Sometimes simple A/B baselining works just fine (also called pre-post analysis), but there are very specific conditions that need to be met. If there are other confounding variables (long-term trends, economic indicators, seasonality, etc.), you can draw bad conclusions from single-variable analysis of this sort.
As long as you have some reasonable assumptions that you can hold all other variables constant, year-over-year can be a perfectly valid comparison. If, however, you tried to compare summer of this year to winter of previous, you have the confounding variable of weather/seasonality which may influence your results deleteriously. Likewise, if you ALSO changed prices or had a new competitor come in to the market, or a bed bug infestation, or what have you, "all else equal" doesn't hold anymore. The need to control for the influence of multiple variables is what causes people to turn to more complicated modeling approaches.
Most "AB Test" or "Control Focus" methodologies attempt to measure the influence of a single variable in the fashion you describe (although there are multivariate AB tests too - this just involves using multivariate regression across multiple trial variables). You just need to satisfy yourself that you really can hold all other significant factors "reasonably" constant over the pre-post intervals (or be able to account for the expected variability due to the inability to control for all potential confounding factors).
Control-focus tests typically designate specially-selected control groups that are held constant over the variable in question specifically to allow differential analysis versus the test group with the assumption (usually supported by historical trending analysis) that non-test factors impact the sets equally (or at least within well-defined statistical tolerance). This approach makes impact measurement more robust against external influencing factors not directly tied to the variable being tested.