Shows how randomness appears in clicks, purchases, revenue, surveys, and small samples. You use averages, rates, variance, and sampling error to describe what changed and what may only look changed.

Teaches probability as a practical language for uncertain claims, conditional statements, and evidence. You work with events such as “converted,” “saw an ad,” and “would have bought anyway.”

Connects common marketing outcomes to useful distributions: Bernoulli and binomial for yes/no events, Poisson and negative binomial for counts, normal and lognormal for continuous outcomes, and gamma for positive spend or revenue. You choose distributions by matching them to real data behavior.

Uses Bayes’ rule to update a belief after seeing data. You calculate simple priors, likelihoods, and posteriors for conversion rates, lift, and revenue per customer.

Traces the path from early surveys and direct-mail testing to econometrics, digital attribution, MCMC, probabilistic programming, privacy changes, and today’s renewed use of Bayesian media models. This history explains why modern practice blends experiments, causal thinking, and uncertainty-aware modeling.

Builds regression as the first flexible tool for linking marketing actions to outcomes. You fit linear and logistic models for spend, exposure, clicks, conversions, and revenue while keeping the interpretation tied to decisions.

Shows how to write a model as a story that could have generated the data. You connect assumptions, likelihoods, parameters, and observable outcomes in a way that makes mistakes easier to find.

Teaches how priors can represent scale, business limits, past tests, expert judgment, and weak background knowledge. You also practice avoiding priors that secretly force the answer.

Covers Monte Carlo simulation, Markov chain Monte Carlo, Hamiltonian Monte Carlo, and posterior samples. You turn hard probability calculations into draws that can be summarized and used for decisions.

Shows how to tell whether posterior computation can be trusted. You read trace plots, effective sample size, divergences, R-hat, energy diagnostics, and warning messages instead of treating software output as magic.

Uses modern probabilistic programming to write, fit, and revise Bayesian marketing models. You build small models in tools such as Stan or PyMC and connect model code to data, priors, likelihoods, and posterior summaries.

Teaches posterior predictive checks, residual checks, calibration, and error patterns. You compare simulated outcomes from the model with real campaign, sales, and customer data to see what the model misses.

Covers cross-validation, LOO, WAIC, stacking, holdout periods, and simple benchmark models. You choose models for usefulness and reliability rather than chasing the best-looking in-sample fit.

Turns posterior distributions into choices about spend, targeting, creative, pricing, and risk. You use expected value, credible intervals, loss functions, decision thresholds, and value of information.

Builds partial pooling models for many stores, regions, campaigns, creatives, products, or audience segments. You let small groups borrow strength from larger patterns without erasing local differences.

Covers Bayesian A/B tests, multivariate tests, sequential reads, stopping rules, and power in business terms. You estimate lift and uncertainty while avoiding common testing traps.

Uses causal diagrams to separate correlation from cause in ad exposure, targeting, pricing, and retention questions. You identify confounders, mediators, colliders, and variables that should or should not be adjusted for.

Covers regression adjustment, matching, weighting, difference-in-differences, and sensitivity checks when randomized experiments are not available. You estimate incrementality while being clear about what the data can and cannot prove.

Teaches market-level holdouts, geo experiments, synthetic controls, and Bayesian structural time series for incrementality. You design tests that work when user-level tracking is limited or unavailable.

Builds media mix models that connect spend to sales over time. You model adstock, carryover, saturation, seasonality, controls, baseline demand, and channel-level return on investment.

Covers the shift from cookie-level attribution to aggregated, consent-aware, and privacy-safe measurement. You work with clean rooms, platform-reported aggregates, modeled conversions, and the limits they create for Bayesian media models.

Teaches probabilistic approaches to multi-touch paths, delayed conversions, channel sequence effects, and attribution uncertainty. You also learn where attribution breaks down and when incrementality methods are safer.

Covers discrete choice, multinomial logit, conjoint analysis, willingness to pay, and substitution between products. You model how customers choose among offers, brands, prices, and features.

Builds Bayesian elasticity models for prices, discounts, coupons, promotions, and competitor movement. You estimate response curves while separating short-term sales spikes from long-term value.

Covers retention, repeat purchase, transaction frequency, spend per order, and survival-style thinking. You estimate customer lifetime value with uncertainty instead of relying on a single fragile number.

Teaches mixture models and latent class models for customer groups that are not directly labeled. You build segments from behavior while checking whether the segments are stable and useful.

Builds Bayesian time series models for sales, demand, traffic, and revenue. You handle trend, seasonality, holidays, events, uncertainty bands, and forecast evaluation.

Covers dynamic regression and state-space models where channel effects, baselines, or customer behavior change over time. You model markets that drift instead of pretending last year’s response still holds exactly.

Teaches customer-level treatment effects, uplift modeling, and heterogeneous response. You estimate who is likely to be helped, harmed, or unaffected by a campaign.

Covers Bayesian recommender systems, matrix factorization, uncertainty-aware rankings, and cold-start problems. You connect recommendations to business goals such as conversion, basket size, retention, and customer experience.

Teaches Thompson sampling and other bandit methods for choosing offers, creatives, subject lines, and audiences while results arrive. You balance learning with earning in live marketing systems.

Covers Bayesian optimization for budget allocation, bidding, offer tuning, and experiment design when each test is costly. You use surrogate models and acquisition rules to search for better decisions efficiently.

Shows how embeddings from language, image, and multimodal models can represent ad copy, landing pages, reviews, search terms, and creative assets. You use these features inside Bayesian models while keeping uncertainty, bias, and leakage in view.

Covers variational inference, Laplace approximations, mini-batching, sparse models, and practical tradeoffs for large marketing datasets. You choose faster methods while checking how much accuracy they cost.

Teaches reproducible workflows for data pulls, model runs, versioning, review, and refresh schedules. You monitor drift, broken pipelines, changing channel behavior, and whether recommendations still pay off.

Follows a full project from a business question to data audit, model choice, priors, fitting, diagnostics, validation, stakeholder review, budget recommendation, and follow-up measurement. The project ties together media mix, incrementality, uncertainty, and decision-making.

Covers consent, sensitive data, fairness, targeting harms, documentation, audit trails, and legal constraints that shape marketing analytics. You make model choices that can be explained to customers, executives, legal teams, and regulators.

Maps the roles, specialties, portfolios, and proof-of-skill artifacts for Bayesian marketing analytics. You see how practitioners stay current through papers, open-source tools, benchmark datasets, conferences, and responsible experimentation.