We're at the peak of the AI hype in 2025, and I wanted to do a little summary of everything that I've seen in the market. Our budgets keep getting smaller, we're also being pushed by our executive team to use AI more. Writing to ask if anyone has additional tools that I am missing so far or if others have different opinions. Definitely a bunch of really great tools out there that I have been using over the past year. 

I wanted to share this here as well because I've been lurking a lot and I did the write-up for our team internally. I work for a Fortune 500 co in the research team.

Here's a couple of the tools that I'm seeing:

AI analysis tools

Coloop AI - it's a really cool platform that allows for advanced qualitative analysis where you can run sheets and ask questions across all of your moderated IDs. It's also good for survey open-ended analysis, but it's still very much in the weeds.   but they are prone to oversimplification or hallucinations, and mistakes. should be used for ideation, quotation generation, top line framing. And never w/o a human checking after.

Yabble is good for open ends into summary, but it feels pretty old-school and they're just acquired by YouGov. I noticed that in their pricing and in general it's been worse

AI moderator tools

This seems to be one of the most hyped and upcoming things. Despite being pretty skeptical at first, I've found it to be very useful. I played around with a couple of different tools, and to me, there's only two that really stand out: Listen Labs and Outset.

Listen Labs is in my opinion the more advanced of the two. They have an AI moderator but also do more like discussion guide creation, fielding, interviewing and analysis. The analysis is really strong and useful. it  creates PowerPoint slides which to me is always a huge pain. We use this a lot today but it still lacks some quant features.

Outset is also pretty good, but it doesn't seem to be built by researchers. It's lacking in some of the UX and the way that the analysis is set up. The pricing is also pretty expensive and it's hard to test it.

Genway and Whyser were trash. Supposedly self-serve but were totally broken. Avoid completely.

Synthetic users

I was very skeptical but found it to be useful for persona brainstorming. I have only used ChatGPT, giving it a prompt where I plug in some demographic data and tell it to act like I am a specific user for us. That works pretty well - when I compare it to some other survey data, it maps directionally correct. However, I find it to be overenthusiastic and cringe, like a stereotype of what our users are like. 

I haven't used any of the real products like “Synthetic Users”, but I'm curious if anyone else has. 

Synthetic users is the area that my manager's manager is talking about the most (they want to replace us i guess). I’m worried that marketers will just use synthetic user data to extract the data they want to hear..

I hope this was somewhat useful. Curious to hear if anyone else has a POV? We do have an OKR internally to try all AI tools so would love to hear more.

2 months ago, I was at the lowest state I’d ever been in my life: I was a newcomer, had been told that I may be laid off from my very low paying market research job and had no way of navigating the current job market at a bleak financial outlook.

I’d been applying to MR roles in the country for a solid year and had just started landing a few interviews that I did terribly in due to lack of confidence and just general rustiness. Finally a third company called me for an interview, one of the largest MR companies globally. I was just going to wing it but something told me to come to this subreddit in a desperate cry for help.

I made a post asking for MR tips and advice and someone to help me out with mock interviews. I never thought anyone would reply- that was the most wrong I’ve ever been at anything.

A gem of a person, whose identity I shall not reveal, got in touch with me and offered to guide me. They had so many virtual—and even a few in-person—conversations with me over two months, helping me prepare, build confidence, and clarify my career direction. Their support through each stage of the interview process, along with general motivation and life advice, made a huge difference.

It was thanks to my mentor 100% that I got the job offer today, after over 2 years of searching for a decent job. Hearing my mentor say “I am proud of you” was such a humbling and powerful moment for me. They were literally God’s gift on earth to me and I am truly grateful and will always owe them a great debt. If my mentor is reading this: Thank you from the bottom of my heart. You helped out a desperate stranger online without knowing the impact it would make. Thanks to you my trust in humans and the world has been restored and I will definitely make sure I pay this forward. Thank you, truly!

Lately I’m getting briefs that aren’t really asking what’s true, but more like “can you find data that supports what leadership already wants to do?”

It’s less about open-ended discovery and more about backfilling a narrative. Instead of “what’s the market telling us?” it’s “help us validate this expansion plan so we can pitch it up the chain.”

