John S. Kim

It’s easy to go along with the status quo, even when data quality is slipping. In the Data Gurus panel on data quality, Jon Kay of Intuit shares why that approach is no longer sustainable. He emphasizes that researchers need to start by asking vendors the right questions, pushing them to show how they are addressing today’s challenges instead of relying on outdated methods. By bringing these issues to light and holding partners accountable, we can create the incentives necessary for real progress. Click the link in the comments for the episode. #Analytics #MA #Data #Strategy #Innovation #Acquisitions #MRX #Restech

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Services as a Product (SaaP) is becoming central to how IT services create repeatable value and scale outcomes. Looking forward to the conversations at IDC Directions on how IT services continue to evolve in an AI‑driven economy. #IDCDirections

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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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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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This research has been brewing for a while... It started earlier this year when I started to notice wildly different forecasts for tech spend, tech markets, economic growth, export growth etc. This basically said "we don't have a clue what is around the corner". So if you don't know what's coming, how do you prepare for that future? I did a lot of research, and with some excellent guidance from my Ecosystm colleague Alea Fairchild, we determined the answer is: BACKCASTING You start with the future vision and work your way back to today. This helps sharpen your focus on what matters to get you to that place. It helps you protect your budgets from global leaders who see the wildly different forecasts and feel they need to cut now. Cutting now might not be the answer - but how do you defend that budget? BACKCASTING. Show where you are going, what you need to get there and how cutting will impact your ability to deliver that future outcome. This isn't a new idea. Apple does it. Tesla does it. Governments do it. You should do it - try it at a micro or macro level to secure budgets and achieve your goals in Asia Pacific or your own specific geo, industry or product area. Tell your clients about it to help them lock in budgets for the initiatives that might drive your solution into their business. Check out my blog below and download the full report for more information. And if you need our help in starting or benchmarking your backcasting journey please reach out!

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There’s been an explosion of ‘off-the-shelf’ analytics platforms promising capabilities like MMM, attribution/MTA, segmentation, LTV modeling, , and more. I come from the more traditional approach - working closely with statisticians to build custom models from the ground up These new platforms promise speed, accessibility, and scale. The traditional approach offers deeper customization and often more methodological rigor. But now AI is entering the equation - raising a bigger question: Will AI augment analysts… or eventually replace much of this work altogether? We’re planning a bit of a 'bake-off' internally to compare approaches, but it feels like the bigger shift is structural: how analytics actually gets done may be changing. Curious how others are thinking about this: - Are off-the-shelf platforms “good enough” for most companies? - When does a custom statistical model still matter? - And does AI ultimately reshape—or disrupt—both approaches? Feels like we’re at a real inflection point in the analytics industry. Would love your thoughts and experiences here.

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We're facing more and more questions about the use of synthetic data in market research. I'd recommend this blog post, which really resonated with me. So much of the conversation is "how closely can this replicate human data?" But there are so many other considerations researchers should be thinking about. https://lnkd.in/eSQfSTPj

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