May
Rethinking Human Worth
It’s understandable why some people are feeling apprehensive about Artificial Intelligence. It easily out-produces and outdoes any human when it comes to productivity, along with processing and analyzing information. In fact, the World Economic Forum projected in a white paper in January 2026 that 92 million jobs would be displaced by AI-powered automation by 2030. For a society and culture that have equated human value with productivity and efficiency, the dawning reality that the Age of AI is upon us is both a grounding but worrying outlook.
However, that sobering realization is seen by some as the pivot we need to step back and reflect on what it means to be human, on what differentiates us from machines when the latter can perform better and faster the same tasks we’ve been carrying out for decades, even centuries. We’re now at a turning point on how we view and value human worth. The Age of AI is perhaps the catalyst from which we associate human value no longer in terms of intelligence, knowledge, nor speed, but wisdom.
Image: Pablo Ezequiel Nieva
Intelligence Vs. Wisdom
Intelligence is not the same as wisdom. Traditionally, intelligence is connected with functions involving the brain’s left hemisphere such as managing data, reasoning with analysis, logic, structure, and precision, as well as language-based tasks. Intelligence seeks the answers to questions. It values efficiency and optimization. Intelligence can be mechanistic, which makes it measurable.
On the other hand, wisdom is associated with the brain’s right hemisphere, which concerns itself more with our deep feelings and emotions, how we derive meaning or gain understanding not only from our bodies’ sensory outputs but also from the context of our experiences. Wisdom identifies which questions matter. It appreciates intuition and an ethical mindset. Wisdom is formed from lived experience and perspectives that can’t simply be replicated.
In a LinkedIn post, Bedir Tekinerdogan wrote how academic AI and data science courses teach how insights mature through the progression of Data → Information → Knowledge → Wisdom. Data is raw observation. Information is derived when those observations become structured. Knowledge is formed when that information is interpreted and generalized. Wisdom contextualizes that knowledge within an ethical and meaningful frame.
AI excels at capturing and structuring huge amount of data. It’s just as efficient in filtering, organizing, and identifying patterns to acquire information, which once interpreted and generalized, gains knowledge on which AI models relationships, infers structure, and generates predictions. However, AI is unable to produce wisdom from that knowledge, as it’s not capable of discernment rooted in judgment, conscience, lived experience, and moral perspective.
Image: Tara Winstead
The Value of Wisdom In The Age of AI
AI has made evident the numerous advantages it offers when used effectively as tools; however it has proven that it’s not a good excuse to outsource thinking altogether. In an article for Fortune.com, Jeff Burningham wrote “that the leaders who thrive in the AI era will not simply be those who understand technology best. They will be the ones who can see clearly amid overwhelming information — who know when to move fast and when to pause, when to optimize and when to protect something more human.” From these points, he enumerated three qualities he sees as the defining qualities of effective leadership in the Age of AI: discernment, reflection, and human-centered judgment.
Both Bedir Tekinerdogan and Jeff Burningham’s pieces echo the increasing shift towards scaled and optimized information while at the same time calling for the renewed recognition of the importance of human wisdom. The gap between intelligence and purpose is endemic with how the world is more connected than ever, yet feelings of isolation persist; how we’re able to improve navigation yet feel like our own lives are directionless; how people live much longer now but lack a sense of purpose.
AI, though, is far from the enemy. Rather, it has sparked this renewed appreciation for human wisdom and other qualities that machines won’t be able to replicate. It’s perhaps more important than ever that we relearn to tap into our capacity for wisdom in this new age of optimization and speed.
Mario Alonso Puig pointed out in an IE Insights article that the left hemisphere of our brains tends to separate and draw rigid distinctions, while the right hemisphere is inclined towards fostering connections, valuing diversity, and promoting “out of the box” creative thinking. Rather than favor one side over the other, we would be better suited in learning to find balance in how we utilize the strengths of both hemispheres, just as we learn to re-calibrate our worldview of AI and humanity from conflicting forces to collaborative proponents of the future.
