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The recent IndiGo flight disruptions and mass delays across India sparked nationwide frustration and created a wave of public debate about the reliability of Indian airlines. Thousands of passengers faced cancellations, extended wait times, and unexpected losses — leading to a direct shift in consumer sentiment, not just toward IndiGo but toward the aviation industry as a whole. This incident has turned into a critical moment of insight for airlines, travel companies, and brands dependent on consumer trust.Here’s what the crisis reveals — and why understanding consumer behaviour now matters more than ever. What Triggered the IndiGo Crisis? While multiple operational and staffing-related challenges were reported, what mattered more was how passengers felt: For consumers today, experience is everything, and when it fails — brand loyalty collapses immediately. Consumer Sentiment Shift: What the Public Reaction Shows The aviation industry is witnessing a shift in expectations: The IndiGo situation has become a case study in the power of collective consumer voice, proving that customers now hold stronger influence than brand messaging. Impact on Customer Behaviour & Travel Decisions 1. Switching to Competitor Airlines Travelers are actively comparing airlines and shifting preferences based on service reliability, not discounts. 2. Rise in Last-Minute Bookings & Flexible Travel People are hesitant to commit early due to fear of change or cancellation costs. 3. Surge in Travel Insurance & Refund-Friendly Services Customers are prioritising protection and peace of mind over convenience. 4. Increased Social Media Pressure A single viral complaint can impact brand reputation nationwide. What Brands Can Learn from the IndiGo Crisis This moment teaches every business (not just airlines) the same core lesson:Consumer perception can change overnight — and the only defence is proactive communication and data-driven customer understanding. 🔍 1. Listen to Consumer Sentiment in Real Time Use surveys, live feedback tools, and social listening to measure brand health continuously. 📢 2. Communicate Early, Fast & Clearly Silence feels like neglect — immediate transparency builds credibility. 🤝 3. Human-Centric Experience Wins Empathy, apology and support resolve anger better than policy statements. 📈 4. Invest in Reputation Monitoring Brands must actively track trust, satisfaction levels and online sentiment. 🧠 5. Data-Driven Decision Making is Non-Negotiable Understanding what consumers think, feel and expect helps avoid large-scale damage. Why This Matters for All Brands The IndiGo disruption has become more than a travel problem — it’s a wake-up call for every business: Companies that adopt rigorous market research, customer analytics and sentiment tracking will grow faster and maintain loyalty even during crises. How Cogentix Research Helps Brands Stay Ahead At Cogentix Research, we help brands: In uncertain times, data is the strongest defence and the smartest investment. Final Takeaway The IndiGo crisis proves one thing:Customer emotions shape brand destiny.Brands that listen, adapt and respond quickly will win the future of consumer loyalty.

Why New-Age Formats Matter in Market Research Consumers today don’t just fill out surveys — they speak, record, react, and post. This shift has made traditional research incomplete without capturing these digital behaviours. Voice, video, and social listening provide unfiltered, spontaneous sentiment that structured questionnaires often miss. Voice Insights: Understanding Real Emotion Tone and Emotion Analysis Voice notes and audio responses help researchers understand: Faster, More Natural Feedback Speaking is effortless for respondents, improving both participation and quality of insight. Video-Based Research: Capturing Real Context Body Language & Expressions Video surveys and UGC observation help decode: Short-Form Video Trends Platforms like TikTok, Instagram Reels, and YouTube Shorts reveal: Social Media Listening: Real-Time Consumer Pulse Tracking Conversations at Scale Brands can monitor: Spotting Emerging Trends Early Listening tools help identify: Why These Methods Strengthen Insights More Authentic Data People express themselves naturally on digital platforms, giving researchers richer, unbiased inputs. Real-Time Intelligence Voice, video, and social conversations capture what consumers feel today, not weeks later. Holistic Consumer Understanding Combining these sources creates a 360° view: Conclusion: The Future is Multi-Format Research As digital behaviour evolves, modern market research must integrate voice analytics, video intelligence, and social listening to stay relevant. These formats offer deeper storytelling, real-time signals, and more accurate consumer understanding — giving brands the competitive edge they need in fast-changing markets.

