What is user feedback?
User feedback is qualitative and quantitative data from customers on their likes, dislikes, impressions, and requests about a product. Collecting and making sense of user feedback is critical for businesses that wish to make improvements based on what their users need. Channels for user feedback can include email and phone surveys, as well as third party research, but the most reliable and most responsive channel for user feedback is delivered by in-app messaging and in-product surveys.
What are the different types of user feedback?
NPS: Net Promoter Score is a structured way to sort customers into promoters, passives, and detractors based on their 0-10 point rating for the question “How likely are you to recommend this product to a friend or colleague?”
CSAT: Customer satisfaction rating, typically administered after an interaction with support staff or other transactional events.
Feature requests: Customer feedback on feature enhancements or desired functionality is a key way to assess market demand and learn what to build next.
Release feedback: After the product team has shipped a new product or feature, collecting post-launch feedback is an important way to learn if the offering hit the mark or if there’s more work to be done.
How can I solicit user feedback?
Picklist: The user chooses one option from a pre-selected list. This is usually delivered as a drop-down menu or a radio button. Examples include “What’s your industry?” or “What’s your role?”
Multi-select: This question format allows the user to select more than one option for their answer. It is typically administered in a “check all that apply” type of question. Examples include “Which features would you be interested in testing?” or “What departments at your organization use this solution?”
Rating scale: This question is useful for getting quantitative information for releases or sentiment rating. Formats include NPS (0-10 rating), Likert scale (“On a scale from 1-7, how much do you agree with the following statements?”), 5-star rating (1-5 scale), or even a binary thumbs up/thumbs down rating.
Open text: This question format is an effective addition to quantitative rating scales, and can be helpful to understand the “why” behind any individual number rating. Ask users to provide detail on why they gave their previous quantitative rating.