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Why Human Context Still Beats Machine Data

Chris on December 12, 2025

Data can be accurate and still be wrong for a decision.

That tension is growing as organizations collect more information than they can meaningfully interpret.

Machine-driven insights now process millions of data points instantly, surfacing patterns and reports without human involvement, but the increase in data hasn’t made decisions easier – it has simply raised expectations for what the data should clarify.

The difference? Context. Although AI can identify what people said or did, only humans can understand what the situation meant. Meaning, not volume, is what shapes the right decision. That said, AI has earned its role in the research process by handling the work that doesn’t require interpretation.

Where AI Accelerates Consumer Research Techniques

Video review that once required watching every minute of footage now gets summarized automatically, with key moments flagged for human analysis.

Open-ended responses that took days to code get tagged by theme and sentiment in minutes. Real-time quality monitoring catches bots, speeders, and inconsistent patterns during fieldwork, allowing teams to intervene immediately rather than learning about problems after the fact.

This is where AI excels: processing volume and surfacing patterns with speed and consistency that manual methods cannot match. Once those patterns emerge, the real work begins. Understanding what they actually mean requires human context.

Context Is Not a Vibe, It’s a Variable

The same words can mean entirely different things depending on who says them and when.

“It’s fine” can signal satisfaction, resignation, politeness, or rushed indifference. Pattern recognition flags the phrase. Context tells you which meaning applies.

Contradictions are insight, not error. People might buy one thing but feel another about it. They purchase the healthier option while craving the indulgent one. AI smooths away these tensions because they look like inconsistencies. Humans explore them because they reveal the competing motivations people actually navigate.

Motivation shifts by situation. The reason someone picks a product at 9 AM differs from why they pick it at 9 PM. Household dynamics, stress, time, and social context all influence decisions. These are fluid variables that only emerge through conversation.

The missing data matters most. Embarrassment, fear of judgment, guilt about waste, or pride in a choice they know others would question all shape behavior. These drivers stay hidden unless someone asks the right follow-up question at the right moment.

These layers of meaning are why context isn’t just descriptive. It actively protects decisions from misinterpretation.

Human Context Is Decision-Risk Insurance

Patterns without context can result in confident, wrong decisions.

An AI tool might flag that 60% of participants described a product as “convenient.” That sounds positive until a human moderator probes further and learns that “convenient” was code for “I feel lazy using this.” The pattern was accurate. The interpretation was backwards.

Human-led qualitative research adds decision guardrails by probing edge cases, exceptions, and social drivers that automated analysis misses. Consumer research techniques like in-depth interviews and contextual observation catch moments when what people say diverges from what they do.

In high-stakes categories, this matters even more. Health decisions, financial products, parenting choices, and identity-adjacent purchases all carry emotional weight that determines adoption and trust. Humans surface the hesitations, fears, and unspoken concerns that algorithms cannot detect because participants rarely articulate them unprompted.

Recent testing has shown that synthetic respondents can handle surface-level tasks but struggle when emotional or irrational motivations become central. That gap is exactly where qualitative research delivers the most value.

These limitations point directly to the areas where human judgment creates value, which is where qualitative expertise becomes irreplaceable.

Where Humans Win

Certain aspects of research require human judgment that AI cannot replicate.

Probing changes the answer. Humans hear hesitation, notice avoidance, and ask the follow-up question that gets to the real driver. A participant says they switched brands for “better quality.” A skilled moderator asks what that means personally and learns it was actually about feeling respected after a bad service experience.

Emotion with a cause matters. People rarely explain their emotional drivers cleanly. Skilled moderation helps them articulate what is difficult to say. The frustration is not just about the product but about what it represents in their routine, identity, or household dynamics.

Behavior in context tells richer stories. Ethnographic methods show what people do when nobody is asking them to report it. A shop-along captures what gets picked up, put back, and justified in real time. A mobile diary entry recorded during the decision provides better data than memory-based reconstruction.

Social dynamics drive outcomes. Household roles, identity signals, and perceptions of “who this product is for” often determine choices more than features or price.

Tradeoffs reveal the real decision. Humans map the full decision stack: price, guilt, time, health, convenience, social approval. AI treats choices as single-variable preferences. Humans understand that every decision involves negotiation between competing priorities.

These strengths show up most clearly in the qualitative methods designed to capture context.

Consumer Research Techniques That Require Human Context

These methods depend on human interpretation from start to finish.

Contextual interviews, whether in-home, in-store, or remote, allow researchers to see the environment where decisions happen. A “show me your setup” conversation often provides more insight than any survey.

Mobile diaries capture decisions in the moment rather than relying on memory. Entries recorded during the experience avoid the rationalization that happens when participants reconstruct events later.

Shop-alongs observe behavior as it unfolds. Watching what someone picks up, puts back, compares, and justifies reveals priorities that people cannot always articulate directly.

Projective tasks help participants express what is difficult to say plainly. Personification exercises, “talk to your past self” prompts, or “what would you advise a friend” scenarios create distance that makes honesty easier.

Artifact reviews add depth. Receipts, pantry photos, screenshots, and personal notes provide tangible evidence of behavior. AI can process these artifacts quickly through object detection. Humans interpret what they mean in context.

L&E Research tested this with a workflow involving nearly 200 photos. Object detection reduced review time and flagged technical errors. Human analysts then interpreted the patterns and connected them to participant motivations. The combination worked because each tool handled what it does best.

When to Lean on AI vs. When Humans Must Lead

The decision comes down to what the research needs to accomplish.

AI works well for processing volume, flagging patterns, monitoring quality, and accelerating repetitive tasks. It handles structured data efficiently and can surface directional trends quickly.

However, humans become essential when research involves emotion, high-stakes decisions, identity, contradictions, or tradeoffs. Consumer research techniques that explore motivation, probe behavior gaps, or interpret social dynamics require human judgment. Pattern detection finds what people said, but human context explains why it matters.

The strongest research combines both tools strategically. AI accelerates the processing while humans provide the interpretation, and that balance protects quality while improving efficiency.

The Future Is Human-Led, AI-Supported, and Quality-Protected

Context is what separates insight from noise.

While AI tools will continue to improve, efficiency will mean nothing if the interpretation is wrong. Human expertise leads the work when meaning, motivation, and context shape decisions.