The way consumers shop is about to undergo its biggest transformation since the rise of mobile commerce. By 2026, AI-powered chat commerce will move from an emerging trend to a fully mainstream global shopping channel—powered by rapid advancements in conversational AI, hyper-personalization, and omni-channel automation. Consumers no longer want to browse, search, compare, and click endlessly. They want instant answers, personalized recommendations, and frictionless checkout—all within the platforms they already use daily. In 2026, chat commerce becomes the bridge between convenience, intelligence, and trust. What Is AI-Powered Chat Commerce? AI-powered chat commerce combines conversational AI with shopping experiences, allowing users to discover, compare, and purchase products directly inside chat platforms like WhatsApp, Instagram, iMessage, Telegram, WeChat, and website chatbots. Unlike traditional chatbots, 2026 conversational AIs understand intent, remember preferences, handle complex queries, and mimic human-like assistance—leading to higher engagement and conversion rates. Why 2026 Is the Tipping Point Several fast-moving forces are converging to make 2026 the breakthrough year: 1. Maturity of Generative AI Models AI has moved from scripted replies to context-aware, predictive, and emotionally intelligent interactions. These models understand product details, user preferences, and purchase history to deliver personalized recommendations instantly. 2. WhatsApp & Instagram Commerce Explosion Meta’s integrated commerce ecosystem is expected to expand dramatically in 2026, enabling businesses to set up AI-powered storefronts inside chat apps with one-click checkout. 3. Voice + Chat Coming Together Voice assistants like Siri, Google Assistant, and OpenAI-powered models will blend with chat interfaces, leading to voice-enabled shopping through conversations. 4. Decline of Traditional Website Conversions Long pages, heavy navigation, and crowded product listings are becoming outdated. Consumers prefer messaging-first experiences where the product comes to them, not the other way around. 5. Retailers Want More Direct Relationships AI chat commerce enables direct, personalized communication without relying heavily on ads. Brands see this as a way to reduce cost per acquisition and build stronger loyalty. How AI Chat Commerce Will Transform Consumer Behavior 1. Shopping Becomes a Conversation, Not a Search Process Instead of typing “best running shoes for flat feet,” a user simply asks:“I need shoes for long-distance running with good arch support.”The AI quickly compares brands, prices, reviews, and availability—within seconds. 2. Hyper-Personalization AI will understand: This allows it to recommend the exact product the user is most likely to buy. 3. Instant Checkout Inside Chat Shopping carts and payment pages will be replaced with:“Yes, buy it.” One message. One confirmation. Done. 4. 24/7 Shopping Assistants Unlike human agents, AI can handle thousands of queries at once—making even small businesses capable of enterprise-level customer service. What It Means for Businesses in 2026 1. Higher Conversions & Shorter Decision Cycles AI reduces the back-and-forth that usually delays purchases. The more personalized the experience, the faster the conversion. 2. Reduced Customer Acquisition Costs Brands spend less on paid ads because AI can nurture customers directly on messaging apps. 3. New Sales Funnels Will Emerge Instead of:Ad → Website → Product Page → Cart → CheckoutIt becomes:Chat → Recommendation → Purchase 4. Data Becomes a Strategic Advantage The companies with the richest data ecosystems will deliver the best conversational experiences. 2026 & Beyond: What Comes Next? As chat commerce becomes the default shopping method, expect: The future of commerce is not just online—it is conversational, intelligent, and deeply personalized. Conclusion 2026 will be remembered as the year AI-powered chat commerce becomes mainstream. With smarter models, integrated payment ecosystems, and consumer demand for effortless shopping, brands that adopt conversational commerce early will gain a massive competitive advantage. In this new era, shopping is no longer an action—it becomes a natural, human-like conversation powered by intelligence.