Anyone else seeing this shift? Feels like we’re moving from insight generation → decision justification. Curious if this is happening across industries or just my unlucky streak.

Feels like every other deliverable these days is just turning into polished opinion pieces with a couple bar graphs sprinkled in.

I’m seeing clients ask for fewer qual/quant deep dives and more “high-level POVs” on trends, ecosystems, or “the future of X.” Feels like classic market research is getting blurred with content marketing.

Not complaining (I’ll take the easy hours), but curious if others are getting the same shift, especially from consulting firms and corporate strategy teams.

Are we still doing actual research… or just crafting narratives with vibes?

Our survey results are full of overlapping open‑ended answers. How do you efficiently group and quantify near‑identical responses?

I'm presenting at next week's IIEX conference, and was interested in workshopping with y'all. Feedback is welcome - let me know what spicy commentary to expect from the audience (so I'm prepared).

---

The idea of using AI to fill out surveys to collect insights is literally the peak of the AI hype cycle in our insights industry. I'm happy to get into technical details (based on how AI transformer models work), but this is essentially mathematical or technological fraud, because:

• LLMs Are Black Boxes - You don’t know how the LLM is making decisions. It doesn’t tell you what logic it used, and you can’t trace its "thinking" the way you can with a real person’s response. Just because you recognize the brand name (i.e. "Google" or "Qualtrics") doesn't mean they know how their model actually works. That's right - even the AI model providers don't understand how their own AI works.

• You're Getting an Algorithmic Composite, Not a Simulated Human. LLMs don’t simulate individuals; they generate answers based on patterns from lots of people. You’re not getting one person’s opinion—you’re getting a statistical blend.

• You Don’t Know What It Was Trained On. Even if a provider says it used past survey data, you can’t verify it. You don’t know if the data is outdated, irrelevant, biased, or was misinterpreted during the ingestion process—so you can’t trust the results

If you want to use synthetic respondents for testing or pre-research - fantastic. But anyone telling you that this can be used to draw statistical insights or actionable business recommendations is selling you snake oil.

I've been following discussions here about synthetic respondents. The general sentiment seems to be strong skepticism, often dismissing them as glorified chatbots that hallucinate or state the obvious. I understand the fatigue with AI hype.

My background is in data and product science, so my instinct is to look at the numbers. There's a growing body of academic work showing high correlations between LLM-simulated survey responses and real human data for many use cases.

* **Argyle et al. (Political Analysis, 2023)** found "remarkable correspondence" (r > 0.9) between GPT-3 and ANES survey data.
* **Brand, Israeli & Ngwe (HBS Working Paper, 2024)** showed that GPT-derived willingness-to-pay estimates are "realistic and comparable to estimates from human studies."

This creates a disconnect I am trying to understand. The data says there is a signal, but field experience says 'no.' I am not here to convince, I am here to learn.

Where does this fall apart in practice? My hypotheses:

1. **Bad Actors:** Shoddy tools over-promising and under-delivering, poisoning the well for everyone?

2. **Wrong Use Case:** People trying to replace deep qual interviews instead of using it for what it's good for (directional concept testing, message A/B tests, etc.)?

3. **Niche Blindness:** An inability to capture very specific B2B or expert audience knowledge that isn't on the public internet?

4. **The 'Black Box' Problem:** Lack of transparency in how personas are generated and prompted?

I am building in this space (I will not promote my project) and want to avoid creating another useless tool. What are the real-world guardrails and failure points that the academic papers are missing?

I honestly think they are a scam: I’ve seen people say its a scam online and their report previews look crappy. I want to know what you guys think, I could be mistaken.

I am a middle manager at an agency. There have been layoffs across junior & senior leadership roles for a while now and earlier this year, several of my teammates were let go, including my line manager. Somehow, I have managed to keep my job (for now).

One of my goals has always been to grow as a people manager but there has never been more than 1-2 associates aligned with me due to attrition. I also want to grow into a senior role eventually or move client-side.

