Long before I became a qual researcher, I was an ad guy. In my 20s and 30s, I was part of the agency of record for what was then the dominant name in photography, Kodak. In that role, I helped the company build its film brand, specifically with professional photographers and photo-enthusiasts. I also had a secondary role developing ads that presented Kodak’s latest R&D activities to the scientific community.
Category: General
Follow Your Nose
I love books. I really do love all books, but I have a soft spot for fantasy. There are so many authors who have created a universe so immersive it feels like I never fully left, nor do I want to. Unsurprisingly, Tolkien has created one of those universes for me. In my most recent re-reading, I came across a particular passage in The Fellowship of the Ring that spoke to me not only as a Tolkien junkie but as a technology research moderator.
Unconditional Positive Regard
I’m the first to admit that I can’t carry a tune. And my dancing skills…well, let’s just say Seinfeld’s Elaine Benes and her infamous dance-floor contortions come to mind. While my husband and son may tease me for my lack of coordination, there’s someone in my household who accepts me completely for who I am — off-key voice, off-rhythm boogying and all.
Qual Research Insights
It’s not often you hear T.S. Eliot mentioned in connection with qual research. But there’s a quote from his Four Quartets poem I find particularly relevant to the work we do: “The end is where we start from.” Granted, Eliot’s poem is more about existential quandaries than market research, but it does speak to one important aspect of how we approach our work here at Thinkpiece: beginning at the end.
Brands with Personality
When is water more than just water? Ask the billion-dollar beverage brand Liquid Death. Between 2022 and 2023, Liquid Death’s retail sales grew 100% from $110 million to $263 million. The brand’s valuation is now double what it was worth in 2022. This warp-speed trajectory is particularly remarkable when you consider what Liquid Death sells: water.
The Psychology of Research
Tackling difficult questions is all in a day’s work for B2B qualitative market researchers. After all, the ultimate goal in research is to help clients reveal answers that solving vexing challenges and provide strategic direction. But what about those questions of a more philosophical variety — such as, “are we in control of our own destinies?” Is this type of existential exploration meaningful for B2B qual research and the marketers who use it? In a word, yes.
Why Words Matter in Qual Research: For Meaningful Insight, You Need Meaningful Language
Have you ever repeated a word so often, it starts to lose its meaning? Try it: say the word “flower” 30 times in a row. You may find that after a while, it begins to sound like a random assortment of sounds, devoid of rhyme or reason.
There’s a scientific term for this phenomenon known as semantic satiation. Coined in the 1960s by Leon James, a professor of psychology at the University of Hawaii, semantic satiation occurs when the rapid repetition of a word triggers both sensorimotor and central neural activity in the brain repeatedly. With each repetition, the word loses its intensity. To put it another way, the neurons become filled up with the word until they are completely satiated and unable to consume any more — at which point, they start rejecting the word’s meaning and it becomes gibberish.
In James’ own words, semantic satiation is described as:
“…a kind of a fatigue. It’s called reactive inhibition: When a brain cell fires, it takes more energy to fire the second time, and still more the third time, and finally the fourth time it won’t even respond unless you wait a few seconds. So that kind of reactive inhibition…is what attracted me to an idea that if you repeat a word, the meaning in the word keeps being repeated, and then it becomes refractory, or more resistant to being elicited again and again.”
Fascinating, you might be thinking. But what does that have to do with qualitative research?
Jargon, Jargon, Everywhere
In the realm of qual research, semantic satiation may not seem like much of an issue. But jargon satiation certainly is. As researchers, we’re tasked with extracting meaning from the responses and feedback — that is to say, the words — we elicit from participants during focus group and interviews and distill into reports. Which is why it’s so very important for us to make sure the words we use to steer these conversations also have meaning. That requires being precise, clear, and jargon-free in our word choices — particularly when conducting complex B2B research for the jargon-rife technology, healthcare, and finance industries.