If you’re evaluating where human context matters most in your research, L&E Research can help you design studies that balance efficiency with insight. Contact us today to learn more about what our team can do for your business.

Beyond the Pattern: Insights Only Real People Can Reveal

Chris on December 8, 2025

Artificial intelligence is reshaping the insights industry at an extraordinary pace. AI qualitative research has introduced new tools, new models, and new workflows that continue to push the boundaries of what is possible, tempting researchers with faster timelines and cleaner datasets. Yet beneath that excitement sits an important question: where does synthetic data strengthen the work, and where does it still fall short?

To explore that question, L&E Research conducted two complementary studies. The first took place in the spring and centered on bathroom habits, an intentionally human, messy topic that revealed notable gaps in how synthetic respondents interpret personal behaviors and contextual cues. The second study, completed this fall, examined breakfast habits using a more advanced methodology built inside CondUX. This allowed us to test how synthetic respondents handled logic-heavy branching, object detection, and emotionally driven open-ended tasks, all in parallel with a panel of sixty real people.

Together, these studies gave us a grounded, evidence-based view of what synthetic data can do well and where human insight still matters. The findings are neither alarmist nor celebratory. They are practical and measured, shaped by what happened when we put two respondent types through the same workflows. The results offer a clear direction for researchers who want to use synthetic respondents responsibly and effectively.

What Synthetic Data Did Well

Across both studies, synthetic respondents performed strongly when the task relied on structure, logic, and clear informational cues. They handled formatted questions accurately. They moved through complex branching without friction. They delivered consistent reasoning, tidy patterns, and clean distributions. For general trends or macro-level norms, synthetic outputs often aligned with the human panel. For example, all groups recognized comfort and scent as primary drivers of bathroom product choices, and nearly all showed similar attitudes toward basic hygiene habits and price sensitivity.

In the breakfast study, synthetics performed equally well when the question centered on observable behavior. When asked to estimate the percentage of people who skip breakfast, they offered clean, narrow ranges grounded in public data. When asked about the types of foods people tend to eat in the morning, their answers mirrored common consumer patterns. In these cases, the synthetic panel provided quick, directional insights that were easy to analyze.

The engineering work invested in building L&E’s synthetic panel also made a meaningful difference. The persona engine introduced individuality and response variability. The use of model combinations created a balance between speed and quality. Deterministic logic ensured that the branching path followed expected rules. These choices produced synthetic respondents that were more mature, more consistent, and more capable than in our earlier testing. They were still pattern-driven, but they were better at producing human-like variation within those boundaries.

All of this reinforces that synthetic respondents are valuable for certain uses. They can support early exploration, provide fast pulses before engaging real participants, and act as an efficient test bed for survey logic. They can also reveal formatting issues, help identify biases in question structure, and produce clean bulk data when speed is the highest priority. In these areas, the strengths of synthetic panels can save time, reduce cost, and support stronger research design.

Where Synthetic Data Fell Short

Although the synthetic panel handled structure well, it struggled consistently with emotion, contradiction, and personal context. When asked to share memories or describe feelings, synthetics provided warm vocabulary without lived grounding. Their emotional tone had polish, but not depth. In the smell memory task, synthetics created vivid scenes instead of authentic recollections. They described cinnamon and orange peels simmering on a stove, but never linked those images to real people, experiences, or moments.

This pattern repeated across open-ended responses. Humans spoke about parents, childhood routines, comfort, stress, and identity. They recalled mornings that had gone wrong, the rituals that held meaning, and the ways breakfast shaped their day. Synthetic respondents spoke in structured generalities. They offered interpretations that were plausible, but not personal. They followed linear reasoning, rarely contradicted themselves, and rarely displayed the spontaneity or unpredictability that characterizes real human behavior.

Visual outputs revealed similar limitations. In the bathroom study, synthetic images looked pristine and over-engineered, with little sense of real-world imperfection. In the breakfast study, freezer images generated by synthetics fell apart even as more effort was applied to improve variability. Increased engineering did not consistently yield more realistic results. In fact, the harder the system was pushed to create natural randomness, the more artificial the images became.

The most striking limitation surfaced when the AI was asked to classify the two datasets. It reviewed the human panel and synthetic panel and confidently asserted that panel A was human and panel B was synthetic. It was wrong. When asked why it misinterpreted the data, the model explained that it made its determination based on pattern recognition. It acknowledged that one dataset looked more structured and normalized, so it assumed that one was synthetic. The realization was important. Confidence did not mean accuracy, and pattern-driven thinking did not equate to human understanding.

This moment made something clear. Even when synthetic respondents look convincingly human, especially at first pass, the source of their output is fundamentally different. They respond to patterns. They do not respond to experience. Synthetic data is a powerful tool, but it does not replace the grounding that comes from real human insight.

Why Methodology Matters in AI Qualitative Research

The design decisions in the breakfast study highlighted the importance of building research that is intuitive for people. By leaning into image uploads, video responses, and object detection, the study created moments that felt natural and familiar. Participants engaged with the questions the way they would engage in everyday routines. This allowed us to observe how synthetics navigated the same experiences.

Regions of difference appeared clearly. Humans offered wide ranges of interpretation because lived experience varies. Synthetic respondents stayed within narrow, predictable bands. When object detection revealed inconsistent behavior, humans explained it through life context. Synthetics explained it through clean logic. When mornings went wrong, humans shared stress, panic, humor, and self-reflection. Synthetics shared sequences. The structure of the study amplified the contrast between the two respondent groups, and it made the findings easier to interpret.

The quality of the methodology also demonstrated how a platform like CondUX can elevate insight. Designed for people first, the study flow became smoother and more intuitive. The same design principles can improve synthetic processing by clarifying intent and reducing ambiguity. This dual benefit creates an environment where human-centered design, strong logic, and modern tools support both types of respondents.

A Practical Future for Synthetic Data

The future of synthetic data in AI qualitative research is not a matter of replacement. It is a matter of fit. There are places where synthetic can strengthen the work and places where it cannot. The responsible path is to use it with intention, knowing when it provides value and when it introduces risk.

