I’ve been working on a new online project for quite a while now.
I’m not ready to talk about what it is yet, but lately I’ve been spending a surprising amount of time on something I thought I had already done:
finishing it.
At some point, all the important pieces were working.
The main features were there. The pages worked. Nothing major was obviously broken.
That feels like the finish line.
It isn’t.
Once I started going through everything more carefully, I kept finding little things.
A sentence that sounded awkward.
Something that didn’t look quite right on my phone.
A step that made perfect sense to me because I already knew what was supposed to happen.
None of these were big problems.
But together, they made me realize there’s a big difference between something that works and something that is actually ready.
You Already Know Too Much
I think this is one of the hardest parts of working on anything yourself.
You know what everything is supposed to mean.0
You know the backstory.
The person seeing it for the first time knows none of that.
So instead of asking:
Does this work?
I’ve started asking:
Would this make sense if I had never seen it before?
That’s a much better question.
And it doesn’t just apply to building websites.
This Applies to Marketing Too
This is where I think it gets useful for almost anybody working online.
Think about something you’re promoting right now.
Maybe it’s an email, a landing page, or an ad you’ve been running for a while.
You probably know exactly what it is, what it does, and what you want people to notice.
But would somebody seeing it for the first time know the same thing?
That’s where things get interesting.
Look at your promotion without filling in the blanks yourself.
Is it immediately obvious what it’s about?
Is the next step clear?
Could something be simplified?
Sometimes improving a promotion isn’t about adding more.
Sometimes it’s just about making what’s already there easier to understand.
The Same Thing Happens With Almost Everything
We finish something, make sure it works, and move on. That makes sense.
The problem is that “finished” makes us stop looking.
Sometimes one more pass is where you catch the friction — something confusing, unnecessary, or not nearly as obvious as you thought it was.
Those can be small changes, but they can completely change how something feels.
I’m Learning to Respect the Last 10%
I used to think getting the major pieces done meant a project was basically finished.
Now I’m starting to think the last 10% is where you stop looking at what you made as the person who created it and start looking at it from somebody else’s point of view.
That applies whether you’re building something big or writing a short email.
Before you call something done, give it one more look.
Not as the person who made it.
As the person who is about to see it for the first time.
You may be surprised what suddenly jumps out at you.
Talk soon,
Jerry
P.S. Pick one thing you’re currently promoting and look at it with fresh eyes today. Don’t ask whether it works. Ask whether it’s obvious. That’s a much tougher test.
I’ve been using traffic exchanges for more than 20 years.
Over that time I’ve used a lot of them, but these days my actual surfing routine is pretty simple.
I have a few traffic exchanges I use regularly, a couple of traffic exchange games that help me decide where to spend some extra surfing time, and Harvest Traffic to spread my advertising around even further.
This isn’t supposed to be a ranking of the best traffic exchanges in 2026.
These are simply the ones I’m actually using right now.
Some of the links in this post are affiliate links. If you join through one of them, I may earn a commission at no extra cost to you.
How I Surf
Before I get into the traffic exchanges, I should mention Camel Tabs.
Camel Tabs lets me manually surf several traffic exchanges at the same time. The free version lets you have up to seven going at once.
That’s usually plenty for me.
I load up the exchanges I want to surf, let Camel Tabs keep track of the timers, and click my way through them.
Nothing fancy.
TrafficG
TrafficG is probably the one I come closest to surfing every day.
It has a 1:1 surf ratio, a big audience, and a simple daily incentive I like.
Surf more than five sites and you’re entered into the next day’s credit drawing. The prize is currently 3,000 credits.
I try to at least qualify for the drawing. If I have room in my Camel Tabs rotation, I’ll usually surf more.
TrafficG has been around a long time and still feels worth the time I put into it.
EasyHits4U
EasyHits4U has also been part of my routine for years.
I don’t think it’s quite as strong as it was at its peak, but it’s still a huge traffic exchange and it still gives me a 1:1 surf ratio.
That’s enough to keep it in my rotation.
Simple as that.
Traffic Ad Bar
Traffic Ad Bar is a little different from the normal traffic exchange.
There are rankings, points and other parts of the system you can pay attention to.
I don’t really worry about any of that.
I surf.
Traffic Ad Bar handles the rest and gets my advertising shown through its network.
It’s unique, it has a lot of reach, and I think the time spent surfing there is well worth it.
Hungry For Hits
Hungry For Hits is one of my favorites because it feels like the place where traffic exchange people hang out.
When I surf there, I see a lot of familiar names and faces.
The chat is usually active too.
If I’m promoting something aimed at traffic exchange users, Hungry For Hits is one of the places where I want my ads to be seen.
The Food Game
I’ve written about The Food Game quite a bit over the years.
A lot of traffic exchanges participate in it.
As you surf participating sites, the Food Game logo appears every so often and you collect food.
