Methods and validation
Every estimate we publish comes from a named method: discrete choice experiments, Mixed Logit, ICLV, validated against replicated human studies. These articles explain how that machinery works, where it breaks, and how to interrogate any vendor's accuracy claim, including ours. If you check evidence for a living, this is your shelf.
- Feature Prioritization: Frameworks, Tools, and Evidence
Feature prioritization helps a product manager decide which features deserve the next release by comparing their expected value and delivery effort.
- Lock the Target Group and Stimulus Before Writing Survey Questions
Lock the target group, the decision question, and the stimulus before a single survey question gets written.
- Synthetic Respondents: What They Are and When to Trust One
A synthetic respondent is a language model conditioned on a demographic or behavioral profile, then asked to answer survey and experiment questions the way that profile of person…
- How to get the most out of open-ended questions
An insights director staring down three thousand open-ended verbatims before a launch decision needs one thing: to know which comments describe the real reason people chose what…
- How to Tell If a Causal Vendor's Probability Model Is Auditable
A data science leader vetting a causal behavioral platform for a paid pilot needs one question answered before budget moves: can this vendor show the exact path from its modeling…
- How to compare results across segments with crosstabs
An insights director holding a banner table where Segment A converts 15 points higher than Segment B, with a budget reallocation decision riding on that gap, needs more than the…
- Availability adjustment for conjoint preference shares
A pricing lead deciding whether to raise price off a conjoint simulator's forecast is really asking one question: does availability adjustment make the preference share…
- Do LLMs Understand Real-World Prices? A Pricing Benchmark for Synthetic Consumers
An LLM-based synthetic panel can produce fluent, plausible-sounding survey answers without ever grounding those answers in real prices.
- Pricing Research Methods: Choosing the Right Method Before You Field
A pricing or product leader facing a launch, a tier change, or a repricing decision must choose a research method before fielding: willingness-to-pay, price sensitivity, conjoint…
- From an Unexplained Survey Result to a Validated Why
A four-week brand tracking wave lands on your desk and a key metric has moved in a direction nobody can explain.
- How to manually calculate partworth utilities
Buyers evaluating a conjoint vendor's output have one real decision to make: whether to trust the part-worth utilities enough to act on them, not whether the underlying regression…
- Bayesian Modeling for Modern Marketing: Solving Real-World Attribution & CLV Challenges
Cookie deprecation and shrinking first-party tracking have made channel attribution harder to trust.
- Why a Favorite Still Loses Most of the Time: A Bracket Forecast Under Uncertainty
A single-elimination bracket does not ask a team to be good once. It asks a team to be good repeatedly, against opponents decided by other matches it does not control.
- When a Forecasted-Control MMM Can (and Can't) Answer a Budget Question
A forecasted-control MMM can answer a budget question about known dynamics under expected future conditions, but it cannot tell a team whether an action its historical…
- When a Fixed Forecasting Rule Should Become a Distribution
A forecasting pipeline that hard-codes its shrinkage constant either over-regresses every subject or under-regresses every subject, since a single mis-set constant pushes all of…
- Conversations, Not Checkboxes: A Smarter Way to Run Surveys
A research lead deciding whether to move survey budget from static forms to AI-moderated conversation is really deciding whether the answer they get was ever tested against a real…
- Conversational surveys explained in 6 minutes
A research or insights leader evaluating an AI-moderated interview tool needs one distinction before signing a contract: a conversational survey produces a longer, more textured…
- When a Synthetic-Customer Read Is Enough, and When You Need a Causal Experiment
A product or research leader has to decide, before engineering capacity or launch spend is committed, whether a synthetic-customer read is sufficient evidence for a feature…
- Bayesian Spatial Modeling for Evaluating Hockey Goaltending Performance
A goalie's save percentage answers a narrow question: what share of shots did they stop?
- Why a Frozen CAC Number Misleads Your Next Budget Reallocation
The decision this affects
- Why Per-Test Bayesian Model Loops Stop Scaling
A data science team running many concurrent Bayesian A/B, ABC, and ABCD tests eventually hits the same wall: compute cost and batch runtime scale with the number of tests, not…
- A Regime-Aware Risk Model: What to Validate Before You Trust the Probability
A single risk model that averages across every market environment mispositions risk in both directions: it overstates volatility in calm months and understates it heading into a…
- What is Conjoint Analysis? (with examples)
A product lead deciding whether a conjoint result is solid enough to greenlight a launch needs a straight answer, not a survey lecture.
- The ROI of quality checks: never run consumer panels without them
Fraud screens catch bots. They don't catch a wrong pricing decision. Below is the article.
