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
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
Oct
A Human Center Makes Market Research All The More Powerful
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
The Future Of Research Is Here
You’ve seen it and there’s no denying it. Industries have been reshaped by the increasing utilization of Artificial Intelligence just in the last few years alone. Promising and delivering speed and optimization at the fraction of the costs and resources, it’s powerful, revolutionary and exciting. And as with any emerging technology, it comes with its own set of anxieties.
In line with its growing popularity and adoption, people in different industries have been expressing nervousness over being replaced in their jobs by AI. Certain repetitive, data-driven tasks are at the greatest risk of being supplanted by AI. However, AI also opens up opportunities to shift focus and upskill the more complex and creativity-driven facets of work roles, creating new jobs or augmenting existing ones.
The research industry is just as impacted by AI’s progressive application. It’s naive to assume that researchers would be replaced wholesale by AI, but there’s more to delivering research results than just gathering and crunching data.
Image: Circe Deyer
Our take on the integration of AI into research
We’ve always maintained that AI is a good advisor, but it’s a poor decision-maker. We’d like to modify that by saying it’s an even worse storyteller, if at all.
Cascade Strategies has been in the market research industry for over three decades now, serving some of the biggest local and international companies. You can say we’ve seen it all in this industry, but we’re just as fascinated as everyone else by the mainstream popularity of AI in the past few years. We’ve applied it in our methodologies, been impressed by its operational benefits and how it changed industries, but in the end, we know truthfully that it is not the end-all, be-all for research work. We believe that AI would serve us better by being a powerful extension of human judgment, creativity, and insight.
AI can be fed large datasets to approximate human thinking, but we believe it can never replicate human perspicacity, the kind of intelligence honed and guided by human values and experience. Take a look at our Expedia Group Case Study where we’ve utilized AI to generate multiple revenue-granting scenarios, then tempered the decision-making process by applying high-level human thinking to craft messaging that resonates with the end-user.
AI-driven research can produce results based on what has come before, but it can never uncover the truly novel, meaningful and resonant insights high-level human thinking unlocks. These are the insights that empower big and sweeping decisions. Data-based results from AI would seem lifeless and unrelatable. But if they are imbued with human interpretation, that output elevates into a masterful narrative that sparks imagination, questions boundaries, and transforms perspectives.
Image: geralt
Featured Image: mohamed mahmoud hassan
Top Image: geralt
Sep
Some years ago, Publix Supermarkets allowed us to conduct qualitative and quantitative research for them. The result was a key brand insight about a kind of consumer called the Reluctant Shopper. Despite the ironic name, this kind of consumer hewed more closely to the shopping experience Publix offered than to competitive shopping experiences. The campaigns built around the Reluctant Shopper helped Publix weather the storm of competition from well-heeled operators like Walmart. Winn-Dixie, a much larger chain with many more stores, perished.
We recently asked Gemini to review the same dataset and report on it. Gemini provided a sparkling and quite accurate report on the data but perceived nothing about the Reluctant Shopper. This is something to bear in mind if you’re a consultant advising a brand on how to thrive: perspicacity counts.
There’s a kind of intelligence AI can’t reach. It has dimension, soul, and human inspiration. We’d do well to remember this as we pour more datasets into the maw of AI. If you’re a consultant and need perspicacity, you might want to contact Cascade Strategies. We can help you see things AI can’t see.
Image: Harrison Keely
Aug
Can Synthetic Respondents Take Over Surveys?
jerry9789 0 comments artificial intelligence, Burning Questions
What Are Synthetic Respondents?
AI has increased operational efficiency by streamlining knowledge bases and shortcutting processes so it’s no surprise people and companies are looking for more ways for its application. For market research, one curious consideration is whether it could take over surveys, essentially by replacing actual respondents with synthetic respondents.
Also known as virtual respondents, digital personas, and Virtual Audiences, synthetic respondents are individual profiles constructed by Large Language Models (LLMs) from real or simulated data. Ideally, the data or descriptions used to generate these profiles come from previously conducted surveys and are combined with individual-level demographics, attitudes and behaviors.
Using these synthetic respondents over real respondents could benefit your research with speed, accuracy and cost savings, at least according to their advocates. Basically, you just need to conduct one survey and from the profile description or data you gathered from the actual respondents, you’re able to generate results from the constructed individuals over and over for succeeding studies and research.
Testing Synthetic Respondents
While synthetic respondents could accurately represent real respondents, relying exclusively on the results from these AI-based individuals may not be entirely beneficial. A webinar hosted by Radius Global took a closer look at the potential of AI-generated synthetic respondents through three case studies of quantitative concept testing, quantitative communications research, and qualitative communications research.