There’s an entire game built around cooking recipes, earning points and moving through the system.
I don’t really do any of that. :-)
I usually just trade my food in for gold, which I can use toward traffic exchange upgrades.
What matters to my surfing routine are the Tasty and Delicious traffic exchanges.
Those change every week and give better Food Game rewards.
So if I’m going to spend time surfing anyway, I might as well spend some of it at the exchanges that are paying out the most Food Game rewards that week.
There are also certain traffic exchanges that give boosted rewards each day.
So again, my thinking is pretty simple.
If I’m already going to surf traffic exchanges that participate in Viral Traffic Games, I might as well spend some of that time at the ones giving me the best rewards that day.
Between The Food Game and Viral Traffic Games, the exact mix of traffic exchanges I surf changes quite a bit without me having to think too hard about it.
At just about every traffic exchange I use, I have at least one website slot pointing to my Harvest Traffic co-op link.
I’m upgraded at Harvest Traffic, so I get a 1:1 ratio.
That means every visitor I send into Harvest Traffic earns me another visitor that gets sent back out to one of my pages through the co-op.
I like that setup because it lets me concentrate my own surfing on the places I actually want to use while still getting my pages shown across a much wider group of traffic sources.
I don’t have to personally surf everything.
Harvest Traffic helps take care of that part for me.
My Traffic Exchange Routine Is Pretty Simple Now
That’s really what my traffic exchange routine looks like in 2026.
TrafficG, EasyHits4U, Traffic Ad Bar and Hungry For Hits are the main sites I keep coming back to.
The Food Game and Viral Traffic Games influence where else I spend some surfing time.
Harvest Traffic helps spread my advertising beyond the sites I surf myself.
And Camel Tabs makes the whole thing manageable.
That’s about it.
After all these years, I don’t feel any need to make traffic exchange surfing more complicated than that.
I’d rather spend my time on a handful of sites I like, take advantage of the extra rewards where I can, and let the co-op help spread the traffic around.
Which traffic exchanges are you actually surfing in 2026?
Let me know in the comments. I’m always curious to see where everybody else is spending their time.
And just 20 of those visitors generated 69 of the clicks.
Almost half.
That was the final surprise in my Safelist Attention Project.
Most People Clicked Once. A Small Group Kept Clicking.
Throughout this experiment, I used a persistent browser cookie to identify returning visitors.
That doesn’t guarantee every visitor ID represents a completely separate physical person. Someone using another browser or device could receive another ID.
But it gave me a reasonable way to distinguish between a tracked browser appearing once and one coming back later.
Out of 7,380 distinct tracked visitor IDs, only 94 ever clicked the attention button.
Of those 94:
Clicker Type
Tracked Clickers
Attention Clicks
Share of Clicks
Clicked once
74
74
51.7%
Clicked on multiple visits
20
69
48.3%
Total
94
143
100%
That’s the number that really changed how I looked at the experiment.
74 tracked visitors clicked once and generated 74 clicks.
20 repeat clickers generated 69 clicks.
Nearly the same number of clicks.
From dramatically different numbers of people.
That Small Group Was Really Small
Those 20 repeat clickers represented only 21.3% of the people who clicked at all.
Yet they generated 48.3% of every attention click I recorded.
Put against the entire experiment, the number gets even more surprising.
There were 7,380 distinct tracked visitor IDs.
Only 20 became repeat clickers.
That’s about 0.27% of all tracked visitors.
Now, I want to be careful with that number.
I’m not saying 0.27% of the audience created half the value of the campaign.
They didn’t.
This experiment measured one very specific thing: whether somebody deliberately clicked the button telling me they had noticed the page.
What the number shows is much narrower—and still interesting:
A very small group generated a surprisingly large share of the measured interaction.
Repeat Clicking Usually Meant More Than One Extra Click
At first, I wondered if “repeat clicker” mostly meant somebody saw the experiment twice and clicked both times.
That did happen.
But it wasn’t the whole story.
Of the 20 repeat clickers:
9 clicked twice
11 clicked three or more times
several clicked four, five, or six times
one tracked visitor clicked on eight separate visits
So more than half of the repeat-clicker group interacted at least three times.
And the result wasn’t being distorted by one extreme visitor.
The most active tracked visitor produced eight clicks.
The remaining repeat clicks were spread across a small group that kept interacting when they encountered the experiment again.
Why?
I don’t know.
Maybe they recognized the experiment.
Maybe they enjoyed participating.
Maybe the page became familiar.
Maybe they remembered the unusual image.
Maybe they simply thought, “There’s Jerry’s experiment again. I’ll click the button.”
The experiment never asked them why they clicked again, so I don’t want to invent an explanation.
The safest conclusion is simply this:
The same tracked visitors remained willing to deliberately interact on later visits.
