Hey there! 👋

Sharekh here. Welcome back to The Research Mag - this is where I share fresh market research ideas and practical moves that actually change product outcomes.

Before we jump into today’s discussion, let’s take a quick look at what we covered last time.

Quick recap

Last edition we argued that speed without depth makes brittle decisions.

The problem: most teams pick a method first and hope it answers their question.
The fix: start with the single decision you need to make, then choose the method.

We gave you the exact framework and method pairings to keep speed and depth on the same side. If you missed it, you can catch up on that issue here.

What is new right now

Research is becoming routine rather than occasional. When teams ask for insight more often, standardization beats shortcuts. Expert networks are now a large, buyable market. Domain context is available on demand, and teams are learning to use it with guardrails. Research operations patterns, artificial intelligence tools, and repositories are making depth compound while cycles shorten. That only works when teams set clear rules about how they will use these inputs.

What actually changed

Research is industrializing. Teams want answers faster. Vendors and operations patterns are making that possible. The danger is not speed itself. The danger is speed without rules. Practitioners report turbulence and adaptation. Layoffs, shifting roles, and new workflows are real. Teams are mixing short expert calls with targeted user work.

Expert calls are buyable at scale. Companies are buying domain context on demand. That makes expert calls a standard research input rather than an exception. Use them with guardrails. Research operations, repositories, and artificial intelligence assist tools are the practical countermeasures. They make depth compound while cycles shorten, but only when teams apply the right rules.

This is where we move from problem to practical fixes.

The real choice you face every week

When research becomes routine, you face this decision constantly: standardize or shortcut.

Standardize looks like repeatable templates, mandatory tagging, and a living library you can query six months on from now. Your new product manager searches for pricing objections in enterprise accounts and finds three past studies with linked recordings, not vague summaries. When an executive asks whether the team already looked at something, you can pull up the exact evidence in ninety seconds.

Shortcut looks like quick opinions that feel right on day one and fail on day ninety. Slack polls instead of customer calls. Acting on a single expert memory. Building for the loudest demo user. It feels efficient. It is not. Most organisations do both and assume they are getting the best of both worlds. They are not. Shortcuts create noise that drowns out standardised work. Trust in research erodes. Executives stop asking for input because they cannot tell which findings are solid.

Here is what actually works. Expert calls, artificial intelligence tools, and operations patterns can work together when you have clear rules about what each one does.

Experts provide fast context about market dynamics, competitive positioning, and technical constraints. Artificial intelligence accelerates synthesis by surfacing themes and grouping feedback. Research operations make outputs reusable by enforcing tagging and linking at capture.

Together these elements let you move faster and still show how a decision was reached. You can trace a product pivot back to the five customer conversations that revealed the problem, the expert call that confirmed a constraint, and a short survey that validated demand. That is speed with accountability.

No rules, and you get faster junk. You will ship features that no one asked for, but you will ship them faster.

Principles that keep speed and depth on the same side

Start from the decision, not from the method.
Write the single decision this work must change in one sentence. The product owner signs it before you recruit one participant. If you cannot write it, do not run the project.

Why this matters: When you start with the decision, you keep the study tiny and fast. For example, the question "Should we add single sign on to the enterprise plan?" is a decision. The question "Let us understand enterprise needs" is not. The first tells you who to talk to, what to ask, and what threshold matters.

Use experts to validate constraints, not to replace users.
Protocol: bring three assumptions to the call. Ask the expert which one they would bet on and why. Capture their confidence. Then run one quick test with actual users to validate the most important assumption. Expert opinion informs the plan. It does not replace user evidence.

Make findings traceable, not just readable.
Deliver one page that contains the recommendation, the confidence level, two or three key pieces of evidence, and the single metric you expect to move. Then link to the research log that contains recordings, notes, and transcripts. Executives will get clarity in sixty seconds. Teams will get traceability when they build.If the brief cannot link to the source material, the brief does not ship. No link equals no claim.

Tag at capture and audit weekly.
Minimal fields are: decision, role, date, and confidence. Tag at the moment you finish the study. It takes thirty seconds. Weekly audits catch mistakes while memory is fresh. Retroactive tagging fails more often than it succeeds. The tagging at capture matters because the research you did six months ago should make your next study faster, not invisible.

Place economics next to evidence.
Statistical significance matters. Business impact matters more. A two point increase in add to cart for a low margin item can be statistically real and commercially meaningless. A smaller increase on a high margin add on might be worth shipping even if the test is underpowered, if payback and lifetime value are strong.

Always estimate contribution margin, payback window, or lifetime value. If you cannot estimate precisely, give a sensible range and state your recommendation in that context.

How teams put this to work

Run a small cadence every week. Do not wait for the perfect big study. Use simple templates so more of the team can contribute. Publish a single narrative each month. Keep it to one page with one chart and one call. Lead with the decision, not the method.

Decision led method pairings that remove paralysis.
What to build: five to eight discovery conversations with clear profiles plus a short survey to size demand.
What to fix: ten task based sessions with clear success criteria plus a focused experiment on the top two fixes.
Who to target: five customer calls across segments plus a cohort cut from product analytics.

These pairings combine qualitative and quantitative evidence at the right fidelity. Discovery conversations find the problem. The survey sizes the problem. Task testing shows whether the fix works. Experiments prove it at scale. Analytics show which segment converts.

One page decision brief plus linked research log. The one page brief has four sections: recommendation, confidence level, key evidence in bullet form, and the metric you expect to move. Then link to the full research log with recordings and notes.

Mandatory minimal taxonomy plus weekly tag audit. Tag new work as it happens. Fix errors every Friday. Start now. In six months you will have a library people actually use.

Expert checkpoint with user validation. Use thirty minute expert calls only to validate constraints or edge cases. Follow up with at least one minimal user test. If you cannot validate the expert claim with at least one user conversation or experiment, do not act on it.

Where the industry is heading

Expert networks will become standard research infrastructure. Use experts as a specialised data source that provides fast context and edge case validation. Do not let experts replace user research.

Research operations platforms will behave like operating systems. Expect deeper integrations with recruitment, analytics, and feature flagging. Searchable research libraries will be the expectation rather than the exception.

Job descriptions will reward synthesis and business fluency. Recruiters will ask what decision your research changed and what the revenue impact was. If you cannot answer that question, you will not get senior roles.

Artificial intelligence assist will require human audit. Use artificial intelligence to surface themes and group feedback. Humans must verify clips and add context. Artificial intelligence makes you faster. It does not make you right.

Bottom line

Research is not dying. Research is industrializing. Use speed to free time for judgement, not to replace it. Standardise your workflows. Tag at capture. Use experts with guardrails. Always follow up with users. Place economics next to evidence.

Small habits. Big compounding value.

The research function that survives the next three years will not be the one that ran the most studies. It will be the one that changed the most decisions and showed the revenue impact. That is how research earns its seat in the boardroom.

That is a wrap for this issue of The Research Mag!

What is your take on industrialising research? Have you found ways to use expert calls or artificial intelligence tools without losing depth, or are you still figuring out where to draw the line between speed and shortcuts? Hit reply and tell me what is working and what is still messy.

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Hey there! 👋

Sharekh here. Welcome back to The Research Mag after a short pause. This is where we share fresh market research insights and practical ideas shaping the future of the industry.

Before we jump into this month's insights, let's take a quick look at what we covered last time.

Quick recap

In our last edition, we explored why product managers cannot ignore product research.

• Continuous learning beats one-off studies.

• Staying close to user journeys prevents costly misses.

• Pair speed with depth to make decisions you can stand behind.

What is new right now

• Qualitative work is scaling without losing its reason to exist when teams recruit for relevance and ask fewer, better questions.

• Automation is taking repetitive tasks off researcher calendars so people can frame decisions and carry clear recommendations into leadership rooms.

• Statistical significance is being confused with business significance, and the smart fix is to place economics next to evidence.

What actually changed

Teams are using platforms to remove lag from recruiting, scheduling, and analysis. The best programs still run human conversations and write clean guides. They treat transcripts as raw material rather than the outcome.

Cutting the wait: faster recruiting and setup so learning starts sooner.

Teams that deliver useful findings on a steady cadence tend to do three things. First, they make sure the people they speak with truly fit the question, and only then do they decide how many participants to include. Second, they ask fewer questions so each one pulls its weight. Third, they sketch the debrief outline before fieldwork begins, which forces clarity about the decision the study must support.

Automation changed the tasks. People still own the judgment

• What tools handle: They draft survey blocks, group open-ended answers, and surface themes so teams spend less time on setup and sorting.

• Where tools help most: They speed early analysis and make it easier to see patterns, which helps you plan the next step.

• What people must decide: A researcher still chooses what is worth measuring and what evidence is needed for the decision at hand.

• How quality is protected: Someone has to test whether the instrument’s wording, logic, and sample actually fit the goal.

• Who carries the story: A person needs to take the findings into the room, explain the trade-offs, and make a clear recommendation. That is the work that earns trust.

People are also looking at evidence with a clearer lens. Significance tests still matter, but they do not make the decision for you. Good choices happen when the numbers meet the money. A two-point lift on add-to-cart for a low-margin item can look “statistically right” and still change nothing. A smaller lift on a high-margin add-on can miss the test and still be the better move if payback and lifetime value are strong. Leaders care about impact. Your approach should reflect that.

Principles that keep speed and depth on the same side

1. Start from the decision, not from the method.
   Write down the choice you need to enable. Name the alternative you would take if you did not run the study. List the risks you want to reduce. Only then pick a method. If the question is what to build, pair five to eight discovery conversations with a simple sizing pass. If the question is how to ship, pair task-based usability with a launch-gated experiment. The goal is decision quality.

2. Shorten cycles without shrinking the question.
   Use platforms to compress logistics while keeping the substance intact. A tight screener that locks on the few variables that define relevance is faster than a wide one. A five-question interview that asks the right things produces more signal than a long script that tries to do everything. Short can still be rigorous when the intent is clear.

With the setup complete, now shift from steps to a clear recommendation.

3. Push analysis toward one recommendation.
   Executives do not need a pile of tags. They need a call. Make the call and show your work. Name the assumption that drives the decision. Quantify the cost of being wrong with a sensible range. State the one extra data point you would collect if you had one more week. You will earn more trust by being explicit about uncertainty than by hiding it.
   

4. Place economics next to evidence.
   Add one business line to every readout. Show the expected impact on contribution margin, the payback window, or the shift in lifetime value. If you cannot estimate it, say so and provide a range. Then state your recommendation in light of that range. You will have a shorter meeting and a better outcome.
   

5. Treat quality as a recruiting and moderation problem, not only a tooling problem.
   Fraud controls are necessary. They are not sufficient. The cure for low-quality answers starts with relevance and ends with moderation. Tighten the few variables that define fit. Remove questions that exist only to catch cheaters. Open each conversation by checking context and intent. Exit early when a participant is not who you need. Skilled moderators protect your data more than dashboards do.

How teams put this to work

Run a small cadence of conversations or tasks every week. Use a simple template so more of the team can contribute. Publish one narrative at the end of each month. Keep it to a page with a single chart. Lead with the decision, not the method.

Decision-led method pairings.
Use a short pairing table that anyone on the team can follow.

• What to build: five to eight discovery conversations with clear profiles, plus a quick survey to size demand or risk.

• What to fix: ten task-based sessions with clear success criteria, plus a focused experiment on the top two fixes.

• Who to target: five customer calls across segments, plus a cohort cut in product analytics.

A single source of truth for open decisions.
List the decisions that remain open and the specific evidence each one requires. Assign an owner and a date. Update the list once a week. This is simple to maintain and hard to ignore.

Role clarity that values judgment.
Be explicit about time use. Automate the busy work on purpose. Give senior researchers space to model scenarios, write the brief a VP will sign, and coach the team on what good evidence looks like.

An honest post-launch loop.
Do not just log results. Explain what you learned about user behavior and where your prior was wrong. Close the loop by naming what you will do differently next time.

Where the industry is heading

• Qualitative platforms that behave like operating systems. Expect deeper integrations with recruitment, analytics, and feature flagging.

• Job descriptions that reward synthesis and business fluency. Output will increase. The value sits in judgment and translation.

• Method debates that invite finance to the table. Cost of error and payback windows are now part of the discussion, and that is healthy.

Bottom line

Speed is useful when it frees time for the work that only people can do. Depth is valuable when it explains behavior well enough to move a plan. Put the decision at the center. Keep your cycles tight. Tie your findings to economics. Ask for less data and more clarity. That is how research earns its seat in the boardroom.

That’s a wrap for this issue of The Research Mag!

What is your take on the speed vs depth challenge? Have you found ways to move faster without sacrificing quality in your research, or are you still caught in the trap of choosing one over the other? Hit reply and tell me how you are navigating this, your wins and the places you are still figuring out.

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Hey there! 👋

Sharekh here! Welcome back to The Research Mag—where we break down fresh ideas, market research insights, and the innovations shaping the future of decision-making.

Before we jump into today’s discussion, let’s take a quick look at what we covered last time.

Quick recap

In our last edition, we explored why so many B2B innovations fail. It's rarely because the ideas are bad. More often, it’s because teams skip the foundational step, understanding the real problem. This week, I want to talk about product managers, and why they can’t afford to treat product research as someone else’s job.