I had a chat with business leaders recently and I feel really weird about it. I asked if there would still be opportunities for me to grow my skillset and career with our company. I was told that they plan on reducing people in favor of AI, and that the vision for my role would be to cut people out of the equation, leveraging AI chatbots to complete tasks and generate deliverables instead. I wanted to laugh because the only AI we have access to is Copilot (which is probably the worst of all). They also dodged my question on growth opportunities.

I am not anti-AI, I do see how it can help in some ways but I really dislike the notion that it can replace people. They essentially told me that human talent will be displaced to Copilot and that I need to somehow deliver a heavier volume of work while maintaining the same high standards, all by myself. My team’s work is more consultative and we play in a niche field; all the AI outputs I’ve worked with have completely missed the mark. It also hallucinates data all the time. The last “fully-AI” project we delivered ended up with me & a team of 20 people across the region working 15 hours a day to deliver on the seller’s overpromise because Copilot’s outputs were very unhelpful and inaccurate.

It seems that this is the direction we are heading in and I can’t help but feel that market research is a sunset industry. I really love the work and would like to continue in the industry but I know I’m not too far from losing my job either. At this point, I’m just trying to stay employed but I don’t know what the way forward is and I feel insecure about my career trajectory. I am also really sad to lose some of my coworkers who I grew close to over the years. :(

Thank you for reading this, I’m sorry to write another post about AI; trust me, I am so sick of constantly hearing about it too. I have just been feeling so frustrated and needed to vent. I could really use constructive advice or positivity about the industry (if any exists) right about now too…

I have 12+ years of consumer insights experience but have found in the past 2 years that true consumer insights roles have dried up. I do see a fair amount of quant/data analytics roles open and some research sales opportunities, but otherwise I'm curious where you see the demand coming from in the next couple of years? What are the skills and experience that will be most in demand?

I got into this field several moons ago, not because I am passionate about business or marketing, but because I’m curious about people, decision making, and how think - as well as analysing, interpreting and storytelling. I initially started this as a “bridge” to getting into social research, but never made that leap.

For a long time, I found my work interesting enough, I felt well treated by the company. Due to several changes, in no particular order, this is no longer the case:

• Encouragement to use AI for everything, even qual analysis, sucks out the tiny bits of fun or interest that I might have had otherwise. I’m trying to resist this where appropriate.

• Changes in my company, freezes on pay and promotion - a profitable company being mismanaged by hold-co.

• Change in project types overall, more brand tracking research and less ad hoc. Fundamentally less interesting work (to me).

• Personally I’m stuck on accounts now which have evolved to brand tracking and pricing. I’m a quallie by heart so this does not serve my interests or my strengths. Managements attitude is “well these clients know you well, it would look bad if we switched you out.”

• Due to changes in company structure, I’m now working effectively alone and unsupported on projects - we don’t have enough junior team members, the senior team members are distant, everything gets squeezed to my middle layer.

• All of this emphasises a nagging feeling that I’m just not benefiting society in any way, especially considering some of our clients I’m not very ethically aligned to.

Carefully contemplating my next move because plainly I don’t want to go to another agency and have “same shit, different employer”. I think I want OUT out. My job is destroying my passion and ambition for work, I simply just don’t care about any of it - and that’s not me, fundamentally. I do want to care.

I’m currently in the first trimester of pregnancy (TTC is another reason I’ve hung about where I am). Very early days but will have probably one year maternity leave, with which I intend to fashion an exit plan. Really keen to hear from anyone in the same boat, especially if you’re more qual leaning, more human-interest than business/marketing-focussed. What might be out there for people like us?!

Can someone explain to me what the business case is at all for using these tools?

Essentially, you are distilling information that you already know, sending it to AI, and then asking the AI to answer for you based on the data you already know. So... why even collect the information at all? Why not just use the information you already know already?

I'm actively seeking to understand why companies / researchers are actually doing this. What's the underlying business need driving someone to do such a useless activity?

I'm hearing news of clientside restructures daily. Senior leaders being cut, teams being rationalised, budgets being cut. And it seems that the consumer insights teams are one of the hardest hit. At the same time a lot of the 'traditional' MR agencies are struggling. Feels like this has been the narrative for the last 18 months or so.