Jargon in the business world is everywhere. Best in class. Cutting edge. Synergies. Ecosystem. We’ve all heard these terms bandied about so many times, they’ve lost any meaning they might have once communicated. In fact, according to a survey conducted by American Express, 88% of office workers admitted to pretending to understand business jargon when in reality they had no clue what the words meant. Meaningless jargon is so pervasive, Grant Thornton — a global leader of audit, assurance, tax and advisory services — put together this comprehensive index of the most commonly (and egregiously) overused business buzzwords.
So how can we as researchers make sure we craft our screeners, discussion guides, and interview questions using words that eschew jargon and convey meaning? And how can we guide conversations with respondents that reveal meaningful insight? Yes, avoiding jargon is top of the list. But beyond the buzzwords, there are other steps we can take to communicate and converse with greater clarity, precision, and meaning.
Forego the Fluff
As moderators and researchers who enjoy a good story, we may be tempted to accessorize our screeners, discussion guides, and interview prompts with extraneous words and bloated descriptors that provide more fluff than substance. In addition to clouding the conversation, fluffy words may influence or bias participants to respond a certain way they might otherwise not have. That in turn impacts the quality and usefulness of the insight.
Be Specific
This guideline goes for the questions you ask, and for the responses you receive. Avoid generalized terms like “thing,” “some,” or vague words like “good,” “bad,” “happy,” “sad.” Drill down into the specifics of what constitutes good and bad, what made them feel happy or sad. Make sure your respondents truly understand the question, and if they don’t, rephrase it. If you’re not sure what a participant means by their response, ask for specificity and steer them toward those details.
Account for Interpretation
Words that sound specific and technical may be wind up having different meanings for different people. AI, for example, covers a broad range of technologies, methodologies, and implications. Depending on the level of knowledge and experience of the respondent, AI to some might mean ChatGPT while to others it means a robot or a self-driving car. You could be talking about limited memory AI or self-aware AI or theory of the mind AI. Clarifying squishy terms will help avoid misunderstanding or misinterpretation.
Define Your Terms
When using words that may be interpreted differently, it helps to provide clarifying definitions that get everyone on the same page. So for example, if we’re asking focus group participants about their experience using software as a service — which has become a vague bucket lumping together a wide range of cloud-based software delivery models — we let the group know what we mean by that phrase.
Keep It Simple
Even — perhaps especially — with the complexities of technology, healthcare, and finance, we find that simple is often best. Get right to the point. By distilling a question down to its most basic form, using the fewest words possible, you may be able get your meaning across more clearly and directly. This approach is also more likely to get straightforward answers.
Speak Their Language
As moderators, we want to connect with our respondents. In our experience, the most meaningful insight comes from having peer-to-peer conversations with participants. That requires speaking their language, using words they use in their everyday work and lives. We remind ourselves that at the end of the day, we’re conversing with people — not just software engineers or neurosurgeons or brokers or interview subject #32. So talk to them, one human to another.
Reports, Too
All these guidelines apply to reports as well. To reveal truly meaningful insight, reports need to be simple and straightforward, with zero fluff, bias, or bloat. They need to get to the point as quickly as possible, with clearly defined terms that leave no wiggle room for multiple interpretations. And they need to be crafted for the audience who will be using them; a report written for a CEO should differ from a report written for a brand manager, for instance.
All this to say: words matter. A lot. The power of qualitative research comes from the story it reveals behind the data — and the words used to tell that story. Choosing those words carefully, with precision and intent, ensures that the stories we tell as researchers are truthful, resonant, and impactful. And above all, immune to semantic satiation.
Rethinking the Qualitative Research Paradigm: Embracing Expertise Over Generalization
In the dynamic world of market research, qualitative researchers often find themselves navigating through diverse industries, exploring topics ranging from disposable diapers to deep tech and healthcare. For decades, the industry has favored the archetype of the qualitative researcher as a generalist—a versatile individual capable of rapidly adapting to various domains. Paper towels this week, edge computing the next. I ought to know. This was my life for many years.
But—and I’m veering into hot-take territory here—this approach may not always yield the most insightful results, particularly for B2B companies with complex products and services. I posit that subject matter expertise is the quality that matters most in qualitative research. Matter of fact, I bet my career — and my company — on the conviction that expertise should be at the forefront of qualitative research. Here’s why.
