How we built the Reddit reviews
Short version: we tried 30 software categories and published 12. We read Reddit posts and comments from Prowlo's dataset in November 2026, counted 474 that discussed specific products, and set aside 221 entries from vendors and review farms across the published pages. Each page shows counts and links to threads. You will find no star ratings and no quoted comments.
Disclosure: prowlo is our product, and its dataset supplied these threads.
Where does the data come from?
Prowlo collects posts from the subreddits and keywords its watchers follow. We searched that dataset for each category, read comment threads under questions where people compared products, and ran a limited number of live Reddit searches where the dataset ran thin. Each category's page lists the subreddits and date range it drew on. Reddit changes, so each page carries the month we pulled it.
What counts as a mention?
A post or comment counts when it names a specific product in the category and says something about it or asks about it. One author counts once per product per thread, so a person who keeps replying does not inflate the total. Identical text posted to several subreddits counts once. We count Reddit only, and we skip posts on user profiles.
Whose opinions did we leave out?
We removed posts and comments from anyone with a stake: makers, employees, people pitching their own product, accounts in brand-owned subreddits, and templated review posts with affiliate links. A vendor's view of a rival counts as a stake too. When we could not tell, we left the comment out. Prowlo gets the same treatment as every other vendor, and no Prowlo reply counted toward any page.
How did we code each mention?
An AI model read each item and marked every named product as positive, mixed, negative or no opinion, following one written rule set. Positive means the author uses it and reports that it works, or recommends it. Negative means they report a problem, left it or warn against it. Mixed means both in the same item. We re-read a random sample of coded mentions against the source threads before publishing.
Which product comes first on a page?
A product can lead a page with at least 3 positive mentions and more positive than negative mentions. Products that pass come first, ordered by positive minus negative mentions. The others follow in the same order. When nothing passes, the page says no product led. We show the four counts so you can weigh them differently.
Which categories did not make it?
A category needs at least 30 qualifying posts or comments and at least 5 distinct products. These 18 fell short, mostly because vendors wrote most of the replies once we looked closely: Reddit monitoring tools, Reddit lead generation tools, GummySearch alternatives, social listening tools, brand monitoring tools, AI agent frameworks, cold email tools, email marketing tools, AI visibility tools, social media scheduling tools, landing page builders, CRMs for startups, customer support tools, project management tools, user feedback tools, AI meeting notetakers, bookkeeping and accounting tools, web scraping tools. Our own market is on that list.
Where do these counts fall short?
- The dataset leans toward founder and SaaS communities, so it is one sample of Reddit opinion.
- Some pages draw on a single subreddit. Those pages name it in the answer.
- Most counts are small, and one busy thread can move a product up or down.
- We work from what people chose to post, so unhappy and very happy users are over-represented.
- Stealth marketing can slip past our checks when an account discloses nothing.
- Comments reflect the thread on the day we read it, and later deletions do not change the counts.
Frequently asked questions
Are these ratings?
No. We publish counts of positive, mixed, negative and no-opinion mentions for each product, and you can weigh them yourself. We order products by positive minus negative mentions so the table has a reading order, and we do not publish that figure as a score.
Do you quote Reddit comments?
No. Every page paraphrases what people reported and links to the thread, so you can read the original words in context. Paraphrasing also means a deleted comment does not stay on our site in the author’s own words.
Who decided whether a comment was positive or negative?
An AI model read each post and comment and coded it against one written rule set. We then re-read a random sample of coded mentions against the source threads. Treat any single call as a judgment, and open the linked thread if a mention matters to your decision.
Why is a well known product missing from a page?
A product appears only when someone with no stake in it named it in the threads we read. Our dataset leans toward founder and SaaS communities, so a product popular with enterprise buyers or in other communities can be absent here. Absence says nothing about quality.
Browse the results in the full index, or see how Prowlo serves Reddit data to AI agents.