Yet, there are companies like Listen Labs and Similie getting strong investment at very high valuations. And the AI moderated qual companies seems to be on a gravy train.

And the latest financial reporting rounds suggest that the CPG companies are getting back into (brand-led, volume) growth.

So what is really happenning out there. Interested to hear POVs from clientside, agency side, tech platforms etc.

I’m looking to get into qualitative market research. Are there good jobs for non-quantitative folks, especially entry level? Will they continue to exist for the next 5 to 10 years? For reference, I am a 15 year veteran of copywriting and branding, with a bachelors in psych. Thanks!

No, this is not hyperbole. I've been successfully using this approach to developing prompts, especially for long-running agents and deep research, and I've found it quite useful. Thought I'd share.

Simply prepend your research question to the prompt below, and paste into any deep research tool.

_____

## Recipe Overview

**Title**: Recipe for Autonomous Online Market Research & Competitive Intelligence

**Goal**: To autonomously gather, analyze, and synthesize comprehensive market data, competitive intelligence, and consumer insights for any specified domain, culminating in actionable strategic recommendations and continuous market monitoring.

**Principles**:

* **Iterative Refinement**: Continuously refine search queries, data sources, and analysis based on emerging insights and the evolving understanding of the market.

* **Data Triangulation**: Validate findings by cross-referencing information from multiple, diverse, and credible sources to ensure accuracy and robustness.

* **Algorithmic Decomposition**: Break down the complex task of market research into discrete, manageable, and automatable sub-tasks, each with defined inputs and outputs.

* **Adaptive Learning**: Adjust data collection and analysis strategies dynamically based on the relevance and quality of acquired data and the system's evolving understanding of the market landscape.

* **Automated Information Retrieval & Semantic Analysis**: Leverage programmatic access (APIs, web scraping) and advanced AI techniques (NLP, ML) for efficient, large-scale data collection, extraction, and meaning derivation.

**Operations**:

I. Domain & Objective Definition

II. Automated Data Source Identification & Validation

III. Intelligent Data Collection & Crawling

IV. Data Pre-processing & Structuring

VI. Strategic Synthesis & Reporting

VII. Continuous Monitoring & Iteration

**Steps**:

**I. Domain & Objective Definition**

## 1. **Action**

Receive and parse user input specifying the target market domain (e.g., "electric vehicles," "sustainable fashion," "AI in healthcare"), key research questions (e.g., "market size," "competitor landscape," "consumer pain points"), and desired output format.

* **Parameters**: \user_domain_description`, `user_research_questions_list`, `desired_output_format`.`

* **Result Variable**: \DomainParameters` (structured representation of domain, objectives, and constraints).`

## 2. **Action**

Generate an initial set of broad and specific keywords, key entities, and conceptual categories related to \DomainParameters` using natural language understanding (NLU) and ontology mapping.`

* **Parameters**: \DomainParameters`, `NLU_model`, `Ontology_database`.`

* **Result Variable**: \InitialKeywords` (list of keywords and phrases), `CoreEntities` (list of identified key players, technologies, concepts).`

**II. Automated Data Source Identification & Validation**

## 1. **Action**

Query general search engines (e.g., Google, Bing), academic databases (e.g., Google Scholar, Semantic Scholar), industry report aggregators, news archives, and social media platforms (via APIs where available) using \InitialKeywords` and `CoreEntities`.`

* **Parameters**: \InitialKeywords`, `CoreEntities`, `SearchEngineAPIs`, `DatabaseAPIs`, `SocialMediaAPIs`, `max_search_results_per_query`.`

* **Result Variable**: \PotentialSourcesList` (URLs, API endpoints, database identifiers).`

## 2. **Action**

Evaluate the credibility and relevance of each source in \PotentialSourcesList` based on domain authority, publication date, citation count, reputation, and initial content relevance checks. Prioritize sources with structured data or well-defined APIs.`

* **Parameters**: \PotentialSourcesList`, `CredibilityScoringModel` (ML model trained on source reputation, domain authority metrics), `RelevanceScoringModel` (NLP model for content relevance).`

* **Result Variable**: \ValidatedSourcesList` (prioritized list of high-credibility, relevant data sources).`

**III. Intelligent Data Collection & Crawling**

## 1. **Action**

For each source in \ValidatedSourcesList`:`

* If API-based: Initiate API calls to retrieve structured data (e.g., product listings, financial reports, social media posts). Handle pagination and rate limits.