The decisions that shape products are often made too early, and without clarity

I’ve worked with and learned from some amazing PMs over the last few years. The best ones are not just good at execution. They’re deeply curious. They take the time to understand why users behave the way they do.

But that’s not always the case. In many teams, the roadmap decisions are often made based on speculative thinking instead of validated insights. Sometimes it’s pressure from leadership. Sometimes it’s a loud customer. Sometimes it’s a feeling that “this is what our competitors are doing.”

And that’s how products slowly drift away from what users actually need.

You start building things that sound right in meetings, but fall flat in the real world. You release new features, but users don’t understand how to use them. You fix the UI, but not the core problem. You push for growth, but retention stays stuck.

When that happens, it’s not always a strategy issue. It’s often a research issue.

Product research isn’t a phase. It’s a habit.

There’s a misconception that research is something you do at the beginning of a project. You do some interviews, write up a report, and move on. But the best product managers I’ve seen treat research like a constant thread.

Research isn't a one-time push, it's an ongoing cycle.

They’re not waiting for a quarterly study. They’re listening all the time, whether it’s through direct user calls, usability sessions, or even the things people are saying to the support team.

They know that product research isn’t about confirming ideas. It’s about learning what you didn’t know you needed to ask.

Why understanding the user journey matters

One of the most overlooked parts of product research is mapping and understanding the full user journey, not just the feature you’re working on right now. When PMs look only at isolated screens or micro-interactions, they miss the bigger story.

User journeys show you where real friction builds up. They show you where expectations are set but not met, and where users drop off silently. These journeys are not just diagrams. They are narratives filled with emotion, intent, and decision-making moments.

User journeys aren't just diagrams; they're the roadmap to real insights.

When you spend time tracing how users go from awareness to activation, from trial to habit, you start to see what truly needs fixing. Sometimes it's not the button that needs to change; it's the entire flow.

User journey research gives you that visibility. It connects the dots that isolated feedback can’t. And when product managers understand those patterns, they make better decisions, faster.

How we're improving research at CleverX

I'm a big believer in practicing what I preach. That's why we've been rethinking how research works within our own platform.

We recently launched both moderated and unmoderated usability testing capabilities on CleverX. Not just because it's a feature customers asked for, but because we've experienced firsthand how critical these insights are to building products people actually use.

Those pain points guided our approach. We wanted to create something that removes the administrative headaches while preserving the quality of insights. Something that makes research feel less like a special project and more like a natural part of the product development process.

It's still early, but we're already seeing how this helps teams catch usability issues earlier and build with more confidence. It's one small way we're trying to make research more accessible for everyone involved in product decisions.

You don’t have to be the researcher. But you can’t be disconnected from the research.

Let me pause here for a moment. There's something product teams don't admit enough: it's easy to treat research like an item on a checklist. You check the box, write the report, and move on. But when that happens, you miss the deeper insight, the part that challenges your thinking instead of validating it.

It's not that PMs don't value research. It's that they're often afraid of what it might reveal. Real research surfaces uncomfortable truths, things like "this feature isn't useful," or "our assumptions about users were wrong."

But avoiding that discomfort has consequences. If you're not willing to sit with those hard truths, you're not building value. You're just managing delivery. The best product teams I've seen don't use research to validate, they use it to get uncomfortable early, so they don't waste time later.

When you're making product decisions, you need to be close to the people you're building for. That means asking to join a few calls, going through research notes, or watching recordings; even if it's just a few minutes a week.

Getting closer to user insights means looking beyond the surface.

You need to know what users are struggling with; not just in theory, but in practice. Otherwise, you end up building from guesses. And in this blind calls are expensive.

Speed is good. But building the wrong thing quickly doesn’t help anyone.

There’s always pressure to move fast. And I get it. You want to ship. You want to show momentum. But if your product is growing in complexity without improving in value, something’s wrong.

If you don’t know why your users behave the way they do, or if you’re solving for things that weren’t problems in the first place, it catches up with you. Maybe not in the first release. But eventually, it shows; in churn, in confusion, in support tickets that keep repeating the same issues.

Product research is what gives you context. It gives you confidence. It gives you the ability to say, “We’re not shooting in the dark. We know.”

That’s a wrap for this issue of The Research Mag!

What’s your take?

Have you ever changed course because of something unexpected you learned from users? Or have you pushed forward with a feature despite warning signs from research? I'd love to hear your stories—both the wins and the lessons. Do reach out to me here.

Until next time,

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Hey there! 👋

Sharekh here! Welcome back to The Research Mag—where we break down fresh ideas, market research insights, and the innovations shaping the future of decision-making.

Before we jump into today’s discussion, let’s take a quick look at what we covered last time.

🔍 Quick recap

In the last issue of The Research Mag, we tackled a growing crisis in market research: bad data and fraudulent responses. With the rise of AI and automated tools, misleading data is creeping into research processes, causing businesses to question the validity of their insights.

Here’s what we explored:

• AI-generated responses: How advanced AI can create convincing but fake insights, which can easily mislead research teams.

• The rise of fraudulent research practices: From identity spoofing to survey farms, how participants are faking their way into studies.

• The need for smarter verification systems: To ensure that the data driving decisions is authentic and reliable.

If you missed it, you can catch up on that issue here.

🚀 What’s new?

Last time, we focused on the issue of bad data and how it’s eroding trust in market research. This time, we’re shifting gears to another critical area—B2B innovation and how improper segmentation can lead to failure.

In this issue, we’re exploring how companies often overlook segmentation when developing new products or services, which can result in alienating loyal customers. We’ll show you how effective segmentation can ensure innovation works hand in hand with customer retention, ultimately driving both growth and long-term loyalty.

Innovation vs. customer retention: The balancing act

Innovation and customer retention—every B2B company knows these two are essential to growth. But let’s be real for a second. Balancing the two can be tricky. Some companies get so caught up in pushing for innovation that they forget about the very customers who helped them get to where they are. Others are so focused on keeping their loyal customers happy that they miss new opportunities for growth.

The thing is, innovation and customer retention aren’t as opposing as they might seem. The bridge between the two? Segmentation.

Balancing innovation with customer loyalty

I’ve been thinking about this a lot lately—how segmentation can help businesses get the best of both worlds. It’s not just about targeting the right customers with the right products; it’s about ensuring you continue meeting the needs of your existing customer base while also making room for innovation. By strategically segmenting your market, you can innovate in ways that resonate with both new and existing customers, so you’re not risking the loyalty of your best clients while still moving forward.

In this issue, we’ll take a closer look at how segmentation can be the key to fueling growth, keeping your customers happy, and avoiding the pitfalls that might cause you to lose your best clients.

The Importance of segmentation in B2B market research

Let’s be real—no two customers are the same. Even in the same industry, they’ve got different needs, challenges, and priorities. And that’s where segmentation comes in.

I’ve been thinking about this a lot lately. Too many companies treat their customers as one big group, which feels easier, but it’s not effective anymore. Segmentation isn’t just about dividing people up into boxes—it’s about truly understanding who they are, what they need, and how your product can solve their problems.

Segmentation sparks innovation

Without it, businesses risk missing opportunities and, worse, losing loyal customers who feel like they’re being overlooked.

Effective segmentation lets you tailor your approach, target the right people with the right products, and avoid leaving your best clients behind while innovating. It’s about making sure your growth doesn’t come at the expense of the relationships you’ve worked so hard to build.

In B2B market research, segmentation isn’t just a useful tool—it’s a necessity for businesses that want to stay competitive in today’s fast-paced landscape. Without it, you run the risk of treating all your customers the same, leading to missed opportunities and a lack of personalization.

Why does segmentation matter so much in B2B?

• Customer diversity: In the B2B space, clients come with vastly different needs, pain points, and goals. You could have a large enterprise that requires scalable solutions or a small startup that needs a more affordable, customized product. Segmentation helps you group customers based on these characteristics, allowing you to tailor your messaging, product offerings, and support to each unique segment.

• Maximizing ROI: Segmentation allows you to allocate your resources more effectively. By understanding which customer segments are most profitable, you can focus your efforts on those that provide the highest ROI. It helps you determine where to invest your marketing budget, which products to push, and how to prioritize customer service for your most valuable clients.

• Improved product development: Through effective segmentation, you can understand the specific needs of different groups, leading to better product development and innovation. If you know that one segment values simplicity and another values advanced features, you can innovate accordingly without risking alienating either group.

• Personalized marketing: B2B clients expect personalized experiences that address their specific business needs. Segmentation allows you to create targeted campaigns that speak directly to each segment’s unique pain points, resulting in higher engagement, better conversion rates, and improved customer loyalty.

• Customer satisfaction and retention: At the end of the day, segmentation isn’t just about acquiring new customers—it’s about keeping the ones you have. When you deliver what customers need and ensure they feel valued and understood, you build long-term relationships that foster loyalty. Businesses that neglect segmentation risk losing those relationships to more personalized, customer-centric competitors.

The risk of ignoring customer retention

We all know it’s easier to keep a customer than to find a new one, but too many businesses overlook this basic truth. Customer retention isn’t just a buzzword—it’s essential to long-term growth.

When companies focus too much on innovation and forget about the people who helped them get there, they run the risk of losing their loyal base. And here’s the kicker—innovation without retention is like throwing a party and forgetting to invite the people who’ve always been there. It’s a recipe for disaster.

That’s where segmentation really comes into play. With the right segmentation, you can balance the need to innovate with the need to keep your customers happy. You can launch new products that serve both your existing and future customers, without alienating the ones who got you where you are.

We love our loyal customers!

How innovation and retention intersect with segmentation

Innovation and retention don’t have to be opposing forces. With the right segmentation, both can work together. Here’s how:

• Balance innovation with retention: Without segmentation, businesses risk launching new products that alienate loyal customers. Segmentation ensures you’re meeting the needs of both new and existing customers.

• Targeted innovation: By understanding customer segments, you can create new products or features that align with what different groups value. This way, innovation can excite your loyal customers, rather than making them feel left out.

• The bridge between innovation and retention: Segmentation is the key to maintaining customer loyalty while pursuing innovation. It allows you to tailor innovations to each segment’s unique needs, keeping your current customers engaged while attracting new ones.

• It’s not an either/or: Segmentation helps you make innovation work without sacrificing retention. It ensures both can coexist and drive growth.

Using market research to fine-tune segmentation

Segmentation isn’t just about dividing your customers—it’s about understanding them in-depth. Here’s how market research can help fine-tune your segmentation:

• Data-driven insights: Use market research to uncover deeper motivations behind customer behaviors. It’s not just about who they are—it’s about why they buy and how they make decisions.

• Real-time feedback: Regularly gather feedback to keep your segments up-to-date. Customer needs evolve, and market research helps you stay in sync with those changes, so your segmentation stays relevant.

• Behavioral insights: Track customer behavior over time. This isn’t just about demographics; it’s about identifying trends and shifts in preferences that will keep your segmentation accurate and actionable.

• Refining personas: Through research, you can improve your customer personas, making sure they represent your audience more accurately. As markets evolve, so should your personas.

How segmentation can open new revenue streams

Segmentation isn’t just about improving customer retention—it’s also about finding new ways to grow revenue. Here’s how segmentation helps identify new opportunities for revenue generation:

• Identify high-potential segments: Segmentation helps you spot customer groups that might not be immediately obvious but are ripe for tailored products or services. These segments can be a significant source of untapped revenue.

• Cross-selling and upselling: Understanding the unique needs of each segment allows you to create targeted campaigns for cross-selling or upselling, helping you increase revenue without having to find new customers.

• Personalized offerings: When you personalize your products based on what each segment values, you increase the likelihood of conversions. Segmentation makes your offerings more relevant and compelling, leading to more sales.

• Increase lifetime value: With more precise segmentation, you can focus on retaining your high-value customers by offering products that truly meet their needs, ultimately boosting their lifetime value and ensuring steady revenue growth.

Segmentation is not about splitting your customers into neat little boxes—it’s about understanding what drives them, what they need, and how you can better serve them while continuing to innovate.

But here’s the thing: segmentation is a continuous journey. Your customers’ needs will change, and so should your approach. The goal isn’t just to get it right once, but to keep refining and adapting.

So, what’s your next step? Look at your current segments—are they still aligned with your customer base? If not, it’s time to rethink your strategy.

Before we wrap up, let’s quickly revisit why segmentation is so important. It’s the key to balancing innovation and customer retention. By segmenting your customer base effectively, you can tailor your innovations to meet the needs of both new and existing customers, ensuring your loyal base isn’t left behind while you grow. With the right segmentation, you can drive sustainable growth without sacrificing the relationships you’ve worked so hard to build.

3 steps to improve your segmentation today:

1. Understand customer behaviors: Go beyond basic demographics and dive into why customers make certain decisions. Track behaviors like purchasing patterns, interactions, and feedback to create more actionable segments.

2. Gather continuous feedback: Keep your segments fresh by regularly collecting data through surveys, focus groups, or customer interviews. As your market evolves, so should your segmentation strategy.

3. Test and adjust: Implement your segments in targeted campaigns or product launches and assess their performance. Use the insights to tweak and optimize your segmentation approach, ensuring you're always in tune with your customers' evolving needs.

That’s a wrap for this issue of The Research Mag!

💭 What’s your take?