- Hierarchical Bayesian Latent-Trait Estimation, Explained
A number published without its limits reads as marketing. Trusting a model's output for a pricing or positioning decision means trusting how it handles uncertainty.
- Incrementality Testing: What It Proves, What It Cannot, and How to Run It Causally
A marketing leader deciding next quarter's budget needs to know one thing about incrementality testing before commissioning one: it answers the wrong question at the wrong time…
- Aaru and EY: What a 90% Correlation Claim Actually Covers
A synthetic-research vendor publishes a correlation number against a Big Four partner, and the number circulates as proof the category works.
- When Is a Synthetic Consumer Response Ready to Inform a Real Decision?
A synthetic consumer response is ready to inform a launch, claim, or positioning decision only after it has been checked against human behavior on the same question.
- Why a Media Mix Model Number Needs a Controlled Test Before It Moves Budget
A media mix model can tell a VP of Marketing Analytics that paid social drove 18% of last quarter's revenue.
- Prediction Solves the Wrong Problem for Most Business Decisions
A pricing change, a new message, or a product decision rarely fails because a model predicted the wrong number.
- AI Brand Tracking: Brand Health Without Surveys
A brand or marketing leader who commissions a quarterly tracking wave knows its worst failure mode: a competitor moves, a message lands badly, or a public event shifts perception…
- 5 Best Practices of Online Survey Design
A senior buyer fielding another market survey needs one answer before writing a single question: the five instrument-design practices that 2026's guides converge on are neutral…
- Gaussian Process Geospatial Modeling: Beyond Hierarchical Models
A data science or research lead evaluating a causal experimentation vendor needs to know whether the vendor's model treats geography or segments as related, or as unrelated…
- A Research Operating Model for Matching Evidence to Decision Risk
A research leader should route each question by the cost of being wrong. AI-assisted exploration can surface hypotheses.
- Classification of quality issues in survey sample
A pricing manager deciding whether to trust a survey sample before setting a launch price is really weighing two separate quality questions, and most tooling shipping in 2026…
- Validating a Multi-Step Onboarding Flow Before Engineering Builds It
A Head of Product with a redesigned sign-up-to-first-action sequence has two options: validate it before engineering builds it, or ship on instinct and let production A/B tests…
- What is implicit testing? (with examples)
A marketing leader deciding whether to greenlight a campaign, a package redesign, or a price change will eventually hear a pitch for implicit testing: measure how fast someone…
- What Are Synthetic Consumers? Knowing When to Trust the Answer
Synthetic consumers are AI personas built from a language model with persona conditioning, and trust in their answers hinges on question type: reasoning and preference questions…
- MaxDiff vs. Conjoint vs. NPS: Which Instrument Matches the Decision
An insights lead scoping a study has three common instruments to choose from, each answering a different question.
- New data quality safeguards against fraudulent survey responses
A research director evaluating a fielding vendor for 2026 needs to know what a "fraud-free" certification actually covers.
- Agency vs. in-house marketing measurement: where causal action testing fits
A marketing analytics or data-science leader deciding how to measure effectiveness usually frames the choice as agency versus in-house.
- Simulating Data with PyMC
A simulated output is only as trustworthy as the process that generated it.
- Synthetic Consumer Research: 4 Steps to Check Before You Trust the Result
A synthetic-consumer result is trustworthy only as far as its weakest step.
- How to perform smart sampling and data checking?
A research director who owns a sampling and data-checking protocol is choosing between two different guarantees: that respondents are real, and that the resulting choice model is…
- Busting Market Research Automation Misconceptions
{"opener": "The biggest misconception in market research automation is treating human panels and synthetic respondents as the real choice, when neither produces causal evidence…
- How Key Driver Analysis identifies what matters most
A CX or insights leader looking at a Key Driver Analysis report has to decide whether to spend next quarter's roadmap budget on whatever attribute sits at the top of the ranking.
- Mini-lecture: Conjointly's guide to inflation
Inflation re-entered the forecasting conversation in 2026, and a pricing or insights leader deciding how to respond needs one thing Conjointly's mini-lecture on inflation doesn't…
- Hierarchical Bayesian Models for Customer Lifetime Value Across Cohorts
A marketing analytics team allocating next quarter's acquisition and retention budget across customer cohorts faces a modeling choice first: fit one customer lifetime value (CLV)…
- How to analyse time series results
A brand or insights lead watching a tracker move after a launch, price change, or repositioning needs one thing: proof that the decision caused the move, not just proof that the…
- Bayesian A/B Testing at Scale: Why Millions of Observations Slow MCMC Down
A head of experimentation running an A/B test with millions of observations faces a real trade-off: full Bayesian inference swaps a single point estimate for three richer outputs…
- AI Causal Graphs: 4 Checks Before You Act on One
An AI model can read a short list of variable names, say TV spend, brand awareness, website visits, sales, and return a full cause-and-effect graph without seeing a single row of…
- What is TURF Analysis and When to Use It?
