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Latest updates, guides, and tips from Kicue.
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How Many Questions Should a Survey Have? Keep It Short to Avoid Drop-Off
How many questions should a survey have? The short answer: aim for roughly 5 minutes and ~15 questions, then cut anything that doesn't serve your goal. We explain why response rate and data quality fall as the question count grows, give you 5 steps to set the right length, and show how to trim without losing what matters — grounded in research like Galesic & Bosnjak (2009) and field experience.
Read moreConcept Testing Survey Guide — Measuring Acceptance Before Launch
How to design a concept test that evaluates a new product, feature, or ad copy in a survey before launch. Covers when to use monadic, sequential monadic, and comparative testing; the standard metrics of purchase intent, newness, appeal, and uniqueness; how to read Top Box scores; the importance of comparing against norms; and how to craft the concept stimulus itself — organized around the practical instincts of the field. The entry point to the pre-launch research that precedes PSM, conjoint, and MaxDiff.
Read moreCustomer Segmentation Survey Guide — Dividing Customers with Cluster Analysis
How to design a customer segmentation survey that sorts customers into meaningful segments from survey data. Covers the difference between a priori and post-hoc segmentation (cluster analysis), the four classification axes (demographic, behavioral, needs, psychographic), when to use hierarchical clustering vs. k-means vs. latent class analysis, how to decide the number of segments, and the six criteria for a usable segment — organized through the segmentation research since Smith (1956) and the practical instincts of the field.
Read moreKey Driver Analysis Guide — Finding What Moves Satisfaction and NPS
How to use Key Driver Analysis (KDA) to find what is actually moving overall satisfaction and NPS. We cover the trap of ranking by correlation alone, the multicollinearity trap in multiple regression, the methods that solve it (Shapley value and Johnson's Relative Weights), and the single most dangerous misreading — confusing correlation with causation — organized through the relative-importance literature since Johnson (2000) and hard-won field experience. We also place KDA as the source of derived importance feeding into IPA (importance-performance analysis).
Read moreImportance-Performance Analysis (IPA) Guide — Prioritizing Improvements in 4 Quadrants
Importance-Performance Analysis (IPA) organizes customer-satisfaction results into four quadrants — Concentrate Here, Keep Up the Good Work, Possible Overkill, Low Priority — so you can decide what to fix first. We cover the difference between stated and statistically derived importance, how to choose the axis split point (mean vs. median), the ceiling-effect trap that breaks most analyses, and how to build the scatter plot — grounded in the academic literature since Martilla & James (1977) and the pitfalls practitioners actually hit.
Read moreChurn Survey Design Guide — Turning Cancellation Reasons into Improvement Actions
Designing churn surveys (exit surveys, win-back surveys) that capture SaaS / subscription cancellation reasons in a structured way. We walk through the six common churn reason categories, the design differences between exit and win-back surveys, Avoidable / Unavoidable segmentation, and connection to a closed-loop workflow — grounded in the academic work of Keaveney (1995) and Tax et al. (1998) along with practical SaaS implementation patterns.
Read moreMaxDiff (Maximum Difference Scaling) Design Guide — Measuring Priorities
Avoid the ceiling effect where every item lands on 'important' on a Likert scale, and measure real priorities with MaxDiff (Maximum Difference Scaling, Best-Worst Scaling). Covers experimental design, sample size, score calculation with hierarchical Bayes, and how it compares to conjoint analysis — grounded in Louviere & Woodworth (1990) and the working practices of implementation vendors.
Read moreSurvey Sampling Methods Guide — Random, Stratified, and Cluster
An organized look at how to choose who to survey, split between probability sampling (simple random, systematic, stratified, cluster) and non-probability sampling (convenience, quota, snowball). Built on the academic foundations of Kish (1965) and Lohr (2010), and the practical realities of the online panel era — explained from the editorial desk.
Read moreAggregating Survey Data in Excel — 5 Steps from CSV to Chi-Square Test
A practical 5-step workflow for downloading CSV from your survey tool and aggregating it in Excel. From UTF-8 encoding fixes and PivotTable-based GT/cross-tabs to the CHISQ.TEST function for chi-square testing, charting, and team sharing — complete one report in 30 minutes.
Read moreSurvey Research Ethics — Informed Consent, GDPR, and APPI
Research ethics and privacy protection become unavoidable as survey operations mature. From the three principles of the Belmont Report, to Informed Consent implementation, regional differences across GDPR / APPI / CCPA, PII risks in open-ended responses, and additional care for children and vulnerable populations — this guide organizes operational guidelines grounded in academic foundations and regulatory frameworks.
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