How are you balancing innovation with retention in your business? Have you found segmentation to be the key to driving growth? Hit reply—I’d love to hear your thoughts on this issue and what stood out to you. Do reach out to me here.

Got an idea for a future topic? Let me know! Let’s keep the conversation going.

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Hey there! 👋

Sharekh here! Welcome back to The Research Mag—your monthly dose of sharp insights, evolving trends, and the tough questions shaping the future of market research.

Before we get into this month’s issue, let’s quickly rewind to last time.

🔍 Quick recap

Last time on The Research Mag, we launched something new—our podcast, Research Decoded with Sharekh—with a hard-hitting first episode on AI in market research.

Here’s what we explored:

• AI-moderated interviews are fast, but still lack the emotional nuance and flexibility of human moderators.

• Market research fraud is rising—with people using AI to fake expertise and generate believable (but false) responses.

• The real opportunity? Using AI as an assistant, not a replacement—especially for automation, fraud detection, and processing at scale.

• If we don’t build smarter verification systems now, research teams risk losing trust in their own data.

We unpacked it all with Stacy Thomas and Angie Stahl from GRRR. If you missed it, you can catch the full episode here.

🚀 What’s New?

Last time, we tackled the rise of AI and what it means for the future of insights. But this time, we’re turning the spotlight on something just as critical—and maybe even more urgent.

What if the data itself can’t be trusted?

This issue, we dig into the hidden crisis in market research: bad data, fake respondents, and eroding confidence in research outcomes. Because when your insights aren’t real, your decisions won’t be either.

When data gets harder to reach

Let’s be real—data might be everywhere, but getting it the right way? That’s becoming tougher by the day.

Between stricter privacy laws, the death of third-party cookies, and rising consumer skepticism, researchers are no longer just data collectors. They’re becoming stewards of trust.

In this issue of The Research Mag, we’re digging into how the research world is shifting gears—not just to comply with the rules, but to completely rethink how we collect, manage, and protect information.

Here’s what we’re unpacking:

• Why traditional data pipelines are slowing down—and why that might actually be a good thing

• How consent, transparency, and responsible usage are becoming non-negotiable

• What privacy-first innovation looks like: think contextual targeting, zero-party data, and synthetic alternatives

• What this means for the future of decision-making in research-driven teams

Let’s dive in.

The death of “easy” data—and why researchers need to care

There was a time when tracking users through third-party cookies and massive data panels was the default. It made research fast—but not always ethical.

Now? The game’s changed.

Here’s how:

• Third-party cookies are disappearing -

  Google Chrome is now following Safari and Firefox in removing support for third-party cookies, marking a major shift in how user data is tracked across the web.

  Read more about it → Google Privacy Sandbox

• Global privacy laws are tightening -
  From the GDPR in Europe to CCPA in California, regulations are reshaping how data can be collected, stored, and used—with more regions following suit.

• Browsers are getting smarter about privacy -
  Apple’s Intelligent Tracking Prevention and Firefox’s Enhanced Tracking Protection are blocking many standard tracking methods.

According to a 2024 survey by Statista, 43% of marketers said new data privacy regulations are already having a significant impact on how they approach digital advertising and data collection.

So what now?

It’s not just about compliance—it’s about consent and trust. Researchers must shift from passive tracking to approaches where people know why their data is being collected, and what’s in it for them.

What does “value exchange” actually mean?

Say you’re running a study for a product launch. Instead of burying a data consent clause in legalese, tell participants what their input will shape—a new feature, a better user experience, or even early access perks. Be clear. Be fair. That’s value.

In short?

Data access is becoming harder. Trust is becoming more valuable. Researchers who adapt to that reality will thrive.

When your entire data strategy relied on third-party cookies… 😬

What happens when respondents stop being real?

Let’s be honest—these days, getting a “response” doesn’t always mean it came from a real person.

And that’s not an exaggeration.

📊 According to Research Defender, up to 35% of responses in online research are either low-quality or outright fraudulent.
Add AI-generated open-ends, click farms, and identity spoofing into the mix, and what you end up with is a dashboard full of fiction.

Here’s how fraud is showing up today:

• Identity spoofing is rampant → Fraudsters pose as qualified B2B participants, sometimes using AI-generated LinkedIn profiles or scraped credentials to pass screeners undetected.

• Survey farms are thriving → Entire online communities now exist to help people “hack” research platforms for incentives. (You’ll find Reddit threads and Discord servers with step-by-step guides.)

• AI-generated responses are harder to catch → Many read like thoughtful, articulate answers—until you realize they’re stitched together from LLM prompts, not real experience.

The most dangerous part? These responses look great in your dashboard—until your product fails because the insight behind it was fake.

This kind of fraud doesn’t just waste research budgets—it erodes trust in the entire process. When your insight team starts second-guessing every dataset, strategic confidence nosedives.

That’s why fixing things after the data comes in isn’t enough. Research teams need smarter upfront safeguards:

• Identity verification (yes, even in B2B)

• Behavioral quality checks

• In some cases, live validation for high-stakes studies

  

  When the dashboard looks perfect, but your insights are built on fake data

The slow death of third-party data (and what’s replacing it)

Third-party data used to be the go-to for targeting, segmentation, and audience insights. But that era? It’s fading—fast.

Thanks to privacy regulations like GDPR and CPRA, browser changes (looking at you, Google), and increasing consumer distrust, marketers and researchers are losing access to the cookie crumbs they’ve depended on for years.

And this isn’t just a marketing problem.

It’s a research problem, too.

When third-party data dries up, so do a lot of easy assumptions. Behavioral benchmarks, targeting criteria, even recruitment pipelines for studies—all get messier without passive tracking.

So what’s taking its place? Researchers are moving toward:

• Zero-party data → Voluntarily shared by the participant (preferences, intent, motivations)

• First-party data → Collected directly from user behavior (with consent)

• Contextual insights → Gained from smart, in-the-moment research rather than passive tracking

The upside? You’re getting real signals from real people, not inferred guesses from shady data brokers.
The downside? It’s slower, more intentional, and requires better design.

But here’s the big opportunity:
When companies take the time to ask the right people the right questions—instead of just scraping behaviors—they don’t just comply with privacy laws. They build trust.
And trust scales.

Your dashboard when 35% of the responses are fiction

🛠️ 3 ways to strengthen data quality today

So, how do you fight the flood of bad data? Here are threestrategies your research team can start using right now:

1. Flip the screener -
Use reverse-screening logic—design questions that intentionally catch contradictions or fake experience. Think of it as a truth test for your respondents.

2. Verify, don’t assume -
Add identity verification layers—tools that match LinkedIn profiles, check for consistent digital footprints (IP/location), or analyze behavioral signals in real time. Yes, even for B2B.

3. Spot the weird stuff early -
Look for patterns: survey speeders, generic or gibberish open-ends, copy-paste answers. Flag them before they skew your insights.

💡 It’s not about perfect data. It’s about setting a higher bar for what makes it into your dashboard.

What trustworthy research looks like now

Let’s be honest—“trust” in research doesn’t come from the cleanest dashboard or the prettiest pie charts.

It comes from knowing who your participants are, how the data was collected, and why the insights actually reflect reality.

In 2025, trustworthy research means more than just asking good questions—it’s about asking the right questions to the right people, and being sure they’re real.

So what does good look like today?

✅ Participant validation built in—not bolted on
Tools like Research Defender and Lucid Impact Measurement are leading the way in real-time respondent verification—flagging duplicates, bots, and suspicious behavior before it hits your dataset.

✅ AI-assisted analysis, not AI-generated data
- Using LLMs to sort through transcripts? Smart.
- Using LLMs to replace transcripts? Risky.
- The best teams use AI as an amplifier, not a stand-in.

✅ Smaller samples, deeper conversations
The industry is moving away from “more = better.”
Hyper-targeted sampling, paired with real qualitative depth, is leading to richer, more reliable insights.

✅ Transparency in methods and incentives
More teams are now sharing how their studies were run, who they included, and what incentives were offered. That’s not just good ethics—it’s good business.

Good data isn’t wishful thinking. It’s verified

So, is trust in research broken—or just evolving?

Let’s face it—between privacy pushback, respondent fraud, and AI chaos, it’s tempting to say market research is losing its edge.

But here’s the truth:

It’s not broken. It’s just evolving. Fast.

And that evolution is pushing researchers to get sharper, think deeper, and build smarter systems of trust.

We can’t go back to the days when panel-based quant ruled all, or when identity verification was “just a nice-to-have.” The new era demands better. Not more research—but better, cleaner, and more credible research.

So, what can we do?

→ Rethink what data quality really means
→ Stop over-indexing on quantity
→ Invest in tools that prioritize validation and integrity
→ Ask better questions, and build smarter blends of qual + quant

The researchers who rise now won’t be the ones who know the most tools.
They’ll be the ones who know who to trust—and how to verify it. Let’s make sure we earn that edge.

💸 What’s the ROI of better data?

Bad data isn’t just a research problem—it’s a business risk. A few hours saved during data collection can cost companies millions in bad product decisions, misaligned strategy, or flawed GTM efforts.

On the flip side, investing in fraud detection and identity checks upfront helps research teams avoid costly blind spots—and build credibility inside the org.

Wrap-Up: What’s next for researchers?

If this issue made you feel like research is getting harder—good.
Because it is.

But here’s the upside: it’s also getting smarter.

The challenges we’re facing—privacy walls, fraud, AI noise—aren’t signs of decline. They’re signals that research is becoming more central, more scrutinized, and more strategic than ever before.

It’s not just about gathering data anymore. It’s about proving it’s real, proving it’s reliable, and proving it’s worth acting on.

The future of research isn’t just about scale. It’s about trust.

So here’s a question for you:

When was the last time you really trusted the data you collected?
If the answer isn’t “last week,” it might be time to look deeper.

Let’s keep building, testing, verifying—and yes, trusting—better.

That’s a wrap for this issue of The Research Mag!

💭 What’s your take?

Have you noticed growing gaps in data trust, or faced any challenges validating your sample quality lately? Hit reply—I’d love to hear what you liked about this newsletter. Do reach out to me here.

Got an idea for a future topic? Let me know! Let’s keep the conversation going.

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Hey there! 👋

Sharekh here! Welcome back to The Research Mag—where we break down fresh ideas, market research insights, and the innovations shaping the future of decision-making.

Before we jump into today’s discussion, let’s take a quick look at what we covered last time.

🔍 Quick recap

Last month, we explored the role of market research in Product-Led Growth (PLG) and why just having a great product isn’t enough to drive retention.

Here’s what we uncovered:

➡️ PLG isn’t just about sign-ups—users need the right onboarding, activation, and product experience to stay engaged.

➡️ Many PLG companies fail at segmentation—they assume all free trial users are potential customers, leading to high churn.

➡️ Research-driven onboarding improves retention—companies like Slack & Dropbox use deep user insights to refine activation strategies.

➡️ Competitive research matters—understanding how users evaluate alternatives helps PLG companies position themselves effectively.

PLG thrives when companies remove friction, understand user behavior, and make data-backed product decisions. If you missed it, catch up here🚀 

What’s new?

Exciting news—I’ve just launched Research Decoded with Sharekh! 🎙️ A podcast where we explore the biggest shifts in market research and decision-making.

To kick things off, I sat down with Stacy Thomas & Angie Stahl from Good Run Research & Recreation (GRRR) to tackle a critical question:

Is AI making research smarter or fueling more fraud?

Episode 1: The Evolution of Research

🎙️ Podcast spotlight: Research Decoded with Sharekh

The first episode of Research Decoded with Sharekh is all about AI in market research—how it’s shaping insights but also introducing serious risks like fraud, fake respondents, and unreliable data.

In this episode, we unpack:

• The limits of AI-moderated research—Can automation ever match human intuition?

• Why fraud in market research is at an all-time high (and why companies are struggling to stop it).

• The future of AI + human expertise—where do we draw the line?

Here’s what came out of the conversation. 👇

🎯 Key excerpts & takeaways from the episode

1. AI moderators are efficient—but can they truly replace humans?

💬 Sharekh: “We’re seeing more AI-moderated interviews. But are they really working? Have you seen them deliver insights at the level of a human moderator?”

💬 Angie Stahl: “We’ve tested AI-moderated interviews, and here’s the reality: AI can ask questions, but it can’t think. It doesn’t follow up when something interesting comes up, it doesn’t challenge contradictions, and it certainly doesn’t pick up on emotional nuance. That’s a huge gap.”

💬 Stacy Thomas: “The best insights come when a moderator knows when to push, when to pivot, and when to dig deeper. AI isn’t there yet.”

2. The fraud problem: “People are using AI to fake expertise”

💬 Sharekh: “One of the biggest challenges we see at CleverX is that people can now fake expertise in B2B surveys. They use AI to generate responses—and it’s nearly impossible to catch unless you have the right fraud detection in place.”

💬 Stacy Thomas: “Exactly. We’ve seen ‘experts’ who can answer complex open-ended questions perfectly—but the moment you push back, they have no real knowledge. That’s because AI wrote their response, not them.”

💬 Angie Stahl: “Survey farms are getting smarter too. There are entire communities teaching people how to game research studies for money. If companies don’t start investing in better fraud detection, research is going to become completely unreliable.”

3. AI’s role in research: Assist, don’t replace

💬 Sharekh: “The way I see it—AI should be an assistant, not the decision-maker. It can help us analyze data faster, but we still need human researchers to interpret, challenge, and refine insights.”