The Advantages of Expertise
Why is expertise crucial in qualitative research? For myriad reasons. Entrusting your complex research to subject-matter experts specific to your industry yields several significant advantages: faster learning curve; more meaningful and revealing conversations; an understanding of rapidly evolving industries and topics; and increased trust and credibility between researcher and client. All of which leads to better, deeper, actionable insight that generates enduring results. Let’s explore further (after all, that’s what researchers do).
Faster Learning Curve
The more complicated the research, the more time it takes for a generalist to become comfortable enough to lead productive discussions about it. Researchers who bring an already-substantial foundation of industry knowledge can get up to speed faster and dive into the research more quickly and confidently. This research readiness is especially valuable for clients who need insights sooner rather than later to make business-critical decisions.
More Meaningful Conversations
Specialized researchers possess firsthand experience and knowledge within specific industries or fields, enabling them to approach research from a peer-to-peer perspective. Interviewing a group of highly specialized doctors? Bring in a researcher who once led cardiac and neurosurgery ICUs. Conducting a focus group of Linux kernel developers? Helps if your moderator is also a software engineer. This industry-insider insight fosters deeper connections with participants and facilitates the exploration of nuanced questions that might otherwise go unasked.
Deeper Understanding of Ever-Changing Fields
Nowhere is the need for specialization more apparent than in industries like technology and healthcare, where the pace of innovation is relentless. In these domains, having someone fluent in the industry is not just advantageous—it’s imperative. Technologies evolve rapidly, and healthcare landscapes undergo constant transformation. Without researchers who live and breathe these sectors and follow their changes, valuable insights risk being overlooked or misinterpreted.
Increased Trust and Credibility
Expertise lends credibility to the research process. Clients are reassured knowing that their projects are in the hands of individuals who not only understand their industry but are deeply immersed in it. This trust becomes the foundation upon which fruitful collaborations are built, ultimately leading to more actionable insights and informed decision-making.
Embracing the Expert
Given these clear advantages, why then has the qualitative research industry clung to the generalist model for so long? Perhaps it’s rooted in tradition, or maybe it’s simply a matter of convenience. My guess is that it’s because there are simply not enough subject matter experts out there who turned to qualitative research. Regardless of the reasons, it’s time for a paradigm shift—a reimagining of what qualitative research can and should be.
One last thought: it’s essential to acknowledge that specialization exists within the realm of consumer packaged goods (CPG) research as well. CPG researchers are their own breed of specialists. They possess a unique understanding of consumer behavior, brand perceptions, and market trends within the CPG space. Their expertise enables them to unravel complex consumer dynamics and deliver actionable insights tailored to this specific domain. So the specialized researcher isn’t just for high tech and healthcare; it benefits clients across industries and audiences — B2B and B2C.
By embracing specialization, we can elevate the quality of our research and deliver more impactful insights to our clients. I’d be interested to hear your thoughts and experiences on specialization vs. generalization. Reach out and let’s discuss.
Qualitative Market Research Trends to Track in 2024
As we welcome 2024 and all its possibilities with open arms, we thought now would be the perfect time to explore some of the trends — current and emerging — we believe will have the biggest impact on qualitative market research. Let’s start with the trends we see as gaining momentum in the year ahead.
Digital Qual Research Continues to Dominate
Ushered in by the pandemic, digital qualitative research is now the new norm — and will continue to be so in 2024 and well beyond. Survey findings show that 87% of researchers did over half of their qual research online in 2023, with 85% of them saying they’ll continue to do so over the next 12 months.
We go into the pros (and cons) of remote focus groups and interviews here, but the bottom line is this: digital research can help companies expand their reach of respondents more easily, conduct interviews more quickly and conveniently, while saving resources and money. Digital research extends beyond online focus groups, as well. In 2024, we expect researchers to keep using digital tools for all manner of activities including surveys, social media listening (more on that below), online discussion forums, data-mining, and more.