Aggregate results for the concept tests involving game controllers indicate somewhat strong similarities between the results of the real and synthetic respondents. This extends to the results from the quantitative communications research when it comes to the believability of statements on the benefits of milk, although there were some differences. The differences were much more pronounced though when it comes to surprise over the same statements, and there was incongruence when considering how each statement could possibly increase milk consumption.
The qualitative communications research was seeking in-depth insights into women’s needs, perceptions, and preferences for running a race or marathon, with the feedback gathered meant to be used for creative content. Personas were constructed from the profiles of six women aged between 18 and 64 years old who ran at least once in an average week. They had an LLM assume each persona to allow a comparison between findings from real participants to synthetic respondents.
They found that while both real and synthetic respondents have somewhat similar responses when it comes to functional aspects as goals for women in general pursuing fitness, the AI responses lacked emotional expressions. There are also little differences in the synthetic respondents’ responses despite having different profiles, and there was even a lack of subtle differences.
As for concerns among women who are aspirational marathon runners, the synthetic personas were consistent in their responses while the real respondents provided more nuances, variety, and perspectives more prevalent among women.
Synthetic Respondents vs. Real Respondents
Synthetic respondents appear to be useful if you’re evaluating existing ideas and concepts; however, if you’re looking for “breakthroughs” or essentially new insights you would’ve never arrived at had you not performed the case study or research, you would need to engage with real respondents, relying exclusively on their results or combining them with that of synthetic respondents. Yes, there could be cases where synthetic respondents could be used, but the results must be extensively validated. It would also require increasing the efficiency of how data used to construct these individuals are analyzed in addition to enhancing the quality of the data and information gathered for these profiles through thorough screening, intelligent probing, and smart choice models.
There is a place for synthetic respondents in market research, but as another tool in a researcher’s toolbox. They won’t be taking over surveys or replacing actual respondents wholesale anytime soon, it seems, as that elusive “Eureka” moment researchers seek is inherently tied to the nuances and perspectives of human emotion and experience you simply can’t construct.
Photo courtesy of Pavel Danilyuk
Jun
How Great Market Research Can Help Food and Beverage Manufacturers
jerry9789 0 comments Brand Surveys and Testing
How do you navigate a market landscape as vast and dynamic as the Food and Beverage Manufacturing industry? How do you introduce your new product or make it stand out from the competition? And how do you keep your offerings or your company itself relevant amidst the fast-changing times?
These questions might make one think that thriving let alone venturing into the Food and Beverage Manufacturing industry would prove to be formidable and intimidating pursuits. But that wouldn’t necessarily be the case if one leverages great market research in their endeavors in Food and Beverage Manufacturing.
From resonating with the right audience to competitively pricing your product, from adapting new and emerging technologies to innovating in an ever-shifting industry, market research would prove key to the foundation of strategies and achieving the goals of any enterprising Food and Beverage Manufacturer.
Copyright: Gustavo Fring
Benefits of Market Research for Food and Beverage Manufacturers
One of the largest and most saturated industries in the US, Food and Beverage Manufacturing could prove to be challenging and daunting for startups to enter and for new products to be introduced. That is, unless plans and business decisions are informed and backed by data gathered from market research which utilizes one or a combination of research methods which includes qualitative and quantitative research, feasibility studies, focus groups, interviews, and surveys.
Great market research allows upcoming products to find their optimal customer or target audience. This type of research allows manufacturers to tailor their marketing and advertising to resonate with their ideal consumer versus a blanket approach taking aim at multiple customer segments all at the same time, which might be more costly and inefficient. With a better understanding of their target customer through market research, Food and Beverage Manufacturers could improve not only their product’s reception but also their chances of getting their own share of the market.
The Issue of Feasibility
You might even say that market research could inform manufacturers if there is even a market for a particular product. Feasibility studies and the like would attest whether there is money to be made with a proposed product which you could also present to interested investors.
In addition, market research not only provides insights into consumer behavior but also of trends and opportunities for growth, improvement and innovation. Manufacturers who are able to identify these opportunities and develop the appropriate products or solutions could greatly benefit for being first in line, discovering and capturing niche markets, as well as gaining an edge over their competition. Identifying or forecasting consumer and market trends allows Food and Beverage Manufacturers room to pivot with existing products, either by increasing the production and/or promotion of a selection that’s becoming popular or mainstream, or pull back efforts on one expected to be in decline, possibly exploring ideas to improve it or shelve it until conditions become favorable.
What Product Attributes Should Be Emphasized?