💬 Stacy Thomas: “Exactly. The companies that win won’t be the ones that replace researchers with AI. They’ll be the ones that use AI to remove repetitive tasks, but keep humans at the center of decision-making.”

💬 Angie Stahl: “We should be using AI for data processing, fraud detection, and automation—not for deep qualitative analysis. When you need to understand emotions, motivations, or subconscious drivers, that’s where human expertise is irreplaceable.”

💬 Sharekh: “If AI-generated responses continue at this rate, the research industry faces a crisis. The integrity of our insights is at stake.”

🎧 Listen to the full episode

🎧 Spotify → Listen here

📺 YouTube → Watch the episode

🍏 Apple Podcasts → Tune in here

Fixing AI’s data problem: What comes next?

The good news? We can solve this.

✅ Stronger participant verification → Advanced fraud detection tools can flag duplicate responses, AI-generated patterns, and suspicious inconsistencies.

✅ AI-assisted fraud detection → If AI can be used to generate fake responses, it can also be used to detect them. Smart platforms are already integrating AI-powered screening techniques.

✅ Hybrid research methodologies → The future isn’t AI vs. humans—it’s AI + human expertise. Companies need both automation and human oversight to ensure data integrity.

AI is here to stay, but if companies don’t get smarter about data integrity, research will become dangerously unreliable.

So… is AI a game-changer or a data integrity nightmare?

The answer is it’s both. AI is transforming research, but without strong fraud detection and human oversight, it could cause more harm than good.

What do you think? Have you seen AI-driven research in action? Is it improving insights or making data less reliable? Hit reply and let’s talk.

📢 Stay in the loop

More episodes are coming soon—covering the biggest challenges and innovations in market research. 🔗 Subscribe Now

Let’s keep the conversation going. I’d love to hear what you think! Got an idea for a future topic? Reply to this email and let’s talk.

You can also reach out to me directly here.

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Hey there! 👋

Sharekh here! Welcome back to The Research Mag—where we break down fresh ideas, market research insights, and the innovations shaping the future of decision-making.

Hold up—before we dive in, let’s take a quick look back at last month’s issue in case you missed it.

🔍 Quick Recap: January’s Issue

Last time, we talked about the downfall of traditional surveys and why they’re losing their edge in today’s fast-moving world.

Here’s a quick rundown of what we covered:

• Survey fatigue is real → People are drowning in survey requests, and response rates are hitting all-time lows.

• Bad data is everywhere → Bots, survey farms, and disengaged participants are skewing results, making insights less reliable.

• Static surveys aren’t cutting it → Businesses are shifting toward AI-driven conversations to make research more dynamic, insightful, and human.

If you missed it, you can catch up here.

🚀 What’s New?

Think about the last time you signed up for a new product. Maybe you explored a few features, poked around the interface, but never really got hooked.

PLG assumes users will ‘just get it.’ But most of them are like this ⬇️

When users sign up but have no clue what to do next

That’s the challenge with Product-Led Growth (PLG).

PLG has been the driving force behind the success of companies like Slack, Notion, and HubSpot—letting users discover value on their own, without a sales team pushing them through the funnel. But here’s the catch: PLG doesn’t work without deep market research.

Most companies assume that if they build a great product, users will just “get it” and stick around. The reality?

❌ Users sign up but never activate. They explore for a few minutes, then leave.
❌ Adoption stalls. Features are built, but no one uses them.
❌ Churn creeps up. Customers leave because they don’t see long-term value.

PLG isn’t just about letting users try your product—it’s about understanding who they are, what they need, and how they engage.

This month, we’re breaking it down:

• How market research fuels PLG success—from user segmentation to feature adoption.

• Where most companies go wrong—and why just tracking product metrics isn’t enough.

• How companies like Slack, Dropbox, and HubSpot use research to drive activation and retention.

Let’s get into it.

The flawed assumption behind most product-led growth strategies

Product-led growth (PLG) is built on the idea that the product itself drives acquisition, retention, and expansion—reducing reliance on traditional sales-led motions. Companies like Slack, Notion, and Figma have proven that a well-designed product can create viral loops, remove friction, and scale without a large sales force.

But PLG doesn’t mean skipping market research.

Many early-stage companies assume that free trials and self-serve onboarding will naturally convert users, only to realize that low activation rates, high churn, and stagnant growth prevent sustainable scaling.

In reality, the most successful PLG companies don’t just analyze product usage. They continuously study user behavior, competitive landscapes, and market trends to refine their strategy.

This newsletter breaks down five key ways market research fuels PLG and why companies that neglect it often hit a growth ceiling.

1. Identifying the right audience: PLG doesn’t work for everyone

Let me ask you this—how many times have you signed up for a free trial, explored for a few minutes, and then… never logged back in?

Exactly.

That’s the reality most PLG companies face. They assume that every free trial user is a potential long-term customer. But that’s rarely the case.

When you expect free trial users to convert… but they don’t

Some users sign up just to browse. Others try a few features but never hit that “aha” moment. And then there are the ones who love the product—but never convert to paying customers.

This is where market research makes the difference. It helps companies figure out:

✅ Who actually gets long-term value from the product
✅ What separates power users from casual explorers
✅ Why some users activate and retain—while others churn

Instead of treating every sign-up the same, the best PLG companies use research to double down on the right users—the ones who will actually stick around, engage, and grow with the product.

Why most PLG companies fail at audience segmentation

PLG works when a product fits seamlessly into a user’s workflow—but only for the right users. Many companies assume that broad reach = broad success. It doesn’t.

Here’s where most PLG strategies go wrong:

• They target everyone instead of the right people—leading to high sign-ups, but low retention.

• They assume free trial = intent when, in reality, many users are just testing and will never convert.

• They optimize for volume instead of value, making decisions based on overall sign-up data instead of researching who actually sticks around.

Without research, PLG companies waste time optimizing for the wrong users.

How research helps: Finding the right users before optimizing the product

PLG isn’t just about self-service. It’s about removing friction for the right users while filtering out the wrong ones.

Here’s what great PLG companies do differently:

• Behavioral cohort analysis → Instead of looking at all sign-ups, they track who activates and retains.

• User interviews & Jobs-to-be-Done (JTBD) research → They figure out why people actually use the product (not just what they say in surveys).

• Competitor research → They look for gaps in the market where underserved users exist, instead of copying others.

2. Reducing friction in onboarding and activation

Product-led growth only works if users quickly experience value—but most don’t. They sign up, explore for a few minutes, and leave. If they don’t hit an activation point fast enough, they churn before realizing how useful the product could be.

A major reason for this? Friction in onboarding.

Many PLG companies assume that because onboarding is self-serve, users will figure things out on their own. But that’s rarely the case. The companies that succeed don’t just optimize features—they research where users get stuck, remove unnecessary steps, and redesign onboarding for fast activation.

Where most PLG companies go wrong with onboarding

Self-serve onboarding is great in theory, but in practice, most companies unintentionally introduce friction.

Here are the most common mistakes:

• Too many steps before users experience value → If users have to fill out long forms, watch mandatory tutorials, or go through complex set-up before actually using the product, they’ll drop off.

• Overloading users with too many features upfront → Instead of guiding them toward one clear action, companies overwhelm users with tooltips, pop-ups, and feature explanations.

• Lack of personalization → Not all users need the same onboarding flow, but many PLG products treat everyone the same.

Without research, teams guess at what’s causing drop-offs instead of fixing the real issues.

How research helps: Identifying friction before it kills conversion

Good PLG companies don’t just track how many users complete onboarding. They analyze why some fail to activate and fix those issues.

The best research methods include:

• Session recordings & heatmaps → Watching real users interact with the product reveals where they struggle or abandon the process.

• Drop-off analysis from product analytics → Identifying the exact step where users disengage helps pinpoint the problem.

• User surveys & qualitative interviews → Asking first-time users what confused them surfaces hidden friction points.

Case Study: How Slack used research to optimize onboarding

Slack wasn’t the first workplace messaging tool, but it became the fastest-growing because of how well it guided users to activation.

Instead of just introducing features, Slack’s team focused on:

✅ Encouraging users to send messages early → Onboarding was designed to help users send their first message immediately.
✅ Driving team invites as a key activation step → Slack nudged users to invite colleagues early, increasing engagement.
✅ Personalized onboarding nudges → Slack tailored messaging based on user behavior to keep teams active.

Faster activation = better retention. Slack nailed it.

This research-driven onboarding approach led to faster activation, higher retention, and strong viral adoption as teams invited more colleagues.

Without research, Slack might have focused on adding features instead of optimizing the user experience for engagement.

3. Making data-driven product decisions

Many PLG companies believe they’re data-driven because they track sign-ups, activation rates, and churn. But tracking metrics isn’t the same as making research-backed decisions. However, the harsh reality is most PLG companies ship features based on internal hypotheses, customer requests, or what competitors are doing.

That’s a problem.

Building a great product isn’t just about shipping fast—it’s about knowing which features will actually improve retention, adoption, and expansion. And that requires market research.

Where PLG companies go wrong with product decisions

Many PLG companies fall into the trap of reactive product development. They:

• Prioritize customer requests without deeper validation → Loud feedback doesn’t always represent what most users need.

• Overbuild based on competitor feature parity → Just because a competitor added a feature doesn’t mean it drives adoption.

• Ignore behavioral insights in favor of opinion-driven roadmaps → The loudest voices in a company (usually sales, leadership, or support) can push for features that don’t align with actual user needs.

Without research, product teams optimize based on assumptions instead of real user behavior.

How research helps: Identifying what actually drives retention and growth

The best PLG companies don’t just ask what users want—they study what users actually do.

Key research methods include:

• A/B testing feature impact on retention → Instead of launching features blindly, companies measure their effect on activation and engagement.

• Analyzing user drop-offs in key workflows → Identifying friction points helps teams prioritize fixes that improve conversion.

• Jobs-to-be-Done (JTBD) research → Helps uncover why users adopted the product in the first place, leading to better roadmap decisions.

Case Study: How Dropbox used research to build a viral referral system

Dropbox’s famous referral program wasn’t an accident—it was the result of behavioral research.

Instead of just assuming that users would naturally share the product, Dropbox analyzed why people signed up and what motivated them to invite others.

Their research led to a key insight:

👉 Users who received Dropbox as a referral were far more likely to activate and retain than those who signed up through ads.

This insight drove one of Dropbox’s biggest PLG growth decisions:

✅ Referral incentives → Instead of focusing on paid ads, they gave users extra storage for inviting others.
✅ A/B tested referral messaging → They tested different prompts to see what got users to refer the most.
✅ Made referrals part of onboarding → Instead of treating it as a side feature, they embedded it into the user journey.

Invite. Share. Get free storage. Genius.

As a result, Dropbox’s referral program grew sign-ups by 60% and increased long-term retention.

Without research, Dropbox might have wasted resources on ads instead of doubling down on referrals—a decision that helped it become a market leader.

🔗 (Source: Prefinery – Dropbox Referral Program Case Study)

PLG isn’t about moving fast and breaking things—it’s about building the right things, based on real user behavior.

The difference between companies that scale and companies that stall? One makes product decisions based on data. The other makes them based on opinions.

4. Competitive positioning: Why PLG companies can’t ignore the market

A common mistake PLG companies make is believing that their product alone will set them apart. They assume that as long as their product is great, users will choose it over competitors.

That’s rarely the case.

Users don’t just evaluate products in isolation—they compare them. Even in a PLG model, they explore multiple options, read reviews, test different platforms, and weigh their choices before committing.

Users don’t just pick—they overthink.

And here’s the kicker—if your positioning isn’t clear, your competitors define it for you. If you don’t actively differentiate in the market, customers will default to the loudest, most visible, or most familiar brand.

Where PLG companies go wrong with competitive positioning

Too many PLG companies assume that their product’s features and functionality are enough to win. But the market is crowded, and users are constantly bombarded with choices.

Here are three common mistakes PLG companies make when it comes to positioning:

1) Ignoring competitive research entirely

• They assume their product is unique—without studying how competitors frame similar features.

• They fail to analyze how competitors position themselves to different user segments.

• This leads to blind spots, where companies don’t realize that their messaging is too broad or too generic to stand out.

2) Copying competitors instead of differentiating

• Just because a competitor adds a feature doesn’t mean it’s valuable to your audience.

• Many PLG companies fall into the trap of chasing parity instead of carving out their own distinct value.

• The result? A product that blends into the crowd rather than one that commands attention.

3) Failing to articulate why they’re different

• Your product might be objectively better, but if users can’t tell why in seconds, they won’t care.

• Weak positioning leads to unclear messaging, making it harder for users to see your unique value.

• The best PLG companies define their core differentiation early—and reinforce it at every touchpoint.

Without research, PLG companies struggle to stand out. They either blend in or get lost in the noise.

How research helps: Identifying market gaps and positioning effectively

The best PLG companies don’t guess. They use research to deeply understand the competitive landscape and craft a positioning strategy that makes their product undeniably compelling.

Here’s how research helps answer the three critical questions for PLG positioning:

1) What alternatives are users considering?

How research helps:

• Conducting competitor NPS analysis reveals what users love or hate about other solutions.