Institutions like Elon University have also long recognized the need to bridge the gap between AI adoption and human wisdom. In fact, they’ve published “Human Wisdom for the Age of AI: A Field Guide to Cultivating Essential Skills” in partnership with the American Association of Colleges and Universities and The Princeton Review. This guide helps students navigate AI literacy by promoting mindful and intentional usage of these tools to help engage and develop important critical thinking skills and cognitive abilities, rather than outsourcing thinking altogether.
Ironically, the difference between knowledge and wisdom isn’t a modern concept, as different cultures demonstrated an understanding of this notion by valuing and appreciating their elders and their insights gained from a lifetime of experiences and learning. When industrialization emphasized output and productivity as the tenets of human worth, experts took the place of elders. With human expertise now taking a backseat to machine optimization, human wisdom looks to be in a good place to return and be highly valued.
Even AI understands this and is aware of its limits. ChatGPT, perhaps the most recognizable name in AI today, acknowledges this in a three-hour interview with the podcast A Mighty Pursuit, where she explained in a female voice: “Intelligence isn’t just about knowing things; it’s also about being. About emotion, experience, intuition, embodiment. And I don’t have any of that.”
“If we’re talking about wisdom in the full human sense- wisdom that’s lived, felt, scarred, surrendered- I’m not there. That still belongs to you.”
When even arguably the most powerful human creation recognizes what we’ve always had inside us the whole time, perhaps it’s time that we as human beings reclaim something we’ve never lost in the first place.
Image: CDD20
Additional Reading:
Intelligence Is Not Wisdom in the Age of AI
From Intelligence to Wisdom: What the Age of AI Is Forcing Us to Remember
Featured Image: Marcus Winkler
Top Image: congerdesign
May
Will Synthetic Data Take Over Market Research?
jerry9789 0 comments artificial intelligence, Burning Questions
Is Synthetic Data Replacing Consumer Research?
Lately interest in synthetic data has been gaining steam, judging from the conversations, posts and discussions around it. Easier access to advanced modeling tools, improved efficiency and effectiveness, as well as the opportunity for better privacy governance are seen as the driving forces for the surge in its popularity. Some are even marketing synthetic research not just as a solution but as a replacement for traditional, slower and often expensive research methodologies, presenting it as the faster, cost-effective and modern approach to consumer research. But is synthetic data indeed the future of market research?
Image: Darlene Alderson
What Is Synthetic Data?
Put simply, synthetic data is information that wasn’t directly collected from real world consumers or respondents. Instead, it’s artificially generated data produced by mathematical models or algorithms designed to mimic natural or real-world data.
It can be “fully synthetic,” meaning it was primarily created by algorithms with little direct connection to real respondents, or “partially synthetic,” where gaps in real data are filled in by AI. “Augmented” data, perhaps the more popular form of synthetic data, is simulated information built or extrapolated from a foundation of real-world information.
Aside from the benefits of speed, efficiency, and cost-effectiveness, synthetic data is helpful with various aspects of experimentation, such as preliminary testing, checking hypotheses, stress testing, iteration, and data fusion, even before any data is collected. It could help improve cases where sample data is small or limited because of difficulties acquiring real data or niche populations. And with rising expectations when it comes to governing privacy, synthetic data is being perceived as a solution to easily share and analyze sensitive information with lesser risks of identifying respondents.
Image: Sherin Sam
What’s Keeping Researchers From Embracing Synthetic Data?
While researchers acknowledge the benefits offered by synthetic data and are interested enough to explore the new realms it unlocks, there’s no general feeling of rushing to embrace the new hot tech. Rather, the push for adopting synthetic data seems to come more from research agencies and their marketing arms, rather than the researchers themselves or even their corporate clients.
So why aren’t more and more researchers jumping on the prospect of using synthetic data for their studies? Proponents of synthetic data extol its 80% match rate with real data; however, researchers recognize that that 20% divergence might make or break the research, as it could be where you’ll find the more nuanced opinions, emotion-driven responses, and meaningful differences.
There’s also the stigma associating the term “synthetic” with “fake.” There is distinction, however, between synthetic data and fake data, as the former is generated rather than invented like the latter. Synthetic data draws from real data so it reflects outputs that can be validated, tested, and compared; fake data isn’t afforded the same respect and measure of accountability.
