There’s No Such Thing as a Free Lunch… Except Sometimes There Is
In the app world, we’ve all heard the saying: If you’re not paying with money, you’re paying with your data, attention, or time.
That belief is so deeply rooted that when something genuinely comes free, people don’t trust it.
In the app world, we’ve all heard the saying: If you’re not paying with money, you’re paying with your data, attention, or time. That belief is so deeply rooted that when something genuinely comes free, people don’t trust it.
And OwlEye learned that the hard way.
The Free App Backlash
When OwlEye launched publicly, I proudly positioned it as a free app. Ironically, that became a problem. People didn’t see “free” as a benefit; they saw it as suspicious.
Questions started pouring in:
- How are you making money?
- What are you doing with my data?
- Is this safe?
Some users even assumed there had to be a hidden catch. The truth? There wasn’t. But convincing people of that turned out to be harder than building the app itself.
Free wasn’t trust-building. It was trust-breaking.
The Silent Leak
OwlEye sends price-drop alerts with one clear goal: help users save money. Naturally, you’d expect those notifications to convert into purchases through the app. But here’s what actually happens most of the time:
User gets alert → Opens Amazon directly → Buys there.
Owleye did the hard work - tracking, monitoring, alerting - but the purchase bypasses us entirely.
The app drives intent, but Amazon captures the sale. It’s a silent traffic leak that most people never notice.
Indirect Traffic
Most of the sales on OwlEye come from indirect traffic through the Deals page rather than users searching for a specific product. The Deals page acts as a discovery surface where users browse products that have recently dropped in price. To identify meaningful deals, OwlEye continuously tracks historical pricing and calculates the 30-day average price for each product. When the current price falls at least 5% below this 30-day average, the product is automatically flagged and surfaced on the Deals page. This approach helps filter out temporary or insignificant fluctuations and highlights products that are genuinely cheaper than their typical market price, making it easier for users to discover worthwhile discounts.
The Affiliate Misconception
Another hurdle? Affiliates.
Many users believe that buying through an affiliate link means they’re paying extra to cover commission. That’s simply not true. Amazon pays the affiliate from their margin - not from the user’s pocket. The price remains exactly the same.
When “Helping Too Much” Looks Suspicious
At one point, Owleye automatically selected the Amazon region based on the user’s device settings.
The intention was simple: remove friction and make things seamless.
Instead, it triggered doubt. Users started wondering:
How does the app know my region? Is it tracking me? Is this accessing private data?
Even though it was just reading the standard device locale (something every app can do an din-built into Android and iOS), the feature created discomfort. So we removed it entirely and gave users manual control to choose their Amazon region.
A classic product lesson:
Transparency always builds more trust.
Giving Out Too Much Information
In the early versions of Owleye, I went overboard with data.
I thought more stats would make the app look powerful and trustworthy. So I added extra numbers, comparisons, and insights that most users didn’t even ask for - and honestly, I wouldn’t add them again today.
What happened next was unexpected. Some of the users started digging into every detail: questioning minor price fluctuations, overanalysing patterns, asking for even deeper breakdowns
That’s when it clicked.
The more information you give, the more users expect.
And expectations grow faster than features.
Instead of helping users make faster decisions, the excess data was slowing them down and creating confusion. So we intentionally scaled things back and kept only what truly matters.
Owleye’s goal isn’t to overwhelm users with numbers.
It’s meant to answer one simple question clearly:
Is this a good time to buy or not?
Owleye’s journey hasn’t just been about tracking prices.
It’s been about learning how users think, doubt, mistrust, and react.
Building features is easy. Building trust is the real product work.
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