• Identifying the feature gaps competitors haven’t solved helps companies focus on areas of true differentiation.

• Understanding why users switch from one product to another prevents wasted effort on unnecessary features.

2) Why do users choose (or leave) competitors?

How research helps:

• Running win/loss analysis shows what pushes users toward or away from a product.

• Churn surveys and exit interviews uncover the biggest pain points that drive users to switch.

• Competitive research goes beyond feature comparison—it reveals the emotional and functional reasons behind user decisions.

3) What gaps exist in the market?

How research helps:

• Jobs-to-be-Done (JTBD) research helps uncover what users are really trying to achieve.

• Identifying where existing tools fall short lets PLG companies lean into their unique strengths.

• Instead of competing on features, PLG companies can position themselves around solving high-impact problems better than anyone else.

Competitive research isn’t about copying. It’s about understanding where your product wins—and doubling down on it.

5. Driving expansion revenue & retention: The final PLG growth lever

Most PLG companies obsess over acquisition—optimizing for sign-ups, onboarding, and activation. But what separates high-growth PLG companies from those that stall?

They focus just as much on expansion and retention.

Simply put—growth doesn’t come just from new users. It comes from keeping and expanding existing ones.

Where PLG companies go wrong with retention & expansion

Many PLG businesses treat expansion as an afterthought. Here’s where they go wrong:

1. They only focus on new sign-ups → Acquisition is important, but without retention, it’s a leaky bucket.

2. They don’t research why users churn → Without understanding why users leave, fixing retention is just a guessing game.

3. They fail to identify expansion opportunities → Most companies try to upsell based on price, instead of researching what additional value users actually need.

Without research, PLG companies waste time optimizing for the wrong things.

How research helps: Identifying why users stay, leave, and upgrade

The best PLG companies use research to answer three questions:

1. Why do our best users stay?

   • Running longitudinal NPS analysis helps uncover what keeps power users engaged.

2. Why do users churn?

   • Conducting exit surveys and analyzing cancellation data reveals the biggest reasons users leave.

3. What triggers expansion?

   • Studying upsell behavior and feature adoption patterns helps teams build products that naturally grow revenue.

Instead of assuming what features drive retention or expansion, research helps teams identify where the real opportunities are.

Case study: How HubSpot used expansion to scale beyond marketing automation

HubSpot started as a marketing automation tool but successfully transitioned into a multi-product PLG company by expanding into CRM, sales, and customer service software.

Instead of remaining a niche tool, HubSpot identified key expansion opportunities based on customer adoption trends and refined its cross-sell and upsell strategy. This allowed them to move beyond marketing automation and offer a comprehensive growth platform for businesses (Source: New Breed Revenue).

This shift led to:

✅ Built a cross-sell motion → Introduced CRM, sales, and customer service tools alongside marketing automation.
✅ Personalized upsells based on usage data → Users were recommended additional products based on behavior.
✅ Focused retention efforts on multi-product users → The company refined onboarding to drive adoption across multiple tools.

The result? HubSpot evolved from a single-product SaaS tool into a category-leading multi-product platform, driving long-term customer retention and revenue growth. Without a well-executed expansion strategy, HubSpot might have remained a marketing automation tool instead of scaling into a full-fledged PLG company.

The bigger picture

Most PLG companies think growth = more sign-ups. The best ones know that real growth comes from keeping and expanding existing customers. Retention and expansion aren’t afterthoughts. They’re the foundation of sustainable PLG growth.

Conclusion

Product-led growth isn’t about hoping users will figure things out on their own. It’s about understanding them better than anyone else.

The best PLG companies don’t just optimize for sign-ups or build features based on assumptions. They research deeply, analyze behavior, and use insights to drive real, sustainable growth.

Here’s the key takeaway:

• PLG isn’t just about acquisition—it’s about retention and expansion.

• Market research ensures you’re optimizing for the right users, not just more users.

• Companies that scale don’t guess—they use data to remove friction, improve adoption, and drive expansion revenue.

The difference between PLG winners and those that stall? The winners understand their market better than anyone else.

PLG isn’t about just getting more sign-ups—it’s about keeping the right users. The best companies don’t just track numbers; they study behavior, fix friction, and optimize for retention. Are you?

Let’s build smarter, research-driven growth strategies. What’s your take? I’d love to hear your thoughts—reply and let me know!

That’s a wrap for this issue of The Research Mag! What’s your take on the role of market research in PLG? How do you see data-driven insights shaping the way companies drive adoption and retention?

I’d love to hear what you liked about this newsletter. Do reach out to me here.

Got an idea for a future topic? Let me know! Let’s keep the conversation going.

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Hey there! 👋

Sharekh here! Welcome back to The Research Mag—where we touch upon fresh ideas, market research insights, and the innovations shaping our future.

Quick pause before we jump in—let’s take a quick look back at last month’s issue in case you missed it.

🔍 Quick Recap: December’s Issue

Last time, we talked about the growing uncertainty in the market research world. From inflation and budget cuts to survey fatigue and rising data privacy hurdles, businesses are having a tough time navigating these unpredictable times.

Here’s a quick roundup:

• Economic pressures: With inflation and tighter budgets, market research is often the first thing to hit the chopping block.

• Data quality concerns: Did you know up to 30% of survey responses are fake? No wonder trust in the data is slipping.

• Participation crisis: Survey fatigue is real. Engagement rates are tanking, and it’s becoming harder to get people to share their opinions.

We also touched on some potential solutions, like gamified surveys and AI-powered fraud detection tools to clean up the mess. If you missed it, you can catch up here.

🚀 What’s New?

Think back to the last time you got a survey. Did you roll your eyes and click away? Or maybe you started it, got halfway through, and thought, “Why am I even doing this?” (No judgment—we’ve all been there.)

Pretty much sums up how most of us feel about surveys, doesn’t it?

Surveys have been the cornerstone of market research for over a century, but let’s face it, they’re starting to feel… outdated.

Now, imagine this: a world where surveys aren’t just static forms to fill out. Instead, they feel like a real conversation; personalized, dynamic, and, dare I say, human. This is what AI-driven video and audio conversations are bringing to the table, and it’s already reshaping how we collect data.

In this issue, we’ll explore the history of surveys, the challenges they’re facing today, and why AI-driven conversations might just be the future of market research.

A Brief History of Surveys: From Paper to Pixels

Let me ask you this: How many times have you been asked to fill out a survey? On a scale of “Ugh, not again” to “Fine, but I’ll skip half the questions,” most of us know the drill. But have you ever wondered how surveys got here, and why they’re still stuck in the same old format?

Surveys have been around for over 100 years, evolving with technology but rarely changing their core structure. They’ve been the backbone of market research, but as the world has changed, they’ve struggled to keep up.

Let’s take a quick look at how they’ve transformed, and where they’ve fallen short.

Let me ask you this: How many times have you been asked to fill out a survey? Be honest! On a scale of “Ugh, not again” to “Fine, but I’ll skip half the questions,” most of us know the drill

But here’s a thought—have you ever stopped to wonder how surveys even got here? Why, after more than 100 years, are they still stuck in the same old format?

Surveys have evolved with technology, sure. But their core structure? It’s barely changed. For decades, they’ve been the backbone of market research, but as the world races ahead, they’re struggling to keep up.

Let’s take a quick look at how they’ve transformed over the years—and where they’ve fallen short.

1. The Early Days: Paper-Based Surveys

Before the Internet changed the game, surveys relied on good old-fashioned paper. Back in the early 20th century, researchers mailed out questionnaires and then waited, sometimes for weeks or even months to hear back. And when the responses finally trickled in? Someone had to sit down and analyze all that data by hand. It was slow, expensive, and let’s be honest, pretty painful. But for its time, it got the job done.

By the late 1990s, things started to shift with the rise of online surveys. Sure, they were basic, just long forms on clunky web pages but they opened up a whole new world of possibilities. Collecting data in real time from people across the globe? That was a game-changer. If you are curious and intrigued about how surveys have evolved since then? Check out A Brief History of Surveying: The Evolution of Survey Methodology to learn more.

2. The Telephone Era

By the mid-20th century, telephones were ringing off the hook—quite literally. Telephone surveys became the go-to method for data collection, letting researchers reach people far more efficiently than through in-person interviews. This method was a breakthrough for its time. Real-time questioning meant researchers could ask follow-ups, dive deeper, and get more dynamic interactions. But here’s the catch: while data collection sped up, the analysis? Still slow, manual, and a total grind.

3. The Internet Revolution

Then came the late ’90s and early 2000s, and the internet shook things up in a big way. Platforms like SurveyMonkey and Google Forms made surveys digital, transforming data collection practically overnight. Suddenly, surveys were faster, cheaper, and scalable. If you had internet access, you could participate from anywhere, whether you were at home, at work, or, let’s be honest, procrastinating. This digital leap not only saved time and money but also opened the doors to a much broader, more diverse pool of respondents.

Why Surveys Worked?

Surveys were revolutionary because they offered:

• Scale: Reach thousands of participants at once.

• Standardization: Collect comparable data through consistent questions.

• Cost Efficiency: Digital tools made surveys accessible to businesses of all sizes.

But while the delivery methods evolved, the core mechanics stayed the same. We’re still asking people to tick boxes and answer static, one-size-fits-all questions. And as the world has changed, surveys haven’t kept up.

Why Traditional Surveys are Losing Relevance?

Let’s be real—when was the last time you actually enjoyed filling out a survey? Chances are, you groaned, clicked away, or rushed through just to get it over with. Sound familiar? The truth is, traditional surveys haven’t aged well. We’re living in a world that’s faster, more digital, and more dynamic than ever before and the static, one-size-fits-all survey model just isn’t cutting it anymore. What once felt groundbreaking now feels clunky, outdated, and, let’s face it, borderline annoying.

But why exactly are traditional surveys losing their relevance? Let’s break it down.

1. Survey Fatigue: Too Much, Too Often

Surveys are everywhere. Open your inbox, scroll your social feed, or glance at your receipts, there’s always another one waiting for you. It’s no surprise people are burned out. This constant flood of requests has led to what researchers now call “survey fatigue.”

Stat: Email surveys now have an average response rate of just 6-8%. That means over 90% of people are ignoring these requests.

Now, picture this: you’ve just finished a long, frustrating customer service call. Just as you’re about to hang up, the rep asks, “Would you mind staying on the line for a quick survey?” You glance at the clock and think, Quick? Sure. And before you know it, you’ve already hung up. The bigger issue is even when people do respond, the data isn’t always reliable. Rushed answers, half-hearted responses, and general disinterest can quickly turn a survey into a source of noise instead of valuable insight.

2. Sample Bias: The Loudest Voices Dominate

Even when surveys manage to get responses, they often miss the mark. The reason is because the people who respond are usually the ones with the strongest opinions, either they loved their experience or they’re downright furious. And what about the rest? The “silent majority” with more balanced, moderate views? They’re often left out of the conversation. This creates a big problem: businesses end up making decisions based on skewed data, overlooking the actual needs and expectations of their average customers. Stat: A GreenBook study found that extreme responses often dominate online surveys, creating an unbalanced dataset.

Fraudulent Responses: A Growing Threat

Fake data is one of the biggest challenges in research today, bots, survey farms, and fake participants are undermining the reliability of survey results. And the damage? It’s significant. Just imagine that you’ve spent months collecting responses for a critical research project. At first glance, the numbers look great. But then you realize a huge chunk of the data came from fraudulent sources. Just like that, your insights are unusable, and you’re back to square one.

This isn’t an isolated issue—it’s widespread.

Fact: Researchers are discarding up to 38% of collected data because of quality concerns and panel fraud, according to Kantar.

Fraudsters are exploiting online surveys in much the same way they gamed digital ads. And it’s not just about wasting time and money—bad data leads to bad decisions, and those decisions can ripple across entire organizations. But here’s the thing: there’s hope. Various tools are stepping in with AI and machine learning to detect fraud in real time. These systems flag suspicious patterns and unverified participants, giving researchers the tools to protect their data and their reputations. The next time someone says, “Surveys are simple,” remind them it’s not just about collecting responses. It’s about ensuring those responses are real.

Lack of Nuance: The Limits of Numbers

Numbers can only tell part of the story. Metrics like NPS (Net Promoter Score) and CSAT (Customer Satisfaction) give you a quick idea of how your customers feel. But here’s the issue: they don’t tell you why. Think about it. You get a score of 7 out of 10 on a survey. That looks okay, but why wasn’t it a 9? Or a 5? Was the customer annoyed about a late delivery? Did they like the product but find the packaging wasteful? Or were they just confused by unclear instructions?

Surveys often focus on numbers because they’re easy to measure. But in doing that, they miss the emotion, the context, and the little details that make insights truly valuable.

It’s like trying to judge a movie based on its Rotten Tomatoes score alone. Sure, the percentage tells you something, but it doesn’t explain why people loved or hated it.

And here’s the real problem: without knowing the “why,” businesses are left guessing. They might fix things that aren’t broken or, worse, ignore the actual issues that matter to their customers.

Numbers Alone aren’t Enough

Numbers are great for spotting trends, but they don’t give you the whole picture. To really understand your customers, you need something deeper—real, human-like conversations. This is where AI-driven surveys shine. They don’t just capture what customers say—they dig into how they feel. Now, imagine swapping out those static, one-size-fits-all surveys for something more dynamic. An AI that can ask follow-up questions in real time, like:

• “You gave us a 7—what could we have done to make it a 9?”

