Lllucky
Report · August 2026Create workspace

20% of customers are lost after they have already written to you.

Not at the ad, and not at the price — in the gap between the incoming message and the booking. Below: what we found in real conversations, how to check it in your own inbox, and what it costs you.

13–18 h
the real average time to a first proper answer
36–48%
of enquiries arrive outside working hours
×5–31
difference in cost per booking between ads that look identical — the budget drains into the expensive ones

See where your own leads stall and what to fix first. Free, no obligation.

What we found

Six findings that repeat almost everywhere.

None of them are about the size of your ad budget or the quality of your service. All of it happens after somebody has already written to you — inside the conversation, the one place nobody looks, because it seems like everything there is fine.

01

Answered — and never invited them in

That many people were given a price and not a single invitation to come in. Formally the question is answered and the conversation looks successful — while the person walks off to compare prices elsewhere. This is the cheapest loss of them all: they had already written, they had already been answered, all that was missing was a time.

How to check it yourselfOpen the last ten conversations where somebody asked about a price and count how many of them offered a specific time.
11–44%were given a price and no invitation to come in
Where we see it
  • 2–19%were told to call instead of being booked right there in the chat
  • 6–16%show a booking in the chat — and what happened next is visible nowhere
  • $230–350is what the person who was never invited had been ready to spend
02

Identical numbers in the ad manager — very different cost per booking

Take two ads with the same impressions, the same click-through rate and the same cost per conversation. In the ad manager they look equally successful, and both keep running. But connect every message to a business outcome: did the person book, how often did they come back, and how much did they spend? It turns out that the cost per booking can differ by five to thirty-one times between them, while some ads never produce a booking at all. The ad manager cannot show this because the outcome of a conversation sits outside of it. Delivery, meanwhile, watches whether the conversation goes anywhere after the click, so a slow inbox quietly raises what you pay for impressions.

How to check it yourselfTake two ads with a similar cost per conversation and count by hand how many bookings each produced. That is the number worth optimising for — not the cost per conversation.
×5–31difference in cost per booking inside one ad account
Where we see it
  • 8–22%of the budget goes to ads that produce no bookings at all — $80–220 of every $1,000
  • 61%of ad spend lands in conversations that were answered late, or never
  • 60%of paid clicks never name a service — a click was bought, not a customer
  • $1.25–2.08the real cost of a single paid conversation
03

A third to a half of all enquiries arrive outside working hours

That share of messages comes in at night and on weekends — at every business in the sample. This is not junk traffic: where these enquiries started getting answered straight away, the night began producing up to half of the hottest leads.

How to check it yourselfTag a month of incoming messages by hour and by day of the week. The out-of-hours share is usually higher than the owner expects.
36–48%of enquiries arrive at night and on weekends
Where we see it
  • up to 35%of all bookings started at night once night messages were answered
  • halfof the hottest enquiries arrive outside working hours
04

The real reply time is not what it feels like

Counting every enquiry as it is, nights and weekends included, that is the average time to a first real answer. The typical case looks perfectly fine — the losses live in the tail, and the tail is exactly what an owner never sees. Where these messages started getting answered immediately, those same hours turned into seconds.

How to check it yourselfTake a month of conversations and measure the time to a real answer — not to the auto-reply. Look past the typical case at the slowest ten percent: that is where the money goes.
13–18 haverage time to a first real answer
Where we see it
  • 3–29%of enquiries get no answer at all — roughly one in ten at a typical business
  • 9–41 his what the slowest tenth of answers waits — for most, more than a day
  • 11 min – 5.4 his what the typical case shows — which is exactly why the problem stays invisible
  • 5–8%asked for a price and never received a single number
05

The auto-reply hides the problem from you, it does not solve it

That many "first answers" in the conversations we went through were a template. Your own statistics look fast afterwards, the conversation is marked as answered and slides down the list — and your manager stops seeing it. Meta, incidentally, does not count an auto-reply as an answer at all: until somebody replies properly, as far as it is concerned you are silent.

