Asklet widgets ask quick, finely-tuned questions that get better feedback from your customers and users. Let users respond by voice or text, and get the rich detail you need to identify issues faster and easier.
AI-powered NPS widgets that collect natural voice feedback
Asklet widgets ask quick, finely-tuned questions that get better feedback from your customers and users. Let users respond by voice or text, and get the rich detail you need to identify issues faster and easier.
Everyone hates surveys. Users are sick of getting ten-question forms every time they interact with a company, and very few people even bother responding (~6% is typical). Even when they do respond, it's often just a score. We've come to realise that feedback is only effective when it has detail. I worked for years on employee engagement surveys at Peakon, and realised how powerful verbatim comments are at driving action in companies. I would rather have even just a single description of someone'
Very excited about this! I'm so tired of useless, stale surveys, both when I'm asked to fill one out and when reading the generic analysis that goes on top of the dataset. Voice is the new UI!
Having worked in the UX & Tech industry for many years, I know how valuable this could be! Researchers know that user feedback should be as natural and authentic as possible, and given of their own volition. However there's nothing natural about what we do currently with feedback widgets—contriving all their nuanced and rich feedback into a simple score out of 10, or thumbs up. My hope for Asklet is that it helps us bridge that gap between collecting feedback at scale and collecting much mor
Really awesome product with a lot of potential!!
It's been so much fun building out Asklet over the past few weeks, I thought I'd share a little more detail on how it works and what's powering it. Asklet has been built with the same tech we use across the rest of our Surveys platform - Elixir , Phoenix LiveView and Postgres . Why this stack? We've found it incredibly efficient to work with as a small team of 4, and it's built for robust realtime experiences which is exactly the feel we wanted people to have. For infrastructure we're on Amazon'
Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.
Check the similar products section on this page, or browse the category pages linked in the tags above. Each category page shows all products for a given year, sorted by engagement.
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.