Most Returning Visitors Still Never Clicked
There’s another number that keeps all of this in perspective.
There were 288 tracked visitors who appeared more than once during the experiment.
But returning did not automatically mean engaging.
Of those 288 repeat visitors:
247 never clicked
21 clicked on exactly one visit
20 clicked on multiple visits
So only 6.9% of all returning visitors became repeat clickers.
That’s important.
I don’t want to look at these numbers and say:
“Just keep showing people the same ad and they’ll eventually become highly engaged.”
The data doesn’t say that.
Most returners still never clicked.
What happened instead was that a relatively small subgroup behaved very differently from everybody else.
Ten Clicks Can Tell Two Completely Different Stories
This may be the most useful lesson I’ve taken from this final part of the experiment.
Imagine two campaigns.
Campaign A gets 10 clicks from 10 different people.
Campaign B gets 10 clicks from three people who keep returning and interacting.
If you’re only looking at the click counter, both campaigns say:
10 clicks.
But those numbers are telling very different stories.
Campaign A shows more breadth.
Campaign B shows more repeat engagement.
Which would I rather have?
That depends on what I’m trying to accomplish.
If I’m looking for new leads, I’d probably be more interested in reaching more different people.
If I’m trying to build recognition or familiarity, repeat interaction may also be useful.
And if I’m trying to make sales?
Then neither number tells me enough.
I’d want actual conversion data.
That’s something this experiment has kept reminding me:
The value of a metric depends on what you’re trying to learn from it.
Different Safelists Produced Different Patterns
This also helped explain something I had noticed earlier in the project.
Not every safelist produced the same kind of attention.
Mister Safelist generated 14 attention clicks from 14 different tracked visitors.
That’s broad participation.
SendCircle produced 32 clicks from 20 tracked clickers, including five repeat clickers who generated 17 clicks between them.
My Daily Mailer showed a similar mix: 30 clicks from 19 tracked clickers, with five repeat clickers generating 16 clicks.
Then there were sources such as Zodiac Mailer, where eight clicks came from two tracked visitors who both interacted repeatedly.
I don’t look at those patterns as better or worse.
They’re different.
One source may produce broader participation.
Another may produce stronger repeat engagement from a smaller responsive group.
The important thing is that the total click number by itself doesn’t show the difference.
Why I Want Two Numbers From Now On
Before this experiment, if somebody told me an ad received 100 clicks, my first instinct would probably be to compare that number with another source that received 80 or 120.
Now I want another number sitting beside it:
How many different tracked visitors produced those clicks?
Because:
100 clicks from 100 people tells me one story.
100 clicks from 50 people tells me another.
100 clicks from 20 people tells me something else entirely.
None of those outcomes is automatically good or bad.
But without knowing both total clicks and unique clickers, I don’t really know what kind of response I’m looking at.
That’s a lesson I can use far beyond safelists.
The same idea could matter with email, content marketing, recurring advertising, social media, or anywhere else people can interact with something more than once.
One More Interesting Connection To The Eyes
There was one other result I couldn’t help noticing.
Earlier in this series, I looked at what people said made them notice the experiment page.
The eyes in the image barely won among people’s first responses.
But among the 29 additional follow-up answers submitted by repeat respondents, 21 selected the eyes.
That doesn’t prove the eyes caused repeat engagement.
It doesn’t even prove they were the reason somebody remembered the page.
But it does make me wonder whether that visual became especially recognizable once people had seen the experiment before.
Maybe the creative wasn’t only noticeable.
Maybe, for some people, it became familiar.
That’s something I’d have to test separately.
What This Doesn’t Prove
There are a few important limits to these results.
The visitor IDs were cookie-based, so they represent tracked browsers rather than guaranteed individual human identities.
A repeat click does not equal a new lead, a sale, or stronger buying intent.
The experiment didn’t ask people why they clicked again.
Visitors weren’t randomly assigned a certain number of exposures.
And this wasn’t a normal sales page.
The page openly told people they were part of an experiment and asked them to click a button if they noticed it.
Someone who returned later may simply have recognized the page and remembered what I was asking them to do.
That’s still deliberate interaction.
But I wouldn’t assume a commercial offer would produce exactly the same behavior.
What This Whole Experiment Changed For Me
When I started this project back in July, I really thought I was going to find out which safelists were best at getting attention.
I did learn quite a bit about the safelists.
But that wasn’t the most interesting part.
I learned that traffic and attention aren’t the same thing.
I learned there can be more than one kind of winner, depending on what I measure.
I learned that people notice different things.
I learned that some people don’t respond the first time they see an ad.
And now I’ve learned that even after people do respond, the attention itself isn’t necessarily spread evenly.
Some of it was broad.
Some of it came from repeat engagement.
Some visitors never responded at all.
And a very small group generated a surprisingly large share of the visible activity.
