I built five free outbound calculators from eight years of our own campaign data
I run outbound programmes for a living and got tired of every benchmark online being a vendor survey or a number someone invented in 2019 and everyone has recycled since.
So we published ours: 389,890 prospects, 15,018 meetings, 41 client programmes between 2018 and 2026, and fifteen controlled A/B tests where prospects were split at random inside each account so industry, geography, seniority and company size stay constant on both sides.
Then I built five calculators on top of it. No signup, no email, nothing stored, everything runs in your browser:
Reply rate calculator. Enter your sends, replies and meetings. Get the benchmark for your exact persona, company size and region, plus the fixes ranked by measured effect.
ICP fit scorer. Score a segment out of 100 before you build the list, with expected reply rate, meeting rate and deal size.
Message generator. Builds a first message in the format that replied at 44.5%: two and a half sentences, question-led, 240 to 420 characters, no pitch.
Voice note script generator. 25 to 40 seconds with timing marks.
ROI calculator. Meetings, pipeline, cost per meeting, payback.
Three findings that surprised me most, in case you never click:
Targeting beat copy by a mile. The same sequence got 13.0% replies on a job-title list and 51.9% on a list where we'd verified per person that the problem was actually theirs.
Answering a reply within 24 hours converted at 21.4%. After 48 hours, 3.6%.
Seniority buys a reply, not a meeting. Directors replied more and booked half as many meetings as the level below them.
Disclosure: I run Prospectio, which is a LinkedIn and email outreach tool. The data and the calculators are free and ungated, and you can use any of it without touching the product.
Really solid approach — I'm juggling something similar myself (building Xstream4K on the side), what's been the hardest part for you so far?
Thank you. nice, any discount codes?! I think it's the time it is taking to get folks correctly set up on the system and how I need to balance that with my runway of cash!
With the calculators ungated and usable without touching Prospectio, are you seeing meaningful product adoption from users, or is their primary value proving demand for the data itself?
The plan is for users to review this data and build campaigns that are much more relevant to prospect. I built prospectio.ai because I hated the spray and pray approach from so many SDRs.
If users are using the calculators but not touching Prospectio, what behavior would tell you the calculators are actually moving them toward the product rather than just validating interest in the data?
Curious how long it took before you saw the first real results?
About 3 months. But I had the joys of running a team of over 100 SDRs so lots of testing at the same time which removed seasonality variants from the results
The useful distinction here is turning benchmark data into a next decision, not just a score. I’d show the comparison group and sample size beside each result, then suggest one controlled change for the next batch. The reply-speed finding is especially actionable: breaking it out by segment or channel could reveal where a 24-hour response actually changes meetings rather than only replies.
Indeed and the piece missing is are those positive or negative replies. usually I'm seeing 30% of replies are interested so it's those that you don't want to go cold
Makes sense. Are you planning to charge for it, or keep it free for now?
This is free but the idea is to drive folks to set up linkedin automation that works on the basis of the data prospectio.ai
The seniority finding is the one most outbound advice gets exactly backwards. "Go to the C-suite" is what everyone says, but your data shows Directors reply more and book half as many meetings. The person who can say yes in a meeting is almost never the same person who replied to your email.
The 24-hour reply window finding matches my own experience doing B2B cold outreach. I used to treat reply handling as low priority and batch it once a day. Once I moved to same-day replies the conversion rate changed noticeably. If someone replied to a cold email, they were in problem-solve mode at that moment, and that window closes fast.
The targeting versus copy gap is the one worth pushing on further. 51.9% versus 13.0% is a huge spread. Do you have enough runs to separate whether the message format mattered at all in the high-fit segment, or did targeting wash out everything else? Curious whether your 240-420 character format was as important in the verified-problem group as it was in the average-fit group.
Good question. It was about the copy and not so much the account list. The reps would have had a book of business. Your point on how everyone targets c-suite is so true! pet peeve of mine actually. Finding a champion who whats to make a name for themselves is more valuable and often intro's you to the csuite where you'll be armed with more insights into the companies objectives.
Proprietary data is about the only real moat left in content, so the raw material here is strong. Two things I would change: five calculators split the attention and the links, one of them will end up doing ninety percent of the work, so find that one and put the whole push behind it. And no signup is the right call, but the second a calculator tells someone their reply rate sits in the bottom quartile you have the most motivated prospect you will ever meet, so give them somewhere to go on that screen even if it is only a calendar link.
Nice work shipping it. What has been the biggest challenge since launch?
It's finding bandwidth to onboard customers!
Good day
Really relatable. How much time do you put into this each week?
Every Friday I'd sit down with marketing to review the testing. That really paid off after about 3 months. For now my entire time is spend onboarding new customers to prospectio!
The methodology sentence is doing more work than the calculators and it is buried. Splitting prospects at random inside each account, so industry, geography and seniority stay constant on both sides, is what separates this from the vendor surveys you are reacting against, and most readers will skim straight past it. I would lead with that. The related thing worth adding is sample size per segment inside the calculator itself. A benchmark for a narrow persona built on a few hundred prospects deserves a visible range rather than one figure, and showing that is what makes the confident numbers elsewhere believable.
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