Across all 5,756 conversations, 26.6% got at least one reply from the recipient. For scale: cold email reply rates are commonly reported in the 1-5% range. Even taking the generous end of that range, a targeted Reddit DM is roughly 5x more likely to start a conversation. The catch is the word "targeted": every DM in this corpus was sent to someone who had just posted about the exact problem the sender's product solves.

Short DMs won, and it was not close: messages of 40 words or fewer replied at 30.6%, messages of 50 or more at 20.6%. The embarrassing part: 47% of the DMs in this corpus ran past 40 words even though the AI prompt writing them claimed to enforce a 40-word cap. Asking a model to be brief is not the same as making it be brief. We now enforce the cap with a deterministic validator after generation.

DMs opening with a casual "Hey ..." replied at 32.8%. DMs opening with "I saw your post" or "I noticed ..." replied at 17.9%, nearly half. Recipients have learned that "I saw your post about X" is how every bot and growth hacker opens. It announces surveillance instead of starting a conversation. One honest caveat: opener style is partly tangled with length (the "I saw your post about your struggle with..." crowd also writes long), so we enforce length hard and treat the opener as a secondary lever.

DMs that shared a specific, relevant experience ("I shipped my first SaaS to dead silence too, took me 4 months to get 10 users") replied at 36.1%, the strongest positive lever in the study. Generic empathy filler ("I totally get how frustrating that must be") replied at 23.8%, below baseline. Readers can tell the difference between someone who has been in their seat and someone running a script that says to act like it.

This was the single largest negative lever we found. DMs containing at least one word from the list below replied at 10.4%, against the 26.6% baseline: a 16.2-point penalty for sounding like a LinkedIn post. These words are how Redditors detect marketing. Our DM generator now blocks all 23 of them with a hard validator, because prompting the model to avoid them was not enough.

Everything above is about the message. The audience matters more. Holding the product constant, realized reply rates ranged from 2% in the worst community to 53% in the best, a 26x spread that no amount of copywriting closes. The kicker: the AI relevance model rated the 2% community's leads at 71/100 on average and the 53% community's leads at 55/100. The model's confidence and reality pointed in opposite directions.

The practical consequence: we stopped trusting predictions and started reordering each campaign's communities by their realized reply rate, so the daily DM budget flows to audiences that actually answer. Communities with fewer than 20 sends keep a neutral prior until they prove themselves.

Every Reddit tool (ours included) scores each lead 0-100 for how relevant it is. So we checked whether the score predicted replies. It did not. Leads scored 80-89 replied at 25.4%. Leads scored 90-100, the model's most confident picks, replied at 21.4%, the lowest of any band. The mean score of leads that replied and leads that never replied was identical: 66.8 vs 66.8. The spread across all score bands was under 5 points, statistical noise.

Why: the old scorer summed topic relevance and audience fit, so an advisor giving marketing tips scored as high as a founder asking for exactly your product. Topical is not the same as in-market. We rebuilt ours as a buyer-intent triage (advisors, learners, and competitors are hard zeros now). If you pay for any tool with a relevance score, ask the vendor for this chart. If they cannot produce it, the score is decoration.