When regression models perform poorly, there are typically several reasons. Here are some:

Important variables have been omitted from the model. These may include latent (unobservable) variables, such as socio-economic status.

Errors in the data and missing data.

Measurement error. This is not the same as data errors.

The wrong type of regression was used - there are many kinds of regression models. For example, OLS linear regression was used for count data.

Moderated effects (interactions) were ignored or improperly modeled. A simple example would be when the relationship between age and purchase frequency depends on gender but this has not been accounted for.

Curvilinear effects have been ignored or modeled improperly. For example, the relationship between age and expenditures often does not follow a straight-line path.

Heterogeneity has been ignored or modeled incorrectly. There may be several segments of consumers with different drivers (preferences), for instance.

Our expectations were wrong - the data are low-signal and there's little value to be extracted whatever we do.

Often these errors can be avoided or fixed with little effort, but the person doing the modeling must know how. Unfortunately, some people using regression have little understanding of it.

✴️ @AI_Python_EN