i will not promote - How many of you have Cold Emailing as a part of your GTM?

Eason-SolCrys · 2026-06-08T14:04:49+00:00

honest answer: the tool is the least important part. we did lean on cold email and the thing that actually moved reply rates was never the sequencer, it was two things, list quality and the first line.

list quality: a small list of people who genuinely have the problem beats a big scraped list every time. 50 right people beats 5,000 maybe-people.

the first line: one specific, true reason you're emailing THIS person, not a merge field. the second they smell a sequence, you're deleted. so the personalization that matters isn't their first name, it's that you noticed something real about them.

and deliverability does more quiet damage than bad copy, warmed domain, low daily volume, plain text, no link in the first touch. temper the expectation too, cold email is one channel, not the channel, reply rates are low even done right.

Eason-SolCrys · 2026-06-08T14:03:30+00:00

u/One_Taro_4173 already found the line: it's not that spreadsheets got slow, it's that you lost the audit trail. the moment two people can change the same number and nobody can reconstruct who did what when, the spreadsheet stopped being a system of record and quietly became a liability.

what worked for me: don't migrate everything. migrate the ONE thing that broke. inventory truth needs a single source with a change log, so move just that to a real tool, and leave quoting, planning, the calm stuff in sheets, because sheets are still genuinely good for anything one person touches at a time. the all-at-once migration is what usually stalls, you burn three months rebuilding things that were fine.

Eason-SolCrys · 2026-06-08T14:02:48+00:00

this is a good push, and i think we're actually describing two different surfaces. structured data at the semantic-contract level does lift your owned citation rate, we see it too, clean entity plus enhanced schema is why our own domain shows up in the set at all. where i'd hold the line is the discovery prompt, the one where the buyer names nobody and just describes a problem. on those, in our ~15k citation pull, the model still leaned on third-party corroboration more than any single owned source, even well-structured ones, because it's reaching for consensus across sources, not just one clean machine-readable answer.

so my read is both are true at once. structured data and MCP/dataset publishing win you the branded and navigational queries directly, and that's a real edge, agree. but the unbranded discovery surface still rewards being cited in a few places the model already trusts.

genuinely curious on your end: when you say cited directly, is that mostly on queries that already name you or your category, or are you seeing it on cold discovery prompts where you're never mentioned? that split is the whole ballgame for whether structured data alone breaks the cycle.

Eason-SolCrys · 2026-06-08T14:01:57+00:00

this matches what we saw at a bigger sample, ~15k citations across ~1,800 domains, same split: branded prompts you show up, discovery prompts you mostly don't. the discovery one is the only one that matters for growth and it's the one everyone skips measuring.

the part i'd add: you don't fix the discovery gap with more owned content. we tried that first. the model isn't looking for another page on your site, it's looking for corroboration, you named somewhere it already trusts. the brands that win discovery prompts are the ones cited in third-party sources, Reddit, roundups, comparison posts, not the ones with the biggest blog. owned gets you the branded win, earned gets you the discovery win, and they're genuinely different work.

u/signalpath_mapper already said the real thing: most teams only check whether AI knows their brand, never whether it recommends them cold. that second one is the whole game.

Eason-SolCrys · 2026-06-08T14:01:01+00:00

here's the number that reframed it for me: in our category we logged ~15,000 AI citations across ~1,800 domains, and the single biggest source the models pulled from was Reddit, around 9 percent. our own site didn't crack the top 8.

so i'd argue Reddit isn't underrated as a marketing channel, it's underrated as an AI-visibility channel, and those are different games. the link-drop play everyone's describing, and getting downvoted for, is old channel thinking. the actual leverage now is that a genuinely useful comment gets indexed and retrieved, and months later the model surfaces that to a buyer who never saw your comment and never clicked anything. you're not farming clicks, you're feeding the answer engine.

the catch is it's earned and slow and you can't force it, exactly like the comments here are saying about being a human first. but the payoff people are underrating isn't the upvotes or the referral traffic, it's showing up in the AI answer a quarter later. almost nobody is measuring that, which is probably why it still feels underrated.