Smart Use of Mobile Market Research
Mobile devices are a key part of the digital conversation. Smart phones are ubiquitous; in 2023, 92% of Americans owned a smart phone, and spent around four hours a day on these devices. Researchers have realized that mobile phones provide an excellent platform to engage with respondents who are perfectly comfortable communicating via screens, taps, and swipes.
There are advantages to mobile phone research, starting with a low barrier to entry. Respondents don’t have to be at a specific place at a certain time to participate in a mobile-based interview or focus group, respond to a survey, or provide in-app feedback. They can just whip out their phones on the spot to provide in-the-moment, real-time reactions.
Respondents can also share responses in a wide range of ways — via text, voice messages, video, emojis, and even gifs. Mobile devices also allow researchers to conduct location-based research (thanks, GPS), as well as ethnographic research via daily video journals and notes apps. As such, we expect the mobile research trend to continue building steam.
More Social Media Research
The use of social media is also part and parcel of the shift toward digital qual market research. Researchers have already been using social media as a platform for asking questions, conducting polls and surveys, soliciting feedback, testing concepts and messaging, and showcasing prototypes. With Americans spending around two hours a day on social media apps, these platforms are an obvious place to find and connect with respondents.
In 2024, we predict advances in technology will make it easier and more attractive for qual researchers to embrace social media listening for sentiment analysis. There’s a growing list of shiny new tools that enable researchers to track keywords, hashtags, mentions, reviews, and sentiment across multiple social media platforms. Tracking this info can provide valuable insight into emerging trends, unmet needs, consumer preferences, and brand perception.
Ethnographic Research for B2B
Ethnographic research that’s immersed in the actual real-world environments where respondents live, work, and play has been around since the 1950s. So while it’s long been a staple for consumer research, we believe ethnographies offer great potential for B2B research as well — particularly in the field of technology.
We’ve recently tested out this theory ourselves, by using an ethnographic approach with one of our technology clients. For this particular study, we had respondents — specifically, software engineers — take over and physically use our moderator’s computer to play around with an application and provide real-time feedback. The insight we gleaned from this exercise was invaluable, and our clients were thrilled with the results.
Emotion Recognition and Neuromarketing Research
Emotion recognition technology uses algorithms and machine learning to identify and classify micro facial expressions that signify joy, sadness, surprise, fear, anger, disgust, contempt and more. Neuromarketing research takes it a step further, employing medical procedures including functional magnetic resonance imaging (fMRI) and electroencephalograms (EEG) to track and measure physiological changes in the brain to indicate and identify emotional responses.
Emotion recognition and neuromarketing research aren’t new concepts; some of the largest market research firms even have their own in-house labs set up for these kinds of studies. And if you don’t have the budget for these methods, there are lower-cost and less obtrusive devices that can track and measure a participant’s heart and respiration rates which indicate an emotional response as well.
With the emergence of AI, it will be interesting to see where these types of research approaches will go. AI-enabled eye tracking, for example, maps out eye gaze and movement to understand where a person is looking, what they’re looking at, where their gaze lingers, as well as pupil dilation (an indication of arousal). This is definitely a space we’ll be keeping our eye on.
Merging Qual and Quant
We’ve seen an increased need for and openness to a hybrid research approach that combines qualitative and quantitative, and we expect this merging to continue in 2024 and onward. And we’re here for it.
While they may seem diametrically opposed, quant and qual together can strengthen each other while providing a more holistic view of the research. Quant alone can get bogged down in data, making it harder to connect the dots and reveal the insight. Qual, on the other hand, can sometimes be hard to measure, with a lack of quantifiable “proof” to clarify how conclusions were drawn.
Integrate the two research worlds, and you have the what, why and how behind the data. This can result in more meaningful reports that also provide clients the proof-points they need to believe in the insight. Advances in technology and systems are making it easier for researchers to combine qual and quant data in a single platform, which also make it faster and less cost-prohibitive to do both.
A Case for Agile Market Research
Agile market research has been around for a while, as well, and there’s been some debate as to its effectiveness as a methodology. We see the increasing value of agile market research, but also the need for it to be done exceedingly well.
