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 WeekMost 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 work the other way round. Every campaign enters as a hypothesis with a number attached and a threshold that would tell us to kill it, and when the number comes back we either scale that channel or shut it off and move the budget somewhere that is working.
We are not going to promise you revenue, and you should be wary of anyone who does. What we will promise is that you stop guessing: a short list of the channels that reliably return more than they cost, the ones you can rule out for good, and the proof behind both.
And you pay for the engineer's assigned time, full stop - no percentage of your ad budget, no management fee stacked on media spend, no carrying charge. It is how every Designli team is billed, and it matters more here than anywhere: an agency paid a slice of your ad spend has a reason to tell you to spend more, and your engineer does not.
Introducing Hypothesis-Driven Growth.
Designli's product teams run Hypothesis-Driven Development - every change is a stated bet, shipped with the instrumentation to tell you whether the bet paid. Hypothesis-Driven Growth is that same discipline pointed at winning users instead of at building features.
It is the scientific method with a marketing budget attached, and it is why your engineer can tell you not just what happened but which decision caused it.
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.
Meet your GTM Engineer.
A Go-to-Market Engineer is a growth operator with an engineer's discipline, and they are the thing you are actually buying here. We staff them the way we staff product teams: half-time or full-time on your business, sitting inside it rather than splitting attention across a dozen logos.
They own the traction plan end to end. Choosing which channels to test, building and running the experiments, wiring up the measurement so a result can be trusted, and coming back to you with what the numbers actually said. Each channel below has a page showing exactly what that looks like - the six steps, the ramp, and the share of a week it takes.
They are also the reason paid dollars can be traced to something real. A GTM Engineer works from the same product analytics your developers do, so a click can be followed past the form fill into signup, activation and payment - which is what turns a marketing number into a business one.
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.
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.
Two surfaces run separately, because they do different jobs: your company page carries the proof, and your founder's profile carries the argument that reaches buyers. Posts get drafted in your founder's voice for them to publish under their own name, alongside a company calendar planned a month at a time. Everything else on your plan gets a second life here - webinars, research, awards, reviews - and reporting ties it back to who subscribed and who was reading, not to follower counts.
SEO and AI visibility.
Keyword research in the words your users actually type, 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. And we track AI visibility alongside it: whether your product comes up when somebody describes the problem you solve to an AI assistant.
Content and original research.
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.
It starts with an Impact Week.
We spend a week getting to the bottom of two things: every go-to-market activity you have running today, and the state of the product those activities point at. What each one costs, what it returns, and which questions your current setup simply cannot answer.
The product half is not us angling for a build. Even if all you want is the growth help, we need the foundation underneath it - event tracking, analytics, the funnel instrumented from first visit to first payment - because without it your dollars are unattributable and every channel decision after that is a guess. Getting that right first is what makes the spending trackable to an outcome.
You walk away with a scored technical findings report, a refreshed design direction for the screens that carry conversion, a read on your current channels, and a customized 90-day go-to-market plan built around your segments and your unit economics. Not a template, and not a channel list recycled from somebody else's account.
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.
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.