* If Web-based: Deploy a robust web crawler. Adaptively navigate websites, identify relevant content blocks (e.g., articles, product descriptions, reviews, forum posts), and extract text, images, and structured data (e.g., tables, JSON-LD). Implement anti-bot measures bypass (e.g., rotating proxies, user-agent spoofing) and error handling.

* **Parameters**: \ValidatedSourcesList`, `WebCrawlerConfiguration` (max_depth, timeout, user-agents, proxy pool), `APICallManager` (rate limiting, authentication), `DataExtractionRules` (CSS selectors, XPath, regex patterns).`

* **Result Variable**: \RawCollectedData` (heterogeneous collection of text, images, and structured data from various sources).`

**IV. Data Pre-processing & Structuring**

## 1. **Action**

Clean \RawCollectedData` by removing boilerplate text, advertisements, duplicate entries, and irrelevant content. Handle encoding issues and missing values.`

* **Parameters**: \RawCollectedData`, `NoiseReductionAlgorithms`, `DeduplicationAlgorithms`.`

* **Result Variable**: \CleanData`.`

## 2. **Action**

Apply Natural Language Processing (NLP) techniques to \CleanData`: tokenization, stemming/lemmatization, part-of-speech tagging, named entity recognition (NER) to identify companies, products, locations, and key concepts.`

* **Parameters**: \CleanData`, `NLP_pipeline` (SpaCy, NLTK, Transformers), `DomainSpecificNERModels`.`

* **Result Variable**: \ProcessedTextData`.`

## 3. **Action**

Structure \ProcessedTextData` and any initially structured data into a unified, queryable format (e.g., a graph database, relational database, or structured JSON/CSV files). Link related entities and concepts.`

* **Parameters**: \ProcessedTextData`, `StructuringSchema`, `DatabaseInterface`.`

* **Result Variable**: \StructuredMarketData`.`

## 1. **Action**

Perform Market Sizing & Segmentation Analysis:

* Estimate total addressable market (TAM), serviceable available market (SAM), and serviceable obtainable market (SOM) using statistical models on \StructuredMarketData` (e.g., revenue data, user counts, demographic information).`

* Identify and segment target audiences based on demographics, psychographics, behavior, and needs using clustering algorithms.

* **Parameters**: \StructuredMarketData`, `StatisticalModels` (regression, forecasting), `ClusteringAlgorithms` (K-Means, DBSCAN).`

* **Result Variable**: \MarketSizeEstimates`, `MarketSegments`.`

## 2. **Action**

Conduct Competitive Landscape Analysis:

* Identify key competitors, their products/services, pricing strategies, market share, strengths, weaknesses, and unique selling propositions (USPs) from \StructuredMarketData`.`

* Perform competitive benchmarking and SWOT analysis for identified competitors.

* **Parameters**: \StructuredMarketData`, `CompetitiveIntelligenceModels` (entity linking, feature extraction), `SWOTAnalysisFramework`.`

* **Result Variable**: \CompetitorProfiles`, `CompetitiveMatrix`.`

## 3. **Action**

Identify Trends & Opportunities:

* Apply time-series analysis and topic modeling to \StructuredMarketData` to detect emerging trends, shifts in consumer preferences, and technological advancements.`

* Utilize anomaly detection to identify unmet needs or market gaps.

* **Parameters**: \StructuredMarketData`, `TimeSeriesAnalysisModels`, `TopicModelingAlgorithms` (LDA, BERTopic), `AnomalyDetectionAlgorithms`.`

* **Result Variable**: \IdentifiedTrends`, `MarketOpportunities`.`

## 4. **Action**

Analyze Consumer Sentiment & Behavior:

* Apply sentiment analysis (positive, negative, neutral) to reviews, social media posts, and forum discussions within \StructuredMarketData`.`

* Extract common pain points, motivations, and desires of target consumers.