Are you making million-dollar decisions based on fake data? If you’re using online surveys, there’s a 1 in 3 chance you are. Last month, I reviewed a client’s market research data that informed a $200,000 service expansion. The numbers looked compelling: 78% interest rate, strong willingness to pay, positive sentiment across the board. There was just one problem—when we ran the data through fraud detection protocols, 38% of responses were flagged as fraudulent. The real interest rate? 52%. The expansion projections? Off by nearly half a million dollars. This isn’t an isolated case. It’s an industry-wide epidemic that’s quietly destroying the credibility of market research—and costing businesses billions. The Shocking Reality of Survey Fraud in 2025 Let’s start with the numbers that should terrify every decision-maker: The 29% Crisis A recent Rep Data study analyzing responses across 4 major panels and 2 panel exchanges found an average fraud rate of 29%—ranging from 21% to 38% depending on the source. And that’s just the confirmed fraud rate. When you factor in inattentive respondents (those who straight-line answers or provide gibberish just to collect incentives), the total “problematic response” rate approaches 40%. Think about that for a moment. Nearly half of your survey data could be unreliable. The Financial Impact According to industry research: But here’s what makes this truly insidious: most companies don’t even know they have a problem. How Did We Get Here? The Perfect Storm The survey fraud epidemic isn’t happening by accident. Several industry shifts have created the perfect environment for fraudsters: 1. The Aggregator Economy Once dominated by well-managed double-opt-in panels, the sampling ecosystem has devolved into a complex web of aggregators. Most suppliers now blend sources from multiple providers to meet quotas, timelines, and budget constraints. The result? A CASE4Quality study found that just 3% of devices completed 19% of all surveys. Even more alarming: 40% of devices entering over 100 surveys per day successfully passed all other quality checks. 2. The Professional Survey Taker There’s now an entire class of “professional” respondents who: 3. The VPN & Emulator Epidemic Fraudsters are increasingly sophisticated: 4. The Race to the Bottom Market pressure for faster, cheaper research has created a system that prioritizes volume over validity. When speed and cost become the primary metrics, quality inevitably suffers. The Real-World Cost: Case Studies Case Study 1: The 90% That Wasn’t A major NYU study claimed that nearly 90% of NYC transit workers faced assault or harassment. The finding sparked public panic and policy debates. The problem? The survey link was shared publicly on Facebook, allowing unauthorized participants to skew results. Fake ZIP codes and responses from non-transit workers rendered the findings unreliable. The reality: MTA internal data showed only 11% of workers had experienced such incidents. The damage: Reputational harm to NYU, misleading public discourse, and nearly implemented excessive safety measures based on faulty data. Case Study 2: The Bleach Panic In summer 2020, a CDC survey suggested 4% of Americans had ingested bleach to prevent COVID-19—implying 12 million people. After filtering fraudulent responses (respondents who failed attention checks), the percentage dropped from 4% to 0%. The impact: Unnecessary public panic and widespread misinformation during a critical health crisis. Case Study 3: The Insurance Investigation In 2025, major U.S. insurers including Aetna and Elevance Health were sued under the False Claims Act for submitting inflated, self-reported data to Medicare. The lesson: When decisions based on fraudulent data reach regulatory levels, the consequences escalate from business mistakes to legal liability. Your Fraud Detection Checklist: 20 Red Flags Here’s a practical checklist to audit your survey data right now: Device & Technical Indicators Response Pattern Red Flags Content Quality Issues Behavioral Anomalies The True Cost Calculator Let’s calculate what bad data is actually costing your business: Formula: Example Scenario: Mid-size consultancy conducting market research: Calculation: Total Annual Cost: $597,500 Your Turn – Quick Calculator: Scenario 1: Small Business Scenario 2: Medium Enterprise Scenario 3: Large Corporation The multiplier effect is what kills businesses. It’s not just the wasted research dollars—it’s the cascading cost of wrong decisions based on fake data. Solutions: How to Protect Your Research Investment 1. Choose Your Sources Wisely Direct panels consistently outperform aggregators. In a recent IntelliSurvey study: Avoid third-tier vendors entirely—pilot studies routinely find fraud rates exceeding 80%. 2. Implement Multi-Layer Fraud Detection Don’t rely on a single method. Best practices include: 3. Design Better Surveys Fraud prevention starts with survey design: 4. Pay for Quality, Not Just Quantity The cheapest sample is rarely the best value: Remember: you’re paying for quality responses, not total completes. 