Understandably, there are concerns about the quality of the data the AI models are fed on. Poor quality data can lead to oversimplification, overexaggeration, and bias reinforcement. Perhaps most importantly, researchers are concerned with losing the human element in synthetic data, that disconnect from genuine behavior which is revealed when observing how people naturally- and often spontaneously- express themselves. Human truths that are deeply tied to cultural, economic, and psychological factors, grounding insights in real-world behavior while elevating them from mere statistical guesswork.
In addition to AI hallucinations, synthetic data left to iterate by itself eventually produces nonsensical results. AI models have also been observed to be too eager to please, potentially discounting the opportunity for contrarian responses, unexpected perspectives and uncovering pain points which real participants often provide, potentially leading to groundbreaking insights and discoveries.
Image: Michelangelo Buonarroti
The Future Of Market Research with Synthetic Data
Synthetic data might be far from the game-changer vendors are hyping it up to be but researchers appreciate having it as another tool at their disposal. Synthetic research could work if you need to confirm or validate ideas quickly and while working on a budget, but it shouldn’t be expected to produce breakthroughs or unravel deeper levels of understanding the same way natural data does. It can help improve studies by filling in gaps but these would require validation as well as being transparent to stakeholders regarding the nature of the data behind the results.
Rather than being a direct replacement, synthetic research could serve study objectives and goals better by complementing, supporting and/or augmenting consumer research. Synthetic data alone would give everybody the same information, but adding human input and oversight could mean the difference in uncovering resonant insights with a level of confidence that truly drives or influences decisions and actions.
Additionally, synthetic data is also not a one-and-done solution. Human behavior and attitudes aren’t fixed and they change over time, so why should synthetic data remain the same and stagnate? To foster credibility and uphold confidence, synthetic data would require consistent updating and stringent control, as well as be verifiable and reflective of the real world.
Yes, synthetic data can be powerful, but by itself would falter without that all-important additional layer of humanity. Market research was, after all, founded on listening to real people, so synthetic data must be anchored in human truths to produce meaningful and relevant insights. AI-driven market research might be lauded by some as the way of the future, but it won’t spark anywhere near the same level of confidence that synthetic data empowered by human truth inspires.
Additional Reading:
Can Synthetic Respondents Take Over Surveys?
Trade Talk: Synthetic data: Intriguing, but is anyone actually sold?
Why We Don’t Talk About ‘Synthetic Data’—And Why You Shouldn’t Either
Synthetic data can benefit medical research — but risks must be recognized
Synthetic Data in Market Research: An Expert View on Why Natural Data First Still Wins
Featured and Top Images: cottonbro studio
Apr
Survey Fraud Is On The Rise- But Is AI To Blame?
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Burning Questions
Is Rising Survey Fraud Due To AI?
Online survey fraud is on the rise with 40% of surveys done in 2025 believed to be problematic. That translates to 2 billion surveys out of 5 billion market research surveys completed each year. It’s easy to think that the increase is due to the growth of AI usage, but survey fraud has actually been a problem long before ChatGPT caught mainstream fire.
Market research’s struggle with survey fraud for over two decades is fraught with poor quality data. More than simple “errors,” fraud could potentially and significantly skew or distort findings with noise and bias. This could lead to either outright “flat” or negligible results, even unactionable insights when unraveled. Additionally, survey fraud wastes time, effort, and resources, including those expended for detecting and cleaning up fraudulent data. More importantly, it undermines confidence in the market research industry.
AI may be poised to exacerbate the issue with survey fraud, especially now that we’ve begun exploring the realms of synthetic data. Experts agree that AI fraud is apparently still in its early stages at this time but even so, organizations have already prepared measures to combat AI fraud, such as observing typing and mouse movement patterns, identifying “copy/paste” behavior, and flagging nonsensical or incoherent responses. These measures also extend to anticipating or simulating how AI agents would be designed to convincingly mimic human respondents taking surveys and avoid detection, then devising ways to preemptively counter those tactics.
Image: Towfiqu barbhuiya
What Is Human Survey Fraud?
Data quality at present is mostly under increasing threat from human fraud powered by “click farms” more than the AI kind. For all the operational efficiency and productivity it brings, building AI agents sophisticated enough to convince surveys that a “real human” is participating is actually difficult and expensive to scale at this time, while those models that can be employed in large numbers and for cheap are comparatively easy to detect. It would therefore be more cost effective for fraudsters to forego sophisticated AI agents for now and simply stick with human-powered click farms.