Synthetic data is useful for the early stages of research. It can help test surveys, explore broad ideas, compare multiple concepts, and simulate missing segments. It is efficient for bulk analysis and can generate large sets of open-ended comments when the goal is volume rather than nuance. It is a valuable tool for reducing cost and saving time, particularly in early exploration or when the task does not rely on emotional depth.

Human participants remain essential for the parts of research that require meaning. Emotion, trust, comfort, cultural context, loyalty, fear, hesitation, and memory are not yet areas synthetic panels can replicate. Humans tell stories. They contradict themselves. They surprise us. They make decisions that do not always follow logic. All of this matters when the goal is to understand why people behave the way they do.

The findings from these two studies reinforce a simple conclusion. The future of insights is hybrid. Synthetic provides speed and structure. Humans provide depth and truth. Together, they can help researchers balance quality with efficiency.

The Takeaway for AI Qualitative Research

Synthetic respondents have come a long way in a short time. They offer significant advantages in speed and consistency, and they can support researchers in promising ways. At the same time, they cannot yet replicate the complexity, unpredictability, or emotional richness of human behavior. When the study is simple, those gaps are visible. When the study becomes complex, they grow.

What our research showed is not a verdict about technology. It is a reminder that tools and people serve different roles. Synthetic is a supportive partner, not a substitute. Used wisely, it can strengthen the research process. But the heart of qualitative work still comes from people. Their contradictions, context, and lived experiences shape insight in ways no model can fully imitate.

If synthetic struggles with simple tasks, it will not be ready for the complex ones. That is not a criticism: it is a direction for where we go next. The work ahead will continue to blend strong methodology, thoughtful design, and human understanding with the power of modern AI. That is how we will keep learning, keep evaluating, and keep improving the tools that will shape the next era of insights.

The Smart Way to Use AI in Market Research

Chris on December 1, 2025

AI in market research has created a spectrum of responses.

Some avoid it entirely, convinced that automation will compromise data quality. Others adopt every new tool without testing whether it actually improves outcomes. The teams getting it right are those who deploy AI where it accelerates research without sacrificing insight, while keeping humans involved where judgment, context, and interpretation matter most.

At L&E, we treat this as practice rather than theory. Testing AI capabilities in real projects shows us where the technology helps and where it falls short. Our recent Qual vs. Bot webinar presented findings from a study comparing synthetic respondents built with retrieval-augmented generation models against real participants recruited through CondUX.

The results confirm what strategic deployment looks like in practice and where AI delivers value without replacing human depth.

Where AI Improves Operational Efficiency in Research

Some research tasks consume time without requiring complex judgment. AI handles these efficiently, freeing analysts to focus on interpretation rather than manual processing. This approach reduces operational costs, allowing teams to spend more of their budget on strategic interpretation rather than processing time.

AI now accelerates work in four key areas:

Video analysis: Summarizes hours of footage, flags emotional cues, and identifies patterns while human analysts interpret what those patterns mean
Open-ended response coding: Tags thousands of responses by theme and sentiment so researchers can decide which findings deserve deeper attention
Real-time quality monitoring: Catches bots, speeders, and inconsistent patterns during fieldwork so project teams can intervene immediately
Recruitment screening: Pre-profiles participants based on behavioral data, improving respondent quality without replacing human recruiters

Across our tests, the dividing line was consistent: AI scales volume and consistency while humans supply context, nuance, and judgment.

Qual vs. Bot: The Study

Theory matters less than evidence when evaluating new research methods.

Our Qual vs. Bot study built synthetic panels using RAG models and compared their responses to real participants across multiple research tasks. The goal was straightforward: determine where synthetic respondents deliver reliable data and where they fail to match human depth.

When Synthetic Respondents Are Reliable

Synthetic respondents handled certain tasks well. They identified surface-level trends, maintained consistent response patterns, and processed information quickly. When asked factual questions or presented with straightforward preference scenarios, synthetic participants provided usable data.

When Synthetic Respondents Cannot Replace Humans

However, the limitations became clear when research demanded emotional insight or irrational human motivations. AI can report that most people prefer soft toilet paper, but it struggles to explain why that preference exists or what emotional drivers influence the choice.

We also hit engineering limits: API quotas from foundational models like Claude, Gemini, and ChatGPT can bottleneck parallel synthetic respondents, and scaling to research-sized panels exposes speed and access constraints.

The Verdict

Synthetic respondents work for specific applications where consistency matters more than emotional nuance. They supplement human participants by handling tasks where emotional depth matters less than data volume, such as codifying straightforward preferences or evaluating surface-level trends.

Technical and scale limits raised one central question: when does human judgment still matter?

Where Humans Still Outperform AI

Human moderators read the room in ways AI cannot replicate. AI can be programmed as a virtual moderator with branching logic to ask follow-up questions based on previous responses, but live moderators adjust their approach based on tone, body language, hesitation, and other subtle cues.

They probe deeper when participants seem uncertain, redirect when discussions lose focus, and create the psychological safety that encourages honest responses.

Human judgment verifies AI-generated findings. AI excels at identifying candidate insights by processing large datasets and flagging patterns. Analysts then determine which patterns actually matter for the research objectives and business context.

Human interpretation adds essential context to AI-drafted reports. AI can generate initial narratives from coded findings, summarizing themes and organizing data into coherent sections. Researchers refine those drafts by incorporating client knowledge, industry context, and strategic implications that automated systems miss. This human-AI collaboration will define the future of market research as technology continues to advance.

The Future of Market Research: What L&E Is Building Next

With those limits in mind, we’ve focused our product roadmap on augmenting human-led research. The engineering constraints and qualitative gaps showed us that AI works best as a support tool, not a substitute.

Across the industry, this means researchers will spend more time interpreting and advising while AI automates processing and pattern recognition.

L&E is currently building three capabilities:

Object detection in visual research: AI analyzes fridge, freezer, and pantry images to identify products and trigger follow-up questions
Dynamic survey logic: AI adapts question flows based on how participants respond throughout a study
AI-enhanced participant engagement: AI interfaces manage interactions in longer qualitative tasks while keeping human moderators involved

Each capability follows the same rule: measure impact, then deploy only if it improves the research outcome. Any responsible use of AI also requires safeguards around bias, transparency, and data privacy to ensure quality remains intact. This principle of measurement before adoption defines how L&E approaches every AI tool.