5. Understand the Fraud-Incidence Relationship When your target audience is niche (low incidence rate), fraud becomes exponentially more problematic: Example: Why? Fraudsters are experts at faking screening criteria. The lower your incidence, the higher the percentage of fraudsters in your final sample. Solution: Use specialized panels or proprietary recruitment for low-incidence studies. The Fraud-Inattention Distinction Here’s something critical that most researchers miss: fraud and inattention are separate problems. Fraudulent respondents: Inattentive respondents: The overlap: Some fraudsters look inattentive, some inattentive respondents look like fraudsters, but they require different solutions. Fix fraud with better detection. Fix inattention with better survey design. Building a Fraud-Resistant Research Practice Step 1: Audit Your Current State Run your last 3 studies through fraud detection: Step 2: Implement Tiered Quality Controls Bronze Level (Minimum Viable): Silver Level (Recommended): Gold Level (Best Practice): Step 3: Demand Transparency Ask your sample providers: If they can’t answer clearly, walk away. Step 4: Calculate ROI of Quality Investment Quality tools cost money, but consider: Investment: Return: The math is obvious. The Bottom Line Here’s what you need to remember: The question isn’t whether you can afford to invest in data quality. The question is whether you can afford not to. Action Items: What to Do Today Immediate (Next 24 Hours): This Week: This Month: This Quarter: Final Thought In an era where data-driven decision-making is supposedly our competitive advantage, we’re building strategies on quicksand. The 29% problem isn’t

When Carrie Clayton-Hine, EY’s Chief Marketing Officer, first saw the results, she couldn’t believe her eyes. Her team had just completed EY’s annual brand survey—a comprehensive study targeting CEOs of companies with over $1 billion in revenue. The kind of research that takes months to execute, costs six figures, and forms the foundation of strategic marketing decisions. Then, a synthetic data company called Evidenza approached with an audacious claim: “Give us your survey questions. We’ll create artificial respondents and give you the same results in days, not months.” Clayton-Hine was skeptical. Who wouldn’t be? But what happened next sent shockwaves through the market research industry. “It was astounding that the matches were so similar,” she told AdWeek. “I mean, it was 95% correlation.” Let that sink in. Not 70%. Not 80%. Ninety-five percent. For a fraction of the cost. In days instead of months. Using synthetic respondents who never actually existed. This single study has become the most cited evidence in the synthetic data debate—and it’s forcing every research professional to confront an uncomfortable question: If AI can replicate real human insights with 95% accuracy, what does that mean for the future of our industry? The Study That Changed Everything Before we dive into the implications, let’s understand exactly what happened in this groundbreaking test. The Setup EY runs an annual brand survey that’s strategically critical to their marketing efforts. The survey targets one of the most difficult audiences to reach: CEOs at large companies (those with over $1 billion in revenue). The traditional approach involved: When Evidenza proposed replicating this research using synthetic respondents, EY had nothing to lose. They’d already completed the real survey, so it became the perfect controlled experiment. The Process Here’s how Evidenza approached it: No panel recruitment. No scheduling conflicts. No respondent fatigue. Just AI-generated insights based on learned patterns from similar populations. The Stunning Results When the teams compared the synthetic results to the actual CEO responses, the correlation was 95%. The synthetic data didn’t just come close—it nearly perfectly replicated what real CEOs said about EY’s brand, positioning, and market presence. Why This Number Is Controversial (And Misunderstood) Before the synthetic data evangelists start celebrating and traditional researchers start panicking, we need to talk about what 95% correlation actually means—and what it doesn’t. What It Means: The patterns matched. On aggregate measures—brand perception, attribute ratings, preference rankings—the synthetic data showed the same statistical patterns as the real responses. The methodology worked. For this specific type of research (brand perception among a well-defined audience), synthetic data proved capable of replicating real-world findings. Speed and cost advantages are real. Days versus months. Five figures versus six. These aren’t marginal improvements—they’re transformational. What It Doesn’t Mean: It’s not perfect. That 5% gap isn’t trivial. On critical decisions, even small differences can matter significantly. It’s not universal. This study tested brand perception among CEOs. That’s different from understanding emerging consumer trends, exploring emotional drivers, or capturing nuanced cultural insights. The synthetic data still needed real data to train on. This is crucial: The AI models that generated these synthetic CEOs were trained on vast amounts of real executive data. Without that foundation, the 95% correlation would be impossible. The Provocative Argument: Were the Humans Wrong? Here’s where things get really interesting—and controversial. One observer made a striking point about the EY study: “The 95% correlation does not necessarily mean that synthetic data has 5% of ground left to cover. It’s more likely that the inherent sampling errors, subject distractions and signalling biases mean it is the human subjects who were off the pace.” Think about that for a moment. Real executives get bored. Survey research shows that CEOs lose patience with questionnaires longer than 20 questions. Their