'0' is an arbitrary label you've used for non-response. Treating it as a number would imply that non-response expresses less satisfaction than any of the other values on the scales—as you've said you don't have any idea why people didn't respond, that hardly seems sensible. What if you'd coded non-response as '99'?

Mapping '0' to any of other the values on the scale is similarly problematic. @Carl [suggests](https://stats.stackexchange.com/a/333841/17230) mapping to '3', citing a study that claims it makes little difference whether the 'midpoint' on a Likert scale is labelled 'undecided' or 'neutral': I can't see how that bears on the matter of how to treat non-responses (moreover—is '3' in any sense a midpoint in your Likert-*like* scale?). Potential reasons for someone's not responding might include their never having used that feature, not understanding the question, skipping the item by mistake; so it's a courageous decision to equate it with selecting a response that, after all, they could have selected—indeed were prompted to select—had they wanted to. You'd have to be pretty sure you could tell why people didn't respond, & that it made no odds to lump them in with people who did pick some particular answer, at least for the purposes of your study. Better in general to concede simply that they didn't tell what they think & see where you can go from there.

Excluding non-responses raises two questions. First, what's the population you're interested in making inferences about? Unfortunately it's unlikely to be just people who respond to survey questions about a feature. But it may well be just people who are familiar with a feature, so no harm in excluding people who aren't, if that's who the non-responders are. Second, are non-responders typically going to have different opinions to the responders (within each group)? Probably not when they skip an item owing to a momentary distraction; possibly when they can't follow the question; probably when they think they'll get into trouble if they say they're not keen on a feature.<sup>&dagger;</sup>

I'll note in passing that it's good practice to use feedback from a pilot study to tweak the design to minimize rates of non-response, & to follow up some non-responders to investigate further.

Of course, you know your survey better than me, so you'll have to decide what assumptions you can sensibly make to justify excluding non-responses, if you can at all. That you say there are "a few" cases is grounds for optimism. A sensitivity check of some kind is advisable: a simple, though stringent, one would be to set all the non-responses to 1 for Group 1 & 5 for Group 5, then vice versa; & see just how much difference it makes to your conclusions. It might be a good idea to compare non-response rates between the two groups, if that can help test your assumptions.

A more complex approach is to impute the missing values. If you can predict someone's response to the item on Feature 2, say, fairly well from their response to the items on Features 1 & 3, then you've a way to fill in the blanks for non-responders. The assumption here is that the non-responders on a particular item are typically *not* going to have different opinions to the responder *given their responses to other items*. You'd probably need to do some studying or get some help if you wanted to go down this route.

&dagger; To be a little more technical, *casewise deletion* is appropriate when responses are *Missing Completely At Random* or *Missing At Random* with respect to a model's predictors (here just 'Group'): no biases are introduced into estimates, & imputation (based on those same predictors) would add no extra information. The early chapters of [Little & Rubin (2014), *Statistical Analysis with Missing Data*](https://onlinelibrary.wiley.com/doi/book/10.1002/9781119013563) introduce these ideas.