Summary of changes: rebuilt the intro's two overloaded sentences into short declaratives (buyer/decision split; "use it / stop / no mechanism" split); fixed the 93% sentence's…
- Why a Bayesian Marketing Mix Model Still Needs Calibration Before You Reallocate Budget
A Bayesian Marketing Mix Model (MMM) can tell you which channel looks most effective.
- What to Demand Before You Trust a Synthetic-Respondent Vendor's Accuracy Claim
A vendor's self-reported accuracy number is a marketing claim until you can reproduce it.
- What Is a Simulated Buyer, and When Should You Trust One?
A simulated buyer is a model of a real audience member, built from demographic, behavioral, and prior-response data, that answers a research question the way that audience…
- UX Survey Methods for Product Teams
A product team about to ship a redesign usually reaches for a survey.
- Likelihood Approximations Through Neural Networks: A Validation Checklist Before You Trust the Output
A behavioral model that reports a choice probability or a causal effect is only as trustworthy as the likelihood behind it.
- How To Interpret Marginal Willingness To Pay
A pricing lead deciding whether to ship a feature at a premium is really asking how to interpret a marginal willingness-to-pay (WTP) number from a conjoint study.
- Download free Excel template for the Van Westendorp PSM
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- Why a 20% Retention Rate Means Different Things for a 10-User Cohort and a Million-User Cohort
A marketing analytics lead looks at a dashboard and sees a young cohort retaining at 20%. The number reads the same whether it came from 10 users or 1 million.
- Why Synthetic Consumer Ratings Need Better Elicitation
Synthetic consumers are easy to generate and hard to trust. Ask a language model for a score on a 1-5 purchase-intent scale and it may overuse the middle, avoid extreme answers…
- Estimating a Private-Market Benchmark When There Is No Public Price
A public equity index is built from transaction prices that happen constantly: every trade updates the number.
- Mini-lecture: Conjointly's guide to sample selection
A research director deciding whether to follow Conjointly's sample-selection guidance is really deciding one thing: how many respondents a study needs, and whether that respondent…
- Criticisms and counter-criticisms of Kano model
A product leader deciding whether to greenlight a feature for the next roadmap cycle needs a straight answer on whether Kano's basic, performance, and delighter categories can be…
- Counterfactual Causal Inference in PyMC
Prediction forecasts the outcome under current conditions. Causal inference instead estimates the outcome that a different action would have produced.
- Marketing Mix Modeling: A Complete Guide
Marketing mix modeling is a statistical technique that decomposes historical sales into a base level and the estimated incremental lift attributed to each marketing channel, using…
- Are My New Product's Sales Incremental or Cannibalistic?
A new product launch into a saturated category can grow share, or it can just move units the company already had.
- Can You Trust a Vendor's Uncertainty Intervals? A Test Case in Honest Uncertainty Modeling
An uncertainty interval that never changes shape, never widens near a data gap, and never reacts to a structural break in the underlying process deserves a second look.
- Why Running Surveys Is No Longer Enough
Running surveys alone stops being enough once AI can field the same instrument cheaply, leaving evidence-tier judgment and stakeholder defense as the researcher's real, remaining…
- Creating a correlation matrix for conjoint simulations
A senior insights buyer evaluating a conjoint simulator needs to know what a correlation matrix can and cannot tell them before they trust its forecast.
- What reviewers should ask about synthetic market research data
A research director vetting a synthetic-respondent vendor has one decision to make: sign off on the data or send it back.
- How to get the most out of video interviewing in surveys?
A research buyer weighing video interviewing for an upcoming study is really deciding how much weight to put on what respondents say on camera versus what a randomized experiment…
- Claims Test Methodology
Claims test methodology decides which candidate message gets media spend, and for a brand leader or general counsel, that decision carries real risk on both sides.
- Sample Size Calculator and Guide to Survey Sample Size
A research director sizing a discrete choice or conjoint study is really deciding how much to trust the number that comes out the other end, and that decision starts before the…
- Bayesian Marketing Measurement When Individual Tracking Weakens
When individual-level attribution loses coverage, marketing teams should not treat the remaining tracked journeys as the whole market.