attention wanders. They rush through sections. Real respondents have bad days. They answer surveys when they’re stressed, distracted, or multitasking. Their mood affects their responses. Real people give socially desirable answers. They want to appear smart, fair, forward-thinking. They signal rather than reveal their authentic views. Synthetic customers never falter. They don’t get tired. They don’t misread questions. They don’t give inconsistent answers because they’re thinking about their next meeting. Could it be that synthetic data isn’t just close to human accuracy—it might actually be more consistent and reliable in certain contexts? That’s a mind-bending question that challenges everything we’ve assumed about research quality. Beyond Correlation: What Synthetic Data Can Do That Humans Can’t The EY study proved synthetic data could match real data. But the story doesn’t end there. Synthetic data doesn’t just replicate traditional research—it opens possibilities that simply aren’t feasible with human respondents. The “Helen” Moment Mark Ritson, the marketing professor and columnist, shared a jaw-dropping experience with Evidenza’s synthetic data technology. After receiving synthetic research for his Mini MBA product—including category entry points across 10 countries—the team asked if he’d like to chat with “Helen” about one of the findings. A synthetic persona appeared on screen. An attractive woman in her 40s looked back at him patiently. “Who is Helen?” Ritson asked, suddenly realizing what he was looking at—a conversational AI persona representing a segment of his target market. He could ask Helen questions. Probe deeper on her responses. Explore her motivations and concerns. All in real-time. This isn’t just faster research. This is research that was previously impossible. Limitless Scenario Testing With synthetic data, you can: Test 100 product concepts instead of 5, without budget constraints Run simulations across 50 markets simultaneously Ask follow-up questions to synthetic segments any time Model “what-if” scenarios instantly—what if we changed the price? The positioning? The packaging? Traditional research requires choosing priorities because of time and cost constraints. Synthetic data removes those constraints. The Real-World Business Impact Let’s move from theory to practice. What did this 95% correlation actually enable for EY and others? Strategic Speed In a traditional research timeline: By the time you have insights, market conditions may have shifted. Competitors have moved. Opportunities have passed. With synthetic data, that four-month process compressed to days.

Walk into any market research conference today, and you’ll hear it—the question that’s dividing our industry down the middle: “Are surveys dead?” Some experts are declaring that synthetic data will revolutionize everything we know about consumer research. Others are calling it overhyped nonsense that threatens the very foundation of authentic insights. And honestly? Both sides have compelling arguments. If you’re a market researcher, brand manager, or insights professional feeling confused about where synthetic data fits into your toolkit, you’re not alone. Let’s cut through the noise and examine what’s really happening in this heated debate. The Statement That Started a War The 2024 conference circuit exploded with provocative declarations. “Synthetic data is as good as real!” proclaimed some vendors. “Traditional surveys are obsolete!” shouted others. The buzz was impossible to ignore. Here’s what sparked the controversy: 69% of market research professionals used synthetic data in their research last year. That’s not a small pilot group—that’s the majority of the industry experimenting with something fundamentally new. But here’s where it gets interesting: When asked about their experience, only 31% rated the results as “great” when synthetic data was used on its own. So what’s the truth? Is synthetic data the future of research, or is it fool’s gold? What Exactly Is Synthetic Data, Anyway? Before we dive deeper into the debate, let’s make sure we’re all speaking the same language. Synthetic data is artificially generated information created by AI models to mimic real-world survey responses and consumer behavior. Think of it as AI creating “digital twins” of your target audience—virtual respondents who answer questions based on patterns learned from actual human data. Here’s how it works in practice: Sounds futuristic, right? It is. And it’s already happening at scale. The Case FOR Synthetic Data: Why Believers Are All In Let’s be fair to both sides. The advocates of synthetic data aren’t crazy—they’re responding to very real pain points in traditional research. 1. Speed That Seems Impossible Remember when getting survey results took 4-6 weeks? With synthetic data, you can have initial findings in days or even hours. Real-world example: When EY tested synthetic data against their actual annual brand survey (which typically surveyed CEOs of US companies with over $1 billion in revenue), they got results in days instead of months. The synthetic approach cost a fraction of the price of traditional methods. That’s not incremental improvement—that’s transformation. 