Build vs. Buy: The Hidden Cost of Building HDYHAU Surveys

The Challenge

Every engineer has seen it happen. It starts small: a field in a signup flow, a new table in the database, a quick query in the BI tool. In the beginning, it works. The team gets a little signal, everyone’s happy, and the assumption is that the system can grow with the business.

Attribution surveys often start this way. How did you hear about us? feels like the simplest possible question. But once that question sits in front of thousands of users, it stops being a side project and starts being infrastructure. And infrastructure is where things get complicated.

Why attribution surveys matter 

The reason teams bother implementing a HDYHAU is real: traditional attribution is breaking down. Pixels and cookies are fading, iOS 14.5 gutted tracking, and GA4 can’t follow customers across devices or offline moments. Meanwhile, word-of-mouth, podcasts, TV, and influencers drive huge portions of revenue,and none of them show up in analytics.

Attribution surveys fill that gap. They give you ground truth data straight from the customer, not just (often misleading) last click data. Last-click data may over-index on lower-funnel "closing" channels, while under-valuing upper-funnel discovery channels. Attribution surveys solve this issue and help validate your models, calibrate your spend, and reveal qualitative context you’d never get from clickstream data. 

The catch is that the value only emerges if the surveys are executed well, at scale, with high response rates, clean data, seamless integration into your stack, and the flexibility for marketers to update questions without engineering intervention. This is where DIY systems falter. On top of that, there is constant upkeep required to maintain data integrity and system health.

How a quick project turns into a burden

In the early weeks, a homegrown survey looks fine. But then engineering starts getting inundated with requests to:

• Add follow-up logic: “If they say ‘Podcast’, ask which one.”

• Enable & disable response options on the fly as channel spend is reallocated

• Add auto-suggest question types to help improve influencer data cleanliness

• Optimize the mobile survey experience

• Build integrations to send the survey data to social platforms, their CDP, etc.

Each issue is solvable, but the backlog grows faster than it gets resolved. Within a few months, maintaining the survey becomes a burden that never gets prioritized. Measuring marketing performance drops off. We’ve talked to brands that simply stopped updating response options because the overhead was too high.

That’s how a simple survey ends up costing money: it stagnates to the point of being unusable. Teams fall back to spreadsheets, data quality erodes, and marketing starts making spend decisions on incomplete or outdated data. The result is budget flowing to the wrong channels—underfunding the ones that actually work—and leaving millions in growth on the table.

The invisible costs

What rarely gets tallied:

• Engineering time diverted from core product.

• Specialized data science work for normalization, bias detection, and extrapolation.

• The constant churn of maintaining integrations as platforms evolve.

• Organizational trust eroding when numbers don’t line up with other systems.

• Frustration when marketing teams feel blocked from having control over their own surveys.

The hard part isn’t building the survey. It’s maintaining credibility and flexibility in the system over time.

Why Fairing exists

Fairing productizes the lessons learned from years of these edge cases. Instead of a fragile internal system, you get:

• High response rates driven by tested UX.

• Intelligent follow-ups that capture structured data.

• Categorization and statistical extrapolation.

• Pre-built integrations across your stack.

• Enterprise-grade compliance without extra lift.

• A self-serve interface that lets marketers edit, test, and launch questions without engineering support.

Build vs. buy, reframed

There’s nothing wrong with wanting to build, but building your own survey tool is like building your own payments system: unless it’s your business, it’s a distraction. Every sprint spent debugging survey logic is a sprint not spent building the product that sets you apart.

Fairing gives you the same control and flexibility as a homegrown tool, without the ongoing tax of maintaining one.

Turn Surveys Into Signal, Not Overhead 

Attribution surveys look deceptively simple, but they hide statistical, compliance, and organizational complexity. They demand a level of flexibility that’s nearly impossible to deliver in-house without constant engineering overhead.

Fairing takes that complexity off your plate, giving you answers in minutes and engineers their time back. Your team can stay focused on what actually matters: building the product only you can build.

→ See how Fairing can get you attribution clarity in minutes, not months.