- A Donor Value Model Told This NGO Who Would Give. It Didn't Say What to Change
A data science team inside a global children's rights NGO spent months building a Bayesian model to forecast donor value. It worked.
- Bayesian Media Mix Modeling for Marketing Optimization
A marketing analytics team allocating budget across TV, paid social, and direct mail usually starts from last-touch attribution or a spend-to-revenue heuristic.
- Funnel-Aware MMM: A Bayesian Architecture for Full-Funnel Marketing Optimization
A standard marketing mix model (MMM) treats every channel as independent: spend goes in, conversions come out, and each channel gets its own response curve.
- Can an LLM Stand In for a Human Survey Respondent? What One Benchmark Found
A large language model prompted to answer as a person with a given age, income, and education can predict that person's political party about as well as a supervised model trained…
- A Latent Timing Segment Is a Hypothesis, Not a Reason to Move Budget
A model that groups customer activity by day and hour can hand you a clean story: "this segment is active Tuesday mornings." That pattern is already in the data.
- The Model Was Right. The Decision Came First Anyway.
A data scientist builds a strong predictive model. Executives respond with a barrage of what-ifs: what happens if the budget shifts, if volume drops 10%, if three variables move…
- Why Thin Segments Need Partial Pooling Before You Trust a Causal Estimate
A segment-level causal estimate can look strong for a reason that has nothing to do with the segment.
- Predicting Swinging Strikes with Bayesian Additive Regression Trees
Bayesian additive regression trees predict swinging strikes by summing many shallow trees fit through MCMC sampling over pitch-tracking features like velocity and spin rate…
- How to Pressure-Test a PRD Before the Engineering Kickoff
A PRD flaw that slips past review is the costliest bug a product team can ship.
- Should You Trust a Raw Tracking-Poll Average? A Hierarchical Model Answers That
A raw tracking-poll average is not trustworthy on its own; a hierarchical model separates the real trend from pollster bias, method bias, and sampling noise, and only what remains…
- Survey Response Rates Are Falling. What Replaces the Survey for a Decision That Needs a Causal Answer?
A research or insights leader facing a declining-response survey program has five common replacements: behavioral analytics, continuous in-product feedback, social and community…
- Pareto/NBD: Finding Silent Churn Before It Shows Up in Revenue
A customer who buys on demand, not on a contract, never clicks "cancel." They just stop.
- Which Causal Method Fits Your Data When a Randomized Trial Isn't Possible
A marketing or analytics leader wants to know whether a pricing change, a campaign, or a launch actually caused a shift in customer behavior.
- What a Rigorous Causal-Inference Pipeline Checks Before You Trust Its Output
A causal-modeling vendor's output is only as trustworthy as the engineering discipline behind the pipeline that produced it.
- Compiling Code Is Not Validating a Model
A data science team asks an LLM agent to generate a PyMC model from a plain-language description, and the code compiles on the first or second try.
- What a World Cup Forecasting Model Teaches About Trusting a Model at All
A model can hit its headline accuracy target and still be wrong on the exact numbers a decision depends on.
- Causal AI: What It Is and What It Buys a Business Decision
Three price points sit on a CPG pricing lead's desk before a shelf reset; none of last year's sales data covers any of them.
- Simulated Marketing Panels: What They Test Well, and Where Real Buyers Still Decide
Marketing teams increasingly run early positioning, pricing, and messaging questions through a simulated panel before committing production or media budget.
- Models for calculating preference shares
A senior buyer evaluating a simulated market-share forecast needs to know which model turned utilities into shares before greenlighting a launch.
- How Partial Pooling Supports Decisions from Sparse Survey Data
A sparse survey can support a segment decision when the model shares information across related groups and the result carries its uncertainty.
- How to Develop Effective Likert Scale Questions?
Four rules make a Likert item effective: one idea per statement, neutral and unidimensional phrasing, a fixed 5- or 7-point format chosen before fielding, and a pilot test to…
- How to get quick feedback for survey testing?
Quick feedback on a survey comes from guerrilla tests, five-second tests, or unmoderated tools run on small samples of five to eight people, delivering a same-day read on…
- Download free Excel template for the Kano Model
Caption arithmetic (25 vs 200 is 1/8, not 1/7), table false-comparability (reliability coefficient vs replication accuracy are different metrics), unsourced "feature factory"…
- Can LLM-Generated Open-Ended Survey Responses Match Real Issue Distributions?
A consumer insights leader deciding whether to trust LLM-generated open-ended answers before fielding qualitative research needs one question answered: do those answers reflect…
- When Is a Simulated Behavior Model Trustworthy Enough to Act On?