2. The Cost Equation Changes Everything Traditional surveys are expensive. Between panel recruitment, incentives, data collection, cleaning, and analysis, costs add up quickly. For many companies, this means research becomes a luxury reserved for major decisions only. Synthetic data flips this model. Once you’ve trained the model, the marginal cost of additional research approaches zero. Suddenly, you can test ten product concepts instead of two. You can run monthly brand trackers instead of annual ones. For startups and smaller companies especially, this democratizes access to insights that were previously out of reach. 3. Reaching the Unreachable Try surveying 500 C-suite executives at Fortune 500 companies. Good luck with that—and your budget better have six figures allocated. Synthetic data can help augment samples from hard-to-reach populations, boosting representation without the nightmare logistics of actually recruiting rare audiences. 4. Privacy Compliance Made Easier With GDPR enforcement hitting €1.6 billion in fines in 2023 alone, privacy isn’t just a nice-to-have—it’s business-critical. Synthetic data offers a privacy-preserving alternative. You can analyze patterns and behaviors without handling actual personal identifiable information. For regulated industries like healthcare and finance, this is huge. 5. The EY Study That Turned Heads Here’s the stat that made even skeptics pause: When EY compared results from one thousand synthetic personas to their actual survey results, they found a 95% correlation. Let that sink in. That’s not “close enough”—that’s nearly identical results at a fraction of the time and cost. This wasn’t some softball comparison either. This was their annual brand survey targeting CEOs—exactly the kind of strategic research where accuracy matters most. The Case AGAINST Synthetic Data: Why Critics Are Sounding Alarms Now, before you rush to cancel all your survey panels, let’s hear from the other side. Because the critics aren’t Luddites resisting change—they’re raising legitimate concerns about accuracy, ethics, and what we might lose. 1. The “Overhyped” Reality Check The Market Research Society released a comprehensive report that didn’t mince words: Claims that synthetic data will completely replace primary data collection are unlikely, and the technology is “currently over-hyped.” Why the harsh assessment? Synthetic data is fundamentally retrospective. It can only replicate patterns that already exist in the training data. It cannot capture emerging trends, shifting cultural dynamics, or genuinely novel consumer behaviors. When the pandemic hit in 2020, no amount of synthetic data could have predicted how dramatically consumer behavior would shift. You needed real people, experiencing real situations, sharing real reactions. 2. The Bias Amplification Problem Here’s a scary truth: If your training data has biases (and it probably does), your synthetic data will amplify them. Remember Amazon’s infamous recruiting AI that accidentally learned to prefer male candidates? That’s what happens when bias lurks in training data. One researcher studying synthetic data outputs discovered that AI models consistently replicate and sometimes magnify the biases present in their source material. An ethical nightmare waiting to happen. 3. The Emotion Problem Synthetic data struggles with something humans excel at: emotional nuance. As one associate professor studying AI outcomes notes: “Our brains and emotions are highly complex. AI can provide good analysis of what someone has said, but it’s less effective at understanding the emotions that underpin people’s responses.” When a consumer says they “like” a product, are they: Real human researchers pick up on these subtleties. AI? Not so much. 4. The Missing Context Here’s what synthetic data can’t capture: The lived experience of being human. One research agency shared a powerful example: They conducted qualitative research for a global bank in the UAE and discovered that the use of English in campaigns significantly impacted effectiveness across different ethnic groups.

Let’s cut to the chase: If your organization isn’t doubling down on AI investment in market research right now, you’re already playing catch-up. And the numbers don’t lie. A recent industry study revealed something remarkable—83% of organizations already using AI in market research are planning to significantly increase their investment in 2025. But here’s what makes this statistic truly fascinating: This isn’t about jumping on a bandwagon. These are companies that have already tested AI, seen the results, and are now voting with their wallets. So what’s driving this unprecedented wave of investment? Let’s dig into the real story behind the numbers. The Wake-Up Call That Changed Everything Picture this: It’s 2024, and 89% of market researchers are already using AI tools regularly. Fast forward to today, and we’re witnessing something even more dramatic—organizations aren’t just experimenting anymore. They’re scaling up. The shift is undeniable: But what changed? Why the sudden urgency? The answer is simple: Companies that adopted AI early aren’t just seeing incremental improvements—they’re experiencing transformational results. And their competitors noticed. The Real Reasons Behind the 83% 1. Speed Is the New Currency Remember when market research projects took weeks or even months? Those days are gone. AI is revolutionizing research timelines in ways that seemed impossible just a few years ago: As one Customer Insights Director at a Fortune 500 company put it: “AI is going to level the playing field in terms of speed and efficiency. Whoever can dig deeper—whether using AI or not—will be the winner in 2025.” But it’s not just about being faster. It’s about being faster with better insights. 