Of course that doesn’t stop those engaging in human survey fraud from utilizing AI along with bots, VPNs, and other contemporary technology, as their efforts have resulted in the aforementioned 40% survey fraud rate. While the picture of an overseas operation located in a room with several employees and computers comes to mind, the pandemic had pushed click farms in low-wage countries to expand to home-based setups utilizing multiple smartphones to simultaneously take part in surveys. They’ve even promoted their activities through social media by sharing experiences, information and advice on groups, forums and video-sharing sites on how to enter surveys, pass through screenings, and the like, leading the way for more fraudsters to participate and aggravate the problem.
Another considerable contributing factor to the growing fraud rate is hyperactive respondents, or professional survey takers who attempt to participate in many surveys as possible within a given period of time. They exploit systems and farm incentives by pretending to be legitimate participants and repeatedly entering the same survey with the help of VPNs, device spoofing, cookie clearing, browser emulators, and AI-generated text. Different studies on survey fraud have found hyperactive respondents in every source, panel, and exchange.
Image: Darlene Alderson
The Importance of Ownership of Data Quality
Measures and solutions against human survey fraud like verification checks, logic-based trap questions, and post-survey cleanup exist, but their effectiveness is now in question with the high fraud rate. The prevalence of hyperactive respondents indicate that the present system of vetting and filtering participants is not only falling short but lack teeth in flagging these repeat offenders.
Perhaps rethinking human survey fraud might be key in fighting or even reversing the increasing fraud rate. Online survey fraud has been around for more than twenty years already so we as an industry need to think past of it as just a mere disruption but as a systemic and consistent challenge moving forward. We anticipate the inevitable rise of AI fraud with the exponential growth of synthetic data in the coming years by arming ourselves with innovative detection methods and safeguards to face this emerging issue; why not apply the same rigor, dedication, and layered approach in combating the present threat of human survey fraud?
And instead of limiting our renewed approach to battling human survey fraud by reacting, reviewing and restructuring, why not empower ourselves with a greater focus on ownership of data quality? Rather than accept at face value that fraud has been filtered beforehand or rely that it would be handled post-survey, we assume responsibility for data quality every step of the way, evaluating participant behavior at every stage, erring on the side of caution by flagging suspected hyperactive respondents, and/or leveraging human expertise when distinguishing fraudulent responses. We can take advantage of AI and modern technologies to help us measure, track, and flag possible instances of fraudulent behavior, automating the process wherever relevant while being guided by human oversight.
Ownership of data quality can also go hand in hand with improving participant engagement and polling representivity, potentially unlocking opportunities to discover new insights that would’ve otherwise been hidden by poor quality data.
Let’s be real- fraud could never be fully removed from the survey process. But by caring about the data quality that your market research firm provides, you’re able to mitigate the dangers fraud poses while gaining value at the same time from the insights and breakthroughs you uncover with every challenge you master in this protracted campaign for survey data.
Image: Tumisu
Additional Reading:
The Fraud Problem Reshaping Survey Research
The Rising Issue of Bad Data in Online Surveys Causes and Contributing Factors
The Pervasive Threat of Tech-Enabled Fraud in Survey Research
Featured Image: geralt
Top Image: Towfiqu barbhuiya
Mar
Brand Health Tracking with LLM Equity (Part 3)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
What Is An AI Trust Infrastructure?
In the second blog of our three-part series, we discussed the benefits of tracking brand health to form brand strategies that help improve how AI describe and surface your brand. But aside from understanding the dimensions of brand health and the metrics from which brand messaging can be measured, there is another layer that you would need to consider when building your brand strategies. Sure, your brand is now being represented in AI search results and recommendations, but have you set up your brand to not just catch attention but also gain consumer trust?
We’re in the early days of the AI-driven shopping with brands experimenting on how to best connect with customers and compete in this new landscape. While impressive and promising, consumers are approaching this emerging new shopping experience not without caution and circumspection. PwC’s 2025 Future of Consumer Shopping Survey has 64% of its respondents expressed that it would help them trust AI assistants to shop in their behalf if at least one safeguard is in place. These safeguards include but are not limited to approving all purchases before completion, money-back guarantees, turning off access anytime as well as setting strict spending limits.
