In reply to Peter Lancashire.

Like you, I struggle with this issue. I don't know that I (or anyone) can definitively answer your question. Deviation from a straight line indicates that the data have excessive skewness or are heavier in the tails than the reference distribution. Sometimes the deviations are important, sometimes they are not. For example, if you are examining a normal Q-Q plot of residuals to check the assumptions for OLS regression, then moderate deviations from normality are usually ignorable because the inferences in OLS are moderately robust to normality.

Rather than talking about "rejecting" the model, I like to think that the Q-Q plot gives insight into the applicability of the model. The Q-Q plot might indicate that the model is applicable for the "typical" customers (the center of the distribution) but less effective at modeling the "extreme" customers (very active or very rich or very young...).