Feb
What’s Happening Nowadays With Survey Samples? (Part 2)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World, Burning Questions
Why The MR Industry Should Start Collectively Caring About Data Quality
In his recent LinkedIn post, JD Deitch offered two explanations on why the sample market is what it is right now and has been for the past two decades: clients either don’t know or don’t care how bad the quality of data sample they’ve been receiving. Between not knowing and not caring, the latter is the more egregious of the two.
In Part 1 of this series, we touched on the challenges presently faced by the sample market: participant engagement, polling representivity and fraud as illustrated by the Op4G / Slice MR scandal. Of the three, fraud captures the most attention, the one that makes headlines, the one that stirs up the most discussion and calls for resolution. The threat of fraud is in everyone’s mind, and that’s why there are measures and protections in place and constantly being developed to detect and address it.
Fraud, however, may not be the key issue out of the three. In the Greenbook podcast, Deitch has pointed to participant engagement as a long-standing challenge that the industry has always been aware of and has tried multiple times to solve. Fraud has always been tagged with large sum of dollars lost or deceptively gained; what most don’t see or take into account is how much revenue or opportunity is missed because of bad or low quality data generated by poor engagement. Yes, fraud undermines credibility and trust in the industry but there always has been avenues to regain them; market failures due to poor data quality may not be as visible but the damage they create linger and influence. And that damage through the decades has now translated to the indifference clients feel towards sample data being produced. As Deitch puts it, “Most survey research projects just don’t matter enough for clients to demand better.”
The current product coming out of the sample market has been commoditized enough that they hardly affect business decisions. Clients don’t see enough value or endorse the same level of confidence in the present product to justify spending more to learn or find out what people are thinking. And if an alternative like AI comes along, clients are roused enough to spend and explore what the other options offer.
“Companies will always want to know what people think. That need isn’t going away.” And this is why renewed focused on the participant experience becomes key. Rather than settle for respondents who have time to fill out questions, find and attract people that are the most invested and involved in the subject matter. Incentivize them for their time and underscore why their thoughts and feelings matter. Connect and foster a healthy yet professional relationship with them. Encourage them to find or refer similar personalities. Build and maintain an engaged panel of quality participants.
New and emerging technology excites clients and investors so look into leveraging them into your methods and processes. Learn the best way to implement AI. Don’t simply deploy new tech to cut costs and time; discover where AI would complement human talent the most and where human supervision is most critical. Collaborate with tech people and developers to design and build systems and applications aligning with your goals and values.
The level of care and effort market research agencies put into their research work would always reflect in the end-product. At Cascade Strategies, we believe excellent and high quality data resonates, and we’re confident it will strike chords in clients to make them care enough. And when clients care enough, they’ll be willing to find out and demand more.
To learn more about how we leverage inspired human thinking with modern cutting edge technology to achieve high quality market research for our clients, please contact us here.
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Jan
What’s Happening Nowadays With Survey Samples? (Part 1)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World, Burning Questions
What is The Op4G / Slice MR Scandal?
Op4G (Opinions4Good) and its offshoot Slice were US-based market research companies whose senior leaders were indicted in April 2025 for selling fake market research over the course of a 10-year period, generating $10M in fraudulent revenue. While they marketed their business model of maintaining “a quality, engaged membership panel” of individuals eligible to participate in surveys, they began recruiting in 2014 certain individuals called “ants” to complete surveys to increase revenue despite producing fabricated market data. Companies that purchased survey data from Op4G or Slice between 2014 and 2024 are encouraged to contact the U.S. Attorney’s office.
The scheme opens up questions on how much these fraudulent market data has permeated the industry, especially when Op4G and Slice presented their survey findings as high quality backed by ISO certification. It brings to light the importance of upholding transparency and accountability in the market research industry despite the availability of certain shortcuts to cut cost and time.
Image: jesben
What is Enshittification?
The Op4G / Slice MR scandal is perhaps emblematic of the enshittification of platforms. Popularized by Canadian writer Cory Doctorow in a 2022 blog post, Wikipedia defines enshittification as “a process in which two-sided online products and services decline in quality over time.” JD Deitch, who cited in a Greenbook podcast Doctorow’s article as inspiration for writing his ebook, described enshittification as “what happens in platforms when they start to seek yield and profitability and growth.”
Together with Lenny Murphy on that Greenbook podcast, JD touched on how enshittification compounds the long-standing issues in the sample market when it comes to producing high quality and reliable market data: those of participant engagement and polling representivity. The participant experience has been neglected and treated as an afterthought by the industry for so long that attracting a wide and diverse pool of engaged and relevant respondents has remained a constant challenge. When participants aren’t incentivized enough to engage with the survey experience, the quality of the data and insights produced risk falling short of their true potential. And when you simply aren’t attracting enough respondents or even give a reason to change the minds of those who aren’t really inclined to participate in surveys, you’re missing out on the opportunity of tapping into subsets of the population that could’ve given new and interesting perspectives.
The emergence of AI exacerbates issues and attitudes towards the participant experience. When client companies have not just years but decades worth of survey data and studies, they could simply shift spending away from participant-driven research to developing AI that could produce synthetic data from their stock. And when research market companies don’t own or have access to such kind of survey information, desperate firms might resort to taking shortcuts like programmatic sampling or like in the case of Op4G and Slice, fraudulent means to generate survey data and revenue.
The quality of the synthetic data being produced from all that past data and studies comes to mind, too. Yes, it would depend on the quality of the training data Large Language Machines (LLMs) is fed. Excellent synthetic data would enable scaling and efficiency. However, excellent synthetic data would be tethered to the subject matter it excels on; deviation from the subject matter might produce less than desired outputs and far from potential breakthroughs or new discoveries. And despite AI’s best attempts to optimize based on what it was trained on, there’s also always the risk of it hallucinating. When one cares enough to understand, working or investing with flawed data is simply intolerable.
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Featured Image: andibreit
Top Image: Tima Miroshnichenko
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