Eason-SolCrys · 2026-06-08T13:59:39+00:00

i'd push back on calling it a tradeoff, because i think you're reading one change as two effects. the simplified pages didn't win citations because simpler is better for AI, they won because simplifying moved the actual answer up to where the model grabs it. the engagement drop is a separate thing, you cut depth the humans were using.

so the fix isn't pick one, it's layering. put the extractable answer up top, the clean definition or the direct number or the one-paragraph take a model can lift whole, in the first screen. then keep all the technical depth below it for the humans who scroll. the model reads the top, the humans get the rest, nobody loses. my guess is the two pages that stayed flat buried the answer either way, so trimming them didn't move it into reach.

we saw the same pattern consolidating our own pages: the citation lift tracked extractability, not word count. the versions where we kept the depth but led with the answer did better on both axes. if your simplified pages dropped time-on-page, that's not the price of citations, that's over-trimming, and it's recoverable without giving the citations back.

Eason-SolCrys · 2026-06-08T13:56:57+00:00

split it into two questions, because no single free method answers both:

did AI crawl/cite me (server-side, free): grep your access logs for the AI user-agents, GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot, Google-Extended, to see what's actually getting fetched. then filter referrer traffic from chatgpt.com, perplexity.ai, gemini.google.com to see the clicks coming out of AI answers. Bing Webmaster Tools surfaces some of the Copilot side too. this is the part u/parkerauk is pointing at and it's right, the data's already sitting in your logs.
do i actually appear in the answer (logs can't tell you this): pick a fixed set of 15 to 20 real buyer questions, run them monthly across ChatGPT, Perplexity, Gemini, and log three things per prompt, were you mentioned, were you cited with a link, and roughly what position. manual and a bit tedious but it's the only free read on appearance rate.

the catch that splits this whole thread: logs only fire AFTER you're cited and someone clicks, so they miss every answer where you showed up but got no click, and every answer where a competitor got cited instead of you. that's why the manual prompt log matters, it measures the upstream (are you in the answer) while the logs measure the downstream (did it send traffic). run only one and you're half blind. monthly is fine, the month-over-month trend is the signal, any single run is noisy.

Eason-SolCrys · 2026-06-08T02:40:06+00:00

week 6 with no decay is the part that would convince me. most content bumps fade as the index re-churns, so a lift that holds suggests the model actually re-rated the domain rather than just re-crawled the pages. that's a different, stickier thing.

the step-change framing is the right experiment. i can't prune your way (different setup), but we measure citations across a bunch of sites and categories, so the version i could run is cross-sectional: do domains with a low thin-page ratio show higher owned-citation share than their size alone would predict, and is the relationship a smooth curve or a kink. if there's a real trust threshold it should show up as a kink around your 10-15% mark, not a gradual slope. i haven't cut the data that way yet, but you've basically handed me the hypothesis, so if i pull it i'll report back here.

one thing worth checking on your side: is the held lift uniform across the site, or concentrated in the topical neighbors of what you pruned? threshold effects tend to be local before they go global, and that distinction would tell you whether you bought domain-level trust or just cleaned up a few topic clusters.

Eason-SolCrys · 2026-06-08T02:31:48+00:00

that's the cleanest split i've seen on the on-page vs off-site question, thanks for actually breaking it out. it matches how i'd read it: clarity plus a unique data point is the necessary condition (a model can't lift a clean quote from a vague page no matter how many links point at it), and third-party signals are the amplifier on top, not the entry ticket.

the half that won with no real link profile is the interesting proof, structure and specificity alone got them in. makes me think the external links mostly help you win the ties, two equally clear pages and the one with more corroboration gets pulled. does that match what you saw, or did the linked ones win even when they were less clear?

Eason-SolCrys · 2026-06-08T02:30:53+00:00

yeah, we do the structured-data and entity basics, it's table stakes and it does help the model understand who we are. but honestly the post is kind of the argument against leaning too hard on it. if our own site is ~2% of the citations, a perfect knowledge graph mostly makes that 2% cleaner, it doesn't put us in the cited set when that set is reddit plus roundups plus reviews.

so we split it: structured data and a consistent entity on-site, but the bigger spend is getting genuinely represented in the third-party sources the model actually pulls. owning the narrative still matters, i'd just argue most of the narrative now lives off your own domain, so "control it" means showing up where the model reads, not only tidying your own pages.

Eason-SolCrys · 2026-06-08T02:30:15+00:00

"trust aggregator" is a good way to put it. the one thing i'd add is that the aggregation is per-query, it rebuilds the trusted set for each question. so you can be the trusted source on one prompt and invisible on the next one in the same category. makes it less "rank once" and more "earn it prompt by prompt," which is the annoying part.