The Strategic Approach That Works

Smart AI deployment in market research starts with testing rather than assumptions.

L&E built synthetic panels and compared them to real participants because evidence matters more than trends. We incorporate AI in recruitment screening because it measurably improves participant matching. We use AI-assisted coding because it frees analysts to focus on interpretation rather than categorization.

This measured approach matters more as AI capabilities expand. The research industry will see continued development in synthetic respondents, automated analysis, and AI-enhanced data collection. Organizations that deploy these tools strategically will produce better results and maintain client trust. The future of market research depends on this evidence-based approach rather than blind adoption.

Success belongs to teams that understand both the technology and the research. AI makes certain tasks faster and more scalable. Human expertise ensures the resulting insights actually answer the questions that drive business decisions.

Ready to Build an Evidence-Based AI Strategy?

The difference between AI hype and AI value comes down to evidence.

L&E Research has tested synthetic respondents, automated coding, and AI-assisted analysis across real projects to understand where these tools improve outcomes and where they fall short. The results guide how we deploy AI and shape the recommendations we make to clients.

Watch our Qual vs. Bot webinar for the complete study or connect with our team to identify where AI can support your research without sacrificing quality.

Real Spaces, Real Scenarios, Better Healthcare Decisions

Chris on November 18, 2025

At L&E Research, we believe that the most meaningful insights come when people and places align. Our healthcare research facility in Chicago, nestled on Michigan Avenue, offers an immersive, purpose-built space for healthcare research and simulation.

When paired with our specialized healthcare recruitment team, it becomes a powerful two-pronged capability for our clients.

A Space Designed for Authenticity and Impact

Our Chicago site features a state-of-the-art medical simulation suite that has been carefully designed to mirror real clinical environments, so that the behaviors, interactions, and learnings you capture are as natural and reliable as possible.

The suite replicates authentic clinical settings: wall-mounted oxygen, suction, electrical outlets, infusion pumps, carts and monitoring units are all integrated.
It offers a modular layout: we can recreate a hospital room, a home-care setting, or a hybrid simulation. Moveable furnishings, partitions and adjustable lighting all support flexible scenarios.
The space is large and easily accessible, designed to accommodate substantial medical equipment: Whether you are bringing in robotics, endoscopy towers, or reprocessing machines, the suite can manage the size, power, and access requirements.
Private observation capabilities: clients can observe via one-way mirrors or through real-time streaming from a separate lounge, without interrupting the session.
Adjacent utility support space: to support longer or more complex sessions, we provide prep counters, refrigeration, and storage so that your materials and logistics are handled right on-site.
Multi-purpose training and client use room: ideal for device training, learning retention studies, decay periods, or briefing/debriefing your participants.
Location and built-in technology: with our suite located on Michigan Avenue, convenient to transit, hotels, and amenities, and equipped with the latest HD streaming and recording tools, you can conduct high-stakes research with efficiency and flexibility.

The Value of a Healthcare Research Facility in Chicago

When the setting feels real, participants respond as though they are in their actual environment. Our clinical-grade simulation suite enables you to:

Validate protocols, workflows, or medical device interfaces in lifelike conditions.
Explore interactions among healthcare professionals, caregivers, patients and technology under controlled yet realistic settings.
Capture behavioral, emotional, and ergonomic data with fidelity thanks to the integrated observation and streaming technology.
Run scenarios that span acute care, home care, hybrid environments; the flexibility of the space is built in, so your design isn’t constrained by location limitations.
Provide your stakeholders with a comfortable, discrete observation experience while still maintaining participant authenticity.

In short: this is a purpose-built environment for healthcare research, supporting reliable insight and meaningful outcomes.

The “One-two Punch”: Facility Plus Team

While the facility itself is impressive, what truly sets us apart is the combination of infrastructure and human expertise. Our specialized healthcare research team at L&E Health complements the space by delivering:

A dedicated recruitment team experienced in healthcare research, including patients, physicians, clinicians, and allied health professionals.
An extensive, diverse database and technology-enhanced sourcing strategies, enabling efficient recruitment for even complex or niche segments.
A research partner mindset: we’re not just supplying space or participants – we’re helping you design studies that work, with attention to practicalities, participant experience, and data quality.

By combining our premium simulation facility with our focused recruitment and research support, we enable you to move from concept to execution with clarity, confidence and speed.

Use-Case Highlights

Here are some ways our healthcare research facility in Chicago can support your research:

Device usability testing in a clinical or home-care environment, capturing how users interact with equipment under real conditions.
Workflow studies for care teams in switching rooms, emergency response simulations, or tele-health/hybrid models.
Training and learning retention: using our client use/training room to brief participants, then later observe decay or retention in a simulated setting.
Protocol validation for clinical trials: simulate infusion pumps, carts, monitoring, environmental variables (lighting, space constraints) to refine steps and reduce risks.
Mixed-method research: combine quantitative measurement (time, errors, efficiency) with qualitative insights (caregiver/patient feedback, interaction cues) in one facility.

Location-based Advantages

Being centrally located on Michigan Avenue in Chicago means:

Easy access by public transportation and major airports, improving participant convenience and reducing attrition.
Ample nearby accommodations, dining, and services – helpful when you bring in participants or stakeholders from out of town.
A modern, comfortable client lounge and observation spaces aligned with professional research-grade expectations.

Bringing it All Together

When you partner with us at L&E Research, you gain access to a full research-ready ecosystem: premium simulation suite, robust space, integrated technology, dedicated healthcare recruitment, and a partner mindset.

The Chicago Medical Simulation Suite gives you a space designed for accuracy and realism, but what elevates the experience is the partnership behind it. Our healthcare team supports every stage of your study, from planning and recruitment to facilitation and insight delivery. Together, the environment and the people who guide the work create a research experience that is efficient, reliable, and built around real human perspectives.

Let us help you bring your healthcare research vision to life, using an environment built for accuracy, a team built for partnership, and a process built for results.

If you’re ready to explore how our healthcare research facility in Chicago can support your next study, we’d love to talk further. Contact us today!

The Proxy: The Magic Bullet of Healthcare Market Research

Chris on November 17, 2025

Healthcare innovation continues to advance beyond common conditions. The focus now includes developing new treatment options and improving quality of life for individuals diagnosed with rare diseases.

