Question 1

What practices improve reliability in price elasticity studies?

Accepted Answer

To improve reliability in price elasticity studies, design the research to mimic real-world buying conditions and force realistic trade-offs. Use scenarios that simulate actual purchase environments: for instance, show products alongside competitors or within a budget, so respondents react as they would while shopping. Advanced techniques like choice-based conjoint or simulated purchase experiments yield more stable data than simply asking "Would you buy at $X?" because they capture true trade-offs, especially if you randomize price presentations to avoid order bias. Finally, whenever possible, cross-validate survey findings with real sales or past data to ensure the elasticity estimates align with actual behavior. By grounding the study in realism and validating the output, you'll get price elasticity insights you can trust for decision-making.

Question 2

What are the challenges for research with professionals in regulated industries?

Accepted Answer

Research with professionals in heavily regulated industries comes with unique hurdles like access barriers, compliance constraints, and heightened confidentiality requirements. For example, recruiting participants such as doctors, financial advisors, or legal experts can be difficult — they have limited time and often need managerial or legal approval (and NDAs) to take part in a study. Researchers also must craft questions carefully to avoid sensitive or off-limits topics (for instance, healthcare studies must steer clear of patient-identifiable information), ensuring professionals don't feel they're violating any regulations by responding. Moreover, these professionals can be guarded about sharing data or candid opinions due to compliance fears or reputational risk if information leaks. To navigate these challenges, research teams typically coordinate with compliance departments, provide clear confidentiality assurances, and use appropriate incentives that respect industry ethics, so that busy experts are willing and able to contribute meaningful insights.

Question 3

What's a clean plan to validate laddering from benefit to values?

Accepted Answer

To validate a laddering sequence (from product benefits up to personal values), follow up with a quantitative study designed to test those links across a larger sample. Take the laddering insights you found qualitatively (e.g., Feature X provides Benefit Y, which connects to Value Z) and turn them into survey questions or statements. For example, ask respondents how important Benefit Y is to them and separately measure how strongly they care about Value Z or believe the product delivers on that value. Then use statistical analysis — correlations, regressions, or even structural equation modeling — to see if people who rate Benefit Y highly also place high importance on Value Z. You could also use a choice-based approach: present scenarios or messaging that emphasize the benefit versus the underlying value and see if the preferences line up as the ladder predicts. Make sure your sample is large enough to check consistency across segments. By quantitatively confirming that each step (attribute â†' benefit â†' value) is correlated in the expected way, you provide hard evidence that the laddering insights hold true broadly.

Question 4

How does research via online portals differ from call-center journey research?

Accepted Answer

Researching an online self-service portal versus a call-center journey requires different approaches and reveals different issues. For an online portal, you rely on digital analytics and UX testing: track where users click and drop off, and use on-page surveys or usability tests to learn why. This might show, for example, that many users abandon the process at a specific step, pointing to a UI or content issue on that page. The insights here are about navigation, design, and information clarity. For a call-center journey, you turn to conversation analysis and service metrics: listen to call recordings or read transcripts to identify common frustrations, and note operational factors like if calls are getting transferred multiple times or taking too long. This can reveal issues such as customers having to repeat themselves or unclear processes that lead to long handle times. In short, portal research highlights where the self-service experience is confusing or failing, while call-center research highlights where the human support experience needs improvement. Both perspectives together give you a more complete picture of the customer journey.

Question 5

How can CI better cover startups and emerging disruptors?

Accepted Answer

Competitive Intelligence (CI) teams can better cover startups and emerging disruptors by casting a wider net of sources and using modern analytics to catch early signals that traditional reports might miss. For example, monitoring startup databases (e.g., Crunchbase or PitchBook) and funding news, tracking patent filings and key hires, or even following accelerator demo days and niche tech forums can reveal new players before they become obvious. CI professionals are also using AI-driven tools to scan social media chatter, specialized blogs, and job postings for digital breadcrumbs that hint at a startup's activities and momentum. By expanding into these non-traditional channels and engaging industry networks, CI can spot disruptive entrants sooner and give leadership an early heads-up on potential threats or partnership opportunities before those startups hit the mainstream radar.

Ironwood Insights Group, LLC

Why choose Us

What is Ironwood Insights Group, LLC?

Recognition

Services and Expertise

Market Research FAQs with Expert Insights

What practices improve reliability in price elasticity studies?

What are the challenges for research with professionals in regulated industries?

What's a clean plan to validate laddering from benefit to values?

How does research via online portals differ from call-center journey research?

How can CI better cover startups and emerging disruptors?