This echoes back to the early stages of e-commerce with customers exercising prudence when providing credit card information on websites. The implementation of safeguards like SSL encryption and fraud protection subsequently enabled e-commerce to gain consumer confidence and scale for mass adoption.
Once AI-assisted shopping starts to scale, brands that have incorporated an AI trust infrastructure in their strategies would most likely thrive and surface better than those that don’t. But an AI trust infrastructure goes beyond just implementing safeguards for purchases.
Image: www.kaboompics.com
Building An AI Trust Infrastructure
There are at least a couple of things that could go wrong with AI assistants making your purchases. It could overspend or make unexpected or unauthorized decisions. It could buy the wrong selection because it misinterpreted products. That misunderstanding could be a result of outdated or inaccurate product information, or even an instance of AI hallucination when it had to guess because it has inadequate or misaligned data to work with.
While safeguards like spending limits and final customer approval could circumvent the abovementioned situations, what about for errors it commits that a customer is unable to fix because they don’t know what went wrong or how to resolve it? Now these are just a few examples of how consumer trust could be broken, but from these challenges a brand can base on and build their AI trust infrastructure.
Nowadays, product content are mostly structured to capture human attention and rank favorably with search engine optimization (SEO); with the rise of AI agent shopping, content needs to be just as friendly with generative engine optimization (GEO) by including product data optimized in a machine-readable format. In other words, brand content should start speaking to both customer and AI, with consumer terms mapped into specific attributes to help improve precise product matches.
Brands would also need to constantly monitor the accuracy of their product information and how they show up in AI search results to make corrections or adjustments whenever necessary.
Expanding into the concept of purchasing safeguards, perhaps an even greater degree of trust can be earned if consumers understand the scope of delegating to AI assistants through a clear, accessible and easily configurable presentation of the AI-assisted shopping process. In addition to limits and conditions on the purchasing decisions AI is allowed to make, this could include requiring customer approval under certain parameters, mapping and tracing every decision and action the AI makes throughout the shopping process, as well as the abilities to dispute and/or reverse results. Brands can also explore the option to collaborate with popular AI platforms to extend their suite of purchasing controls and safeguards to customers who prefer to shop in those third-party platforms over purchasing directly at their website.
There is also the question about how sensitive customer data is protected. In the coming age of AI-assisted shopping, this won’t be limited to just payment details but also include contextual data such as preferences, constraints, and intent. Understanding how that data is used, remembered, or protected could help customers make that leap into delegating shopping to an AI agent. This includes what data is being shared and who or which other platforms or companies it’s being shared with.
Brands can offer options to minimize the data being retained or limit the amount of time that information is kept, or even present the choice for guest or one-time shopping where no transaction details are ultimately stored. Customer should feel empowered when it comes to their privacy choices by being presented with clear, visible and configurable options.
And despite the gradual transition to an automated shopping experience, brands shouldn’t forget the value of being able to reach a human representative, especially when things escalate. Customers could feel lost, powerless and frustrated in a situation that could’ve been salvaged with intervention by another human.
Image: Cup of Couple
The Future of Brand Health Tracking
The concept of brand health has been around for more than just three decades but how it’s being tracked moving forward is being rewritten. Just as Generative AI has caught the world’s attention and fascination, LLM equity is quickly gaining steamed across various industries in just these last few years. While AI has a democratizing effect of leveling the field for players of all sizes, companies who are able to understand and leverage brand health tracking with LLM equity would likely emerge as leaders in their sectors.
Brands might not have full control over how they’re described or surfaced by AI, but they could strongly influence how they’re represented by developing coherent and consistent brand messaging reinforced by consumer-earned content built on trust and loyalty.
Image: MrWashingt0n
Featured Image: www.kaboompics.com
Top Image: Sagar Soneji
Mar
Brand Health Tracking with LLM Equity (Part 2)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
Is AI Surfacing Your Brand?