2. The ROI Isn’t Theoretical Anymore—It’s Proven Here’s where things get interesting. Companies aren’t increasing AI investment based on promises. They’re doing it based on results they’ve already seen. The numbers speak for themselves: Take fraud detection in financial services as an example. AI systems now process millions of transactions per second, identifying patterns humans could never catch. The result? Significant cost savings and reduced risk—measurable benefits that justify increased investment. 3. The Competition Isn’t Waiting There’s another powerful force driving this 83%: competitive pressure. When your industry rivals are cutting research timelines in half, uncovering insights you’re missing, and responding to market changes in real-time, you have two choices: adapt or fall behind. The market research industry itself is now worth $150 billion, and AI capabilities have become the deciding factor. In fact, 67% of researchers say AI capabilities are either critical or a key factor when choosing research vendors. Translation? If you’re not offering AI-powered solutions, you’re not making the shortlist. 4. Natural Language Processing Changed the Game Here’s something that doesn’t get talked about enough: AI hasn’t just made existing research methods faster—it’s unlocked entirely new types of insights. Natural language processing (NLP) is revolutionizing how we understand consumers: One B2B SaaS company recently used AI-driven sentiment analysis to analyze customer reviews and social media comments. The result? They quickly pinpointed customer pain points, leading to faster product updates and improved customer retention. This isn’t just efficiency—it’s a completely new lens for understanding consumer behavior. 5. Predictive Analytics Offers a Crystal Ball (Sort of) Perhaps the most compelling reason for increased investment? AI doesn’t just tell you what happened—it helps predict what’s coming next. Predictive analytics powered by machine learning enables researchers to: A B2B manufacturing company using predictive analytics can now forecast demand for their products with remarkable accuracy, allowing them to optimize inventory, reduce waste, and improve cash flow. That’s not a nice-to-have—that’s a competitive advantage. The Challenges Keeping Leaders Up at Night But let’s be real for a moment. If AI investment in research were all sunshine and roses, we wouldn’t be seeing some organizations hesitate. The honest challenges companies face: 1. Data Infrastructure Debt Many organizations are discovering their data isn’t ready for AI. It’s siloed, inconsistent, or simply not organized in a way that AI can effectively use. This “data debt” is becoming the biggest bottleneck to AI adoption. 2. The Skills Gap Having AI tools is one thing. Knowing how to use them strategically is another. Organizations are realizing they need people who understand both research methodology AND AI capabilities—a combination that’s currently rare. 3. The “Move Fast, Don’t Break Things” Paradox AI is advancing so rapidly that 30% of enterprise AI projects are expected to stall due to poor planning, inadequate risk controls, or unclear business value. The pressure to adopt quickly is real, but so is the risk of implementation failure. 4. Cost vs. Value Calculation While AI promises efficiency, the initial investment can be substantial. Leaders must carefully evaluate whether the expected ROI justifies the upfront costs—especially when 30% of projects might not deliver as expected. What Smart Organizations Are Doing Differently So how are the successful 83% navigating these challenges? They’re taking a portfolio approach to AI investment. The winning strategy looks like this: Short-term (0-6 months): Focus on AI applications that enhance existing operations—automating repetitive tasks, improving data quality, speeding up basic analysis. Medium-term (6-12 months): Invest in AI that enables new research capabilities—advanced sentiment analysis, predictive modeling, real-time consumer insights. Long-term (12+ months): Explore transformational AI applications—synthetic data generation, AI-powered research design, autonomous insight generation. This staged approach allows organizations to build capabilities incrementally, learn from each implementation, and adjust strategy based on real results rather than projections. The Human Element Isn’t Going Anywhere Here’s something crucial that often gets lost in the AI hype: The most successful organizations aren’t replacing human researchers—they’re empowering them. As one industry expert noted: “Human oversight in the age of AI is non-negotiable. Creativity, empathy, and the ability to interpret nuances—these are things only humans bring to the table.” The future of market research isn’t AI vs. Humans. It’s AI + Humans. The best outcomes happen when: Think of AI as the world’s most capable research assistant—one that never sleeps, never gets tired, and can process information at inhuman speeds. But it still needs a skilled researcher to ask the right

