When I clicked on a Crash type gambling ad, I encountered something very interesting.
When I switched different accounts to enter the same Crash ad, the first time I entered, I could see the betting guide and the recharge entrance right away; but after switching accounts and entering again, I found that I could play the first two rounds for free without anything, and only after playing a few more rounds did the betting prompt appear.
Later, after repeated comparisons, I realized that the two ads were actually different, and I realized that the first time was in the "strong conversion group", while the second time I clicked into the "weak conversion group".
This incident actually explains a problem that is often overlooked in the industry: what exactly are we testing when promoting gambling mini-games?
Using this case, let's discuss the testing logic of gambling mini-games (especially Crash games) during the promotion phase from the perspective of AB testing.
From a promotional perspective, ➡️ AB testing is not only used to find "better solutions", but also to differentiate user types and decide subsequent scheduling methods.
In reality, Crash users can generally be divided into two categories:
🔴One type of user, who comes in with a very clear goal, is to bet and multiply, highly sensitive to guidance and conversion;
🔴Another type of user, more inclined to "play for a while first", tends to resist early recharge or betting prompts.
If the same conversion strategy is applied to everyone, the result is often displeasing both ends: those who want to bet complain that your guidance is not direct enough, and those who want to experience think you are "too eager".
The role of AB testing is exactly to judge through small traffic comparisons which promotional strategies are more suitable for which type of user, and even find a "one-size-fits-all version".
👾 So, what should be the focus of AB testing during the promotion phase?
In the promotion testing of Crash type games, the truly valuable test content almost entirely revolves around "whether users enter the betting state as soon as possible under guidance".
🔴The first category is the matching test of materials and landing pages.
Even with Crash ads, some materials emphasize "high multiples explode in seconds" "double in one go", while others emphasize "controllable profit stopping" "steady operation".
Then the focus of AB testing is to see which type of material brings in users who are more likely to make their first bet in the first few rounds.
🔴 The second category, the same material with different conversion strengths testing.
For example, whether a betting prompt appears on the first screen after entering the game, whether it forcibly guides users to place their first bet, whether the recharge entrance is clearly displayed in the first round.
Through AB comparison, it can be very clearly seen: whether pre-conversion is accelerating betting, or creating player loss in advance.
🔴 The third category is user behavior path testing.
That is, one of the core issues of the game: finding which users are more enthusiastic about "playing" rather than "charging".
Through AB grouping, some users can be deliberately given weaker conversion guidance, only observing their playing depth, betting frequency, and return rate; while giving another group of users a clearer betting and recharging path, comparing the differentiation in subsequent behaviors between the two groups. This type of testing can provide a basis for subsequent fine-tuning.
👾 So, how to complete user scheduling through AB testing?
The key here is whether our testing is clean enough. Each AB test should ideally answer only one question and draw one conclusion.
For example: "Are users coming in with this material more likely to bet in the first round?"
Or: "The same material, weak conversion path, will it leave more high-frequency playing users?"
When designing the experimental group and the control group, in addition to the only variable, other conditions must be kept consistent, including game visuals, odds, explosion logic, gameplay rhythm, etc., to avoid misjudging the difference in mechanisms as promotional effects.
Traffic distribution also needs to ensure that the structure of the two groups of users is similar in terms of source, device, and region, to avoid drawing incorrect conclusions due to differences in user quality.
After the test is over, focus on observing the stability of user behavior after multiple entries.
When these tests are run, the real value of AB testing will emerge: it can roughly conclude which users are worth strong conversion, and which users should be "nurtured" rather than everyone coming in with the same set of rhetoric and paths.
AB testing can differentiate users, control the pace, and reduce the risk of buying volume. The test is not just about a certain material, a certain copy, a certain channel, but also about distinguishing whether users are willing to bet faster or prefer to stay and play under different strategies.
Once these issues have been validated by data, there is truly a basis for scaling up.
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