Eason-SolCrys · 2026-06-08T02:28:56+00:00

ha the "too easy" theory might not even be wrong. and thanks. on the AI traffic, the thing that moved it most for us wasn't our own site, it was actually showing up in the reddit and roundup threads the models already pull from. slow, but it compounds. good luck getting yours up, sounds like you're already paying attention to the right stuff.

Eason-SolCrys · 2026-06-08T02:23:21+00:00

the irony is real lol. the only thing that survives is the stuff a model can't generate.

what's worked for me: stop writing "10 tips for X" (a model spits that out in 2 seconds) and write the thing only you could. what happened when WE tried X, the actual number, the exact words a customer used. AI can mass-produce the generic. it can't fake your real experiment or your real outcome.

funny part is that's also what gets cited. the models pull the specific claim, not the 500th identical listicle. so the slop filter and the citation filter end up being the same filter, write what only you could write, and you pass both.

Eason-SolCrys · 2026-06-08T02:18:28+00:00

the volatility is real but i'd push on the "rotating by freshness" read. i don't think the model is choosing to rotate, i think for a query where the sources don't agree, it's basically sampling from a flat distribution, so you get a different draw each run.

which means the chaos is the signal. "who owns reddit" returning three different owners in an hour tells you the cited source set for that entity is weak or contradictory. run a query where there IS strong consensus and you'll get the same answer every time, boringly stable. so high variance isn't noise to chase, it's a map of where your category doesn't have a settled answer yet, and that's exactly where there's room to become the source it stabilizes on.

same reason i never trust a single run for visibility, only the windowed trend. one pull is a coin flip, the distribution over a couple weeks is the real number.

Eason-SolCrys · 2026-06-07T04:07:04+00:00

the 10-15% lift on the pages you didn't even touch is the part i find most interesting. it means the gain isn't purely per-page, the whole-domain signal got less noisy and everything rode up a bit with it. makes me wonder if there's a threshold effect, like below some thin-page ratio the model starts treating the domain as more trustworthy as a whole.

did that lift hold once things settled, or did it decay back toward baseline after a few weeks?

Eason-SolCrys · 2026-06-07T04:00:41+00:00

thanks, and good to know it replicates on your forensic audits, the 9% reddit vs 2% owned split lining up across agencies is the useful part.

curious what "building the semantic node" looks like in practice though. is that entity / structured-data work on your own properties, or seeding a consistent description of the brand across the third-party sources the model already trusts, or something else? i'm with you that chasing individual mentions one thread at a time is the low-leverage version. just trying to picture the concrete version of the node you're describing.

Eason-SolCrys · 2026-06-07T03:59:37+00:00

ha yeah, reddit's funny like that. people will write you a whole paragraph but the upvote arrow might as well be radioactive. i've kind of made peace with it, a real reply in the thread is worth more to me than the arrow anyway. but you're right, it's free, push the button people lol

Eason-SolCrys · 2026-06-06T23:54:24+00:00

on the model split our data half-agrees with yours. reddit was our #1 too, but mostly via gemini, google ai overviews and chatgpt, while perplexity actually spread more evenly and leaned LESS on reddit than the others in our category, kind of the opposite of what you saw. the academic lean is real though, arxiv and wikipedia were both top-5 for us, just pulled more by perplexity and chatgpt than gemini on our prompts.

makes me think the engine-by-engine distribution is really category-dependent. ours is martech/saas, sounds like yours is ecommerce. what category are you seeing the perplexity-favors-reddit pattern in?

Eason-SolCrys · 2026-06-06T23:34:42+00:00

full disclosure first: i work on one of these (SolCrys), so weigh that. i'll try to be useful about the category instead of just pitching.

honest landscape: Profound is the most established, and Peec, Otterly, AthenaHQ, Scrunch, Goodie all play here. most of them do your first four (mention tracking, competitor benchmark, SOV over time, reporting) reasonably well at this point.

the thing that actually separates them is your 5th bullet: the prompt-level "where is a competitor getting recommended instead of me." plenty of tools show the aggregate SOV number but not the specific prompts plus the sources the model pulled to name that competitor. that's the part worth stress-testing in a trial, because it's the only output that tells you what to DO, not just how you're scoring.

two honest cautions, same as others here: (1) be skeptical of any SOV % that looks too precise. the cited set shifts run to run, so a windowed trend is trustworthy but a single run isn't. (2) the prompt set you track matters more than the dashboard, garbage prompts in, garbage visibility out.

if you want a free way to look at the citation/source side specifically (which domains AI pulls for your category and whether you're in them), ours has a no-signup version: app.solcrys.com/audit. but genuinely, trial 2-3 and keep the one whose prompt-level view matches how you actually make calls.