• “What stood out to you about your experience with us?”

• “What’s one thing we could improve for next time?”

Turn good experiences into great ones by asking the right questions

AI uncovers the nuance that numbers can’t, helping businesses make smarter, more informed decisions. So yes, numbers set the stage. But it’s the story behind them that drives real change.

Enter AI-Driven Conversations: The Next Frontier

AI-driven conversations are changing the way surveys work. Instead of static forms, you have a short, engaging chat that feels natural and intuitive. No endless scrolling. No ticking boxes!  Here’s how it works:

• Starts with Open-Ended Questions: The AI begins with simple, thoughtful prompts like, “What did you like most about your experience?” or “How can we improve?”

• Adapts in Real-Time: If you pause or seem unsure, the AI notices and gently follows up with, “Can you tell me more about that?” or pivots to a related question to keep the conversation going.

• Captures the Full Picture: After the chat, the AI analyzes tone, pauses, and emphasis not just the words but to uncover in-depth insights.

It’s a more dynamic and personalized way to understand customers, where the conversation feels less like a survey and more like being heard.

Why is this a Game-Changer?

1. Personalization: Each interaction feels tailored to the participant. It’s not a one-size-fits-all approach. It’s a dynamic, engaging experience that makes people feel truly heard.

2. Rich Insights: Numbers are helpful, but they don’t tell the whole story. AI conversations go beyond data collection. They interpret context, tone, and emotion. This helps businesses act on insights that aren’t just accurate but genuinely meaningful.

3. Scalability: Need to interview 1,000 people? No problem. AI can handle thousands of conversations at the same time without breaking a sweat. Compare that to traditional qualitative research, where every interview takes time, money, and a lot of effort.

Stat: AI surveys can reduce data analysis time by up to 50% (Phonic).

Traditional surveys treat participants like data points. AI-driven conversations, on the other hand, see them as people. It’s not just about collecting answers—it’s about understanding their stories, emotions, and motivations. Think about it: businesses that go beyond the numbers and connect with the humans behind them gain insights that truly matter. That’s the power of AI.

Who Benefits from AI Surveys?

AI-driven surveys aren’t just shiny new tools, they’re transforming how entire industries collect and act on data. Let’s take a look at some of the biggest beneficiaries:

Market Research: Beyond the Trends

Traditional market research often focuses on trends: what people buy, how preferences shift, or which products gain traction. AI surveys take it a step further by uncovering why customers make these decisions. For example, AI sentiment analysis helps businesses identify emotional drivers like nostalgia, convenience, or trust in a brand. With these insights, marketers can create campaigns that really connect with their audience. Insight: AI-based sentiment analysis allows companies to interpret consumer emotions and make data-driven decisions (Appinventiv).

Customer Service: Real-Time Sentiment Analysis

Let’s face it—post-call surveys don’t get much love. After spending time on a support call, most people are either too relieved their issue is resolved or too frustrated to bother answering more questions. The result? Incomplete feedback and missed chances to understand what customers really need. AI-driven surveys change the game by analyzing the call itself. While the conversation happens, AI picks up emotional cues like tone, hesitation, or frustration, providing instant insights into customer satisfaction.

How AI Changes the Game?

• Real-time insights help businesses address recurring issues quickly.

• No need for follow-up surveys, reducing friction for customers.

• Companies can improve agent performance based on detailed sentiment analysis.

By making feedback effortless, AI ensures businesses don’t just hear their customers—they understand them.

Healthcare: Empathy Meets Innovation

Gathering patient feedback has always been challenging. After a medical procedure, getting patient feedback has always been tough. After a medical procedure, patients are often overwhelmed and skip traditional surveys, leaving valuable insights untapped. AI offers a better approach. Conversational surveys engage patients naturally, asking open-ended questions like, “How are you feeling today?” or “Was there anything we could have done better?” This creates a space where patients feel heard and are more likely to share meaningful feedback.

The AI Advantage

• Patients are more comfortable sharing concerns in a conversational setting.

• Hospitals can identify gaps in care, like unclear instructions or long wait times.

• Healthcare providers can act on insights to improve care and patient satisfaction.

When feedback feels less like a chore and more like a conversation, everyone benefits, especially the patients.

The Challenges: What’s Holding AI Back?

AI-driven surveys are exciting, no doubt about that. But like any new technology, they come with their own challenges. To unlock their full potential, businesses need to tackle these hurdles head-on.

1. Privacy Concerns: Balancing Innovation and Compliance

When it comes to handling sensitive data like audio and video recordings, privacy isn’t optional—it’s essential. Regulations like GDPR (Europe) and CCPA (U.S.) have strict rules about how data is collected, stored, and used.

For companies using AI surveys, this means:

• Being transparent with participants about how their data will be used.

• Using strong encryption and secure storage to protect sensitive information.

• Regularly auditing systems to ensure compliance with privacy laws.

What’s at Stake?
Non-compliance doesn’t just mean fines and legal trouble. It can damage your reputation and, most importantly, cost you participant trust. People need to feel safe when sharing their thoughts—it’s as simple as that.

2. User Comfort: Building Trust in AI Conversations

Let’s face it—talking to an AI might feel a little weird at first. Some participants might hesitate to share personal opinions with what feels like a machine.How can companies fix this?

• Design with warmth: Make your AI sound human and approachable so it feels less robotic.

• Set clear expectations: Start the survey by explaining how the AI works and why it’s being used.

• Reassure participants: Let them know their responses are confidential and valued, no matter the format.

Quick Tip: A short demo or introductory video can do wonders. It helps participants understand the process and feel more comfortable engaging with AI-driven conversations.

3. Adoption Costs: The Price of Innovation

AI systems can be expensive. From purchasing the technology to training teams and integrating it into workflows, the upfront costs can feel like a big hurdle—especially for smaller businesses. But here’s the thing: while the initial investment is significant, the long-term benefits often outweigh the cost. AI can save time by automating manual tasks, scale effortlessly, and deliver deeper insights that make decision-making easier.

How to Approach it?
Always start small. Try AI surveys in one department or for a specific project. This way, you can test how well it works, measure its impact, and make adjustments before committing to a larger rollout.

Facing the Challenges Head-On: AI-driven surveys have the power to transform data collection, but success takes careful planning. By tackling privacy concerns, earning participant trust, and managing costs wisely, businesses can overcome these challenges and fully unlock the potential of AI-driven insights.

The Future: A Hybrid Approach

The future of surveys isn’t about scrapping traditional methods altogether—it’s about combining the best of both worlds. A hybrid approach blends the efficiency and depth of AI with the structure and familiarity of traditional surveys.

What does this Hybrid Model Look Like?

• AI for Open-Ended Responses: Let AI handle free-text answers, analyzing them to uncover patterns and trends quickly.

• Shorter, Smarter Surveys: Swap out long, boring forms for conversational interactions that feel more dynamic and engaging.

• Human Expertise: Free up researchers to focus on interpreting insights and making strategic decisions, rather than getting stuck in manual data analysis.

This balanced approach makes surveys scalable and efficient, without losing the depth and nuance needed for meaningful insights. It’s the natural next step in how we gather and act on data.

Conclusion: A Call for Change

Surveys are evolving, and it’s time to embrace the shift. AI-driven conversations aren’t just about collecting responses. They’re about connecting with people, understanding their stories, and uncovering insights that drive real action.

The question now is: Are we ready to listen?

The future of surveys is conversational, insightful, and human-centric. Let’s build that future together. What’s your take? I’d love to hear your thoughts—reply and let me know!

That’s a wrap for this issue of The Research Mag! What’s your take on the future of surveys? How do you see AI reshaping the way we collect insights?

I’d love to hear what you liked about this newsletter. Do reach out to me here.

Got an idea for a future topic? Let me know! Let’s keep the conversation going.

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Hey there! 👋

Sharekh here, founder of CleverX, where we're revolutionizing the recruitment of verified business participants for fraud-free research!

It’s been a while since we last connected, but we’re back with more insights, ideas, and updates to keep you at the forefront of the research world.

Welcome to this edition of The Research Mag! 🚀 In our last issue, we explored AI’s role in market research—its power, its limits, and why the human touch still matters.

🔍 Quick Recap: Last Issue Highlights

• AI in Market Research: Tools like predictive analytics and AI-generated personas are reshaping the industry.

• The Good: AI offers speed, efficiency, and deeper data analysis.

• The Limitations: It can’t replicate human intuition or tackle broader structural challenges.

But AI isn’t the only story. The market research industry itself feels increasingly unpredictable. Budgets are shrinking, clients are demanding faster results, and economic pressures are mounting. For researchers, it’s getting harder to predict what’s coming next.

In today’s issue, we will be discussing why the industry feels so unpredictable. From global economic pressures to U.S.-specific challenges, we’ll explore what’s driving this uncertainty. Plus, I’ll share some strategies to help navigate these shifting tides and come out stronger.

Let’s get started!

What’s Uncertain in the Market Research Industry?

Starting off with an example: just few months ago, a Deloitte report highlighted that 61% of CFOs are bracing for tighter budgets in 2024. On top of that, people are losing trust in data, and fewer are willing to take surveys. It’s creating a perfect storm for the market research industry. Let’s break down the reasons:

Economic Volatility
Inflation’s like an uninvited guest, showing up and forcing everyone to cut back on spending. For businesses, market research budgets are often the first to go. Rising raw material costs are forcing businesses across industries to make tough decisions—and market research budgets are often the first to go. 

When inflation makes everything too expensive, even your budget feels like dinner

Erosion of Data Trust
Have you ever filled out a survey and thought, “Is anyone else actually answering this seriously?” You’re not alone. In fact, bots, survey farms, and fake profiles are making it harder than ever to trust the data we collect.

Me, halfway through a survey, wondering if I’m the only one answering honestly

In addition, a study by GreenBook found that up to 30% of online survey responses are fraudulent, driven by bots, survey farms, or unverified participants. Imagine building your business strategy based on answers from people who might not even exist! For instance, a global retail brand recently reported inconsistencies in participant data, raising red flags about data quality

Falling Participation Rates
Here’s the thing: people are tired of surveys. It’s hard enough to get someone to spend 10 minutes answering questions, let alone 20 or 30. Participation rates have been dropping sharply, especially in places like the U.S. and Europe.

A LinkedIn Pulse article highlights this “participation crisis,” noting that survey fatigue and low respondent engagement are making it increasingly difficult to gather quality data. Companies are finding it harder than ever to hear from the people who matter most.

For example, a tech company recently shared that their latest survey targeting Gen Z consumers had less than half the expected responses. Why? Survey fatigue. People are overwhelmed with requests for feedback everywhere from email inboxes to social media pop-ups and they’re simply tuning out.

The Global Landscape: Macro & Micro Challenges Impacting Market Research

Macro Challenges

The market research industry doesn’t operate in isolation. Global economic trends and shifting regulations are creating challenges that even the savviest researchers must navigate carefully. Let’s break these down with examples and insights.

Economic Pressures

Global economic challenges are reshaping business priorities. From rising costs to shrinking budgets, companies are being forced to make tough decisions—and market research often gets deprioritized in the process. Before diving into specific challenges like inflation and supply chain disruptions, let’s take a closer look at how these pressures are playing out across industries.

Inflation’s Ripple Effect: Inflation is more than just an annoyance, it’s a cascade of higher costs for businesses and consumers alike. Companies are scaling back spending on non-essential activities, and unfortunately, market research often falls into that category.

Take, for instance, the retail sector: a global apparel brand recently cut its research budget by 20% in 2023, citing increased raw material and production costs. Similarly, the IPA Bellwether Report for 2023 highlighted that market research spending has seen its fifth consecutive quarterly decline, reflecting how economic pressures are forcing businesses to reevaluate their priorities.

The problem is, cutting research to save money in the short term can have long-term consequences. Without consumer insights, companies risk launching products or campaigns blind, a gamble that’s harder to justify in a volatile market.

Supply Chain Disruptions: Let’s talk logistics. Imagine you’re a manufacturer, and your suppliers are delayed for months because of geopolitical tensions or shipping bottlenecks. Are you likely to spend your budget on market research, or will you funnel it into fixing your supply chain?

That’s the reality for industries like automotive and consumer electronics. A Capgemini Report (September 2023) highlights that automotive organizations have restructured supply chain management, with a 22% reduction in offshore procurement over the past two years to tackle disruptions. This shift shows how businesses are prioritizing resilience over other initiatives, like research projects.

It’s not just isolated sectors. Research projects reliant on stable production pipelines, like those in FMCG and tech, are being delayed indefinitely. For businesses, the logic is simple: you can’t research a market when you’re struggling to get products onto shelves.

The Data Privacy Conundrum 

Data privacy regulations are becoming stricter every year, and for market research firms, this isn’t just an administrative headache—it’s a cost driver.

Take GDPR in Europe, for example. If you’re running a study in Germany, you’ll need explicit consent for every piece of data you collect, plus secure systems to store it. Now, imagine trying to share that data with your U.S. headquarters—welcome to a maze of legal compliance!

According to the latest OECD report, compliance costs for research firms have risen by 25% in the last three years. Smaller agencies, unable to bear these costs, are retreating from global projects altogether.

