straight back into the CRM. Whitepapers on the topics your market is actually arguing about, gated for unidentified traffic and ungated for the cohort we're already sequencing. Webinars run end to end - registration, production, delivery, and on-demand republishing. And PR outreach to the trade publications your buyers genuinely read.
Partnerships.
Community posting inside the groups, forums, and chapters we ranked during ecosystem mapping - showing up as a useful participant rather than as an ad. And strategic partnership creation: finding the companies already selling to your buyer without competing with you, then building the referral or co-marketing arrangement that gets you in front of their list. Partnerships are slower to prove than paid, so they get a longer measurement window and the same kill threshold as everything else.
Meet your GTM Engineer.
A Go-to-Market Engineer is a growth operator with an engineer's discipline. We staff them the way we staff product teams: full-time and focused on one client, sitting inside your business instead of splitting attention across a dozen logos.
They own the traction plan end to end. Proposing the experiments, building and shipping them across outbound, paid, SEO, and content, wiring up the measurement so the result can be trusted, and coming back to you with what the numbers actually said.
That last part is the whole difference. Your GTM Engineer reports the experiments that failed as plainly as the ones that worked, because a channel you've ruled out is worth nearly as much as a channel you've found. Both of them tell you where the next dollar should go.
It's also where a GTM Engineer stops looking like a marketer. Every number above is only as good as the instrumentation underneath it, so your engineer owns that layer too:
Product analytics configuration.
Event tracking across every step that matters: landing, signup, activation, first real use, purchase, and repeat. Session replay on the flows where you're losing people. Feature flags standing by for the pricing and copy tests.
Analytics & attribution wiring.
Product analytics connected to your CRM and to the attribution taxonomy, so a click traces all the way through to a customer and revenue lands next to the campaign that produced it.
Secure document & data handling.
Where a lead magnet or a trial takes an upload, we build secure handling and storage with defined retention and deletion behavior, plus a plain-language description of what happens to a file. Prospects hand over less when nobody can tell them.
Recurring reporting.
Reporting built on the instrumentation above and delivered on a cadence, so every experiment closes with a number instead of an opinion - including the experiments that didn't work.
Growth is the mission. Let's go find your channels.
It starts with a free 30-minute call. You walk us through what you've built and where growth has stalled, and we'll walk you through what an Impact Week would look at, what the first go-to-market plan would cover, and what it would cost.