CLV & Cohort Analysis

Dealing with Truncated Data in CLV Estimation

Understand how incomplete observation windows bias CLV estimates and what to do about it. Learn to distinguish truncation from censoring and apply fixes for fair cohorts.

Right‑censoring means the event may occur after the window; truncation means the event ______ the window entirely.

is measured twice in

always occurs during

is duplicated within

never enters

With truncation, some customers are missing due to entry rules, not just unobserved outcomes. That biases retention upward if ignored.

A common fix for right‑censoring in CLV is to use ______ methods.

survival or hazard‑based

ad server logs alone

pure cross‑sectional averages

cookie aggregation

Survival models account for incomplete lifetimes and estimate time‑to‑churn correctly. They use the likelihood for censored records.

Left truncation occurs when only customers who ______ are included.

churn immediately

purchase every day

belong to a single channel

survive to the observation start

Excluding early churners inflates apparent retention and spend. Adjusting the risk set fixes the bias.

Calibration windows in cohort analysis help by separating model training from ______.

forecast periods

ad viewability audits

brand tracking

creative QA

Holdout periods test generalization and reduce optimism from using the same window for fit and evaluation.

When handling delayed‑entry cohorts, you should offset exposure time so that each customer’s clock ______.

uses a fixed calendar day

follows ad flight dates only

starts at their own inception

resets every Monday

Alignment by customer start avoids mixing exposure lengths, which otherwise biases retention curves and CLV.

In purchase‑count models with truncation, weighted likelihoods or inverse probability weighting can ______ bias.

correct

create

double

ignore

Weighting rebalances the sample toward what the population would look like without truncation. This yields fairer CLV.

A quick diagnostic for truncation is that very new cohorts show unusually ______ compared with older cohorts.

flat channel mix

high apparent retention

low acquisition cost

stable AOV noise

Missing early churners in new cohorts inflates retention. As the window widens, the metric drifts down toward truth.

For subscription products, using churn‑hazard models lets you include time‑varying covariates like ______.

canonical tags

image alt text

font size

price changes or plan upgrades

Time‑dependent features capture shocks that alter churn risk mid‑life. Ignoring them can mis‑attribute effects.

Non‑contract (buy‑till‑you‑die) CLV with truncation often benefits from a ______ arrival model.

Pareto/NBD‑style

single‑touch attribution

pure ARIMA

CTR curve

Pareto/NBD separates purchase rate and dropout. It handles intermittent behavior and unobserved churn.

To compare cohorts fairly across seasons, compute retention on a ______ basis.

raw calendar month only

creative format type

traffic channel share

homogeneous time‑since‑start

Index by tenure so each cohort has equal exposure length. This avoids penalizing late‑in‑year cohorts.

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.

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