We often get asked what sets Fairing apart from other solutions in the survey space. The answer is fundamental: we're a measurement company first, survey company second.
We exist in the survey space because that's our data collection medium. But our ambition extends far beyond traditional surveys—we're building toward a future where zero-party attribution data (i.e. in-moment survey data) becomes the gold standard for marketing measurement, replacing increasingly unreliable click-stream data and other forms of measurement.
This vision requires focus. The history of software is littered with companies that tried to be everything to everyone, ultimately delivering mediocrity across the board. Marketing attribution is arguably the most challenging problem in digital marketing—one we'll never solve if we follow the same playbook as general-purpose survey tools.
Legacy survey platforms were designed as feature factories, not insight engines. They excel at form-building but struggle with the far more complex challenge of data interpretation. In the world of digital marketing, there's a fundamental truth: data collection is a cost center, while data insights are a profit center. While AI makes the collection part increasingly commoditized, the specialized normalization required for marketing measurement remains beyond the capabilities of feature-focused survey platforms.
Fairing helps digital brands discover their true customer acquisition channels with granular clarity. Our singular focus allows us to build collective intelligence across our customer base, normalizing responses and creating increasingly accurate attribution models that benefit every brand on our platform.
Your typical survey company offers general questions. Fairing has engineered survey questions and syntax specifically optimized for attribution. This isn't a minor distinction—it's our foundation, embedded in our mission: Make marketing efficient.
The Specialist vs. Generalist Approach
Platforms like Qualtrics and Medallia attempt to serve everyone. Companies like KnoCommerce or Okendo in the Shopify ecosystem follow suit. The inevitable result? They lack depth—especially in marketing measurement, which demands specialized expertise.
Marketing measurement via surveys isn't simple. It requires sophisticated analysis on normalized data. For most survey platforms, the survey itself is the product. Fairing inverts this model—the data is the product, the survey is merely the collection mechanism. This, unlike competitors, is why we continue to invest heavily in data science.
Customers of these complementary solutions rarely adopt their full suite. This isn't surprising. Marketers allocate their time to what delivers the highest marginal value. They need tools that solve specific problems effectively.
Every month, brands seeking real marketing attribution migrate from Qualtrics to Fairing. They quickly recognize the value of working with a team laser-focused on the core problem they're trying to solve.
Consider the contrast:
Qualtrics and Medallia are sprawling enterprise suites. They offer tools for every department—NPS, employee satisfaction, customer experience, and buried somewhere inside, attribution surveys for marketing. No one on their teams specializes in statistically validated attribution methodology. This isn't to say these solutions don't have a place in your customer research or voice of customer stack – but we would never recommend relying on them for such an important problem as measuring your marketing efficiency and return.
Fairing exists to solve marketing's most persistent challenge. According to eMarketer's Annual CMO Survey, accurate attribution remains the #1 problem marketers face year after year. When the precision of your attribution model directly determines how effectively you allocate millions in marketing spend, settling for a general-purpose survey tool that treats attribution as a checkbox feature is a strategic error.
The Fairing Difference: Purpose-Built Attribution
When you implement a platform designed specifically for marketing attribution:
1. High-Reliability Data Collection
Our surveys capture responses during peak engagement—immediately post-conversion—achieving response rates up to 85% (even when a user is not required to submit a response). Other platforms might claim to offer in-moment surveys, but their architecture wasn't optimized for this specific use case. We've seen competitors struggle to reach 10% response rates even with in-moment deployment, while Fairing customers consistently achieve between 30-85%—even without making responses mandatory.
2. Intelligent Channel Identification
Fairing starts with broad channels and intelligently narrows to specifics. Our auto-suggest feature helps users quickly identify precise sources—recommending "Huberman Lab" when a user begins typing "Hub..." after selecting "Podcast," or suggesting specific creators after selecting "TikTok." We intentionally avoid complex tree logic—we've optimized for simplicity and completion, not unnecessary complexity.
3. Response Intelligence Built for Marketers
We transform raw responses like "JRE" into structured data like "Podcast > Joe Rogan Experience," ensuring accurate ROI calculations. This distinction can determine whether a campaign appears profitable or not—a critical difference in resource allocation decisions.
4. Integrations and Partnerships Built for Marketing
We integrate with Meta, TikTok, GA4, and have more platforms on our immediate roadmap. Our partnership strategy focuses exclusively on platforms where you'll allocate your next marketing dollar. No survey company can claim this level of marketing-specific integration.
The Real Cost of Using the Wrong Tool
Marketing teams deploying general-purpose survey platforms for attribution face profound strategic risks:
Misaligned measurement leading to misdirected marketing investments. According to Nielsen's Marketing ROI Benchmark Study, brands with inaccurate attribution models misallocate an average of 26% of their digital budgets, resulting in an estimated 15-18% reduction in potential ROAS. This isn't merely inefficient; it's existentially threatening in competitive markets.
Insufficient response rates undermining statistical validity, forcing decisions based on sample sizes too small to be reliable. When your sample represents only a tiny fraction of your customer base, can you trust the conclusions?
Unstructured data requiring manual processing and normalization before yielding actionable insights—burning valuable analyst time on data preparation rather than strategic interpretation.
Fragmented analytics from limited integration capabilities, creating data silos that obscure the true performance of your marketing channels.
When your competitors make decisions using specialized measurement tools, you cannot afford to base your strategy on attribution data of questionable reliability.
A Different Kind of Company
We're not striving to build the world's finest survey company. Our mission is to create the most effective marketing measurement solution—one that captures attribution data directly from the source: your customers' own words, not inferred from pixels.
Fairing is the first company built from the ground up specifically for marketing attribution via direct consumer feedback. We're not a survey company that added attribution features—we're an attribution company that uses surveys as our data collection method.
If you need enterprise survey software for employee feedback or occasional customer research, numerous options exist.
But if you're determined to understand which marketing channels truly drive growth and how to optimize your spend accordingly, you need a focused solution built by marketers, for marketers, with a singular purpose: making your marketing more efficient.
As privacy changes continue to erode traditional tracking mechanisms, direct-from-customer attribution data becomes increasingly valuable. This is why we've dedicated our entire company to solving this critical problem.
Ready to experience what specialized marketing measurement can do for your brand? Get started today with Fairing.