Here’s a real-world example: a boutique research firm recently stopped conducting studies across the U.S. and Europe because navigating privacy regulations became too time-consuming and expensive. Instead, they now focus exclusively on domestic markets.

This isn’t just a challenge for smaller players. Larger firms are also finding it harder to manage cross-border projects efficiently. Delayed data sharing, increased compliance reviews, and rising costs are collectively slowing down the pace of research—just when agility is most needed.

The big picture is that without a clear strategy for navigating data privacy laws, market research firms risk losing both speed and scale, undermining their ability to provide timely, actionable insights.

Micro Challenges

While macroeconomic trends like inflation and geopolitical tensions dominate the headlines, micro-level challenges are quietly reshaping how market research is conducted and delivered. These issues are closer to the ground but carry significant implications for the industry. Let’s dive in:

Talent Shortages in Market Research

The market research industry is struggling to retain skilled professionals, as many are being drawn to higher-paying opportunities in tech and consulting. This talent drain is leaving firms with fewer experienced analysts to handle complex projects, forcing agencies to rethink how they manage workloads.

With shrinking teams, many firms are relying heavily on automation to bridge the gap. While this can streamline processes, it often comes at the cost of depth and nuance—qualities that experienced researchers bring to the table. This shift raises concerns about the long-term sustainability of high-quality insights in the industry

Declining Data Quality

As research shifts online, ensuring the reliability of data has become a massive challenge. Fraudulent responses, bots, and duplicate entries are skewing results, making insights less dependable.

Take this example: a global FMCG brand discovered that 15% of their survey responses were duplicates or came from unverified participants, leading to wasted resources and flawed strategies. 

Increasing Client Expectations

Clients want insights faster, cheaper, and deeper than ever before. But this demand for speed can come at a cost:

• Rushed timelines: A multinational bank recently demanded a full consumer sentiment analysis in two weeks—a project that typically takes a month.

• Sacrificed depth: To meet deadlines, researchers often have to reduce sample sizes or skip qualitative analysis, leaving insights less comprehensive.

• The result? A final report with surface-level insights instead of actionable depth.

Cultural Nuances and Cross-Border Challenges

Conducting studies across regions isn’t as simple as translating a survey. Cultural differences can drastically impact how questions are perceived and answered.

For example, a global smartphone usage study found stark differences in how respondents from Western Europe and Southeast Asia interpreted survey designs. While Europeans preferred concise, minimalistic formats, Southeast Asian respondents engaged more with detailed, narrative-style surveys. Ignoring these nuances can lead to skewed or incomplete data.

Why Macro and Micro Challenges Should Be on Your Radar?

The macro and micro challenges facing the market research industry aren’t isolated—they’re deeply interconnected, creating a cycle that’s hard to escape. Economic pressures and geopolitical tensions (macro) trickle down to how firms operate daily (micro), while issues like talent shortages and declining data quality further amplify the broader struggles.

For example, inflation might force companies to cut research budgets (macro), which in turn means fewer resources to invest in attracting top talent or improving data quality (micro). Similarly, stricter data privacy regulations (macro) make cross-border studies harder, while survey fatigue (micro) exacerbates the difficulty of gathering representative data

U.S. Macro and Micro Challenges: A Closer Look 

The U.S. market research industry is dealing with challenges on two fronts: the ripple effects of macroeconomic changes and micro-level issues that impact day-to-day operations. Here’s what’s happening:

Macro-Level Turbulence

Rising Interest Rates are Holding Back Growth
If you’ve tried taking out a loan recently, you’ll know borrowing isn’t cheap. The Federal Reserve has raised interest rates to combat inflation, but this has created a chilling effect on investments. For small-to-medium businesses (SMBs) and startups, funding market research often becomes a low priority when borrowing costs are so high.

Example: A survey by the National Federation of Independent Business (NFIB) found that 47% of SMBs are cutting discretionary spending, including research due to increased borrowing costs.

Tech Layoffs are Impacting Consumer Spending
Tech layoffs are hitting harder than a bad Wi-Fi signal. Companies like Meta and Amazon have cut thousands of jobs, and it’s not just a tech problem anymore, it’s a consumer sentiment problem. When workers in tech hubs like California tighten their wallets, demand for market trend studies takes a hit too.

Micro-Level Pain Points

Talent Acquisition Crisis
Market research firms are finding it harder than ever to hire and retain talent. Big Tech and consulting firms are snapping up experienced researchers with offers that most agencies can’t match.

Example: A report by SIS International Research revealed a 48% turnover rate in market research firms over the last two years, with many professionals migrating to higher-paying roles in tech or data science.

The Pressure for Faster Insights
Clients want faster insights than ever before. Volatile markets and competitive pressures mean research firms are often expected to deliver comprehensive reports in days rather than weeks. However, these accelerated timelines often come at the cost of depth and quality. Researchers are forced to prioritize speed, limiting the scope of analysis and potentially leaving valuable insights unexplored.

Over-Reliance on Traditional Panels
Traditional panels are no longer cutting it. Many firms are turning to AI-powered tools or platforms like LinkedIn to recruit participants, but these methods aren’t without issues. Scaling AI tools for niche audiences or ensuring data quality from online recruits remains a challenge.

What does this Mean for Market Research?

These challenges aren’t just numbers—they’re reshaping the way research firms operate. To stay competitive, U.S. market researchers need to:

• Invest in Talent Retention: Offer flexible work options and better career development to keep experienced professionals on board.

• Balance Speed and Depth: Streamline processes without sacrificing quality, ensuring that rapid insights still hold strategic value.

• Innovate in Recruitment: Combine traditional methods with AI-driven tools while implementing robust verification measures.

The U.S. market research industry is at a crossroads. By addressing these macro and micro-level issues head-on, firms can position themselves for success even in turbulent times.

However, despite these challenges, the global market research industry has demonstrated remarkable resilience and growth over the years. Take a look at how the industry has expanded globally

Source: Revenue of the market research industry worldwide from 2008 to 2023 with a forecast for 2024 (in billion USD)

Innovations and Strategies to Navigate Uncertainty

What Forward-Thinking Firms are doing

In an industry facing unprecedented challenges, some firms are stepping up with innovative strategies to adapt and thrive. These approaches focus on building trust, improving data quality, and engaging participants in more meaningful ways.

Bringing Transparency Across Research Data
Transparency is becoming a cornerstone of trustworthy market research. Forward-thinking firms are ensuring participant data is transparent at the respondent level. This means knowing where the data is coming from, understanding the context of the participant based on their experience, and recognizing the persona they carry.

This approach not only builds trust but also ensures that insights are rooted in authenticity and relevance, empowering better decision-making for businesses.

Leveraging AI for Verification
AI-powered tools are revolutionizing fraud detection in surveys. CleverX, for instance, uses digital fingerprinting and passive verification techniques to ensure data quality.

Investing in Gamified Surveys
To tackle survey fatigue, firms are experimenting with gamification. Studies show gamified surveys improve completion rates by up to 30%, especially among younger demographics.

Did You Know? 🤔

Market research spending in the U.S. was $22 billion in 2023, a 7% increase from the previous year. Despite uncertainties, industries like tech, healthcare, and fintech continue to allocate significant budgets for insights. (Source: Statista)

Key Takeaways and Action Points

1. Stay Agile: Pivot strategies to adapt to changing client demands and market volatility.

2. Double Down on Data Integrity: Invest in AI and fraud detection to ensure your data remains trustworthy.

3. Upskill Your Workforce: Create programs to retain top talent and train them in emerging tools like AI and blockchain.

By embracing innovation and focusing on authenticity, market research firms can turn the tide of uncertainty into a wave of opportunity.

That’s a wrap for this issue of The Research Mag! What challenges are you seeing in navigating uncertainty? I’d love to hear what you liked about this newsletter. Do reach out to me here.

Got an idea for a future topic? Let me know! Let’s keep the conversation going.

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

For any direct feedback, mail us here.

Hey there, 

Sharekh here! 👋

Welcome back to The Research Mag! 

I’ve been thinking a lot about our last issue on synthetic data, especially after a coffee chat with a fellow founder here in the Bay Area. We talked about how some people calling generative AI to be the biggest thing to have ever happened in technology since the internet, and I’d agree (on some days). Then we talked about how literally every company now has been releasing an “AI” upgrade to whatever products and services they’re selling.

Which makes sense. AI can make things way, way easier on their customer’s end to do certain tasks, automate workflows, get the software to (literally) do their work for them. And it’s a great addition to any product! Well, mostly. But here’s the thing: AI (including synthetic data) isn’t without its issues. In fact, some of the same risks and limitations we covered in synthetic data are creeping into other AI applications.

But I’m not a pessimist, so let’s start with the good stuff.

Chances are, you already know the basics of how AI is making a huge difference in the market research industry, but let’s recap:

• Sentiment Analysis: AI-powered tools are combing through customer feedback in real-time, analyzing not just what people say, but how they feel. It’s like getting instant access to your customers’ emotions.

• Predictive Analytics: Brands are leveraging AI to predict future customer behaviors based on past data, helping them stay ahead of trends.

• Customer Segmentation: AI is no longer just slicing people by demographics—it’s diving deep into behavioral data to help brands personalize experiences like never before.

Timeline of important events in AI
Source: Statisa

These are impressive, but they’re only scratching the surface. Now let’s jump into the fun stuff. Something that’d make you go, “Oh, that’s actually really cool!”

• AI-Generated Personas—Real enough to fool you

  Market research often relies on building customer personas based on survey data. But what if those personas could react like real people? AI is now creating interactive digital personas that can be used to simulate how a target audience might respond to current events, product changes, or marketing campaigns.

  Example: A brand might train AI on past customer data to build digital personas that reflect their audience’s responses to hypothetical situations—without needing to conduct a survey.

An AI generated user persona
Souce: UXPressia

• AI-Enhanced Perception Mapping—Finding hidden insights faster than ever

  Traditionally, perceptual mapping (a way to visualize how consumers perceive different brands) was a time-consuming, resource-heavy process. But AI is changing that. By analyzing vast amounts of data in real-time, AI can create perception maps that help brands understand how their products are viewed compared to competitors—without needing a large survey sample.

  Fun fact: Researchers used synthetic data to generate a perceptual map for the US automotive industry. The results were strikingly similar to insights gained from a traditional survey of 530 real consumers, but were generated in a fraction of the time.

• Facial Coding with AI—Reading your face like a book

  This one is wild. AI-powered facial coding analyzes customers’ facial expressions during product tests or ad viewings to gauge their emotional reactions—without them saying a word. This goes beyond traditional surveys by capturing raw, unfiltered emotions. It’s being used by everyone from movie studios to consumer goods companies.

  Fun Fact: Disney used this tech during movie screenings, collecting 16 million data points on audience reactions, which helped them tweak scenes based on real-time facial feedback.

• AI for Predicting Trends—Is It a Fad or Forever?

  Ever wondered if a viral trend is here to stay or just a flash in the pan? AI is analyzing social media trends, purchase data, and online conversations to predict whether a trend will last or fade away. This allows brands to decide whether to go all-in on a trend or stay cautious.

  Fun Fact: Some brands are using AI to predict how long TikTok challenges will stay relevant, helping them figure out whether to invest in a short-term campaign 

These sound very exciting, and we too at CleverX are pushing some AI features very soon. But as much as I love what AI can do for market research, it comes with significant risks.

Firstly, there could be bias in data: And this one is very serious. We’ve talked about this before, but it’s worth repeating. If AI is trained on biased data, it will perpetuate those biases. In market research, this can lead to skewed insights that misrepresent your audience.

Privacy Concerns: With AI needing large datasets, privacy is a huge concern. Sure, synthetic data helps, but how much personal information are you comfortable using in your AI models?

The Human Element: AI gives you trends, predictions, and insights, but it doesn’t replace the why. For that, you still need human analysis to provide context and interpret the findings.

So, where do we go from here? AI is a fantastic tool—no one’s denying that. But if there’s one thing we’ve learned over these last few issues, it’s that we shouldn’t rely on it too much. So while AI will help you prepare for most of what’s coming, don’t throw away your human intuition just yet. We’re still going to need that. The human element.

That’s all for now! I’ll come back to you soon with more interesting stuff happening in research!

If you liked reading this issue, please leave us your feedback, as well as ideas as to what you’d like to know more about!

You can also reach out to me directly here.

Best,
Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Hey there, 

Sharekh here! 👋

Welcome back to The Research Mag! In our previous issue, we delved into the pitfalls of market research over the years. Today, we’re tackling another hot topic in the world of research: synthetic data.

It’s a fascinating technology with immense potential, but let’s set the record straight—synthetic data cannot replace real human data. Let’s go back to understanding how synthetic data became quite the thing in research, some of the use cases, and limitations, with some historical tidbits along the way.

A Brief History of Synthetic Data

The concept of synthetic data dates back to the 1940s with the pioneering work of Stanislaw Ulam and John von Neumann on Monte Carlo simulation methods. They generated data artificially to simulate and solve complex physical and mathematical problems. Fast forward to the present, synthetic data has become a critical tool in data science, enabling researchers to create datasets for training machine learning models, testing software, and maintaining privacy.

Image courtesy: MIT Technology Review

What Exactly is Synthetic Data?