The tech giants Amazon and Apple have once again outperformed Wall Street’s expectations in their latest quarterly earnings reports. However, both companies have issued cautious outlooks for the coming months, citing growing uncertainty around tariffs and global trade policies. Here’s a breakdown of what’s driving the headlines-and what it means for the broader tech sector. Amazon: Strong Q1, Cautious Q2 Guidance Amazon reported impressive first-quarter results for 2025, with revenues climbing to $155.7 billion (a 9% year-on-year increase) and net income soaring to $17.1 billion-both figures beating analyst expectations. The company’s core businesses, including Amazon Web Services (AWS) and digital advertising, continued to deliver robust growth. AWS revenue jumped 17% to $29.3 billion, while ad revenue surged 19% to $13.92 billion, underscoring Amazon’s expanding influence in cloud computing and digital marketing. Despite these strong numbers, Amazon’s stock slipped more than 4% in after-hours trading. The reason: a softer-than-expected forecast for the second quarter. Amazon projected operating profit between $13 billion and $17.5 billion and sales of $159 billion to $164 billion, both below Wall Street’s estimates. CEO Andy Jassy specifically highlighted the unpredictability of future performance, pointing to “tariff and trade policies” as a major concern. He noted that while consumer demand remains steady, potential tariff hikes could force price increases in the coming months, impacting both profitability and customer sentiment. Apple: Record Services Revenue, But Tariff Headwinds Loom Apple also posted a strong quarter, with revenue rising 5% year-on-year to $95.4 billion and earnings per share up 8% to $1.65-again, both surpassing analyst forecasts. The company’s Services division was a standout, hitting an all-time high of $26.65 billion in revenue, while iPhone sales remained the primary revenue driver at $46.84 billion. However, Apple’s outlook is clouded by the impact of tariffs, particularly those targeting products assembled in China. CEO Tim Cook warned that the company expects a nearly $1 billion hit to profits in the second quarter due to new US tariffs. He described the environment as “very difficult” for forecasting beyond June, especially given weaker-than-expected sales in China and ongoing trade policy uncertainty. Apple’s CFO emphasized that while the company’s fundamentals remain strong-including a record-high installed device base and a 4% dividend increase-macroeconomic and geopolitical factors are making it harder to predict future performance with confidence. Tariffs: The Unpredictable Wildcard Both Amazon and Apple have joined a growing list of major corporations warning investors about the risks posed by shifting US trade policies and tariffs. The unpredictability of these policies is already prompting some companies to revise or even withdraw their full-year outlooks for 2025. “Obviously, none of us knows exactly where tariffs will settle or when,” said Amazon CEO Andy Jassy. “We haven’t seen any attenuation of demand yet. To some extent, we’ve seen some heightened buying in certain categories that may indicate stocking up in advance of any potential tariff impact.”

In today’s fast-moving digital world, numbers and charts alone don’t tell the whole story. If you’ve ever watched a customer share their experience on video-or seen a brand’s story come alive through rich media-you know the difference is more than visual. Video and rich media are transforming market research, making insights more authentic, actionable, and, most importantly, human. Why Video and Rich Media Matter More Than Ever Consumers crave authenticity. Video diaries, unscripted testimonials, and digital interviews offer a window into real lives, capturing emotions, motivations, and subtle cues that surveys can miss. In 2025, market research companies are leveraging these tools to get closer to the truth-because when people speak freely on camera, their stories resonate in ways that tick-box surveys never could. The Power of Personal Storytelling Imagine a participant recording a video diary about their daily routine with your product. You see their genuine reactions, hear their frustrations, and witness their “aha!” moments-all in context. This kind of unscripted, behind-the-scenes content builds a genuine connection between brands and audiences, making research findings more relatable and actionable. AI: Turning Hours of Video into Actionable Insights The challenge? Video creates a mountain of data. Enter AI-powered video analysis. Today’s tools can: With these capabilities, researchers can quickly find that crucial quote or pinpoint the moment a participant’s opinion shifted-without watching hours of footage. AI-driven indexing and search make video insights accessible and scalable, even for the largest studies. Rich Media Means Richer Insights Rich media isn’t just about video. It includes images, audio clips, interactive content, and more. These formats engage participants and audiences on multiple levels, increasing recall and deepening understanding. For market research, this means: SEO and Visibility: Why Search Engines Love Video There’s another big win: SEO. Search engines increasingly prioritize rich media, especially video, in their rankings. Including video content on your website can: To maximize these benefits, ensure your videos are optimized: Real-World Impact: What Leading Brands Are Doing Top brands now use video diaries and rich media not just for research, but also for marketing and social proof. Authentic customer stories, captured on video, are repurposed across campaigns, boosting trust and driving conversions. In fact, 91% of businesses now use video as a marketing tool, and the trend is only accelerating.