In the current business reality, things change very fast. As such, companies need answers to their questions now, which means they need quick, focused research studies and lots of them. Agile market research responds to this demand by taking an iterative approach to gathering consumer feedback to very specific questions along multiple points in the development of a product, service, or campaign.
Instead of taking weeks or months to test and collect respondent reactions, agile market research delivers answers in days. This allows companies to make on-the-fly tweaks as they go, and ideally launch their product/service/campaign more quickly.
In the hands of an inexperienced firm, agile market research can end up sacrificing quality for the sake of speed. That’s why it’s important to have researchers with first-hand industry knowledge and experience, who know exactly the right questions to ask and how to extract the critical insight quickly.
Now that we’ve addressed some of the current trends, let’s turn our vision toward those we see emerging in the year ahead.
AI As a Collaborative Tool
There’s no doubt that artificial intelligence will play an increasingly larger role in qualitative market research. We expect more research firms to embrace AI technology, apps, and services as a collaborative tool to help them drive efficiencies and productivity. A recent Qualtrics survey supports this, finding that 46% of researchers say they’re “very confident” with using AI in research, and 26% reporting they feel “extremely confident.”
We predict a turn toward AI for a broad range of research activities, from developing screeners, surveys and discussion guides, to weeding out bogus or unqualified respondents, to transcribing interviews, to quickly analyzing massive amounts of data from multiple sources, to generating attractive and compelling reports. But we caution that AI should not and cannot replace the human element when it comes to revealing actionable insights that businesses need to make strategic decisions. You can read more about that in our recent blog post here.
Prioritizing Data Privacy
All the hype about AI leads us to the next emerging hot topic for market research: data privacy, for both consumers and clients.
Today’s consumers are ever-more aware of and concerned about protecting their private data — especially online. According to Pew Research, 72% of Americans believe that almost all their online activity is being tracked, which understandably makes them uncomfortable. It can also make them less willing to participate in qual research studies.
In response to rising fears around data privacy, countries around the world are enacting stricter guidelines designed to protect personal information and give consumers more control over their own data. Moving into 2024, researchers need to be aware of and respond to the concerns of consumers as well as the compliance requirements of their governments. That means having clear and formalized data security protocols in place, as well as complete transparency on how data is collected and used.
Researchers will also need to take measures to protect themselves and their clients against malicious actors. GreenBook reports that upwards of 30% of market research qualifies as fraudulent, propelled by respondents who lie about their qualifications, bots and fake traffic completing surveys, and widespread identity theft. As such, it will be critical for researchers to implement robust safeguards from here on out.
We delve deeper into safeguarding data in our recent blog post here.
Let’s Hear it for Voice of Consumer (VoC) Research
Modern VoC research involves using technology and tools that analyze text and consumer sentiment across a range of digital and traditional channels and methods including surveys, interviews and focus groups, social media, feedback forms, online reviews, customer support interactions, customer journey mapping, and more. The goal is to capture what customers are saying about your product, services, and/or brand for authentic insight.
We expect VoC research to gain traction as companies continue to place an emphasis on customer experience and customer-centric strategies. VoC also plays a key role in developing customer personas that help companies better understand and respond to different audience segments.
VoC research, however, can be challenging and complex since it requires an omni-channel approach and sophisticated tools in order to reveal the complete picture of customer preferences, sentiment, and behavior. It’s also important to make sure the VoC data you collect truly represents your target audience, and that you maintain customer privacy and data security.
Diversity and Inclusion in Market Research
In 2022, 44.1% of the U.S. population was non-White. By 2044, over half of Americans is projected to belong to a minority group. The percentage of Americans who identify as LGBTQ+ is growing as well, especially among younger generations; in 2022, 10.5% of Millennials in the U.S. identified as LGBTQ+, while 20% of Gen Z did.
Our country’s population is aging, as well, with the median age coming in at just under 40 years — a number that’s expected to rise. Even as we get older as a country, younger generations are gaining power as consumers. Millennials now represent the largest group of consumers, and Gen Z’s buying power is going up.
