The three sources bring different players to the cashier

Casino streamers, Telegram channels and social media creators bring different players to the cashier. That changes how quickly FTDs arrive, the amounts players start with and what you need to check next. Use the usual pattern to decide what to examine, then judge the source on its test result.

The usual pattern differs for each source

A casino streamer’s viewer has already seen the game. The move from interest to a first deposit usually takes less time, and the average first deposit is usually higher. That is the practical starting point for this source. It gives the brand a reason to watch the early FTD line closely, rather than treating every new reg as equally far from the cashier.

Telegram channels usually bring more regs from the same audience, with a lower average first deposit. Deposits arrive over weeks. A busy reg line followed by a slower deposit line therefore asks for a different reading from a streamer test. Follow those regs through to FTDs before deciding what the source added.

Social media creators reach the broadest, coldest audience. Regs can be plentiful, but the reg-to-FTD rate is usually lower. These are new players for the brand, which is part of the reason to consider this source. The test has to show how much of that wider interest reaches the cashier.

The source overview follows the same position. “Usually” matters in every comparison here. A particular geo can turn the pattern around. A brand can start with the likely player journey and still find that its own test tells a different story.

Choose the result the next test should examine

In source reviews, we often find that everyone is talking about a different winner. One person points to the reg count, another to fast FTDs and another to the deposit total. Each can be describing the same table accurately. The disagreement is about the job the next test should do.

For a ready product, a social creator test examines how much of a colder audience reaches FTD. A streamer test follows viewers already familiar with the game. With Telegram, the brand can follow the larger reg flow through to deposits arriving over time.

Choose the question before the result arrives. Otherwise a source can be called successful for its regs when the test goal was FTDs, or dismissed for speed when the review was meant to cover later deposits. The source row should answer the agreed test question in plain language: what reached the cashier, at what pace, and with what amounts?

One audience size can lead to very different deposit rows

This invented comparison follows the usual pattern, with no forecast for a real source. The audience sizes are assumed equal so the reg counts can be read beside each other. The product and geo are the same for all three groups.

Streamers lead on speed and average first deposit. Telegram leads on regs and repeat-depositor share. Social creators have the lowest reg-to-FTD rate. There is no single winner until the brand says which result answers its test question.

Speed belongs beside the reg-to-FTD rate

A quick first deposit tells you about players who paid. Put the reg-to-FTD rate beside it to see how much of the registration group got that far. A short wait among depositing players can still sit beside a small conversion share.

For streamers, the shorter route is usually part of the appeal. Check whether the completed test carries that through to its FTD goal. For Telegram, follow the later deposits instead of treating the first busy reg day as the whole result. For social creators, compare the wider reg flow with the share that reaches the cashier.

The cohort article handles the window mechanics. Use that method to compare pace fairly, then read what the pace tells the brand. An early streamer lead and later Telegram growth can both be true. The final table needs to preserve the result at the agreed window instead of choosing whichever date flatters a source.

A bigger average first deposit deserves a look at the players behind it

The streamer starting point is a higher average first deposit. The test still has to show how that average was reached. It could reflect many players starting with larger amounts, or a small group lifting the total. Check the spread of first-deposit amounts in the brand’s own data, grouped by geo and creator type. Deposit bands or the median show the difference while player details stay with the brand. If the average first deposit drops sharply when the largest amount is set aside, the brand knows how much that result depended on it.

Use the same formula for average first deposit in every source row; the glossary defines it. Put the FTD count beside it and read the first-deposit sum as well. A source with a lower average first deposit can still bring a larger sum if enough more regs reach FTD. The hypothetical example shows the separate rows instead of hiding them behind a score.

Use repeat behaviour to choose the next test

Repeat behaviour comes from the completed test. It may show a broad group returning or a deposit total carried by fewer players who pay larger amounts. Keep both the repeat-depositor count and the money visible when comparing that part of the result.

Repeat-depositor share answers how widely the return reaches across the FTD group. Deposit total and deposits per depositor show the amounts that came with it. In the example, Telegram has the largest repeat-depositor share while streamers still have the larger deposit total. Both results belong in the discussion.

That difference matters when choosing what to test next. Broad repeat activity may justify checking whether it holds in another test of the same pair. A concentrated total calls for a closer look at the players behind it before scaling. The deposit report puts these rows into the wider result. The brand can request repeats in the report and use those figures for the comparison.

Check the geo’s effect on the source comparison

The same source in two geos can bring different average first deposits and different reg-to-FTD rates.

Geo usually affects the figures more strongly than source type. That is why the test is always a pair: geo plus creator type.

If one brand’s streamer test runs in Portugal and its social creator test runs in Poland, the totals describe two pairs. The difference includes the geo as well as the source. A larger deposit total gives no clean reason to credit the creator type alone. For a source comparison, bring the tests into one geo.

The geo choice article covers product fit, language and the cashier. Check those before the comparison starts. If payment methods or deposit limits change during a test, mark where the change happened and read the affected part separately. A source shouldn’t get all the credit or blame for a product change that altered the route to FTD.

The test result decides where to go next

Put the three source rows beside each other only after the geo, product conditions and windows match. The usual pattern helps explain what you expected. The completed result tells you what happened. If social creators reach FTD faster in this test or Telegram has the larger average first deposit, say so. The actual result doesn’t have to fit the expected pattern.

Return to the test question and the agreed FTD goal. Read the first-deposit amounts and requested repeat rows, then ask whether they give the brand a reason to run more of the same pair. If the answer depends on a payment problem or an unfinished comparison, that problem needs an answer before a larger test.

Keep the geo when the next test compares a different source. Keep the creator type when testing another geo. Changing both may be practical, but the next result will describe a new pair. That is the distinction to carry into the next launch note.