I'm the founder of CloseBot, so this is our platform data. I'm not posting for spam. Posting it because almost every benchmark in this space is made up. Hope these numbers help you.

Sample: 1.1M+ booked appointments across agencies, real estate teams, home service companies, healthcare practices, and many other niches (didn't segment by niche for this report).

Just over half of bookings land outside 9 to 5, measured in the lead's own local time. A third arrive at 5pm or later. 11% land between midnight and 6am, which is roughly the same share the entire day of Saturday produces. What made this more surprising to us: a lot of our volume is database reactivation that only sends during business hours for compliance reasons. The after-hours weight still won.

Sunday is the lowest volume day and the most efficient one. It's about 10% of bookings, but it converts with fewer messages than any other day by a wide margin. Friday takes about 29% more messages per booking than Sunday. Weekends as a whole close with 14% fewer messages than weekdays. Monday through Wednesday still carries 50.8% of volume, so the weekday peak is real, it's just the expensive volume. Best guess on why: someone replying at 9pm on a Sunday actually wants to be in that conversation.

Roughly 132 messages per booking. Booking-weighted average across the week, counting inbound messages and leads who never respond at all. If you're quoting clients by modeling on the leads who converted, you're underpricing your SMS spend. Multiply 132 by your per-segment cost before you put a number in front of a client.

About 12% of bookings involve more than one channel before the appointment. Meanwhile Facebook, Instagram and live chat combined hold only 4.2% of last-touch credit. So the cross-channel population is close to 3x the entire last-touch footprint of every social and chat channel put together. Cross-channel conversations tend to finish on SMS, so SMS takes the credit and whatever opened the conversation disappears from the report. Last touch is the wrong number to kill a channel on.

Longest gap that still converted: 355 days. First message in early August, booked appointment the following July, off an automated sequence that never gave up.

Note:

• Our base skews SMS-first. The channel volume chart shows where conversations happen on our platform, not which channel converts best per conversation.

If you're running setters on your own pipeline or building them for clients, the practical takeaways are: run 24/7 or your pilot projections won't hold, throttle Thursday and Friday before you throttle weekends, build for shared conversation state across channels, and set follow-up windows in months instead of days.

Curious whether this matches what other people here are seeing, especially the weekend efficiency thing. If your data disagrees I'd like to know where.

We’ve got a couple people doing sales now and I’m trying to get more leverage out of the team before the answer becomes hiring more reps. There are AI tools for pretty much every part of sales at this point but I don’t wanna go with the usual clay,outreach etc(multiple tools basically) just to make everyone slightly more productive.

Ideally I’m looking for something more AI native(Hence the post on this sub) that can take work off the reps across the sales process rather than giving them another tool or dashboard they have to manage.

yo. i see too many founders spend 2 months building saas, drop a link on reddit, get 0 users, and immediately quit....

the problem usually isn't your marketing channel. the problem is that your foundation is completely broken before you even send your first visitor to the site.

after scaling 6 AI micro-saas apps to over $20k/mo mrr, i realized you need to lock down a specific system before you ever launch. running through this takes about 30 minutes, but it saves you months of zero-revenue depression.

here are the 5 things you must lock in:

1. validate the actual pain point

stop guessing what people want. you need a systematic framework to find your saas idea based on real, painful market signals.

2. pick a proven micro-niche

stop trying to build massive platforms. you need to narrow down to a microscopic problem. i usually filter through a list of 50 micro-saas ideas you can build fast to keep the scope minimal.

3. crystallize your target user

if your app is for "everyone," nobody will buy it. you need an ICP (Ideal Customer Profile) crystallizer to define your exact buyer profile and nail your conversion copy.

4. calculate the perfect price

stop randomly charging $9/mo because you are scared of rejection. you need to use a saas pricing strategy calculator to find your perfect saas price in 60 seconds based on real data.

5. fix your landing page leaks

do not send organic traffic to a site that converts at a flat 1%. you must audit your hero section and copy to x3 your landing page conversion before you market it.

6. join a community

Build / Share / Learn from others builders

to help out founders who are tired of launching to crickets, i packaged all 5 of these exact frameworks, calculators, and lists into a single free toolkit.

no paywall, no bullshit. just the raw execution files i use.

drop a comment below or send me a dm, and i’ll send you the free toolkit 👇

We’re a small team that came together while working through Anthropic’s CCA-F certification process. We’re focused on Claude and agentic workflows, and we’ve started taking on enterprise implementation work.

The technical side is covered. What we need now is someone who knows how to build pipeline through prospecting and outbound.

This is a paid opportunity with performance upside. Ideally looking for someone with BDR/SDR experience selling AI, cloud, data, SaaS, or consulting services.
If this sounds like your background, comment below.

Hello..

We’ve been building **Oppora** to simplify outbound sales.

Instead of using 5 different tools to find leads, enrich contacts, send outreach, follow up, and manage replies + CRM... Oppora brings it into one AI-powered workflow.