A causal effect estimated from a simulated buyer population is not automatically trustworthy. It becomes trustworthy after someone checks it against real behavior.
- Seven Survey Biases That Distort Market Research Numbers
A consumer insights lead is about to greenlight a launch, a price, or a message on a survey number: does it describe what the market will actually do, or how the market wanted to…
- What Should Transfer to a New Context? A PyMC Walkthrough for Evaluating Causal Simulation Predictions
A causal simulation predicts a segment, product, or time period it never observed directly.
- Calculation of volume, revenue, and profit in simulations
A pricing lead reviewing a conjoint or discrete choice simulator needs one answer before signing off on a forecast built from it.
- How to compare simulated and human experimental results
Compare simulated and human experiments only when they measure the same alternatives, population, and outcome.
- An Automated MMM Says Shift Budget. Should You Act on It?
A marketing mix model (MMM) fits historical spend and outcome data to estimate each channel's contribution, then automates the data prep and Bayesian modeling choices behind that…
- When a Marketing Mix Model Recommends a Budget Shift, Test the Claim Before You Move the Money
A production Bayesian marketing mix model (MMM) can trace spend through a real funnel: upper-funnel spend shapes lower-funnel demand, demand runs into budget caps, caps shape…
- Understanding the margin of error in simulations
A VP of consumer insights deciding whether to trust a synthetic panel's read on a pricing decision needs one number before signing off: the real margin of error on the study about…
- Willingness to Pay: What It Is and How to Measure It
A pricing lead deciding what to charge for a new product needs an answer to one question: how much will customers actually pay, not what they say they would pay on a survey.
- Getting the top and bottom levels from conjoints
Getting the top and bottom levels from a conjoint means pulling each respondent's utility scores for the highest- and lowest-scoring levels and running a matched-sample test on…
- From Uncertainty to Insight: What Bayesian Reasoning Means for a Business Decision
A marketing or analytics leader deciding how much stock to buy, how to price a product, or when to worry about churn usually has a forecast in hand.
- Choosing Between MaxDiff, Conjoint, and a Controlled Experiment
A research team choosing a study design before fieldwork has to answer one question: does the business decision need a preference ranking, an importance score, a satisfaction…
- Quantify the Uncertainty Before You Pick a Risk Policy
A planner who commits capacity, budget, or inventory against a single-number forecast is committing to a guess about the future. The forecast is usually close.
- Should I include adcepts in preference share simulations?
A senior buyer weighing whether to greenlight a new product does not need a preference-share number that has been massaged by an ad execution nobody randomized.
- Is Your Marketing Model's Answer Data-Driven, or Just Your Priors Talking Back?
A statistical model hands a marketing team a channel-attribution number, and the team has to decide: act on it now, or check it first.
- LLM-Simulated Panels: When a Persona-Conditioned Survey Result Is Enough to Act On
A persona-conditioned survey result is enough to act on as a triage signal for general sentiment on common opinion questions among well-represented groups, before a causal…
- Classifying Types of Conjoint Analysis
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- Why a Flat Choice Model Gets Cannibalization Wrong
A consumer-goods pricing or revenue-growth leader planning a new product launch needs to know one thing before committing trade spend: will this product mostly take share from…
- Survey scripting best practices in market research
CAVEMAN MODE ACTIVE
- Why a Single-Number Forecast Hides the Decision You're Actually Making
A demand forecast that returns one number is answering a question nobody asked.
- Best tips and tricks for drafting surveys for response quality
A research or insights leader who owns survey design is really deciding one thing: whether to trust a clean-looking dataset or to build in the randomization that makes an answer…
- Why a Confounder Can Make a Marketing Channel Look Effective When It Isn't
A marketing team sees sales rise whenever Google Ads run. The obvious read is that the ads work. The obvious read can be wrong, and a worked Bayesian example shows exactly how.
- A Faster MMM Pipeline Doesn't Answer Whether the Numbers Are Causal
Marketing Mix Modeling teams spend most of their time wrangling data: pulling spend, impressions, and conversions from a dozen ad platforms into one schema before a model ever…
- When an AI-Accelerated Marketing Mix Model Is Trustworthy
A marketing mix model can now be configured in hours instead of months. That speed changes how often a team can rebuild the model.
- Why Causal Effects Come With a Spread, Not a Single Number
A vendor hands you a causal effect with a confidence interval attached.
- What a Validation Gate Is, and Why It Should Decide What an AI Agent Ships
An AI agent's output looks finished the moment it stops generating text.