This shift highlights the importance of accessing insights from these unique populations, their caregivers, and healthcare providers. One of the most challenging aspects of market research in the healthcare sector is recruiting participants from the rare disease cohort. To meet required quotas, researchers often need to introduce flexibility within audience requirements, leading to the inclusion of proxy participants.

When carefully selected and well-defined, proxy participants can contribute to the success of a study. However, poorly chosen or inadequately defined surrogate participants can undermine the legitimacy of the insights and compromise the integrity of the data. To navigate these challenges successfully, it’s essential to understand how rare diseases impact the research process.

Rare Disease Impact on Research

While rare conditions pave the way for medical advancements through research, the more niche a condition, the less accessible those diagnosed become for participation in market research, particularly for studies requiring in-person involvement.

A rare condition is defined as one that affects fewer than 200,000 individuals in the United States and fewer than 1 in 2,000 people worldwide. Although each condition is uncommon on its own, more than 7,000 rare diseases collectively affect over 30 million people across the U.S. This limited patient recruitment pool makes traditional recruitment strategies impractical for rare disease studies.

For in-person qualitative research, incorporate proxy patients into the recruitment approach from the outset, provided the research is not a clinical trial and does not involve treatments or medication administration.

By pre-defining acceptable surrogate patients as a flexible component of the recruitment strategy, researchers can ensure a predictable pace, schedule, and budget. This minimizes the need for additional approvals and unexpected accommodations during the research process.

The Power of the Proxy

When evaluating the impact of including representative patients in a rare condition study, there are four key elements to consider:

Prevalence

Pre-identifying and approving surrogate populations to represent rare cohorts can elevate a project’s incidence rate (IR). It increases the likelihood that the project will meet its defined quota(s) within the specified timeframe and budget.

Accessibility

Reasonably budgeted in-person research requires clusters of patients in defined central locations. For rare conditions, clustering is challenging, if not impossible, without requiring patient travel.

Due to the underlying diagnosis qualifying them for the research, patient travel is often unrealistic.

Budget

Rare condition in-person research is costly. Without the inclusion of surrogate patients, researchers face two options: research team travel or patient travel.

Funding for rare disease research is often limited due to the perceived low return on investment for therapies prescribed to a small patient population, coupled with the high cost of drug development.

Stamina

The most researched rare conditions are often in the specialties of oncology, neurology, and pulmonology.

These diagnoses are frequently accompanied by decreased stamina due to disruptions in energy production, muscle function, and nerve transmission. Patients may decline market research study opportunities, especially those that are longer or multi-step, to preserve and protect their energy and health.

Defining Your Proxy

These four elements establish why proxy participants matter. The next step is defining who qualifies as an acceptable proxy for your specific study.

Evaluate and prioritize the negotiable versus non-negotiable qualifiers related to your study participants. Whether focusing on patient journey, preference research, or device/software usability, consider what can be replicated by a population with higher prevalence but similar pathways in terms of symptomatology, provider pool, and treatment experience. Strategic patient recruitment planning at this stage prevents delays and budget overruns later.

From there, acceptable surrogate patients can be defined.

Examples of successful patient proxies in qualitative market research:

Supplementing Tardive Dyskinesia patients with those diagnosed with Essential Tremor.
Recruiting stroke-induced aphasia patients to meet quotas for Landau-Kleffner Syndrome.
Utilizing cirrhosis patients with a diagnosis of Chronic Obstructive Pulmonary Disease (COPD) to recruit for Alpha-1 Antitrypsin Deficiency studies.

Patient Recruitment Considerations & Recommendations

Once proxy participants are defined, execution becomes critical. The following recommendations help ensure successful rare disease patient recruitment:

Proof of Diagnosis: When recruiting rare and representative patients, require participants to provide proof of diagnosis, typically in the form of an EMR sent via secure transfer to the recruitment partner.
Decentralize: Whenever possible, conduct the research remotely. This approach increases the pool of rare patient participants available while keeping costs low.

For small device testing, consider shipping the prototype to the patient with prepaid return arrangements.

Simplify: Availability and stamina are prevalent concerns.

Keep the research simple by eliminating long engagements, multi-step, or multi-day study methodologies.

Transportation: Make transportation easy. Depending on the required diagnoses and/or the age of the participants, consider arranging transportation or providing an extra incentive for a family member or friend to assist with transportation.
Over-Recruit: The recommended over-recruitment rate for remote rare patient research is 20 percent, increasing to 25 percent for in-person research. Cancellation rates are higher among the chronically ill due to the unpredictability of their health on the study date and limited scheduling options.

To ensure quotas are met within the designated timeline, a 25 percent over-recruitment is recommended.

Your Recruitment Partner

The right partner makes these best practices work.

A successful rare patient recruitment effort depends on the quality of your recruiting partner. Look for vendors with clinical expertise who can help design a workable recruitment strategy.

Partners with an established presence in the industry, affiliations with rare patient groups, and a proven track record in rare patient research deliver the best outcomes.

Proxy participants in rare disease market research offer a powerful strategy for overcoming the inherent challenges of recruiting these unique populations. Careful selection of surrogate patients, decentralized research efforts, and simplified study designs enable researchers to gather meaningful insights that drive healthcare innovation.

Quality and reliability of data improve with these practices, supporting effective treatments and improved quality of life for individuals affected by rare diseases. As medical research continues to push boundaries, thoughtful and strategic recruitment approaches remain essential in advancing our understanding and addressing the needs of these special populations.

Ready to Get Started?

If you’re planning a rare disease study, our healthcare research team can help you recruit the right participants efficiently and reliably.

With extensive experience in rare disease and specialty populations, we design studies that meet quotas, stay on schedule, and produce high-quality, actionable insights.

5 Reasons to Conduct Your Taste Tests at L&E’s Columbus Facility

Chris on November 11, 2025

Product decisions depend on taste, aroma, and texture. Choosing the right test kitchen matters as much as the formulation itself.

Many food and beverage brands settle for rented commercial spaces. These makeshift testing areas weren’t designed with sensory evaluation in mind. As a result, compromised data, limited flexibility, and missed insights often follow.

L&E’s Columbus Test Kitchen was engineered specifically for culinary research. Built to support everything from formulation testing to final presentation. This Ohio facility offers the control, adaptability, and expertise sensory studies demand.