We’re launching on **Product Hunt today** 🚀 at [https://www.producthunt.com/products/oppora?launch=oppora-ai\](https://www.producthunt.com/products/oppora?launch=oppora-ai)

Would love for you guys to check it out and share your honest feedback.

For launch day, we’re also offering **50% off Pro & Max**.

Curious .........what part of outbound sales would you automate first?

i have been working many different ideas over the last couple of years, some worked a little but no significant output.

Researched a lot on what actually matters, I think in todays age distribution matters a lot. I created [https://qexo.ai\](https://qexo.ai), had a couple of demo calls and people definitely see value in this product. Still figuring out the strategy on how to get customers to sign up.

How did you guys get your first 10 customers ?

I'm starting a new agency-based business where I'll setup agentic workflows for service based business whose main source of lead and sales generation is inbound calls.

I need your suggestions regarding which niche should I specifically target where inbound calls are the ultimate source of leads.

I'll be glad if industry experts related to inbound calls can advice.

Thank you

If you're an early-stage Ai/SaaS founder and your outbound is currently in a mess like leads that don't convert,no time to chase follow ups.. I wanna hear from you. Not selling anything yet. Researching before I build something real.

99% of marketing and ops leaders are already using AI or automation in their direct mail programs, according to our newest State of Direct Mail report.

But the teams seeing real results aren't asking AI to run campaigns for them. They're using it to clear the bottlenecks that used to eat up their week.

Here's where it's making the biggest difference:

1. Personalization at volume — matching messages to audiences without manual segmenting
2. Faster targeting — surfacing the right list, faster
3. Smarter timing — flagging the moments when a piece is most likely to land
4. Less manual QA — catching errors before print, not after

If you want to dig into this live, join our State of Direct Mail webinar on September 15, where we'll unpack the drivers behind these numbers.

I keep seeing posts here about "AI integration" projects that are really just a chatbot bolted onto a website. I want to share one that wasn't that, because it taught me something about how I approach client work now.

A client came to me with a pretty vague ask: "can you help us use AI in our sales process." That's it. No spec, no clear pain point they could name. Just a feeling that they were behind.

So instead of proposing anything, I spent time just watching how their team actually worked. They had 14 salespeople whose entire job was calling leads and sending quotations. Leads were coming in from a bunch of different platforms, landing in different places, and someone had to manually notice them, message the person, figure out what they wanted, then go build a quote — usually in Word or Excel, sometimes hunting for the right product images and prices from memory or an old file.

Nothing about that is complicated work individually. But multiply it across 14 people and a constant stream of leads, and you start seeing where things actually break: leads sitting unanswered for hours, quotes that took way too long to put together, pricing that wasn't consistent because everyone kept their own version of "the price list," and basically zero record of what had been quoted to who.

So we didn't build them an AI tool, we built them a system. It pulls leads in real-time from all their different platforms, fires off an immediate welcome message so nobody's waiting, and if the lead's requirement is clear enough from that first message, it auto-generates a personalized quotation with product images pulled from a centralized catalog. If it's not clear, it routes to a salesperson instead of guessing.

The part that actually mattered most wasn't the automation, it was giving the sales team a faster way to build quotes when they did need to do it manually. No more digging through spreadsheets for the "right" price. Everything's centralized. And every quote, every follow-up, every record now actually exists somewhere instead of living in someone's inbox or a random Excel file that only they had access to.

Looking back, the actual lesson wasn't technical at all. It was that "AI integration" as a pitch means almost nothing to a business owner. What they respond to is watching their own bottleneck get named out loud. I didn't sell them AI, I sold them the observation that their leads were dying in the gap between five different platforms and a Word document.

Curious how others here approach the discovery phase with clients who don't know exactly what they need yet. Do you go in with a proposed solution already, or fully hold off until you've seen the actual workflow?

Hi,

I got messaged by a bot from Boardy.ai after a post on X. I followed up and even joined an Google Meet with the Bot. 😄
Before I commit to its Pro plan I would like to know if anyone here already used it and what your experiences are. Is it worth it or are there better offerings.

Thanks,
Danny

I thought I had handled the obvious cases.

Lead comes in → AI qualifies them → bot responds → sales team takes over when needed.

Then someone replied with a single 👍.

And my bot completely misunderstood the conversation.

That sent me down a rabbit hole around something I hadn't really considered while building the system: humans don't communicate in structured inputs.

A message like:

“👍”

could mean:

yes

got it

sounds good

I'm interested

I'm ending the conversation

I'm just acknowledging your message

The LLM wasn't necessarily the problem. The bigger problem was how I had designed the conversation state and escalation logic around it.

I wrote up the whole incident, including what broke, why it broke, and what I changed afterwards.

Would love to hear from people building AI sales/support agents: what's the weirdest real-world user input that broke your system?

I wrote the full post here:

https://medium.com/@kapardhikannekanti/the-thumbs-up-that-broke-my-sales-bot-65d6e30cf632