Tyler Perkins

At Research Results Inc. stand for quality in everything we do; from the work we deliver to the people behind it. So extending that commitment to data quality, in a business built on data, wasn’t just logical... it was essential. We’re incredibly proud of the work that went into Radar, and even more excited to share it with our clients.

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Absolutely. Internal data can show what customers did, but not why they did it. Real understanding comes from research that digs into customer motivations, perceptions, and emotional responses. We use Customer Journey Mapping as a framework to explore these layers through both qualitative and quantitative research. Without this foundation, it's easy to make assumptions that lead to the wrong conclusions, or the wrong strategies. If you're not researching behavior, you're only seeing the surface. Learn more on our blog post at: https://lnkd.in/emDcjvc8 #shopperinsights #insights #consumerinsights #consumerresearch #customerinsights #customerjourney #consumerjourney #pathtopurchase #customerjourneymapping #journeymap #CX #P2P #marketresearch #b2bresearch #b2cresearch #personas #cxinsights #cx #customerexperience

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Advanced market research analytics is a lot more than just clicking buttons in a software package. It’s about creative problem-solving, strategic business insight and a strong research mindset. A good methodologist: 🎯Knows the right questions to ask to refine and clarify study objectives. 🎯Can draw from a wellspring of experience but is not limited by it, remaining open to new approaches and innovative solutions. 🎯Finds that sweet spot between rigor and pragmatism, creating solutions that address study objectives in the most efficient way possible. 🎯Can defend the study design to all audiences, friendly and hostile, experienced and inexperienced, adapting communication style as needed. 🎯Can discuss methodology in terms that are simple but not patronizing, comprehensive but not pedantic, ensuring clarity for all stakeholders. 🎯Is easy to work with and fosters a collaborative environment. So, the next time you are tempted by the low cost of off-shore resources or “automated insights platforms,” remember that when it comes to advanced analytics, experience consistently outperforms inexperience, and Real Intelligence delivers more meaningful results than Artificial Intelligence every day of the week. Low cost does not always equate to good value. Want to find out what it’s like working with a real methodologist? Drop me a line on LinkedIn or at Shiv@RamanAnalytics.com.

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AI used wrong can hurt your reputation. Yesterday, I had an experience that perfectly illustrates why implementing AI without strategy can destroy customer relationships and KILL REPUTATION. I needed to schedule a service appointment at a Ford dealership. After calling and getting no answer, I turned to their website's chat feature to speak with "Paul." What followed was maddening: Me: "Is there anyone I can speak to besides calling that number?" Paul: "Yes, call this number." Me: "That's the number I just called. No one answered." Paul: "Here's our contact page." (Same number.) Me: "I need an alternative to that phone number." Paul: "Please contact an advisor by phone." Repeated identical responses. Out of frustration, I typed "Are you fooling me?" and Paul ended the chat. Only after re-reading the exchange did I realize Paul wasn't a person at all—it was a poorly configured chatbot cycling through canned responses, completely missing the context of my request. Here's what bothered me most: I didn't feel like a valued customer. I felt like a transaction. And now? I'm looking for a new dealership, possibly a new car brand entirely. The lesson isn't that AI is bad. It's that AI without thoughtful implementation is worse than no AI at all. Before deploying automation, ask: Does this genuinely help customers, or does it just check a "digital transformation" box? Can it handle real-world scenarios, or will it frustrate people into leaving? AI is a powerful tool—but only when we remember there's a human on the other end.

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So true! An impactful segmentation study designed to deliver actionable insights that are embraced and integrated into a brand's organization can serve as a critical foundation for strategic growth, sharper marketing, and deeper customer relationships.

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Are your survey scales skewing results? Unlabelled numeric scales can confuse respondents and create bias. Try this: Label every point for clarity and confidence. One small change can have a big impact on data quality. Want more tips for writing unbiased survey questions? Learn more here: https://lnkd.in/e3-8GYmv