I started with a traffic question.
I ended up with a measurement lesson.
The number on the counter is only the beginning of the story.
From now on, if I see that an ad got 100 clicks, I don’t think I’m going to be satisfied with knowing there were 100 clicks.
I’m going to want to know one more thing:
Who produced them?
That’s probably the biggest lesson I’m taking away from the Safelist Attention Project.
Don’t just count the clicks. Understand who is producing them.
And with that, I’m going to call this the final core article in this experiment.
I’ve gotten far more out of that strange little page than I expected when I started sending it through safelists in July.
I’m not finished experimenting, though.
So now I’d like to turn the next one over to you.
If I run another marketing attention experiment, what would you like me to test?
This time, I wanted to answer a different question:
What happened when somebody saw the page more than once?
If they didn’t click the first time, was that opportunity gone?
Or could another exposure give the ad a second chance?
Most People Only Saw It Once
The final analysis included 8,392 recorded visits from 7,380 tracked visitor IDs.
Most of them appeared only once.
7,092 appeared once
288 appeared more than once
So only about 3.9% of the tracked visitors returned for another recorded exposure.
For this article, an exposure simply means another recorded page load under the same visitor ID.
That could have come from another mailing, a return visit, a delayed click, or even a refresh. It does not necessarily mean somebody received the exact same email twice.
When Did People First Click?
There were 94 distinct tracked visitors who clicked the attention button at least once.
Here’s when they clicked for the first time:
The biggest takeaway is obvious.
72 of the 94 people who eventually clicked did it on their first recorded exposure.
That’s 76.6%.
So most of the attention happened right away.
But that also means 22 people — 23.4% of all eventual clickers — did not respond the first time.
Nearly one-quarter of the people who eventually clicked needed another opportunity.
That seems worth paying attention to.
The Second Exposure Was Especially Interesting
On Exposure #1, all 7,380 tracked visitors were eligible to become first-time clickers.
72 clicked.
That works out to a first-click rate of about 0.98%.
Now look at Exposure #2.
Among people who returned without having clicked the first time, 269 were still eligible to become first-time clickers.
13 of them clicked.
That works out to 4.83%.
So the first-click rate among those returners was nearly five times the rate on the first exposure.
That sounds dramatic.
But there’s an important catch.
The Second Group Was Different
I’d love to say the second exposure made people five times more likely to click.
The numbers don’t quite let me say that.
Everybody was included in Exposure #1.
Exposure #2 only included people who came back.
Those people may have been more active safelist users, more curious, more likely to revisit pages, or exposed to my campaign differently.
They were a self-selected group.
I didn’t randomly divide people into a “see it once” group and a “see it twice” group.
So this does not prove that repetition caused the higher click rate.
What it does show is that among the people who naturally returned without responding the first time, the second encounter created quite a few new responses.
That alone is useful.
A First Non-Response Wasn’t Always the End
This may be the most practical lesson for me.
It’s easy to run an ad, see that somebody didn’t respond, and assume the message failed.
Sometimes that may be true.
But in this experiment, 22 eventual clickers passed through once without clicking and responded later.
Thirteen responded on Exposure #2.
Another nine responded after that.
I can’t say exactly why.
Maybe the page looked more familiar.
Maybe they were less distracted the next time.
Maybe curiosity had another chance to work.
I don’t know.
What I do know is that one missed opportunity did not always mean the ad had lost them forever.
But More Repetition Wasn’t Automatically Better
The extra response also faded pretty quickly.
After the 13 new clickers on Exposure #2, the numbers dropped to:
3 on Exposure #3
3 on Exposure #4
2 on Exposure #5
1 on Exposure #6
0 after that
In this dataset, every tracked visitor who eventually clicked had done so by their sixth recorded exposure.
I would not turn that into some kind of “six exposure rule.”
The later groups were small, and this was one unusual advertising experiment.
To me, the pattern is simpler:
The first exposure did most of the work. The second created another useful opportunity. After that, new response became increasingly rare.
So How Long Should You Keep Running an Ad?
I don’t think this experiment gives us one perfect number.
What it does suggest is a reasonable middle ground.
If you have an ad that you believe is strong, don’t necessarily abandon it just because someone didn’t respond the first time.
Give it another chance.
Maybe even a few.
But don’t use repetition as an excuse to run the same ad forever either.
Eventually, you may be better off testing another headline, changing the creative, rotating the ad, or bringing it back again later.
For me, the takeaway is:
A first non-response is not always the end of the story.
Most people who noticed my experiment responded right away.
Some responded only after seeing it again.
And eventually, the supply of new attention dried up.
That feels a lot more useful than trying to force the results into some magic advertising rule.
There’s one more strange pattern hiding in the repeat-visitor data.
A small group didn’t just return and click for the first time.
They kept clicking on later visits even after they had already responded once.