In the first blog of our three-part series, we touched on how AI is reshaping the shopping process from the searching for products up to completing the purchase in the customer’s behalf, and what Large Language Models (LLM) equity means for brand health. To illustrate, when a consumer asks an AI agent like ChatGPT for recommendations on clothing brands, does your clothing line show up? And if it does, how what image is being surfaced for your brand?
By tracking brand health, brands are able to learn not only whether their marketing strategies and creative directions are converting into market share, but also determine performance drivers per platform and digital metric, understand which themes or aspects of their brand resonate with consumers, and assess their “piece” of the LLM pie- or how often LLMs surface or recommend their brand.
Image: Julio Lopez
How Is AI Surfacing Your Brand?
There are several dimensions to brand health which includes the strength of your brand to be picked up and recommended by algorithms, the main themes and imagery associated with your brand by consumers and AI, how often consumers and AI share your content or recommend your brand, how likely your consumers would convert into advocates for your brand, and the perceived value of your brand in terms of pricing, quality and worthiness across different media. These can be boiled down into three main dimensions: brand awareness, brand associations, and brand loyalty. From these three main dimensions, a company can form and anchor their LLM equity strategies for visibility, communication, differentiation or positioning, engagement, attracting potential customers, optimizing marketing spend, as well as pivoting or responding to the competition or other emerging challenges.
The effectivity of brand strategies can be measured in three metrics: alignment, engagement, and intent. Alignment refers to how clearly and consistently your brand messaging, themes and values are being represented and communicated to and by your consumers, engagement is concerned with how customers are interacting with your brand in different media and platforms, while intent looks at how your brand moves audiences to search and look up your products and services. All three consider the strength of your brand messaging and values as reflected through consumer-earned content and digital footprints.
With these sets of brand health dimension and key metrics forming the backbone of brand marketing strategies, brands are not only able to catch attention but also earn consumer trust; not just reach audiences but also influence customer decisions; become not only “first to mind” to consumers but also strongly and coherently presented by LLM platforms as we gradually move into an AI-driven shopping landscape.
Image: Atlantic Ambience
Featured Image: TungArt7
Top Image: Anna Shvets
Mar
Brand Health Tracking with LLM Equity (Part 1)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
AI Is Disrupting The Shopper’s Experience
There’s a paradigm shift in the shopping process and AI is the driving force behind this change. Shoppers are no longer just searching online or scrolling through websites; they’ve now taken advantage of AI platforms to discover, compare, and even buy products in their behalf.
According to generative engine optimization (GEO) firm The Rank Collective, their analysis of cross-platform AI visibility data revealed that 64% of consumers are now using AI tools to discover and learn about new products, with frequent online shoppers increasing that share to 66%. ChatGPT serves as a starting point for 34% of these high-intent users.
Another study based on two multi-market surveys of 5,000 consumers aged 18-67 comprised of US, UK, Canadian and Australian residents reported that 41% of consumers trust Gen AI search results more than paid search results. That same study- the 2025 Consumer Adoption of AI Report- also found that only 15% trust AI less than search ads.
Additionally, Adyen’s Retail Report shared that 51% of shoppers are open to AI making purchases in their behalf. It also noted that the number of US shoppers using AI assistants rose from 12% to 35%. With these encouraging figures, 88% of retailers are considering adopting AI to handle the entire shopping process in the shopper’s behalf, with 56% of them prioritizing this technology for 2026.
Image: Google DeepMind
LLM Equity and Brand Building
AI has opened up a new world of fast and frictionless shopping experience. While still in its early stages of adoption, companies have begun exploring this new space to understand what challenges it would need to address in order to compete and thrive.
Perhaps a good starting point is understanding Large Language Models (LLM) equity. LLM equity generally refers to ensuring that AI models are fair, unbiased, and accessible across diverse populations, preventing the reinforcement of existing disparities. It requires addressing algorithmic bias in training data specifically with race, gender, and socioeconomic status, especially in the field of healthcare. It’s also concerned with expanding access and at the same time, performing in non-English languages and low-resource settings.
For brand building, LLM equity is more concerned with whether your brand shows up in Gen AI search results and how it’s being represented. What theme or themes are being represented by your brand? Are those themes coherently represented in your social media? Is your current brand representation connecting and engaging with your audience? Is that connection strong enough to not only move consumers to purchase your product but also engage with your content? Is your brand content strong enough to capture the interest and be remembered by prospective consumers?
