π’π’ Are you wanting to do an EOY Customer Voice survey???
Maybe it's time for your annual/biannual NPS Survey.
Did you just release a new feature and you want to do a vibe check?
So... something to get you thinking - are you still including the neutral position in your Likert Scale?
Neutral statements often have multiple meanings, and if you're not planning for them, you could be skewing your data unintentionally!
It might just be an escape route - users opting out, not disliking, just disinterested! Don't let valuable feedback slip away! On longer surveys, folks tend towards the neutral to skip through questions. While an NPS survey is generally one question, voice surveys and feature analysis surveys tend to be longer (5-7 questions, often with multiple subparts).
When we start to hit fatigue, we look for shortcuts. And, the easiest shortcut is to opt out. When someone opts out, they don't want to give false information - so they aim towards neutral statements.
In this case, neutral statements are not a representation of customer sentiment towards the product. They show a disinterest in the survey.
Avoid confusion by placing your surveys strategically. Catch users in the middle of the action for relevant responses!
When talking about existing features (especially new ones), neutral can have double meaning - "I haven't used it" and "I've used it and I'm neutral about it." Double meanings hurt data analysis!
If the goal is to pick one feature to act on, adding a neutral point essentially throws away vote, giving you less customer voice in your next feature design.
Put NPS or survey starters in your product in the middle of the product path. For example, if you want to get their feedback on a new messaging flow, rather than attaching the survey to the start of the flow or outside of the flow, have it right before the finalization button within the flow.
In general surveys, considering ask "Have you used [feature] before?" with the responses of "yes, 3+ times," "yes, 1-2 times," and "no." If they say they "3+ times," get their feedback. For the rest, skip them over those questions.
While this may give you less data points, that's okay. We want quality data - not a bunch of useless data!
The ideal meaning for a neutral value is that the user does not like, but does not dislike, the feature/product. They're ambivalent.
We want to find ambivalence in the data. Ambivalence shows us features that are unassuming.
While ambivalence can seem like a scary thing to have, there are actually parts of your product that you WANT to be ambivalent.
Are they concerns to address, ignored defaults, or just bundled into positives? Neutrality could reveal hidden gems - features users are indifferent about.
No one wants users to have a neutral opinion of the product, so these could be things you want to improve. However, we should only improve things that we think will garner positive reactions or things that people have workarounds for.
Let's think of feature that we might not notice within LinkedIn - writing articles. The system is rather clunky and it's hard to find your draft articles. However, most of us have come to get used to this. We have workarounds, like bookmarking pages, to deal with the missing features. We're ambivalent because the poor experience is expected.
This ambivalence is a sign of resignation - we assume the product won't get better, so we form workarounds to make it better. Most people start to assume their workarounds are the bets way forward, and begin to associate their workarounds with a positive experience. Addressing workaround will lead to an overall positive increase.
Neutral is the default for corporate IDGAF, so you wouldn't be wrong here.
Take for instance the "post" options in LinkedIn - you have the standard draft, post, and schedule.
Again, these are standard options. You probably aren't mad at this feature, but you're also not cognizant of it - you're ambivalent. This ambivalence shows that the feature is working as intended. And, unless you're looking to revolutionize post settings, this is likely something you can ignore.
For me, this is a red flag and I see it a lot in product data. When folks do this, it's because they don't like their standalone positive score, and the "neutral + positive" score makes them happier. They then LOVE to use these inflated numbers for marketing campaigns...
If you are using the neutral as positive point, ask yourself if the feature meets the industry standards and if the feature has no workarounds for improvement. If either of those statements are false, you should steer clear of using this as a positive.
Get your team together for a Neutral Talk! π£οΈ
If you haven't had the Neutral Talk with your team, now's a great time! Find out what neutral means to them and how they will use it. Set company guidelines for neutral points in scoring and where they will be used.
Remember: neutrality isn't as neutral as we think it is. #DataWisdom #SurveyGameStrong