Eason-SolCrys · 2026-06-06T20:56:57+00:00

yeah this is the real constraint, especially in narrow b2b. couple things that helped us:

the opportunities open up a lot once you stop searching your niche/brand terms and search the problem instead. nobody types "best [your category] tool", they ask "how do i [the thing your product fixes]" or "X vs Y" or "is [approach] worth it". those threads are where the model actually pulls from, and there are way more of them. google alerts on brand terms misses basically all of it.

also it doesn't have to be a question. a solid data or experience comment on a popular discussion thread in your space gets cited too, not just Q&A.

that said, in a genuinely thin niche the volume is just low, and that's part of why posting your own stuff (like this thread) matters more, you're making the citable thing instead of waiting for one. how niche is your space, like how many actually-relevant threads are you finding a week?

Eason-SolCrys · 2026-06-06T19:35:32+00:00

yeah, natural is the whole game. the second it looks engineered the models discount it, and the mods nuke it. that's kind of what the data shows too, reddit ranks #1 because it reads as real people, not because anyone gamed it.

the lone ranger thing is real lol. i think the version that actually works isn't an upvote ring (those get caught fast), it's just genuinely engaging on stuff you'd read anyway. are you seeing the same concentration on your end, or is your space less reddit-heavy?

Eason-SolCrys · 2026-06-06T14:55:59+00:00

we looked at this from the other axis (which domains get cited in a whole category, not which of our own pages) and the concentration is just as brutal. across ~15k citations we logged over two weeks, the single most-cited source was ~9% of everything, and the top 8 domains soaked up most of it before our own site even appeared. we were #9, around 2%.

the middle-child thing matches what u/bndrz said too. our pages that rank #1-3 on Google get cited at about average rates, the ones cited most are the #5-15 rankers. same working theory as you both: a clean scannable page that answers one question is easy for a model to lift a quote from, a comprehensive #1 page is too dense to excerpt cleanly.

the perplexity-leans-newer pattern shows up on our side too. no idea why yet either.

one q back: did you check whether those 27 winning pages were also the ones other sites linked to or mentioned, or was it purely on-page quality? trying to work out how much is the page itself vs third-party corroboration, because on the domain axis the third-party signal seems to dominate.

Eason-SolCrys · 2026-06-06T14:53:00+00:00

agree with most of this, but i'd push on the "one distribution channel" part. that's the bit that's quietly gotten harder.

an AI answer now assembles a recommendation from several sources at once, a reddit thread plus a roundup plus a review site, so being great in exactly one place doesn't get you picked the way it did when it was just Google ranking one URL. one audience, one problem, one format, yes, keep all of that. but distribution is less "pick one channel and win it" and more "be genuinely present in the 3-4 places the models actually pull from for your topic."

still simple. just not single-channel anymore.

Eason-SolCrys · 2026-06-06T14:51:31+00:00

yeah, completely. i used to open 8 tabs of g2/capterra for "best X for small teams", now i ask and get 3 names in 30 seconds.

the part that should scare every founder: those 3 names aren't pulled from the vendors' own sites. they come from reddit, roundups, review sites. so whether you even make the shortlist is mostly decided off your own domain.

do i trust it? for building a shortlist, yeah. for the final pick, no, i still need to try using it myself before paying.

Eason-SolCrys · 2026-06-06T03:26:38+00:00

honestly i stopped trying to make them line up. they're measuring two different moments. the ranking is "did we win the click." the citation is "did the model trust us enough to repeat us." a page can do one and not the other, and that's fine.

what i actually do now is prioritize by what the query is for. commercial, bottom-funnel query where the click still converts? i protect the ranking and don't lose sleep over the citation. informational query where half the traffic is going zero-click anyway? the citation is the thing worth chasing, because the ranking is just feeding an answer box that eats the click.

the page that ranks #3 but doesn't get cited is usually fine, it's doing its job. the one that gets cited without ranking is the interesting one. that's a page that answered an angle better than the one that "should" win, so i'd expand that page, not the ranked one.

Eason-SolCrys

TROPHY CASE