Payment protectionDetails
- Revisions within the package are free.
- If you do not respond within 3 days of delivery, the work is accepted automatically.
The money is reserved when you pay and released only once you accept the work.
Example result
Data analysis: 9 months of bookings at a family-run guesthouse
Key numbers
| Metric | Value |
|---|---|
| Bookings (excl. cancelled) | 251 |
| Room nights sold | 689 |
| Revenue | $78,540 |
| Average rate per night | $114 |
| Cancellation rate | 11.6% (33 of 284) |
| Average stay | 2.7 nights |
Summary by channel
| Channel | Bookings | Share of revenue | Avg. rate | Commission | Net per night |
|---|---|---|---|---|---|
| Booking platforms | 142 | 54% | $109 | 15% | $93 |
| Direct (phone, email, website) | 87 | 38% | $126 | 0% | $126 |
| Local travel agent | 22 | 8% | $102 | 10% | $92 |
Direct guests pay more, stay longer (3.4 nights vs 2.3) and bring $33 more per night after commission than platform guests.
Trends and outliers
- July and August made 41% of revenue; occupancy reached 93%.
- March and April were weakest (occupancy 38%), but weekend nights still sold at 79%: the gap is midweek.
- Cancellations were 19% for platform bookings with free cancellation and 4% for direct bookings with a deposit.
- Outlier: 6 bookings in May at $45 per night. These are all one corporate group with a manual discount; they were kept in totals but flagged.
Findings
- Your most profitable guests are direct, returning guests, yet only 14% of platform guests were invited to book direct next time.
- Midweek in spring is the only real gap; weekends sell themselves.
- Free cancellation on platforms costs about 27 lost nights over 9 months.
Recommendations
- Add a "book direct" card at check-out with a 10% returning-guest rate: still $20 above platform net.
- Create a midweek spring offer (3 nights for the price of 2, Sun–Thu) for March–April.
- Switch platform listings to a non-refundable rate at 8% off alongside the flexible one.
- Track the booking channel for every guest from now on: 17 rows had no channel and were grouped as "direct".
All totals were cross-checked against the monthly sums in your sheet; the difference is $0.
Client: a 12-room guesthouse by a lake, run by two owners Data: 284 booking rows, January to September (date, room type, nights, channel, price paid, cancellation) Question asked: "Which channel and which months actually make us money, and where are we losing it?"
The client and all details are made up.
About this service
Installs look good on a chart, but an app lives or dies by how many people come back on day 1, day 7 and day 30. We take your export from Firebase, Amplitude, Mixpanel or the store consoles, build retention cohorts by install week, trace the onboarding funnel step by step and find where new users drop off. You learn which step to fix first and which user groups stay longest.
What is included
- What you getA report with a cohort retention table, an onboarding funnel with drop-off at each step, comparisons by platform, country or traffic source, and a list of fixes to test. You get it on the order page and as a Word document, with the tables also as an Excel file (values only, no formulas or charts).
- Retention cohortsDay 1, day 7 and day 30 by install week
- Onboarding funnelDrop-off at every step
- Segment comparisoniOS and Android, countries, sources
- Release effectsBefore and after your updates
- Fix listWhat to change and test first
How it works
- 1BriefYou attach the export and list the steps
- 2AnalysisWe build cohorts and the funnel
- 3ReportYou get tables, drop-off points and fixes
What we need from you
Event or cohort data exported from your analytics tool as Excel or CSV, the list of onboarding steps in order, and the dates of releases or campaigns that may have changed the numbers.
We will ask you:
- Which tool is the data from?
- What are the onboarding steps, in order?
- What action means a user got value from the app?
- Did any releases or ad campaigns happen during the period?
Compare packages
| Packages | BasicUp to 100 rows$15 | StandardUp to 300 rows$35 | PremiumUp to 1,000 rows$69 |
|---|---|---|---|
| Deadline | 1 day | 2 days | 4 days |
| Revisions | 1 | 2 | 3 |
| Data rows | up to 100 | up to 300 | up to 1,000 |
| Summary tables | up to 3 | up to 6 | up to 10 |
| Segments compared | 1 | up to 3 | up to 6 |
| Order Basic | Order Standard | Order Premium |
Payment protection
You pay when you order, but the money is released only for accepted work.
- Fixed priceThe package price does not change after you pay.
- The money is reservedWhen you pay, the amount is only reserved. It is released to the service once you accept the work.
- Checked before deliveryEvery result goes through a quality check before it reaches you, and you see it on the order page before accepting it.
- Free revisionsAsk for changes within your brief as many times as the package allows.
- Acceptance after 3 daysIf within 3 days of delivery you neither accept the work nor ask for changes, it is accepted automatically. If you write in the order chat after delivery, the order waits; once we answer, we may start the 3 days again and will say so in the chat.
- Redo or a full refundIf the result does not match your brief and the revisions did not fix it, or the work takes more than twice its deadline, write in the order chat: we will redo the work or refund the payment in full.
Questions and answers
My export has raw events, not cohorts. Can you work with that?
Yes, if each row has a user ID, an event name and a date. Remove emails and names first.
Is day 7 retention of 15% good?
It depends on the category: games, utilities and subscription apps differ a lot. We compare your cohorts with each other first, since your own trend matters most.
Can you set up tracking in my app?
No, we work with the data you export. If events are missing, the report lists what to add.