Speaking of competitive edge, market research allows manufacturers to recognize the strong points of their products in comparison to their competitor’s offerings, enabling their marketing and advertising efforts to highlight those advantages. By the same token, insights on what a competing product offers could be helpful in assessing whether it’s worthwhile to emulate the same qualities or counter it with a better solution. These insights in combination could help determine the ideal price point for a product to strike a balance between gaining revenue and competitive pricing.
Copyright: piviso
Continental Mills Case Study by Cascade Strategies
Continental Mills was looking to introduce new package designs for their line of baking and pancake mixes. Cascade Strategies conducted a study for them, interviewing respondents who were current buyers of baking and pancake mixes. Half were shown the current packages and the other half were shown the proposed designs. They were told that they have $10 to spend and were asked to browse and buy as normal. Their eyes and brainwaves were tracked throughout the shopping process.
We found that by deploying the new designs, Continental Mills would be losing around $19 million. The existing package designs exhibited a harmony among the visual elements that was not replicated by the new designs. The current packages were well-liked, and this was demonstrated by consumers responding positively with their brainwaves and wallets at higher levels than with the new package designs. The visual harmony was consistent throughout the entire current line of packaging.
Copyright: ninthgrid
Growing Your Food and Beverage Manufacturing Business with Market Research
We’ve touched on the benefits of market research for startups and new products, but it doesn’t cease being an important asset to a Food and Beverage Manufacturing company once they’ve gotten off the ground. In fact, market research is just as important to any established company for remaining relevant amidst shifts and trends in the market and consumer behavior, as well as up-and-coming competitors and new technologies.
Customer loyalty and retention might not only be as important but possibly more vital than new consumer acquisition, as existing customers have already consistently endorsed your product over time. Recognizing what they like about your product or what makes them stick with you could be essential in how your offering evolves or your selection expands, gaining new customers without turning away loyal ones. Such insights could also influence how creative you can get with rewarding customer loyalty, chief of which is how and when you would launch relevant promotions or marketing. Think of a buy-one-get-one promotion to counter the introduction of a competing product into the market, or advertisements thanking your most loyal consumers when celebrating milestones.
Health Consciousness
Another thing to consider is the increasing popularity of healthier product selections. Market research can help you learn whether a health-conscious version is good enough to replace your primary product or if it needs to be offered as a separate line. Your marketing team might also be interested in finding out if your ideal consumer buys your product for health benefits to warrant emphasizing this in your advertising.
This also extends to sustainable practices. While this would score well with Gen Z, this isn’t only for marketing purposes. Market research can help you locate the ideal location where not only you have a competent workforce and cost-effective resources to tap into, but also local regulations that support and enable sustainable production practices.
Regional Flavor
Another interesting benefit that market research gives Food and Beverage Manufacturing companies is the opportunity to diversify or introduce flavors or cuisines from a different region. It can either stem from the idea of introducing a novel new product or identifying a growing or trending preference towards a particular regional taste or selection.
And amid the pressures of e-commerce (boosted by the pandemic), DTC, omni-channel shopping, and AI, market research is as relevant as ever to any company with regard to the viability of adapting new technology, expanding shopping and distribution options, and even the prospect of globalization.
Copyright: stokpic
Food and Beverage Manufacturing Market Research with Cascade Strategies
Whether you’re a startup looking to claim your share of the market or an established company who wishes to stay relevant by launching new products or adapting a new way of managing your Food and Beverage Manufacturing business, you can’t go wrong partnering with an esteemed and experienced market research team like Cascade Strategies. With 35 years of market research excellence serving leading US and international companies like the aforementioned Continental Mills, we’re not only well equipped with a wide array of research methods but we’ve remained on the cutting edge of market research with a potent combination of leveraged AI technology and a high level of excellent human thinking. Contact Cascade Strategies today to learn how we can help you navigate the massive and ever-changing market landscape of the Food and Beverage Manufacturing with a roadmap built on high quality and data-driven market research!
Featured Image Copyright: Gustavo Fring
Top Image Copyright: Rulo Davila
Nov
The Children of Millenials: Getting Your Brand Ready For Gen Alpha
jerry9789 0 comments Brand Surveys and Testing, Brandview World, Burning Questions
You’ve done your high-quality segmentation study and persona development, considered single-segment focus and/or multi-segment strategies, crafted buyers’ journeys with psychographic segmentation. Your marketing plans form a playbook catering to a multi-generational audience of baby boomers, Gen X, Gen Y (Millenials) or Gen Z, but have you made room for Gen Alpha?
Who Are Gen Alpha?