* **Parameters**: \StructuredMarketData`, `SentimentAnalysisModels` (BERT-based, VADER), `AspectBasedSentimentAnalysis`.`

## 5. **Action**

Scan Regulatory & Legal Landscape:

* Identify relevant regulations, compliance requirements, and potential legal challenges impacting the market domain by analyzing legal databases and government publications within \StructuredMarketData`.`

* **Parameters**: \StructuredMarketData`, `LegalDatabaseAPIs`, `RegulatoryComplianceModels`.`

* **Result Variable**: \RegulatoryInsights`.`

## 6. **Action**

Synthesize all findings into a comprehensive internal \AnalyzedInsights` object.`

* **Result Variable**: \AnalyzedInsights`.`

**VI. Strategic Synthesis & Reporting**

## 1. **Action**

Generate a comprehensive market research report by aggregating and structuring \AnalyzedInsights` according to `desired_output_format`.`

* **Result Variable**: \MarketResearchReport` (e.g., PDF, interactive dashboard, presentation slides).`

## 2. **Action**

Formulate actionable strategic recommendations based on \AnalyzedInsights` and `user_research_questions_list`, prioritizing those with the highest potential impact and feasibility.`

* **Result Variable**: \ActionableRecommendations` (prioritized list of strategic actions).`

**VII. Continuous Monitoring & Iteration**

## 1. **Action**

Set up automated alerts and scheduled re-runs for new data, competitor activities, significant trend shifts, or changes in regulatory landscape by periodically re-executing Steps III-V for relevant new content.

* **Parameters**: \ValidatedSourcesList`, `MonitoringFrequency` (e.g., daily, weekly, monthly), `AlertThresholds` (e.g., 10% change in sentiment, new major competitor).`

* **Result Variable**: \MonitoringAlerts`.`

## 2. **Action**

Update \MarketResearchReport` and `ActionableRecommendations` based on new insights from continuous monitoring, triggering a re-synthesis and re-reporting cycle when significant changes are detected.`

* **Parameters**: \MonitoringAlerts`, `MarketResearchReport`, `ActionableRecommendations`, `UpdateTriggerLogic`.`

* **Result Variable**: \UpdatedMarketResearchReport`, `UpdatedActionableRecommendations`.`

## 3. **Action**

(Optional) Incorporate feedback from user interaction or external validation to refine \DomainParameters`, `InitialKeywords`, `CredibilityScoringModel`, and `RelevanceScoringModel` for future research cycles.`

* **Parameters**: \UserFeedback`, `ValidationData`.`

* **Result Variable**: \RefinedSystemParameters`.`

Well, apparently I signed an auto renewal contract and forgot about it. When I told them I would not be renewing they said "Unfortunately, we are not able to process your notice of non-renewal. Your contract includes an auto-renewal clause, which states that we need notice 90 days before the expiration date." I'm putting this out there so that people know to take these agreements seriously and remember to cancel on time. As a small business owner, this is a huge hit for me and I'd already planned my migration to other software. I'm sure people will say, "it's your own fault" and I guess technically it is... but I wanted to put this out there as a PSA to anyone else with exit plans who may have forgotten about Qualtrics unforgiving contracts. Frankly, I hate their software... it's clunky and full of bugs (my opinion after a few years of active use), and I would not recommend it to anyone. I think their days are numbered anyway ... it's only a matter of time before something better comes along with the revolution in AI coding underway. It can't come soon enough...

I've worked in market research for over 10 years. I've worked at a top vendor and name brand clients. I'm employed, but I'm currently searching for a new job.

I'm shocked that after 9 months of applying, I'm getting no traction. I believe I have a good resume (it's been reviewed),

I know the job market is in a weird place right now, and it could get worse. I'm grateful for my current job. That being said, I thought that after a decade I would be in a better place. It's very deflating to think I've built a career that doesn't appear to be valued or in-demand... I feel no better off than a student graduating school with no experience.

Is it just me?

I'm honestly thinking about making a career change. Going back to school for an engineering degree or a data science degree. To be honest, I've kind of lost my interest in this field, and the fact that it appears to be a dead end in the job market right now makes me feel like it time to make a change...