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Leading or lagging? CX Insight #36 The line between lagging and leading CX indicators has gotten somewhat fuzzy. Let’s try to clear it up a little, so you don’t end up measuring the wrong things. I’ve sat through enough CX strategy sessions to recognize the moment someone realizes they’re looking at history, at something that was the case in the past. It’s when a C-level exec asks: “Our NPS jumped 12 points. So why are we still losing customers?” The answer’s typically hiding in the “leading indicators”. Something is happening there that NPS will only tell later. Definition: - Lagging indicators describe 𝙘𝙪𝙨𝙩𝙤𝙢𝙚𝙧 𝙟𝙪𝙙𝙜𝙢𝙚𝙣𝙩, 𝙖𝙛𝙩𝙚𝙧 𝙩𝙝𝙚 𝙚𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚 𝙖𝙡𝙧𝙚𝙖𝙙𝙮 𝙝𝙖𝙥𝙥𝙚𝙣𝙚𝙙. - Leading indicators describe 𝙚𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚 𝙞𝙣𝙨𝙩𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝙗𝙚𝙛𝙤𝙧𝙚 𝙟𝙪𝙙𝙜𝙢𝙚𝙣𝙩 𝙛𝙤𝙧𝙢𝙨. Yes, you need to measure the “late” ones that show what already happened: satisfaction scores, loyalty metrics, churn rates, retention. But here’s the catch: it’s done. The business reality today may be different. Five common lagging CX indicators: ∙ NPS – measures willingness to recommend after everything’s played out ∙ Customer Lifetime Value – calculates total value after the relationship’s run its course ∙ Churn Rate – counts who left after they’ve already gone ∙ Retention Rate – shows who stayed after the retention window closed ∙ Revenue per Customer – tallies what they spent after transactions wrapped up By contrast, leading indicators tell you what’s coming. They’re early signals that show up later in both your lagging metrics and your financials. Five typical leading CX indicators: ∙ Customer Effort Score – predicts churn before it happens (high effort = future exit) ∙ First Contact Resolution Rate – shows whether problems get solved or multiply ∙ Repeat contact frequency – early warning that customers are stuck ∙ Product return rate trends – signals dissatisfaction before loyalty tanks ∙ Onboarding completion rate – predicts whether customers stay or bail The distinction matters because you can’t change what already happened. But you can change what happens next. Automotive industry example: Lagging: ∙ Vehicle repurchase rate (did they buy another car from you 5-7 years later) Leading: ∙ First service appointment satisfaction (within 90 days of purchase—predicts the entire ownership relationship) ∙ Service appointment booking abandonment rate (customers who start booking online but don’t finish—signals friction that compounds) Petrol retail example: Lagging: ∙ Relationship NPS over the past year ∙ Average transaction value trends Leading: ∙ Pump-to-payment time (friction indicator—long waits predict site abandonment) ∙ Loyalty app usage rate (engagement drops weeks before customers switch stations) Typical CX dashboards are full of lagging indicators, while the leading ones are lacking. So think of balancing your CX dashboard to give voice to those early signals.

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What Every Researcher Should Know About Clean Survey Design: A few tips from someone who builds surveys every day. As a Survey Programming Lead at iResearch Services, I spend a lot of time turning ideas into live questionnaires. And I've noticed something: -Even the best research designs can run into problems if the survey isn't built cleanly. Here are a few practical things I've learned that really improve the final outcome-for both the team and the respondents: 1. Keep Logic Simple and Clear When the logic's clear, surveys just make more sense, for us and respondents. Bad logic creates confusion or worse, errors. Before handing it off, check: → Does this skip pattern actually make sense? → Could this be structured in a simpler way? A little cleanup upfront saves a lot of time later. 2. Think Mobile First More and more people take surveys on their phones. That means: → Avoid big grid questions (they're painful on mobile) → Keep text short and easy to read Always preview the survey on mobile before going live. 3. Randomization Helps Want better data? Randomize answer options or concept orders when possible. It reduces bias—and it's easy to miss if it’s not planned early. 4. A Clean Layout = Better Responses Good design isn't about looking fancy, it's about making it easy to answer: → Keep spacing consistent → Avoid long blocks of text → Make sure questions don't feel overwhelming on one page This helps reduce dropouts and improves data quality. 5. Always Do a Soft Launch Please don't skip this step. A soft launch helps catch: → Broken logic → Mislabels or missing translations → Unexpected timing issues Just a few completes can save a full re-launch. We once saved a full relaunch just by catching a logic issue in soft launch. It was a five-minute fix that avoided days of rework. Final Thought I'm not a researcher, but I care a lot about helping teams get the best data possible. Clean survey design is one of the easiest ways to make that happen. If you ever want to talk through logic, mobile layout, or just get a second set of eyes before launch-happy to help. Just sharing a few things I've learned from the trenches. #SurveyProgramming #ThoughtLeadership #SurveyMethodology #DataQuality #ResearchOps

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That means it’s expected to more than double. (Source: Behavior Analytics Market Research Report 2024–2029, Research and Markets) Why the surge? Because companies are realizing that understanding behavior isn’t a nice-to-have—it’s foundational. From preventing threats to personalizing customer journeys, organizations are waking up to the power of behavior-based insight. But here’s the catch: the data can tell you what’s happening, not why it matters. If you’re looking for a research and marketing partner who actually knows how to translate behavior into strategy, here’s what to look for: ✅ They go beyond the dashboard. Look for teams who aren’t afraid of qualitative complexity —who listen, observe, and spot patterns. ✅ They focus on action, not just observation. Insight is only useful if it drives decisions. ✅ They understand timing. Behavioral data is about anticipation, not just hindsight. ✅ They bring human context. Behind every pattern is a person. Ask how they uncover the emotional or functional why behind the what. Clarity leads to confidence, and confidence to decisive action.

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