Audience profiles

Raw source preview

Raw, unprocessed file text shown below. Preview truncated for readability.

---
title: Audience Profiles — Preliminary
status: draft
created: 2026-04-12
purpose: Research-backed profiles for client-side enterprise insight teams and agency-side researchers. To be used as foundation for audience-specific web pages in phase 2.
---

# Audience Profiles — Preliminary

Two profiles based on online research from industry sources (GreenBook GRIT, ESOMAR, Cint, Bloomfire, Carnegie Mellon, Federal Reserve, a16z). Each profile is a starting point — not prescriptive. The themes below are grounded in external data, supplemented by what we know from our own pilot customers and positioning work.

---

## Profile 1: Client-side Enterprise Insight Teams

### Who they are

In-house researchers and insights professionals at mid-to-large companies (typically 1,000+ employees). They sit inside the business — in marketing, strategy, CX, or product departments — and conduct research for a single organisation, not for clients. They're not agencies. They're permanent staff whose job is to understand the customer on behalf of the business.

Team sizes vary: from a solo Head of Insight to a team of 10–15 researchers with junior, senior, and specialist roles. They may manage external agency relationships, run studies themselves, or a mix of both.

### The core pressure: speed vs. rigour

The most consistent tension these teams face is being asked to move faster than good research allows.

Decision-makers — CMOs, heads of product, commercial leads — increasingly expect 72-hour turnaround on customer insights *(Cint, 2025; a16z, 2025)*. Long-form research timelines feel slow by comparison. The team knows that rigour matters and that shortcuts have downstream consequences. The business doesn't always see it that way.

The nuance worth holding onto: **the trade-off between speed and rigour is context-dependent.** For lower-stakes decisions, directional answers are often fine. For decisions where the cost of being wrong is high (new product launches, manufacturing decisions, major strategic bets), rigorous research is worth the time. Insight teams carry this knowledge; stakeholders often don't.

### Being bypassed: the workaround problem

Marketing, product, sales, and customer success teams now bypass central insight functions entirely *(GreenBook GRIT 2025; Qualtrics 2025 market research trends)*. They run quick polls via SurveyMonkey, ask ChatGPT questions about customers, pull from social listening dashboards, or commission lightweight agency work independently.

95% of researchers now regularly use or experiment with AI tools — but the meaningful shift is that **non-researchers are now using these same tools** to generate "insight" themselves. The gap between what the insight team can produce and what a product manager can produce with ChatGPT in 20 minutes is closing — at least in appearance.

The uncomfortable truth: sometimes these workarounds produce results that are good enough for the decision being made. The person running a SurveyMonkey poll doesn't know what they don't know — and often, neither outcome surfaces to prove them wrong.

This makes the insight team's job harder. They can see the fragmentation building: inconsistent methods, no single version of what the customer thinks, no audit trail, no way to build on what's been learned. But making that case, when the workarounds feel like they're working, is the real challenge.

### Institutional knowledge: research that disappears

Research gets delivered, then buried *(Bloomfire, The Market Researcher's Guide to Knowledge Management)*. Decks live in folders no one opens. Studies are done and filed, not indexed or reused. When a senior researcher leaves, their knowledge and context go with them. Every new project starts from scratch as if nothing came before it.

This is a structural problem, not a personal failure. Most research teams don't have the tools or infrastructure to make past work accessible. Knowledge that took months to gather becomes dead weight rather than a compounding asset.

### Credibility and stakeholder trust

Evidence-based decision-making inside organisations is fundamentally a social endeavour — it depends on relationships, interpersonal dynamics, and the perceived credibility of the research function *(Carnegie Mellon, 2024 — evidence-based decision-making study)*.

Research loses credibility when findings aren't confirmable. When a stakeholder challenges a conclusion and the researcher can't immediately show the underlying evidence, trust erodes. Teams that can trace every claim back to its source data — and can say "here's exactly where this came from" — hold a stronger position.

The flip side: teams that deliver confident-sounding findings that can't be traced, checked, or connected to other evidence are building on sand. It may hold for now. But one challenged presentation can undo months of relationship-building.

### What they want

- **Speed without sacrificing quality** — tools that help them deliver faster without cutting corners
- **A centralised body of truth** — one place where all customer insight is stored, searchable, and building over time
- **Defensible outputs** — research they can stand behind when challenged
- **Researcher empowerment** — AI that makes each step of their existing workflow sharper, not AI that does the thinking for them
- **ResearchOps infrastructure** — systems, governance, and shared workflows that reduce operational burden and let researchers focus on insight rather than logistics *(GreatQuestion, UserTesting ResearchOps guides)*

### The fear underneath it all

That the team stops being seen as the people who understand the customer — and starts being seen as a bottleneck. That other teams stop asking. That leadership decides the insight function isn't worth the cost when ChatGPT and SurveyMonkey cover 80% of the need.