Here’s why brands choose Columbus for their most critical product research. We start with the foundation: a facility built for culinary innovation.

1. Purpose-Built Test Kitchens Designed for Research, Not Rentals

Our Columbus test kitchen was built with the same purpose-driven approach as our Cincinnati sensory facility, with a space designed specifically for food and beverage research rather than generic commercial use.

The culinary insights suite includes dedicated zones for every stage of testing:

The Prep Step: Handles cold assembly, packaging, and final plating with stainless steel workspace, open shelving, and a movable condiment station that adds versatility when preparing for evaluation.
The Cold Hold: Features large walk-in freezer and refrigerator space, ensuring products are stored safely and properly. Brands can mimic real-world usage conditions and maintain product integrity throughout studies.
The Heat Suite: Brings professional-grade cooking capability with fryers, gas ovens, and flat-top grills. Whether running large-scale tests or preparing samples in small batches, this zone simulates real-time cooking conditions with precision.
The Scrub Hub: Maintains flow and hygiene during complex studies, equipped with a triple-compartment sink, commercial dishwasher, and designated sanitization areas.

In addition, clients don’t retrofit the space to fit their study. The environment flexes around research objectives, whether evaluating a single product or running comparative tests across multiple formulations. Our test kitchens are designed to eliminate guesswork in product evaluation and support detailed flavor profiling at every stage.

When the foundation is built for precision, everything else follows.

2. Real-World Testing Captures What Digital Can’t

Some product decisions require in-person evaluation.

Sensory attributes like flavor, scent, and mouthfeel demand real-human observation under conditions that mirror actual use. Digital tools serve their purpose, but when it comes to food products, nothing replaces direct participant feedback captured in context.

However, Columbus fills that gap. The culinary insights suite simulates authentic cooking and consumption environments, capturing reactions in real time through moderated sessions, recorded observations, or live client viewing. This setup ensures every taste testing session reflects real-world conditions, capturing how a sauce performs during cooking, how packaging shapes first impressions, and how texture evolves over time.

The immediate feedback loop supports faster, more confident product development decisions.

A proper environment removes variables. The right setup captures the truth.

3. Integrated Recruiting Brings the Right Participants to Your Study

Access to test kitchens means little without access to the right people.

L&E handles recruiting, moderation, and logistics so studies run without friction. Our Columbus team ensures participants match exact specifications, whether dietary preferences, cooking habits, or behavioral segments, and that the right questions are being asked throughout.

We combine advanced technology with expert oversight to prevent fraud, verify identity, and ensure qualified participants. As a result, reliable, high-quality data that drives confident decision-making follows

Support includes:

Targeted recruiting for dietary or behavior-based segments
Sensory-trained moderators who guide sessions with precision
Video capture and live-streaming for remote client observation
End-to-end logistics, from participant intake through data analysis

This integrated workflow means fewer handoffs, tighter execution, and cleaner data. Clients focus on insights. We handle the infrastructure.

4. Flexible Configurations Support Fast Iteration

Great insights often come in the second round of testing.

In fact, product development rarely happens in a single round.Recipes get refined. Packaging gets adjusted. Preparation methods evolve. Our Ohio facility was designed to keep the product innovation process moving without unnecessary downtime or logistical friction.

Mobile prep stations allow for quick reconfigurations between sessions. Multiple zones support parallel testing of different prototypes. Client-focused observation areas let brands adjust approaches based on what they’re seeing in real time.

This setup supports:

A/B testing of flavors, textures, or preparation methods
Rapid feedback on packaging, instructions, or concept combinations
Multi-stage product refinement processes that require iterative testing

When the space adapts to your timeline, consumer testing moves faster and delivers more actionable results.

Speed without sacrifice means research keeps moving. Even the best space, however, needs the right team behind it.

5. The Columbus Team Operates with Precision and Partnership

Behind every detail of the Columbus culinary insights suite is a group of professionals who understand what’s at stake in sensory testing.

From maintaining pristine conditions and controlled environments to delivering timely reporting, our staff operates with both precision and empathy. They know that small variables like temperature, timing, and presentation can make or break a study. Participant comfort affects data quality. Your brand’s reputation depends on getting the research right.

When food and beverage brands choose Columbus, L&E-level service comes standard in every session, every detail, every deliverable.

Ready for Better Product Research?

L&E’s Columbus facility in Ohio offers more than square footage and appliances. This culinary insights suite provides a complete research solution: purpose-built space, expert recruiting, integrated support, and a team that treats your study with the same care you give your products.

Whether you’re refining a recipe, testing new packaging, or launching a new product line, our Columbus test kitchen delivers the environment, expertise, and consumer insights your next taste testing study deserves.

Ready to see the space for yourself? Contact L&E Research to schedule a facility tour or discuss how our Columbus team can support your goals

From Race to the Bottom to Rise of AI

Chris on October 10, 2025

Each year, the Future Trends webinar gives us an opportunity to pause, reflect, and take stock of where the future of market research is headed. This year’s discussion was especially striking. Artificial intelligence (AI) is no longer a distant prospect on the horizon; it is here, shaping how we work, think, and deliver value.

As with every wave of innovation, AI forces us to reckon with what we’ve learned from the past. The insights industry has already lived through its own growing pains. For years, the “race to the bottom” drove down costs but left behind an enduring problem with data quality. That legacy continues to shape how we approach the work ahead.

The challenge before us now is simple in statement but complex in execution: how do we ensure that new tools like AI serve as a force for higher-quality insights, not just faster and cheaper outputs?

The Legacy of the Race to the Bottom

The story of the last decade in research is, in many ways, the story of a marketplace caught in a cycle of underbidding.

To win projects, companies slashed costs, often at the expense of participant incentives. That decision may have been expedient in the short term, but the long-term consequences were significant.

Participants became fatigued, undervalued, and, in some cases, disengaged altogether. Fraud crept in through the cracks. The result was an erosion of trust in the data itself, the very foundation of our work.

At L&E Research, we saw this problem emerging early and took it seriously. We invested in “research-on-research,” asking participants directly about their experiences, not just with us but across the industry. How did incentive levels affect their willingness to participate? How quickly did they expect to be paid? How did they feel about moderation and engagement styles?