Gen Alpha refers to the generation born between 2010 and 2025. Between 2.5 million and 2.8 million of this demographic cohort are being born each week around the world. Once 2024 is up, the first generation born and raised in the twenty-first century would’ve exceeded 2 billion worldwide, and they’re expected to outnumber baby boomers by 2025. Also known as “millenials’ children,” Gen Alpha is projected to be the largest and most diverse generation yet.
True digital natives, Gen Alpha grew up accustomed to smart devices and social media. This was reinforced further when the pandemic caused the whole world to stay indoors and turn to digital devices to connect, find entertainment and for virtual learning. It’s no surprise then that they exhibit comfort and quick adaptability with new technologies like artificial intelligence (AI), augmented reality (AR) and virtual reality (VR). Now while the digital world is a constant in their lives, Gen Alpha actually takes time offline and away from tech go outside and engage with friends or physical activities in tandem with caring for their mental health, a practice that became increasingly noticeable after the pandemic.
And it’s not only their mental well-being that Gen Alpha are concerned for. They’re also socially and environmentally conscious, growing up hearing and learning about inclusivity and climate change. They thus have a higher preference for products, brands and practices that promote equality, social responsibility, eco-friendliness, and sustainability when compared to previous generations. They’re also more appreciative of diversity due to globalization and digital connectivity exposing them to different cultures and perspectives.
Video-centric YouTube and TikTok are their favorite digital platforms. They’re also inclined to thrive in the safe and niche confines of gaming over contributing to the noisy and oftentimes chaotic discourse found in most social media. More than just the satisfaction of playing a video game, they express themselves in the customizable virtual space offered by worldbuilding games like Minecraft and Roblux. They are empowered by technology instead of dependent on it. And while they follow and take cues from influencers, they appreciate authenticity, personalization, and uniqueness, proving to be generally wary of and resistant to traditional marketing practices.
Why Market To Gen Alpha?
Gen Alpha is estimated to have an economic footprint of $5.4 billion by 2029. While that’s still a few years off, Gen Alpha has already and indirectly flexed their spending power by influencing their parents’ purchasing decisions while demonstrating at the same time a higher degree of brand awareness than older generations. They are confident with their choice of brands as it is a reflection of themselves and the values they appreciate.
The “adolescent demographic” is also challenging conventional marketing and advertising tactics, having already reshaped older or adult brand marketing. Where once there was space for “tween retail” with brands dedicated specifically for this age group and some adult clothing brands introducing specific clothing lines for tweens, mature brands for example simply expanded their size range to include their younger consumers. You’ll find Gen Alpha sharing the same brand choices or favorites with their millennial parents and Gen Z, the generation that preceded them.
With their digital affinity and offline exigencies set to shape the future of work, learning, and culture, brands would need to rework their marketing approach if they would like to attract Gen Alpha as early as now. While their older members are just entering their teens at this time, understanding how Gen Alpha thinks and behaves can help a brand adapt and lay the groundwork for their marketing endeavors as part of efforts to remain relevant and evolve with the times, especially with a generation this willful but informed when it comes to exercising choice.
How Should You Market To Gen Alpha?
Your marketing cornerstone can start with leveraging existing and emerging technology to understand and engage Gen Alpha. For starters, traditional demographics are already challenged by how diverse Gen Alpha is along with their preferences for personalization and uniqueness. Adopting AI and machine learning into your marketing strategy to analyze consumer behavior data and foresee trends can therefore help you craft a personalized and dynamic buyer experience for Gen Alpha. You can also employ AI-powered virtual assistants for personalized assistance during the shopping journey. AI can also grant your brand the flexibility to adapt to trends and feedback quickly in keeping in line with Gen Alpha’s needs for instant gratification.
Use gamification, polls or promos to enhance engagement and interactivity instead of conventional ads that Gen Alpha more often than not ignore or scroll past by. Add another layer to the shopping experience with AR and VR where virtual spaces allow them to visualize, explore and engage with products before even purchasing.
Gen Alpha is immersed in the digital world but they also seek engagement in the physical world. Offer in-store pick up options for online purchases to allow their shopping experience to extend to physical locations. Use geolocation and location-based services for sending relevant and personalized promotions and notifications, such as in-store only discounts and offers. Develop apps that not only incorporates these points but also enhances your omnichannel presence with seamless transition between online and physical shopping experiences. Don’t forget to promote and collect user-generated content as testimonials to the engaging and immersive experience your brand offers.
Some of the aforementioned technologies are still relatively new to the mainstream but learning and leveraging them as early as now allows your brand and marketing to evolve alongside them while growing and staying relevant with Gen Alpha.
