Mapping Brand Stigma with Remesh — and Why Segment-Specific Loops Matter

Stigma: The Growth Brake No One Budgets For

Stigma is the quiet social whisper: “People like us don’t use that.” It’s often unspoken, but it shapes adoption. Researchers point to seven distinct forms: Self-Stigma, Perceived Stigma, Public Stigma, Stigma-by-Association, Label Avoidance, Structural Stigma, and Health Practitioner Stigma.

Each acts as a balancing loop. The more intense the stigma, the more it counteracts momentum. Trial stalls. Advocacy flatlines. Growth hits resistance nobody accounted for.

Listening in the Right Room

To surface these loops, I ran a qual-quant study on Remesh. One session. Twenty-five minutes. One hundred and eleven (111) consumers debated and polled each other live. It gave me numbers and nuance at the same time.

Remesh’s live clustering sorted open-ends as they came in, mapping key themes to specific segments in real time. From one simple statement - “I feel comfortable telling people I use this brand” - we saw four repeatable audience segments emerge for each brand tested:

• Rejectors (Strongly disagree)
  
  
• Hesitant (Disagree)
  
  
• Discreet (Neutral)
  
  
• Mainstream Users (Agree or Strongly agree)
  

Because the open-ends were tagged to one or more of the seven stigma types, every respondent got a stigma “brake score” that let us see both segment dynamics and the emotional load behind them.

Designing the Guide Twice: First with Synthetic Data

Before going live, I ran a synthetic dry run. I asked GPT-4 to generate mock respondents and walk through the discussion guide. The pretest exposed a key blind spot: asking about the category (“plant-based meats”) softened or masked the sharper brand-specific tensions (e.g., Beyond vs. Impossible). That one insight led to a fast, cost-free pivot in framing.

It also tightened language. Several questions became clearer. Response rates went up. No people harmed.

Loops that Reveal Creative Whitespace

I borrowed from Peter Senge’s Fifth Discipline model, treating segments as “stocks” and each stigma as a “brake.” Thanks to Remesh’s ability to link verbatims to segment tags, we could map which brakes held which stocks back.

Example: Tinder’s Hesitant Users

• Public stigma (“Dating apps are creepy”)
  → reluctance to admit use
  → fewer stories of success
  → stigma reinforced.
  

That’s a textbook balancing loop. But it’s also a leverage point: a safety-forward PR campaign featuring shareable stories could shift that loop, nudging some users from Hesitant to Discreet.

Mainstream Users, meanwhile, reported a different brake: Self-Stigma around “doom-scroll dating.” They’d need a different narrative entirely.

What Synthetic Expansion Added

After the session, I used ChatGPT to generate 400 synthetic lookalike respondents. This second dataset confirmed the segment splits and shrank my confidence intervals. More importantly, it revealed emerging micro-clusters like Gen-Z Hesitant OnlyFans users open to educational creator content.

In short, it expanded my creative whitespace without needing a second recruit.

AI as Thought Partner, Not Oracle

ChatGPT wasn’t just a respondent simulator. It drafted the clustering logic, built quick causal loop diagrams, and even automated the reporting deck via Google Apps Script. I focused on the hypotheses while the model handled the scaffolding.

Boundaries, By Design

A few caveats:

• Synthetic ≠ validation. It helps tune the logic and fatten the segments—but live responses are still the benchmark.
  
  
• Model = simplification. This loop map doesn’t factor in media weight or competitor action. (Next phase, it will.)
  
  
• One loop rarely fits all. Beyond Meat’s brake is taste stigma. Tesla’s is brand association. Knowing which loop constrains which group matters because generic messages waste money.
  

Where This Goes Next

In the upcoming webinar, I’ll unpack these loops and show how Remesh’s live text analytics helps brands zero in on the exact audiences and extract beliefs that hold growth back. And where small, well-placed nudges can finally release the brake.

Register for the session here.

About the author

Yogesh Chavda brings over 25 years of global marketing experience, having led pivotal initiatives at category-defining brands like Spotify, Pinterest, Amway, Kimberly Clark, and WS Audiology. His career also includes a 16-year tenure at Procter & Gamble, where he held senior leadership roles across six countries. He helps brands identify growth blind spots through systems thinking, audience segmentation, and smart research design. At Y2S Consulting, he works with organizations to define their growth roadmap leveraging AI capabilities he’s built.

Understanding stigma isn’t just a social good, it’s a growth imperative. This blog unpacks how Remesh and synthetic data helped map the emotional “brakes” slowing brand adoption.

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