These weren’t academic questions; they were existential.

When participants don’t feel valued, the quality of insights deteriorates. That’s why we aligned ourselves with industry-wide initiatives through the Insights Association and built fraud mitigation into our processes well before it became the industry’s headline concern.

The race to the bottom is part of the research industry’s legacy, but it is not our future. Having acknowledged how we got here, we now have the opportunity to move forward with stronger footing.

Data Quality in the Age of AI

Today, the conversation about speed and cost has been reignited by AI. Procurement departments push for faster, cheaper research. Sales teams feel pressure to deliver. And once again, quality risks being left behind.

However, the tools themselves are not the problem; it’s how we use them. AI can accelerate processes, but it can also strengthen outcomes if we put quality at the center of our applications. The choice is ours.

At L&E, we’ve seen firsthand how AI can be deployed to improve accuracy while also saving time. A recent case study with our CondUX platform is a powerful example. A client asked us to analyze nearly 200 photos submitted by participants. Traditionally, this would have taken a team of humans more than 18 hours to review and categorize. Using CondUX’s object detection capabilities, we reduced the process to just two and a half hours, including setup and quality control.

The time savings alone were impressive, but even more importantly, the AI surfaced errors that the human reviewers had missed. By flagging low-confidence images for human verification, CondUX didn’t replace human oversight; it enhanced it.

This shift is significant. Qualitative research has long relied on asking participants to describe their behaviors and environments. Object detection allows us to observe instead. Rather than asking what’s on a kitchen counter, we can see it directly. Observation has always been at the heart of qualitative work, and AI now gives us new tools to scale it without losing authenticity.

The lesson here is clear: AI doesn’t have to perpetuate the mistakes of the past. If used wisely, it can reverse them. Instead of cutting corners on quality, AI can elevate it.

The Human Factor: Training, Oversight, and Storytelling

Yet even as we embrace new tools, one truth remains unchanged: humans are central to research. AI may be, as one panelist described it, “the best intern you’ll ever have.” But even the best intern still needs a manager.

AI can synthesize information, but it cannot think critically. It does not problem-solve. Left unchecked, it can amplify errors rather than resolve them. The risk of over-trusting AI is the risk of making high-stakes business decisions on faulty insights, a mistake no brand can afford.

That is why human-in-the-loop oversight is non-negotiable. Researchers must continue to bring context, domain expertise, and discernment to every AI-assisted output. AI may help answer “what,” but humans must still interpret “why.”

This balance between technology and humanity is not just relevant for today’s practitioners; it also defines the training of tomorrow’s researchers. Academic institutions play a critical role here. Just as earlier generations learned math without calculators, students today must learn the fundamentals of research without over-relying on AI.

If researchers don’t understand the basics, AI becomes nothing more than a “yes-person,” agreeing, generating, and emulating without questioning. Only those who have mastered curiosity, empathy, and storytelling will know when the machine is wrong, and more importantly, how to use it responsibly.

The future of market research belongs to those who can balance both: the efficiency of AI and the empathy of human interpretation.

Looking Ahead with Optimistic Caution

The insights industry is entering a period of remarkable transformation. Investment in AI and other technologies is accelerating, and the potential to make research faster, more scalable, and more accessible is undeniable.

Optimism must be paired with caution. If we lean too far into speed and cost, we risk repeating the mistakes of the past and recreating the very data quality challenges we’ve worked so hard to overcome.

The way forward is not about rejecting efficiency. It is about balance. AI should help us achieve all three points of the triangle: speed, cost, and quality, without sacrificing one for another. That balance is not easy, but it is possible. And it is necessary if we want our work to remain meaningful, relevant, and impactful.

What gives me confidence is the spirit of this industry. Time and again, researchers have shown the ability to adapt, innovate, and lead. We are not passive recipients of technology; we are active shapers of how it is applied. If we keep people – participants, clients, and researchers – at the center of our work, then tools like AI will not just make us faster or cheaper. They will make us better.

Shaping the Future of Market Research

The future of market research is not defined by technology alone. It is defined by how we choose to use it. The race to the bottom taught us that neglecting participant experience and data quality comes at a high cost. AI gives us the chance to learn from that history and write a different story, one where speed and cost efficiencies are balanced with quality, and where human expertise guides every technological advancement.

At L&E Research, we believe the path forward is not about replacing people but empowering them. With the right balance of tools and talent, the future of market research can deliver insights that are not only faster and more efficient, but also deeper, richer, and more reliable. That is the future we should all be working toward.

Security and Quality Aren’t Perks. They’re Prerequisites.

DWG Admin on September 15, 2025

When it comes to choosing a research partner, trust isn’t a luxury.

It’s the baseline.

That’s why two questions should always be front and center:

How do you protect my data?
How do you make sure nothing gets missed?

At L&E Research, we believe that security and quality are non-negotiable for ISO certified market research.

That’s why we’ve invested in dual ISO certifications: one for information security and one for research quality.

Very few partners hold both. Fewer still bake them into every project the way we do.

What It Means To Hold Both Certifications

At L&E, we understand that trust in ISO certified market research comes from two places: data security and process quality.

These two ISO certifications work together to cover both.

ISO/IEC 27001:2022 is the international standard for information security management. It ensures that your data is protected through formalized policies, risk assessments, employee controls, and encryption practices.
ISO 20252:2019 is the international standard for managing market, opinion, and social research. It ensures that every research project is executed with consistency, documentation, and methodological rigor across all phases.

Holding both means that we don’t just protect your data or deliver your research well.

We do both, every time.

Fewer Than Five Firms Hold This Dual Certification

The Insights Association, through its audit body CIRQ, has certified L&E Research to both ISO standards.

Fewer than five companies hold both certifications for ISO certified market research.

The dual status is rare and difficult to achieve. It represents a deep investment in systems training, oversight, and continuous improvement.

When you work with a partner that holds this distinction, you choose a level of excellence that goes beyond standard vendor relationship.

