Methods and validation

How the evidence is produced, and how to check it.

• 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 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.

• Can LLM-Generated Open-Ended Survey Responses Stand In for Real Verbatims?

  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…

• Should You Trust a Raw Tracking-Poll Average? A Hierarchical Model Answers That

  A tracking metric moved this month. Before a research or insights leader acts on that move, one question decides everything: is this a real change in the trend, or noise from…

• 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.

• Feature Prioritization Without Surveys: Choosing a Ranking Method

  Most roadmaps carry more candidate features than a team can ship in a single cycle: twenty on the list, capacity to build five, and a dozen stakeholders pulling in different…

• LLM-Simulated Panels: When a Persona-Conditioned Survey Result Is Enough to Act On

  Conditioning a large language model on a demographic or psychographic backstory and asking survey questions produces an opinion distribution, not a causal answer. Argyle et al.

• When a Forecasted-Control MMM Can (and Can't) Answer a Budget Question

  A marketing analytics lead just allocated $30 million for next quarter's campaigns.

• Predicting Swinging Strikes with Bayesian Additive Regression Trees

  A model that returns one number is asking for trust it hasn't earned. It earns that trust with an uncertainty band, checked against data the model never saw.

• 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.

• 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…

• 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…

• 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.

• 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…

• 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.

• 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.

• UX Survey Methods for Product Teams

  A product team about to ship a redesign usually reaches for a survey.

• 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…

• 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…

• 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…