Logistic regression vs. survival analysis for journey modeling: does timing matter to you?

Logistic regression vs. survival analysis for journey modeling: does timing matter to you?

When you build a custom attribution model instead of buying one, the statistical backbone you pick encodes what you think conversion is.

The two framings
Logistic regression treats conversion as a binary outcome: given this set of touches, did the user convert, yes or no? It estimates each channel's contribution to conversion probability. Survival analysis (hazard modeling) treats conversion as a timed event: given exposure, how does each touch change the rate of converting per unit time, and how does that rate evolve as the journey ages?

What changes between them
Logistic regression ignores when touches landed and whether non-converters simply haven't converted yet — it treats a user who'll buy next week identically to one who never will (right-censoring, which it cannot handle). Survival models explicitly accommodate censored journeys and let you ask whether a channel accelerates conversion or merely co-occurs with it.

The cost
Survival analysis demands clean timestamp data and careful handling of time-varying covariates; it's harder to explain to stakeholders than an odds ratio.

Bottom line for practitioners: If your sales cycle is short and you mostly care about "did it convert," logistic regression is simpler and sufficient. If your cycle is long, leads sit in pipeline, and you care about velocity — does this channel pull conversions forward — survival analysis is the honest model, because it refuses to label not-yet-converted users as failures. Neither, on observational data, proves causation; both need experimental calibration.