Why ISO Certified Market Research Matters To You

When you partner with an ISO certified market research firm that holds both standards, you gain tangible benefits across security, quality, and operational efficiency:

Peace of mind for IT and compliance teams. ISO 27001 certification assures that every layer of your project data, from client records to video files, to survey data, is protected by one of the most respected security standards in the world.
Confidence in research integrity. ISO 20252 certification ensures your qualitative and quantitative research is managed with consistent documentation, governance, and methodological accuracy.
Faster onboarding, fewer surprises. Auditable standards reduce friction in vendor approval processes, especially in healthcare, financial services, and tech where security and governance matter most.
Proof of excellence, not just promises. These certifications are independently verified, maintained through regular surveillance audits, and publicly listed through CIRQ. They reflect an organization that holds itself accountable.

Trust Is Earned

When we say we’re built for your peace of mind, it’s not just a promise.

It’s a process.

One that’s been extremely audited, globally validated, and continually improved to meet the ISO certified market research standards you deserve.

Looking for a partner that holds itself to the highest global standards? Let’s talk.

The New ISO Standard Is Here. We’re Already There.

DWG Admin on August 26, 2025

At L&E Research, staying ahead of the curve isn’t just a business goal: it’s how we build trust. That’s why we are finalizing our certification to ISO 27001:2022, the latest update to the international standard for information security. This new version brings stronger safeguards, clearer structures, and more relevant controls for today’s digital landscape.
We are not waiting for a deadline to act. We are meeting the future of data protection now.

What is ISO 27001?

ISO 27001 is the international benchmark for information security management. It defines how organizations should structure, implement, and maintain safeguards that protect sensitive data. Being certified means our security practices have been reviewed and approved by an independent, accredited body through a formal audit process. For our clients and partners, it is a clear signal that we take information protection seriously and that we have the policies, procedures, and culture in place to prove it.

What’s Different about the 2022 Version?

The 2022 update introduces structural and practical improvements to the standard. While the core principles remain the same, the refinements help organizations better align with modern digital environments. Here’s what changed:

A more streamlined framework. The original 114 controls have been reduced and reorganized into 93, grouped into four categories: organizational, people, physical, and technological. This makes the standard easier to manage and apply.
New areas of focus. Eleven new controls were added, including items like cloud service security, data deletion, and threat intelligence. These additions reflect the realities of today’s digital ecosystems.
Improved clarity and alignment. Language updates throughout the document make the standard easier to understand and integrate with other ISO frameworks, such as those for quality or risk management.

While the changes may appear technical, the intention behind them is simple: to make security stronger, clearer, and more adaptable.

Why it Matters for Our Clients

Our upgrade to ISO 27001:2022 is about more than keeping up with industry standards. It reinforces our promise to protect the data and relationships that power your research. Here’s what it means for you:

Greater assurance that your data is secure. The updated controls reflect current risks and ensure that our practices remain aligned with best available guidance.
Less time spent on vendor assessments. Certifications to the latest version helps meet IT and procurement requirements faster and more efficiently.
Confidence that your partner is continuously improving. Our upgrade shows that we don’t wait for compliance deadlines to take action. We invest in systems that benefit you directly.

Part of a Larger Commitment

L&E Research is also certified to ISO 20252:2019, the international standard for quality in managing research projects. Together, these two certifications represent our focus on protecting both the integrity of your research and the information it contains.
We believe security and quality go hand in hand. Our commitment to ISO 27001:2022 is one more example of how we bring that belief into practice.

Want to learn more about how our certifications support your research goals? Let’s start a conversation.

Smoother Projects. Smarter Decisions. Fewer Surprises.

DWG Admin on August 22, 2025

Getting research right isn’t just about who you recruit or what you ask.

It’s about how every step is managed – before, during, and after fieldwork.

That’s where certification matters. At L&E Research, our quality systems are built on globally recognized best practices, helping you avoid missteps, reduce friction, and deliver cleaner insights from the start.

We’re proud to be ISO 20252 certified, the international quality standard for managing research projects.

That certification exists in the background of everything we do, but the outcomes speak clearly for themselves: smoother execution, better alignment, and fewer surprises.

So what does that mean for you as a client or research partner?

Here are five advantages of working with an ISO 20252 certified organization.

Built-In Quality At Every Step With ISO 20252

ISO 20252 outlines strict process requirements for every phase of research project execution.

That means quality is not something we add at the end. It’s embedded from the very beginning.

From proposal development through fieldwork and reporting, each step is guided by documented procedures, checks, and controls. These measures reduce error and improve consistency.

This translates to research outcomes that are not only reliable, but also replicable across projects, methodologies, and markets.

Confidence In Methodological Rigor

ISO 20252 includes annexes that define specific standards for various research methodologies, including qualitative interviews, online surveys, ethnography, data management, and more.

L&E’s certification covers all six annexes. As a result, our entire portfolio of services operates under the same high standards.

When your project involves a specialized audience or a complex methodology, you can trust that the execution will meet globally recognized expectations for rigor.

Clear Project Documentation And Transparency

Documentation is a core requirement of ISO 20252.

Every project is governed by clearly defined protocols and procedures. This is something clients often don’t see until they work with a certified partner.

The structure and clarity this provides helps clients understand exactly how recruitment, moderation, data handling, and reporting are managed.

Whether you’re onboarding a new vendor or expanding research to a new region, working with an ISO 20252 certified partner can eliminate uncertainty and align expectations from the start.

Audit-Ready Operations

ISO 20252 certification requires an external audit by an accredited body (like CIRQ) every three years, with annual surveillance audits in between.

This level of oversight ensures that certified organizations remain compliant, current, and continuously improving.

In regulated industries or when dealing with high-stakes data, the assurance that your partner is audit-ready is not just comforting. It’s critical.

A Commitment To Excellence, Not Just Compliance

Any company can claim quality. Fewer can prove it.

Certification to ISO 20252 is a signal to clients that your research partner has gone through an independent, rigorous evaluation process.

It reflects a commitment not only to doing the work, but to doing it well. That means consistently, transparently, and with your project outcomes in mind.

At L&E Research, ISO 20252 isn’t just a credential. It’s part of our operational DNA.

Looking For A Research Partner You Can Rely On?

Whether you’re sourcing qualitative participants, conducting a national survey, or managing a complex, multi-market study, working with an ISO 20252 certified partner ensures your insights rest on a foundation of reliability and process excellence.

Let’s have a smarter conversation. Contact us to learn how L&E Research can bring quality, consistency, and clarity to your next project.