Introducing: Bulk Recategorization, Turn "Other" Into Attribution Signal

On average, ~6% of ‘How did you hear about us?’ responses fall into “Other.”

At first glance, that might not seem material. But it is.

“Other” isn’t random noise. It’s highly repetitive, high-signal responses - especially from hard-to-measure (HTM) channels like podcast, influencer, OTT/CTV, and word-of-mouth.

It’s:

• “Huberman Podcast”

• “TikTok creator”

• “My daughter”

• “Saw it on a YouTube”

When those responses remain unstructured, three things happen:

1. High-impact channels get undercounted

2. Budget decisions drift from reality

3. Teams spend hours manually cleaning recurring write-ins

This isn’t a survey problem. It’s a data structure problem.

And it’s solvable.

Introducing Bulk Recategorization

We built Bulk Recategorization to eliminate recurring manual cleanup and recover lost attribution signal.

From the Responses tab in Fairing, you can now recategorize a response and apply that change to all identical historical matches - instantly.

For example:

• Map “Huberman” → “Podcast + Huberman Lab”

• Move “YouTube + Huberman” → “Podcast + Huberman Lab”

• Standardize “a friend” → “Friend or Family”

Instead of repeatedly cleaning the same write-ins, you can convert free-text responses into structured, aggregated attribution data directly in Fairing.

Your Analytics updates immediately.

No extra tools.
No repeated ops work.
No more recurring signal trapped in “Other.”

Why This Matters for Hard-to-Measure Channels

Hard-to-measure channels disproportionately show up in free form text.

If these responses stay unstructured, your attribution model quietly undercounts the very channels that require the most strategic budget conviction.

Bulk Recategorization strengthens attribution fidelity by turning repetition into clean, standardized reporting.

Across customers, we consistently see that “Other” responses are not random - they are highly recurring. That means automation can progressively eliminate leakage over time.

A Real-World Example

This challenge isn’t theoretical.

Mando, a modern personal care brand investing heavily in podcast and YouTube, faced a familiar attribution problem: their highest-impact awareness channels weren’t being fully captured in click-based models.

Like many brands measuring podcast attribution, they knew certain shows were driving performance, but the data wasn’t telling the complete story.

The breakthrough came from diving into the “Other” category.

With Fairing, Mando asks every new customer: How did you hear about Mando? Inside the open-text “Other” responses, customers were naming specific podcasts, creators, and influencers.

By recategorizing those responses into structured channel data, Mando:

• Decreased “Other” responses by 3.12%

• Improved Podcast CPA by 9.2%

• Realigned reporting to reflect true offline and upper-funnel performance

After structuring their “Other” responses, the team saw a materially different CPA picture, giving them the internal confidence to protect and expand podcast budget.

By turning free-text signal into structured attribution data, Mando avoided cutting high-performing shows, reinvested confidently in top creators, and built a stronger internal case for hard-to-measure channels.

Bulk Recategorization makes that process scalable.

What once required deep manual analysis can now be applied across thousands of responses in seconds, strengthening attribution fidelity without adding operational burden. Read the full case study.

Phase 1 of Fairing’s Automation Roadmap

Bulk Recategorization is the first step.

Next up: recategorization rules that will automatically apply to future incoming responses - eliminating recurring cleanup altogether.

Response recategorization is foundational to improving attribution survey accuracy. Done correctly, it:

• Increases attribution accuracy

• Doesn’t require manual effort

• Strengthens measurement for podcasts, influencers, OTT/CTV, and word-of-mouth

If you’re investing in hard-to-measure channels, structuring free-text responses isn’t optional.

Because “Other” isn’t noise.

It’s signal waiting to be structured.

Not a Fairing customer yet? Schedule a demo today.