Explore how Bayesian models estimate purchasing and retention with priors and uncertainty. Learn where hierarchical and posterior predictive approaches improve CLV decisions.
A key advantage of Bayesian CLV models is that they provide ______ around forecasts, not just point estimates.
fixed ROAS tiers
full posterior uncertainty
purely frequentist p‑values
deterministic bounds only
In Bayesian purchase‑arrival modeling, the Beta‑Binomial or Beta‑Geometric framework is used to model ______.
session‑level bounce rate
fixed CAC over time
inventory depletion directly
repeat probability with a prior
Hierarchical priors help when segments have little data because they ______ information.
oversample bots for
hide
borrow
discard
Posterior predictive checks are used to ______ model fit to held‑out behaviors.
inflate
validate
randomize
ignore
Compared with MLE point fits, Bayesian CLV can naturally incorporate ______ like churn priors or seasonality.
informative priors and constraints
ad viewability
DNS TTLs
cookie windows
When data are limited, credible intervals for CLV often get ______ than in large samples.
wider
unchanged by design
skewed to zero by default
narrower regardless
Bayesian updating lets you refresh CLV estimates as ______ arrives.
creative pixels
organic impressions only
new transaction data
shared logins
For high‑value outliers, robust likelihoods (e.g., Student‑t) can reduce undue influence compared with ______.
Dirichlet priors
logit links
Gaussian errors
Poisson arrivals
Mixture models can capture latent subpopulations in CLV by allowing multiple ______.
URL parameters
SKU barcodes only
ad exchanges
purchase‑rate components
Posterior decision‑making can optimize offers by maximizing expected ______ under the posterior.
pageviews
profit or utility
click volume
reach duplication
Starter
Good start—review definitions and setup to solidify your foundation.
Solid
Nice work—your grasp is strong; refine edge cases and assumptions.
Expert!
Outstanding—your CLV intuition and technique are practitioner‑level.