All this to say: in 2024, it will be imperative for researchers to make sure their research samples and respondents reflect the diversity of their audiences — including ethnicity, cultural background, lifestyle, gender, age, and sexual preference. It will be especially important to include traditionally underrepresented populations such as women of color, people with disabilities, and members of the LGBTQ+ community.
Focus groups comprised of respondents who have similar experiences, preferences, and biases can often lead to skewed results. We get it: sometimes you need to find respondents who check off specific attributes — a certain age group, profession, demographic. But within the confines of the screener, and whenever possible, diversifying your groups and interviewees can help reveal richer, deeper insight that leads to better decisions.
Diversity and representation are an even bigger challenge when we consider how difficult it’s becoming to find respondents. Today’s consumers are increasingly likely to ignore phone calls, emails and texts from unknown sources, including field managers trying to recruit for research studies. Which leaves only those self-selecting consumers to respond and participate — and who often lack diversity. To address this challenge, qual researchers will need to lean hard into relationship-focused recruiting.
Beyond diversity, researchers must also work to ensure inclusivity with their studies. That requires creating environments where every participant feels listened to, valued, respected, and involved — regardless of who they are.
What Did We Miss?
Call us optimists (we certainly embrace that title), but we’re excited about the emerging tools, technologies, and methodologies researchers can leverage to unlock even greater insights that drive tomorrow’s innovations. As is the nature of our industry, we’re sure this list will continue to evolve as the year advances, and we’re curious to see what develops. We’d also be interested to hear about any trends you’d add to the list. Reach out, and let’s discuss.
9 Tips for Using AI Safely in Market Research
There’s been plenty of hype around Artificial Intelligence (AI) and how it’s going to transform our world — including the world of market research. What’s lacking is practical guidance for using AI in our daily work as researchers. For the next series of Thinkpiece blog posts, we’re focusing on less hype-ful and more helpful AI insight.
For the first of this series, we’re sharing our top tips for using AI safely. True, AI offers the potential to help make research more efficient and effective. But AI technology also comes with a host of potential risks, particularly when it comes to data protection and privacy. After all, AI apps are designed to collect as much information — including sensitive info — as possible to keep learning and becoming “smarter.”
Before you invest in that shiny new AI app or tool, here are nine basic safety practices to keep your, your clients’, and your respondents’ data secure and protected.
1. Choose Your AI Apps Wisely
Before you start using any AI app or tool, take some time to research the company behind it. You’ll want to make sure the developer is legitimate, offers good customer support, and has a solid reputation particularly around security.
Avoid fly-by-night and sketchy apps, and do some digging into the tools you’re considering. Check out the company’s portfolio to see what they’ve done before, and the quality of those products or services — keeping a lookout for any bad press or reviews. Make sure the company is well versed in the latest AI and machine learning frameworks, and is clearly an expert in the field. Last but not least, scrutinize their commitment to security (see tip number 3).
2. Don’t Overshare
Assume that any data you share with an AI app will be used to improve its machine learning — and is at the mercy of the app developer. Which means you should also assume that this data is vulnerable to breaches.
To that end, avoid sharing any personal, confidential, or sensitive data — especially your clients’ or respondents’ info. For example, any proprietary client information, such as a new product that’s being held under wraps, should definitely not be shared with any AI app or tool. A general rule of thumb we follow: if you don’t want it blasted on social media, don’t share it with an AI app. And if you come across AI-generated content that requests sensitive information, run the other way.
You might also want to invest in additional security tools designed to prevent oversharing of confidential and sensitive data. LLM Shield, for instance, prevents language learning machines (LLMs) like ChatGPT from leaking your clients’ or respondents’ personal or private information.
3. Peruse the Privacy Policy
Reading the fine print may be a pain, but it’s important to know what you’re signing up for. So be sure to peruse the privacy policy, terms, and conditions for any AI app you’re thinking about using. This will let you know how the app plans to collect, store, protect, and use any data you share with it.
As previously mentioned, most apps use the data you share to make it “smarter.” But the app’s developers may also be exploiting data for other purposes, such as selling personal information to third parties who want to completely freak you out with personalized ads. The app’s privacy policies should give you confidence that the developers won’t do anything dubious with your data and are taking thorough measures to protect your info from hackers.