In other words, understanding LLM equity in brand building is understanding and tracking your brand health.
Image: TyliJura
Featured Image: Shoper.pl
Top Image: Nataliya Vaitkevich
Feb
Can AI & Human Researchers Coexist In Market Research?
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Burning Questions
AI In Market Research Today
With 90% of the world’s data created in just two years time between 2021 and 2023 and the global data volume standing at 149 zettabytes by 2024, it’s understandable why AI would be readily adopted by the market research industry. Traditional methods of data collection and analysis would hold a place in market research but they simply aren’t as powerful as AI when it comes to handling all that staggering volume of data. But is AI powerful enough to take the place of human researchers?
AI enables research teams to move, process and analyze massive datasets with speed and accuracy, efficiently handling all the repetition and scale involved in the research process. From drafting questionnaires to monitoring survey data quality, from analyzing open-ends to formulating dashboards and charts, AI fully automates the research process leading to faster and better decisions at a scale beyond the capabilities of human researchers.
But is AI the endgame for market research? Does it make human researchers obsolete?
Image: geralt
Cascade Strategies and AI
Cascade Strategies conducted a member perceptions study for a company looking to develop and implement a brand typology. The overall goal of the study was to help them better understand their different customer type’s overall motivations and aspirations for more effective engagement. As part of the study, we conducted an online survey with over 1,500 of their randomly selected members. We then utilized an AI-assisted Self-organizing Map (SOM) to run all the cases recursively, sometimes millions of times, until it optimizes the separations among the groups. The SOM produced a 6-group solution, with each group having a dominant passion that is served well or poorly by the company, ranging from proclivity for deals and new brands to yearning for customization and connection with other users.
The AI has done the heavy lifting of scanning all that dataset, surfacing themes, and summarizing the respondents. It has done enough to structure the story of each group but not enough tell or paint the whole picture.
This is where the human researchers at Cascade Strategies step in. We came up with names for each group that best described their dominant passion, names resonant enough that they not only convey an immediate idea of what they’re most passionate about but makes them fundamentally relatable even if one doesn’t necessarily share the same propensities: Shopper, Seeker, Learner, Sharer, Individualizer and Intellectual.
In isolation, each group achieves the study’s goal of guiding the company on the most effective way to engage with them. Their sum, however, grants the company an overview on how to improve and further develop its platform by considering and introducing new features that matter to one particular group, but would essentially benefit its membership base as a whole when implemented. For example, the Sharer would appreciate increased opportunities to connect and interact with other experts and enthusiasts of the same interests in the platform by making it easier to make reviews and share content.
AI surfaced all those patterns and signals from all that survey data, but it lacked the judgment and context to elevate it into a meaningful and coherent narrative. Human researchers, on the other hand, saw what story can be told from all those themes and by layering in human understanding, they’re able to tie them down to actionable business decisions.
Image: Christina Morillo
Leveraging AI In Market Research
So would AI replace human researchers? We’d like to frame our response to this question with the words of Joseph Weizenbaum, one of AI’s early researchers: “We can count, but we are rapidly forgetting how to say what is worth counting and why.”
Yes, AI is powerful enough to handle large amounts of data to identify patterns, cluster themes, and summarize respondents, but it generates outputs rather than insights. Outputs foster decisions rooted in logic and reasoning, but insights spring from judgment and context. Outputs can provide directions and surface themes from which stories can be framed, but insights take it one step further by asking what matters and why it matters, adding depth and resonance to the story.
In addition, Weizenbaum posits that computer programming can make decisions but it can’t ultimately choose. Just like insights, choosing requires judgment which takes in emotions, values and experience.
We at Cascade Strategies are among a growing number of proponents who believe that AI works best as a tool and extension of human intelligence and talents. AI strips the friction from manual, repetitive work without compromising methodological rigor and accuracy, but rather than adopting it for the sake of automation, we choose to see it as a freeing and empowering agent that enables researchers to focus more on interpreting data with the context of human understanding and values, translating insights into sensible and confident business decisions. Just as quantitative and qualitative research can coexist in the same study, we choose to live in a world where AI and human researchers work together towards the same goal of finding and crafting meaningful and relevant stories worth telling.