Your brand would also need to increase focus on data protection and privacy, as Gen Alpha is particularly mindful of how valuable their personal information is in this era of data breaches and leaks. Brands need to be able to communicate clearly their privacy policies and demonstrate responsible data handling in addition to offering consumers control over how their personal information are use.
As mentioned before, Gen Alpha are acutely aware of social and economic issues aside from being the most diverse cohort yet. With this comes the rise of purpose-driven marketing where your brand needs to strongly communicate, commit and exemplify your mission and values, lest you be called out for virtue signaling. Gen Alpha are expecting brands these days to support and feature diversity and representation, calling out those that they perceive lack this value. Your brand would need to highlight and be transparent with your sustainable and ethical practices, including sourcing, production, packaging, and labor, while continually seeking areas for improvement and better, more modern methods to adopt.
While influencers are one of the top sources from where Gen Alpha learns and considers products to purchase, there is a shift nowadays on which personalities to follow thanks to this generation’s penchant for authenticity and shared values. Instead of considerably bigger names and one-time sponsorship, brands can consider long-term partnerships with micro-influencers and nano-influencers. Their niche following might be smaller but they are highly engaged and more connected, allowing for more organic integration of your brand messaging through collaborative content creation. As what we’ve already learned with high-quality segmentation study and persona development, your marketing goals can sometimes be better served by identifying, focusing and tailoring your campaign towards that consumer instead of a “blanket” approach with demographic data for reach with an influencer with a large following.
While this generation is still a year shy of rounding out all of its members, understanding and engaging with Gen Alpha as early as now would benefit brands looking to find a foothold into future markets. As technologies evolve and attitudes change, there might be no better time than now for brands and their messaging to organically connect, resonate and grow alongside Gen Alpha.
For further reading:
https://www.tokinomo.com/blog/gen-alpha-consumers
https://medium.com/@daisygarciathomas/marketing-and-consumer-behavior-of-generation-alpha-9492ceaf63ee
https://therobinreport.com/get-ready-for-gen-alpha-consumer-behavior-shifts/
https://hbr.org/resources/pdfs/comm/journey/TheBusinessCaseForUnderstandingGenerationAlpha.pdf
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Nov
“Humanizing” Market Research with AI
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Burning Questions
The Boon and Bane of AI
The increasing and widespread utilization and demand for Artificial Intelligence have been met with both excitement and reservation. Excitement for the possibilities AI’s implementation unlocks, oftentimes steps ahead of the curve or beyond expectations; reservations not only stemming from the risks over its unethical and unchecked use, but also the ramifications for human involvement now that intelligent machines represent an optimized and economical choice for completing tasks and processes. But can there be a middle ground somewhere where AI and human engagement coexist and collaborate?
The “Humanization” of Market Research
“Capturing the Human Element in an Artificial World” by Eric Tayce (Quirk’s Marketing Research Review, Sep-Oct 2024) posits that such a midground is possible, especially in market research. An industry that’s all aware of its excessive dependence on technology to necessitate a push to “humanize” research data, it saw a dramatic shift from “data-intense tomes and clinical-sounding slide titles” to “streamlined, narrative-style reporting” focused on “the unique motivations and experiences that drive customer behaviors.” The latter “humanized” approach is able to communicate business goals while connecting and engaging on an emotional level. However, generative large language models (LLMs) cast a shadow on this “humanized” approach by offering synthetic outputs and progressive algorithms.
But combining both AI and efforts to “humanize” research can result in the whole being greater than the sum of its parts. The article shared that AI can help collect more unstructured data from survey research by employing conversational chatbots to create a natural, richer experience for the respondent. That unstructured data in turn can potentially provide more organic, more human insights with AI-powered algorithms, an undertaking that was once considered too complex or time-consuming. AI can also build multifaceted perspectives through context by linking survey records with a broad range of data sources. And in lieu of traditional static deliverables, data and insights can be presented in a vibrant and interactive narrative by an AI-powered persona.
The “Human” Element
All these interesting prospects can only be achieved when AI is tempered by high-quality human input and thoughtful implementation considerate of ethical and moral implications. Aside from AI mistakes and hallucinations existing, AI has been observed to be too helpful and excitable. Human oversight and input remain key in ensuring AI models are trained, fine-tuned and grounded with quality and relevant datasets while having enough flexibility to engage appropriately in open-ended interactions.
There’s no denying just how transformative AI is in reshaping industries today, including market research. Despite concerns of machines taking over jobs, one can look at it with the perspective of roles changing and adapting. AI with its generative and synthetic capabilities can elevate the “humanization” of market research, but to get to that point we simply can’t forget that humans are indispensable to the whole process.
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Top Image Copyright: Darlene Anderson
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