4. Customize Your Security Settings
After carefully reviewing the app’s privacy policy and deciding you’re comfortable with it, don’t stop there. Go into the app’s privacy and security controls and change any settings to meet your preferences.
For example, the app may give you the option to have data automatically erased after a certain amount of time, or you may be able to choose to delete the data yourself manually. You might also be able to review and erase search histories and clear conversations to delete anything you and the AI app might have “chatted” about.
5. Train Your Team
It’s a good idea to formalize your own security policies and practices for using AI apps and tools, as well. Guidelines can cover a wide range of topics, including how the app should be used in alignment with your or your clients’ values; complying with laws related to data privacy; identifying and addressing potential AI biases; reducing risks of exposing or mishandling sensitive data; the role of humans in ensuring AI app safety; and steps to take in the event of AI-related errors or disputes.
Train your team and anyone who will be using the AI app on these policies, practices, and guidelines to make sure they’re understood and followed.
6. Keep Up with Compliance
It’s a good idea to familiarize yourself and any other users on your team with current compliance and regulatory requirements around the use of AI. The Health Insurance Portability and Accountability Act (HIPAA), for example, enforces strict guidelines around protecting patient information and privacy which might easily be violated using AI. If you’re conducting research in Europe, you’ll want to be aware of any potential General Data Protection Regulation (GDPR) issues when using AI tools.
Compliance and regulations around the use of AI are ever evolving, so it’s important to stay on top of these changes to protect yourself as well as your clients and respondents.
7. Follow Good IT Security Practices
The growing prevalence of AI makes robust IT security protocols and hyper-vigilance even more critical, now that the area of attack is much larger. Keep employing the usual security best practices, such as creating strong passwords for all apps and websites, making sure all your software is up to date with the latest versions, and employing reputable anti-malware and anti-virus software.
This last point is especially important since it’s now possible to create malware that observes what you enter into an LLM like ChatGPT and then sends that information to a malicious actor for the purpose of stealing sensitive data. It’s also possible to use prompts engineered to “hypnotize” or trick an AI tool into doing things it normally wouldn’t, such as compromising the user’s data or producing incorrect or malicious responses.
These hypnotized AI apps can be used in phishing attacks to steal data from a user who thinks they’re interacting with a reputable source. Following good IT security practices will help protect you against this new crop of AI-enabled hacking.
8. Remember: AI Isn’t Perfect
Far from it, in fact. AI algorithms are subject to bias, which can lead to inaccurate, unfair, and even offensive results — all of which we definitely want to avoid in market research. Case in point: you might have read about the New York-based law firm that used ChatGPT for legal research and submitted a filing that referenced six completely fabricated legal cases, complete with bogus decisions, quotes, and internal citations — putting the firm in legal jeopardy as a result.
Which is why it’s critical that you don’t rely or make decisions based on AI alone. Have an actual human review, vet, and confirm any content or results generated by an AI app — paying close attention to potential inaccuracies, prejudices, or outright lies.
9. Just Keep Learning
One lesson we can glean from AI: the more we learn, the smarter we get. If you’re interested in leveraging AI for market research, we recommend learning as much as you can about the subject before you invest in and start using any AI tools or apps.
One good source of knowledge is Amazon, which recently launched its “AI Ready” initiative with free courses that teach you how to use generative AI (GenAI) to create text, images, and other media. Think ChatGPT and DALL-E. We just completed Amazon’s Generative AI Learning Plan for Decision Makers course, and recommend it for anyone interested in understanding more about using GenAI in their organizations.
You can also schedule a no-cost tutorial session with our in-house AI expert and Director of Technology Research, John Dibling, and download our free resource guides including “Understanding AI & Why It Matters for Market Research” and “8 Tips for Using ChatGPT in Market Research” here.
While one of the aims of AI is to simplify our lives, in reality it’s added a lot of complexity. And that complexity is constantly changing. We’re here to help you safely leverage AI as an effective, collaborative market research tool — human to human. Let us know if you have questions or are looking for more guidance.