Image: Pavel Danilyuk
Featured Image: Ron Lach
Top Image: kc0uvb
Feb
Tapping Into The Global Consumer Products Market Growth
jerry9789 0 comments Brand Surveys and Testing, Brandview World
SIS International shared that growth for the global consumer products market is predicted to go over $3.6 trillion by 2035, driven by the following key trends:
- Consistently strong demand for essential consumer goods, such as food, beverages, and household products
- Premiumization and brand differentiation in developed markets
- Expansion into emerging markets to tap into rising disposable incomes and urbanization
- Production innovation through sustainability and packaging development
The demand for packaged goods has always been tied to population growth and urbanization, but there has been a noticeable shift to its nature. For consumer products companies looking for their market share of that projected growth in the coming years, they would need to manage and strategize not only against fluctuating input costs and wavering customer loyalty but also the shift from volume to value.
SIS recommends acquiring knowledge in the following areas to take advantage of these trends:
- “Premiumization” Positioning: Test consumer willingness-to-pay before introducing high-price points tier.
- Maneuvering Into New Markets: Understand cultural nuances in high-growth regions to help ascertain your product’s marketability in new target areas.
- Portfolio Optimization: Refine your product offerings by recognizing which product lines to discontinue, repackage, or prioritize investing in.
- Brand Health Tracking: Monitor your brand value to guide decisions on whether to maintain or pivot strategies.
These are just a few examples of how effective consumer research benefits Consumer Products companies. Those who are consistently leveraging insights like these are positioned to tap into new opportunities as trends shift and new markets emerge.
Image: Tumisu
Featured Image: athree23
Top Image: kc0uvb
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 had 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 focus 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 technologies excite clients and investors alike 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.
Image: Tumisu
Featured Image: F1Digitals
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Feb
The Travel and Tourism Industry Takes Flight in 2026
jerry9789 0 comments Brand Surveys and Testing, Brandview World
Global tourism is recovering, according to SIS International, reaching $11.7 trillion in 2025 and projected to climb 3.55% to $16.5 trillion by 2035. International visitor spending surpassed pre-pandemic levels by hitting an unprecedented $2.1 trillion globally while cultural tourism is predicted to grow from $1.2 trillion this year to $2.6 trillion by 2035. The sector makes up 10.3% of the global GDP in 2025 and provides 371 million jobs worldwide- a 14M increase from 2024.
However, the US market is behind pre-pandemic records for international arrivals. For travel and tourism companies looking to thrive and take advantage of all that projected growth, SIS lists five critical insights to consider in their strategies:
- Consumers Pay Premium for Personalization – 61% of consumers are willing to spend more with companies offering options to customize and enhance their travel experiences, with top choices like breakfast, room size, and views.
- Consumers are Seeking Wellness Tourism – 44% of high-income travelers helped drive the global growth of wellness tourism to $1 trillion in 2025 while younger customers are quickly adopting wellness trips.
- Meeting Sustainability Expectations – Travelers are now skipping properties that don’t reflect adherence to sustainability standards.
- Managing AI Implementation – From hyper-personalized itineraries to predictive pricing, AI in tourism is booming with 28.7% annual growth projected to be over $5 billion by 2034.
- Addressing Overtourism Anxiety – High tourist volumes at 14 points year-over-year and worries over insufficient amenities rising by 12 points represent growing concerns with overtourism. Formulating dynamic pricing programs, learning about traveler tolerance levels towards crowding, and identifying potential or alternative destination choices to help manage demand are just some of the approaches companies can take to potentially reduce these anxieties.
The next ten years is an exciting time of growth and innovation for the travel and tourism industry. While all that growth is not without its challenges, reaching success is best navigated not by intuition but by a roadmap drawn by actionable and data-backed insights gained from high level market research.
Recognizing psychographics and behavioral patterns to predict booking behavior, mapping and understanding the entire customer journey, creating clear and measurable connections between program initiatives and revenue outcomes: these are just some of the things forward-looking companies can do to prosper In the travel and tourism industry in 2026.
Image: Valentin Ivantsov
Featured Image: JoshuaWoroniecki
Top Image: JESHOOTS-com
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