How to check it yourselfCheck what share of your answers are templates and recalculate your speed without them. The gap between those two numbers is the size of your blind spot.
35–53%of "first answers" are an automatic template
Where we see it
  • 100%of the businesses in the sample were running one of these templates
  • Not countedMeta does not treat an auto-reply as an answer — it will not make your impressions any cheaper
06

Following up is the least-done work of all, and by hand it does not happen

That many conversations at a single business had genuine interest, then the person went quiet and nobody ever came back to them. Follow-up is either not done at all or it is dry and templated, and nobody replies to that. We tried writing individually, reading each conversation properly: it works markedly better than a template, but it is a full-time job that a manager cannot carry mid-flow, reminders or not.

How to check it yourselfCount the conversations where somebody showed interest and disappeared — then count how many of them anyone brought back. The second number is usually close to zero.
1,000+conversations with no attempt to win them back — at one business over five months

Next: what this looks like inside one conversation, and what it comes to on your numbers. Or ask us to review your own customer conversations now.

Get my free report →
Anatomy of one loss

This is what a typical lost enquiry looks like.

Assembled from real conversations — every step below matches one of the findings above.

Good evening! How much does the treatment cost? And could I come in this week?Tuesday, 22:41
Hello! We have received your message and will reply during working hours.Tuesday, 22:41
Auto-reply — the conversation counts as answered, the person has no answer
Hello! It starts from $120.Wednesday, 12:50
A real answer 14 hours later · price given, no time offered
Thanks, I’ll think about it13:02
To book, please call us: 5XX XX XX XX13:04
Pushed to the phone — they never called, and nobody ever wrote again
Your numbers

Run it on your own.

Put in what you know about your business — we will do the rest using the same proportions we measured in the conversations we went through.

Enquiries a month200
Average sale, $100
Ad budget a month, $600
Period
You are most likely losing
  • answered too late — or never answered
  • answered, but never taken to a booking
Separately — the ad budget

goes to ads that produce no bookings at all. That is money spent, not revenue forgone, so we do not add it to the figure above — otherwise the same money would be counted twice: a large share of the ad budget lands in exactly those conversations that were answered late or never answered.

This is an estimate based on averages. On your data the number will be different — and we will work it out precisely: from your conversations and your price list, not from our averages.

Run it on my data →
How we count it

A method, not a hunch.

The findings above come from thousands of real conversations at service businesses that take enquiries through messengers and social media — across several years of inbox history, including the period before any automation was in place.

One enquiryA message from a person after a day of silence, not the endless thread as a whole. Before and after are counted by the same rule on the same account.
Speed is measured to a real answerAutomatic templates are filtered out. We show the unfiltered version alongside, so the difference is visible.
Speed is read in fullThe typical case, the average and the slowest ten percent. The losses live in the tail, and the typical case hides them.
Money comes from non-overlapping groupsEach conversation belongs to exactly one cause, so nothing is double counted. Conversations that went fine are not counted as losses.
Three scenarios, not one numberConservative, base and optimistic, with the assumptions on conversion and attendance stated openly.
Every figure is labelledMeasured from data, estimated from assumptions, or a hypothesis. Different kinds are never added into one sum.
Evidence scopeThe conversation findings use anonymised aggregates from roughly 3,500 enquiries across five service businesses, including inbox history from before automation. The advertising findings use a separate 90-day analysis from 15 May to 13 August 2026 across three service businesses running Meta campaigns optimised for messages.
LimitationsRanges differ by business and period. Bookings are outcomes marked by the business, so they are a measured floor, not a guarantee. The calculator is an estimate; your audit uses your own conversations, prices and outcomes.

Want to see where your own leads are being lost?

We will work out where and how much you are losing using this method, and show you what can be done about it: from reply speed and conversation scripts to pointing your ads at bookings rather than at the number of chats. Ready within one working day after we receive access, free of charge and with no obligation.

or message us on WhatsApp

Ready to put this into your own inbox?Create my workspace

Llucky · updated 21 August 2026 · prepared from anonymised aggregates of real conversations