Which Half of Your Marketing Budget Actually Works?

01 /The Concept

"Marketing is art plus science," declares Haus. "We bring the science to your art."

Coming up with great creative is the art. Spending the budget intelligently – that's the science. Modern companies run a sprawling mix of tools and channels simultaneously, which makes a deceptively simple question nearly impossible to answer: which one actually drove the sale?

The problem is that a typical buyer encounters dozens of brand touchpoints before converting. Attributing the entire purchase to the last ad they clicked before checkout is obviously wrong – that click is usually just the final nudge, not the cause.

"We always had a feeling that our YouTube content was doing far more work than the click-through numbers suggested. Haus let us prove it – and showed us we should be investing even more there," said one of the startup's clients.

Haus's thesis: the only reliable way to understand what's working is to run a controlled experiment.

Instead of counting last-click conversions, the platform measures how total purchase volume changes when a specific channel or tactic is added or removed from the marketing mix. That change in volume is the channel's true incremental value.

Experiments can be run at a granular level too. One client discovered through Haus testing that TikTok ads were actually lifting sales on their Amazon storefront – while a different set of channels was driving conversions on their own website. Without the experiment, those dynamics would have stayed invisible.

There's also the diminishing-returns problem. Pouring more money into a channel produces strong gains early, then progressively less, until additional spend generates almost nothing. The key is to catch that inflection point before you overshoot.

Haus handles this by running three parallel experiments per channel – comparing outcomes at the current budget, a lower budget, and a higher budget – then recommending reallocation toward wherever the incremental return is highest.

The same logic applies to geographic targeting and to discount campaigns. Running a promotion costs margin; the question is whether the volume lift more than compensates. Running a test first turns that into a data question rather than a gut call.

The real elegance of Haus is that it runs all these experiments autonomously and continuously, delivering daily reports on incremental performance. A typical output might show that the current cost per conversion on TikTok is $87 vs. $104 on YouTube – but that at a higher budget, TikTok's CPA climbs to $109 while YouTube's falls to $49, making a reallocation the obvious call.

Haus was founded in 2021 and has grown steadily ever since. The funding story reflects that trajectory: $1.8M in fall 2021, $4M in summer 2022, $17M in summer 2023, $17.5M in April of this year – and now, just three months later, another $20M.

02 /Why It Matters

Haus is another reminder that in business, nobody actually knows what's working. The only honest path to certainty is to run an experiment and measure the result.

That insight explains why investors keep backing startups that make business experimentation faster and easier. A few [related reviews](/review/kogda-pribyl-bolshe) are worth connecting here.

Monocle ([related review](/review/kogda-pribyl-bolshe)) built a platform for D2C brands to measure the incremental impact of promotions and discount campaigns – a focused slice of what Haus does. Despite the narrower scope, Monocle raised $7.5M in its first meaningful round this past May. The hook: D2C brands spend roughly $400 billion a year on promotions, so even a fraction of that market is enormous.

Operating under the provocative banner "A/B testing is dead," OfferFit ([covered previously](/review/kak-povysit-jeffektivnost-reklamnyh-rassylok)) built a platform that optimizes outbound messaging campaigns across a vast array of variables – from which offer to show to the ideal send time for each individual recipient. OfferFit has now raised $39M. Like Haus, its real play is running thousands of micro-experiments in parallel, something no human team could manage manually.

Subsets ([covered here](/review/privychka-vazhnee-chem-polza)) focuses on subscriber retention experiments for digital publishers – testing what keeps readers engaged, extends subscriptions, and maximizes LTV. Notably, Subsets segments readers by behavioral habits rather than demographics. It raised $1.7M after that review was published.

03 /Opportunities

The broad opportunity: platforms that automate business experimentation.

Picking a specific domain – digital publishers, D2C brands, SaaS companies, retail – is a meaningful advantage because it lets you pre-bake the right experiment types for that vertical's problems, rather than building a generic test-anything tool.

Business experimentation has always mattered. What's changed is that AI now makes it possible to run experiments that are genuinely intelligent – varying hundreds of parameters simultaneously and updating in real time. That wasn't feasible at scale until recently.

The practical starting point: identify a domain where you already have intuitions about what drives outcomes but lack the data infrastructure to test them systematically. Build the experimentation platform for that domain first – Haus's own trajectory suggests that a product built close to a real problem consistently outperforms a generic test-anything tool. Once the results speak for themselves, the expansion path is clear.