Pairwise Comparison (also known as Paired Voting or Pairwise Ranking) is a simple yet powerful way of ranking a list of options by breaking them into a series of head-to-head votes. This video explains everything you need to know about Pairwise Comparison, including its origin, use cases, setup tips, and real-world examples.

I reviewed 104 survey questions from all the PMF templates I could find online. Here are 8 things to avoid and 3 researcher-approved templates for you to steal for your next Product Market Fit Survey.

When picking a tool for your PMF survey, you need to consider: price and limitations, filtering and segmentation, branching and survey functionality, and more. To save you the time, I tested and reviewed the 12 most popular PMF Survey tools — here’s everything you need to know.

6 voting, polling, and survey methods you can use to turn a subjective set of names into a ranked list based on people’s strongest preferences (with interactive examples and results).

Product Market Fit Surveys are a great way to benchmark your current level of PMF, but with some simple segmentation analysis techniques, they become a powerful user research and strategy tool for early-stage startups.

We’re removing all limits on free surveys. Users can now create unlimited surveys with unlimited questions that engage unlimited participants — all for free. Here’s why we’re bullish about freemium for OpinionX.

Head-to-head pairwise voting is one of the easiest ways to rank a list of options. This guide explains the best methods and most popular tools to use, along with some real examples.

The Ultimate Guide To Pairwise Ranking — best methods, most popular tools, example pairwise comparison surveys, types of pair ranking, calculation formulas, and more.

The Ultimate Guide To Paired Comparison — definition, best methods, most popular tools, example surveys, types of paired comparison, calculation formulas, and more.

Online surveys are a fast and cheap way to run rapid A/B tests for images, ideas, ad copy, sales messaging, problem statements. Choose between comparative preference testing formats or monadic surveys, and plan ahead for respondent segmentation afterwards.

How do you figure out what to build next when customers are all shouting for different things? Tudor, a UX Researcher at Series C scaleup Labster, learned first hand why you can't always listen to your loudest customers…

Discrete Choice Modeling is a category of research that measures the relative importance of a set of options according to a group of people, including conjoint, maxdiff, pairwise comparison, and more.

Psychographic customer segmentation identifies groups of customers with a similar way of thinking about a topic or issue. Including 5 research methods, 6-step process, a real B2B case study, and how to run a psychographic segmentation survey.

How to translate product ideas into their underlying assumptions, identify the riskiest assumption, design an assumption test, pick from 15 assumption testing methods, and define the success and failure criteria for that experiments.

Customer discovery is about uncovering the unmet needs, pains and desires driving people’s behavior. The best way to do that? Ask good questions. Here are some suggestions…

Everyone loves conjoint analysis — even if they don't really understand it. Here's what conjoint is actually for, how it works, and what most people mistake about it.

Explanations (with examples) for the 13 most popular conjoint analysis methods, including types like choice-based, best-worst, ranking, self-explicated, volumetric, menu-based, adaptive, and more.

Conjoint analysis is an advanced research method for measuring attribute importance in a product mix. It’s complicated, expensive, and can only be used in a handful of research scenarios. Here are 8 examples of alternative research methods to consider instead.

8-step guide explaining how conjoint data is collected, analyzed, and manually computed (with formulas) to calculate part-worth utility scores for each attribute and level in a conjoint analysis survey.

Conjoint analysis is one of the most misused and misunderstood research methods. This guide covers 5 criteria that must be met to justify using conjoint and 10 examples of when conjoint analysis does or doesn’t suit a research scenario.