I'm curious regarding how you are estimating hit rate (raw hit rate true/false per observation or probability of the hit from the logit equation) and how many holdout tasks you are employing. Keith Chrzan (our colleague) has found that unless you have at least 5 or more holdout choice tasks, hit rate reliability for comparing one model vs. another may be low. So, you need to have plenty of holdout tasks to be reasonably confident that changes you are making to the utility estimation actually are having a statistically significant improvement.
Next, when you increase the prior variance, you allow respondent-level utility models to have higher fit (you are increasing the low-level model fit) and you reduce the Bayesian smoothing to the population means (the upper-level model fit). Higher fit in logit models (our HB employs a logit model) means larger utilities and bigger differences between best and worst utilities within an attribute. That's why when you increase the prior variance you are seeing price sensitivity increase. The sensitivity to the first attribute (I'm assuming SKU?) will also be seen to increase.