Synthetic data is artificially generated data that mimics the characteristics and structure of real-world data but does not contain any actual personal information. Created through algorithms and statistical models, synthetic data can simulate a wide range of scenarios and data points.

Growth of Synthetic Data (Gartner)

The Promise of Synthetic Data

Synthetic data holds significant promise. It can be used to:

• Accelerate Development: By providing a sandbox for data science projects, synthetic data helps speed up development cycles.

• Enhance Privacy: When combined with techniques like differential privacy, synthetic data can help protect individual identities in sensitive datasets.

• Augment Data: It can fill gaps in real data, especially when dealing with small datasets or biased historical data.

Developers can expand synthetic datasets with alterations that provide more variety and better AI accuracy. (Source: Nvidia)

The Limitations of Synthetic Data

Despite its benefits, synthetic data has its pitfalls:

• Not Inherently Private: A common misconception is that synthetic data is automatically private. This isn’t true. Synthetic data can still leak information about the original dataset if not carefully handled.

• Distortion: Synthetic data is, by nature, a distorted version of real data. This means any analysis or modeling performed on it can be flawed or incomplete.

• Challenges with Outliers: Capturing outliers and rare events in synthetic data is particularly challenging, which can result in critical gaps in the data.

Another common misconception is that synthetic data is inherently private. This isn’t true. Synthetic data has the capacity to leak information about the data it was derived from and is vulnerable to privacy attacks. Significant care is required to produce synthetic data that is useful and comes with privacy guarantees. This is a crucial insight for those who believe that just generating synthetic data is enough to protect privacy.

Why Synthetic Data Cannot Replace Real Data

1. Quality and Authenticity: Real data, with all its imperfections, carries the richness of real-world scenarios. Synthetic data, while useful, lacks the authenticity needed for final decision-making.

2. Privacy Risks: Despite efforts to anonymize, synthetic data can still reveal private information if not handled correctly. Historical data shows that privacy breaches have occurred even with synthetic datasets.

3. Performance Issues: Machine learning models trained on synthetic data often do not perform as well when deployed on real-world data. The difference in data quality and nuances can lead to significant discrepancies.

One of the main issues with synthetic data is its struggle to capture outliers and rare events. For example, software development, when testing software designed to handle rare system crashes or security breaches, synthetic data often fails to replicate these infrequent but critical events. This can lead to software that performs well under normal conditions but fails during rare and critical situations. Or else, imagine creating a synthetic dataset for a financial application. The generator might miss replicating the behavior of a flash crash, a rare event in the stock market, leading to potential blind spots in the software’s robustness.

Another problem is that linking synthetic datasets can be problematic. If datasets are synthesized independently, the one-to-one match between datasets will be broken. For example, linking lab test results with genetic data from independently generated synthetic datasets would not work effectively. Another scenario: Imagine a company testing a new CRM software where customer profiles and transaction histories are synthesized independently. The inability to link these datasets accurately can result in flawed testing, impacting the software’s effectiveness in real-world scenarios.

Real vs. Synthetic: A Battle of Wits

While synthetic data can simulate a lot, it can’t replace the nuances of real-world data. It’s like watching a movie about a mountain climb versus actually climbing the mountain. The movie might show you the steps, but it won’t give you the experience of the altitude, the wind in your face, or the thrill of reaching the summit.

Synthetic data is a powerful tool that complements real data in many ways, but it cannot replace it. Understanding its limitations and leveraging it appropriately can help enhance research without compromising on data integrity.

That’s all for now! I’ll come back to you soon with more interesting stuff happening in research!

If you liked reading this issue, please leave us your feedback, as well as ideas as to what you’d like to know more about!

You can also reach out to me directly here.

Best,
Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Issue #2: The Pitfalls of Market Research: Lessons Learned and Future Directions

Hey there, 

Sharekh here! 👋

Welcome back to The Research Mag! In our last issue, we explored the rich history of market research, tracing its evolution from ancient practices to modern methodologies. Today, we shift our focus to the challenges and pitfalls that have emerged over the years. Understanding these issues is crucial for improving the reliability and effectiveness of market research.

🔍 Quick Recap:

• Market research has ancient roots, dating back to Roman traders

• The 19th and 20th centuries saw the formalization of market research practices

• Key methodologies like surveys, focus groups, and data analytics have stood the test of time

The Insanity of Fraud in Research

This is what I meant by your research data coming from aliens. Meaning most of it is fraud. Could be a bot, could be an alien behind a screen on Mars. You get my point.

As market research has evolved, so too have the methods of fraud that threaten its integrity. Fraud has become a significant problem, with Discord groups and online forums offering individuals the opportunity to become fake survey respondents for a fee. This practice undermines the integrity of data, leading to skewed results and poor decision-making. Historical trends show that as incentives for participation have grown, so has the sophistication of fraudulent tactics.

It’s estimated that close to 40% of responses in high-volume research done with traditional panel-based methods are fraudulent. Needless to say the 40% that’s recognizable as fishy and fraudulent, no one really knows how credible is the other 60%.

This is typically what you’d like to avoid while doing research

Incentives and Their Challenges

Offering incentives is a common strategy to boost survey participation, but it comes with its own set of challenges. In B2C surveys, participants might receive $5 per response, while B2B respondents could earn $50-100 per survey. This gap is even bigger in developing countries, where such amounts are very tempting, leading to more fraud.

The stakes are higher in B2B research, where the value of each response is significantly greater. Here, the incentive structure often leads to a higher risk of fraud, with respondents motivated to provide false information to gain monetary rewards. In many cases, the lack of proper verification mechanisms can make this issue worse, resulting in data that is unreliable and potentially misleading.

The Identity Crisis in B2B Market Research 🤖

Historically, the copy-paste approach of B2C methods into B2B environments has led to significant discrepancies in data quality. Without proper identity verification, the collected data often lacks reliability. Many companies view market research as a mere formality, opting for cheaper, high-volume data collection methods that do not ensure the quality or authenticity of the responses ((yes, I said it!)

With traditional panel-based surveys that form the biggest chunk of the market research industry, almost all of them do not have the ability to trace where the data of the research is coming from, and whether it’s actually legitimate

Incentivizing Fraudulent Behavior 💰

Financial incentives motivate fraudulent behavior. Respondents may lie about their qualifications or complete surveys multiple times to maximize their earnings. This is particularly prevalent in regions where the offered incentives can represent a significant side income. The problem is further compounded by panel providers who, driven by the need to maintain high response volumes, may overlook the verification processes necessary to ensure data integrity.

It’s estimated that up to 40% of survey responses can be fraudulent.

The lure of monetary rewards can drive individuals to participate dishonestly in surveys, or use means such as bots to do the same.

Lessons Learned ✅

To combat these issues, it’s essential to implement robust verification methods.

Additionally, balancing the act of programming your studies in a way that they are not build just to catch fraudulent respondents but also the programming and technology doesn’t punish legitimate respondents.

Verification Methods: Ensuring the authenticity of respondents is crucial. Techniques such as digital fingerprinting, two-factor authentication, social proof, and AI can help ensure respondent authenticity.and can help verify the identities at participant level, reducing the risk of fraudulent responses.

Ethical Practices: Maintaining ethical standards in market research is paramount. Ensuring transparency and fairness in the incentive process builds trust and encourages honest participation.

Fraud Prevention at CleverX 🚨💪

At CleverX, we understand the critical importance of maintaining the integrity of our market research data. To combat fraud, we utilize advanced technologies and robust verification methods.

Fingerprinting and Device Recognition: One of the primary tools we use is getting a device fingerprint, which allows us to identify and analyze multiple device signals to create unique user profiles. This helps us detect fraudulent behavior by recognizing patterns that are indicative of bots or multiple entries from the same user. By analyzing over 100 unique signals from a device, such as IP address, browser configuration, and device type, we can ensure that each response is genuine and unique.

Passive and Active Verification: We employ both passive and active verification techniques, including LinkedIn verification of all profiles. Passive methods include digital fingerprinting and cross-referencing participant information with multiple third-party databases. This allows us to verify identities without disrupting the user experience. Active methods, like two-factor authentication and real-time behavioral analysis, further enhance our fraud detection capabilities.

What we do at CleverX to prevent fraud in research data

AI and Machine Learning: Our use of AI and machine learning helps us stay ahead of fraudsters by continuously learning and adapting to new fraudulent behaviors. These technologies allow us to detect anomalies and suspicious patterns in real-time, flagging potential fraud before it impacts our data quality.

Understanding the pitfalls of market research is the first step towards improving its practices. By addressing issues of fraud and incentive-induced biases, we can ensure more accurate and valuable insights.

That’s all for today! Stay tuned for the next issue!

If you liked reading this issue, please leave us your feedback, as well as ideas as to what you’d like to know more about!

You can also reach out to me directly here.

Best,
Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

Hi there! 👋

Sharekh here. I’m the founder of CleverX, where we’re building the instant way to recruit verified business participants for fraud-free research.

Welcome to the inaugural issue of The Research Mag! This newsletter is where I'll share my thoughts and insights on market and product research. I'm excited to have you on this journey!

In this first edition, we're exploring the rich history of market research. From its ancient origins to what it looks like now, we'll uncover how market research has evolved and why it's more important than ever.

History of Market Research

Early Examples: Ancient Rome to the Domesday Book

Market research, in some form, has been around for centuries.

Imagine the bustling markets of ancient Rome. Traders keenly observed customer preferences, gathered information on pricing, and adapted their strategies accordingly. This rudimentary form of market research helped them stay competitive and meet consumer demands.

Domesday Book is a detailed survey and valuation of landed property in England at the end of the 11th century. The survey was ordered by William the Conqueror at Christmas 1085 and undertaken the following year.

This extensive survey documented land holdings and resources across England, providing a valuable snapshot of the economy at the time. Though not market research in the modern sense, it exemplifies the long-standing human drive to gather and analyze data.

A page from the Domesday Book.

💡 The information recorded in Domesday:

The Domesday survey was carried out by commissioners holding sworn inquests in local courts, where they asked fixed questions of local men. For each property, each question was asked three times, to cover changes over time.

Questions included in the Domesday Book

Access this free online interactive version of the Domesday Book here.

Development Through the 19th and 20th Centuries

Fast forward to the 19th century, when market research began taking on a more structured form. In the 1820s, the first opinion polls were conducted in the USA, marking the beginning of a more formal approach to understanding public opinion. By the 1890s, companies were using questionnaires to gauge consumer reactions to advertisements, laying the groundwork for modern survey techniques.

The early 20th century saw market research becoming an established industry. The period from 1910 to 1920 witnessed the rise of dedicated market research firms. By the 1930s, the practice was well integrated into business strategies, with companies like Nielsen leading the way in developing robust data collection and analysis methods.

Cover page of the first consumer market survey by Nielsen in 1929.

Key Milestones and Figures 🚨

One of the pivotal figures in market research history is Arthur C. Nielsen, who founded the ACNielsen company in 1923. Nielsen's work revolutionized market research with the introduction of audience measurement and retail index methods, which are still in use today.

The establishment of professional organizations like the Market Research Society (MRS) in the UK and the European Society for Opinion and Market Research (ESOMAR) in the 1940s further cemented the field's importance. These bodies helped standardize practices and promote ethical guidelines, ensuring the integrity and reliability of market research.

A Timeline of Market Research

What Worked 💪

Successful Methodologies and Practices

Throughout its evolution, market research has seen numerous methodologies and practices that have stood the test of time.

Surveys and Questionnaires: One of the most enduring tools in market research, surveys and questionnaires have allowed researchers to gather large amounts of data from diverse populations. The standardization of questions ensures consistency, while the ability to distribute them widely (first through mail, then telephone, and now online) has made them incredibly versatile.

Focus Groups: Introduced in the mid-20th century, focus groups provide qualitative insights that surveys might miss. By gathering a small group of people to discuss a product or idea, researchers can delve into deeper motivations and opinions. This method helps to uncover rich, detailed information that can inform product development and marketing strategies.

Observational Research: Observing consumer behavior in natural settings has provided invaluable insights. This method involves watching how people interact with products in real-time, capturing authentic reactions and usage patterns. It has been particularly effective in retail settings, helping businesses optimize store layouts and product placements.

Data Analytics: With the advent of computers and, later, the internet, data analytics has become a cornerstone of modern market research. Techniques such as regression analysis, clustering, and predictive modeling allow researchers to identify trends, segment markets, and predict future behaviors with high accuracy.

Okay, so what are the things that didn’t work?

Well, that’s a story for the next time. In my next newsletter, I'll spill the beans on the lessons we've learned and give you an exciting peek into the future of market research. You won't want to miss it!

I’d love to hear what you liked about this newsletter. Do reach out to me here.

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn

For any direct feedback, mail us here.

Hi there,

I'm Sharekh, the founder of CleverX. You might already know about us, but today, I'm excited to tell you about something new I'm starting – a newsletter called The Research Mag.

In The Research Mag, I'll be sharing insightful content, industry news, insider opinions, and much more; on market and user research that you won't want to miss. Whether you're a seasoned pro or just curious, there's something for everyone.

If you'd like to keep receiving this newsletter, please 'star' this email and move it to your main inbox tab. This way, you won't miss out on any future issues.

However, if you prefer not to receive these emails, you can unsubscribe at any time by clicking the link below.

Looking forward to staying in touch!

Sharekh,
CleverX
Find me on X and LinkedIn