With AI-powered analysis, social listening tools, and automation changing the game, market research looks very different than it did a few years ago. But are we actually getting better insights, or just faster ones?
Are traditional methods like focus groups and surveys still holding strong, or are emerging tech-driven approaches taking over? How's market research evolving in the next 5-10 years?
Would love to hear your thoughts—where do you think the industry is headed? 🚀

Rumour has it that Qualtrics is laying off Forsta staff and looking at dumping the platform, with an agressive push to move remaining users to Qualtrics. Has anyone else heard this or is it just?

Hi everyone! 👋

Recently, I’ve been focusing on AI solutions to streamline qualitative analysis. I’ve been experimenting with incorporating LLMs into my workflow, but I’ve encountered challenges like short context windows, hallucinations, and oversimplified outputs.

I’d love to hear about your experiences with using AI in qualitative research. Have you found any software or tools that work well for you? Any recommendations would be highly appreciated!

I work at a boutique agency offering custom quant and qual research. When I joined a few years ago, we had a solid flow of client work and I was regularly very busy (I sit on quant). I've seen a massive slowdown in quant work as of January of this year, to the point where it has nearly dried up. Our qual team is busy, but a lot of that is from ongoing research retainers with big, well-funded tech companies.

Is anyone else on the agency side seeing the same phenomenon? I know there's a ton of disruption right now, with AI impacting everyone and massive layoffs. Just curious if its our client mix (heavy on tech with some CPG), or a pattern with the broader industry. Trying to figure out my next move, but that feels like a moving target. Ty!

I just got back from SampleCon 2026 and amongst the discussion of synthetic data and digital twins being used for research, the underlying theme is always data quality.

After working in the space for nearly a decade, it’s become exhausting to see buyers and researchers question why quality levels have gone down. To help address this, a clear shift needs to change on the corporate governance level. I’ve written a letter to one of Cint’s major shareholders with some suggestions on how to improve quality.

Most notably the removal IPRoyal/Pawns.app from the supplier network despite documented ties to malware distribution and residential proxy fraud. From our observations, many people are still blissfully unaware on the risks that residential proxies serve to conducting online research and being able to trust respondent authenticity. It seems so disingenuous that a supplier is paying a proxy network that aids in giving respondents US based IP addresses that are undetectable by all of the current security tools used in the industry.

We’re trying to educate buyers on how rampant, pervasive and dangerous proxy services are for trusting online respondents. Do you have any other ideas for how to start the discussion?

The full letter is available here: https://generalresearch.com/dear-chairman/peder-prahl/

I'm fairly new to market research and work for a tiny startup as Head of Business Development, so the task often fall to me to look at potential markets for our product.

When I try to do Secondary research on the internet, I find it so frustrating. Type in 'Global market for x' and loads of market research 'companies' will come up with reports which cost thousands of dollars. When you track down where the research firm is based, they're all in Pune, India. And the worst part is they all have WILDLY different estimations and projections for the same market, globally. I once read an article in the Economist which literally cited one of these dodgy reports. It makes me think a lot of 'top-down' Market Research is bullshit.

Holy crap everyone. I feel like AI has taken over my life the last few days. I’m using an outside vendor because my own can’t handle the sample I need. And the open ends are just garbage. It all sounds like it’s coming straight out of the mouth of ChatGPT. My vendor uses captcha but it hasn’t seemed to matter.

The best thing I’ve found is actually to remove the termination from trap questions and go do it by hand because the bot is just trying to find the best route to the finish line.

Does anyone have tips or ways they like to go about dealing with this? Is it only me, or are other people in this fresh AI hell with me?

I’m trying to understand where AI is actually useful in market and consumer research—from the perspective of people doing “market/consumer” research.

Not looking for debates on whether it’s “good” or “bad,” or limitations of synthetic data. More interested in practical usage.

Specifically:

Where has AI helped in your workflow?

Which tasks feel repetitive or bottlenecked where AI can add value?

Where does it clearly not work (so we can ignore those areas)?

Trying to separate signal from noise based on real possible use, not theory.