---

## Profile 2: Agency-side Researchers

### Who they are

Researchers working at market research agencies, insight consultancies, and full-service fieldwork firms. They serve multiple clients across industries — designing studies, managing fieldwork, analysing data, and presenting findings. Their business model is fundamentally different from client-side teams: they bill for time and expertise, and they win or lose clients based on the value they deliver.

The spectrum is wide: boutique specialist shops (qualitative, ethnography, neuromarketing), mid-size full-service agencies, and large multi-thousand-person consultancies like Ipsos or Kantar. What they share is the pressure to differentiate in a commoditising market.

### The core pressure: commoditisation

The agency business model is under sustained pressure *(GreenBook GRIT 2025; Quirks 2024)*.

Clients can now DIY basic research in-house using self-serve platforms. They retain agencies for complex problems, specialist access, or strategic interpretation — not for routine survey execution. ChatGPT at $20/month is a credible substitute for junior analyst work on many tasks. Agencies that compete on execution speed or price alone are losing margin.

The data is stark:
- Agencies that repositioned their offerings grew 8% in 2024
- Agencies that expanded services grew 9.7%
- Agencies that made no changes grew just 1.1% *(GreenBook GRIT 2025)*

Standing still is stagnation.

### Data quality: a growing crisis

Data quality concerns increased 40% year-over-year in 2025, driven largely by anxieties around synthetic data and AI-generated respondents *(GreenBook GRIT 2025)*. Survey response rates have declined from approximately 60% pre-pandemic to under 45% currently *(Federal Reserve, 2025)*. Among Gen Z respondents, fatigue is severe.

For agencies, this creates a genuine problem: the product they're selling depends on the quality and authenticity of the responses they collect, and both are under more pressure than they've been in a generation. Clients who can't see the quality monitoring process have every reason to question the validity of the findings.

Agencies that can demonstrate transparency in how they collect, monitor, and validate data — and hand clients proof of that — have a meaningful differentiator.

### The knowledge management opportunity

This is the angle that doesn't get enough attention.

As agencies accumulate research for each client over time, they build something genuinely valuable: an institutional memory of that client's market, customers, and research history. What was asked, what was found, what changed year-on-year, what the brand means to different segments — this knowledge compounds.

Most agencies don't protect it systematically. Research lives in project folders, in individuals' heads, in PowerPoint decks that can't be searched. When a senior person leaves, or when a client switches agencies, that knowledge walks out the door *(Stravito; Dynata Sharpr; Bloomfire)*.

The agencies building towards "knowledge as moat" — positioning themselves as the custodians of a client's customer understanding, not just the executors of individual studies — are creating genuine switching costs. A client who has 3 years of structured, searchable research history built on a platform doesn't walk away easily.

This is a different value proposition from "we run better surveys." It's: **we are the people who remember everything your customers have ever told you, and we make that memory work harder over time.**

### The value proposition shift

The agencies that are growing have moved away from competing on execution. The new value proposition clusters around:

- **Strategic interpretation** — not just "here are the findings," but "here is what this means for your business and here is what to do about it"
- **Specialist access** — niche audiences, hard-to-reach demographics, category expertise that clients genuinely can't replicate in-house
- **Knowledge retention and discovery** — owning the institutional memory of client research so that each new study builds on what came before
- **AI + human hybrid models** — using automation for data collection, coding, and basic analysis to free senior researchers for the thinking that actually matters
- **Proof of quality** — demonstrating, not just claiming, that their data is clean, their respondents are real, and their methods are sound

### What they want

- **Differentiation from DIY** — tools and processes that make their work visibly better than what a client could do themselves
- **Data quality assurance** — the ability to show clients that quality is being monitored and that problems are caught before they contaminate the findings
- **Client knowledge retention** — systems that accumulate and make accessible the research history for each client, creating stickiness and compounding value
- **Speed without commoditisation** — the ability to deliver fast without competing on price
- **ROI demonstration** — evidence that their work changed decisions and drove measurable business outcomes

### The fear underneath it all

That clients keep building in-house capability. That the work agencies do gets automated down to a commodity. That there's no longer a clear answer to "why do we need an agency when we can do this ourselves?" — and that without a compelling answer, the decision-makers stop calling.

---

## Notes for Phase 2

These profiles are inputs to the audience-specific web pages — `/for-enterprise` and `/for-agencies` — which will be written in a second phase. Key copy directions to carry forward:

**For enterprise pages:** Lead with the recognition moment — the salesperson scenario, the bypassed insight team, the knowledge that disappears. Then position the platform as the thing that makes the team indispensable by giving them speed, depth, and a compounding knowledge base.

**For agencies pages:** Lead with the knowledge moat angle. Not "better survey tools" — "one place where everything your clients have ever learned is stored, searchable, and growing." Position the platform as the thing that makes clients hard to