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100% gambling fairness: how to check games results?

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Among the key pillars that influence on the trust of online casino games, Provably fair technology occupies a special place. The request from players for honesty and at the same time the evolution of crypto technologies gave the development of new transparent systems for checking the outcomes of the game. BGaming was the first major iGaming provider to offer a “Probably Fair” feature. With cryptography, players can easily verify that all game outcomes are truly unbiased and random.

Thin end of the wedge
The first semblance of a system of “proven justice for players” was represented by some individuals online casinos as the password-protected archive with thousands of outcomes for the game. This «manual way» implies that after some time the archive was published and players had the chance to check the randomness (of course, provided the player remembered all of the rounds IDs). This took place long before the cryptocurrency rush.

Crypto gave the technology a second breath, but it had a lot of disadvantages at first. The basic systems generated two numbers before the bet was placed: “server seed” by the server and “client seed” by the player. When the bet was placed the RNG made use out of these two numbers to generate the outcome of the game. This “client seed” method was clearly affecting the game result. This is unpredictable, but still affecting the results. The system proves itself, but was too hard for a regular player to use.

Blockchain helps simplify the verification process through controlled code. This code is subject to public audit in a more reliable manner. Instead of the player manually checking each round, the software can be checked once to ensure that there is no form of foul play. You can then run periodic checks to confirm that the code has not been changed.

The impact of BGaming innovations

Bearing in mind the shortcomings of the manual ways, the BGaming team developed its own result verification system for online slots.

Marina Ostrovtsova, director of BGaming explained: “We started developing slots in the early days of the cryptocurrencies boom, so this feature pretty much comes from our history as well. We are proud to state that BGaming was the first slot game developer to introduce Provably fair into the casino content world.

BGaming’s Provably Fair system unites the best aspects from the existing alternatives. Why did we build it? We wanted to offer the players something extra besides the games and we were smart enough to adjust our system to the best user experience.”

In simple terms, BGaming calculates the outcome of each round before the actual bet is placed. Everything happening after a game round is there for the player to verify the fair outcome of the game. We go into more detail on how the BGaming Provably Fair feature works here. The description of the method placed here.


Why it is a trend on the example of BGaming

  • Among top 3 BGaming games in terms of GGR volume in 2019-2020, 2 games have Provably Fair technology support.

  • Interest in BGaming games from crypto projects is growing every year by at least 15%, which creates a separate niche for our business growth

  • Operators are interested in Provably Fair games because it helps to increase their trust level among players, that’s why operators create separate sections with PF games or additionally highlight technology on games icons

The “Provably fair” feature, (developed and first implemented by BGaming) is a great way for players to feel safe. Security and transparency are two extremely important ingredients for a successful provider. Most important for our Slotwolf Casino team is to satisfy our casino players and match their demands for exciting, fair and supreme entertainment,” – noted Harald Pia from Slotwolf Casino team.

What games can be checked by the Provably Fair algorithm?

BGaming’s approach to creating new games and services is focused on regular analysis of player needs and player care. That’s why the team pays extra attention to building a portfolio of Provably Fair games. Today the BGaming’s lineup includes 35 games such as online roulette, video poker, card games and a large number of online slots. Games that support the Provably fair feature maintain leading positions in online casinos. Top brand’s slots such as Aztec Magic Deluxe, Lucky Lady Clover and Mysterious story of Avalon: Lost Kingdom found its place in BGaming’s portfolio of Provably Fair games.

The Provably Fair gaming concept has transformed the way online casinos operate. Now some of online casinos include exceptionally the Provably Fair feature games, as well as platforms aggregators, are looking for new slot providers that could offer proven fair play. In fact, the Provably Fair feature took players’ respect to a new level and opened the world of iGaming to more audiences.

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ASO 3

Pain Points in FB, PPC, ASO 3 Case Studies with Solutions by N1 Partners

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What mistakes do partners most often make at the start of ad campaigns? Why does scaling turn out to be harder than expected, and what stands on the way of getting faster profits?

The N1 Partners team presents the second article in the real case studies series (read the first one here), so you can apply the experience of N1 Partners affiliates in your own campaigns. In this section, you’ll get only practical knowledge and proven approaches from experts.

Read everything about ASO, FB, and PPC traffic in the article — no fluff, with real analytics and specific recommendations. Everything has been tested — take it and apply it!

CASE STUDY 1 (Facebook traffic)

Context

  • GEO: AU
  • Brand: N1 Bet
  • Goal: Increase conversion and reduce duplicate users
  • Bundle type: Creative + PWA App

Initial problem (“Pain”)

  • What exactly wasn’t working?
    Most incoming players were already registered. CTR was quite low, while Reg2Dep remained decent.
  • Where did the funnel break?
    At the creative viewing stage.

What did the analytics show?

  • Which metrics indicated the problem?
    Low CTR and a high number of duplicates.
  • What patterns were noticed (audience / timing / creatives)?
    Low CTR and a highly overlapping audience.
  • What was the main hypothesis?
    The creative had lost its efficiency: due to high audience coverage, new users were no longer interested.

What exactly was tested?

Creative:

  • Format: Video
  • Style: Standard dynamic video featuring a very popular slot

Message:

  • Main focus: Slot mechanics

Audience:

  • Peculiarities: None — broad standard targeting

Problem solution

  • What exactly was changed?
    The creative was replaced, made more unique, with a focus on a different slot.
  • How was the creative aligned with the product?
    Audience activity for slots within the product was analyzed, and a more engaging slot was selected.

Results and insights

  • Which metrics improved?
    CTR increased significantly. Reg2Dep remained stable. Duplicate users dropped substantially.
  • How quickly were the results visible?
    Immediately, CTR and audience stabilised right after the creative became unique.
  • Key insight:
    Don’t use top spy-service creatives without adapting them.
  • Main mistake at the start:
    Rushing for results without proper analysis and preparation.
  • How were campaigns scaled?
    By increasing the number of launched campaigns. Scaling was done quickly.

Final FAQ on Facebook traffic

  • Which mistake or underestimated factor had the biggest impact at the start?
    The biggest issue was rushing. The desire to launch campaigns quickly led to insufficient attention to creative uniqueness, reducing initial performance and requiring additional resource optimisation later.
  • If you were to relaunch this setup, what would you do differently?
    Focus more on creative uniqueness. It’s important not just to copy ideas but to refine presentation — keep the core message while experimenting with visuals, text, and triggers. This helps find more effective combinations faster.

CASE STUDY 2 (PPC traffic)

Context

  • GEO: CA 
  • Source: Google OfferWall
  • Brand: RollXO
  • Goal: Optimize FTD cost and increase conversion

Initial problem (“Pain”)

  • What wasn’t working?
    Traffic was too expensive. Costs needed optimization.
  • Which campaigns/keywords were problematic?
    There was a large number of irrelevant keywords.

What did the analytics show?

  • Which metrics signalled the issue?
    The key metric was CPC. It was 3× higher than the CPC of other partners using the same source.
  • Which keywords/segments performed the worst?
    Mainly keywords related to irrelevant slots and payment methods for the product.
  • What was the main hypothesis?
    The focus was placed on high-CPC keywords that were not aligned with the product.

What exactly was tested?

Keywords:

  • How did the approach change?
    The team added negative keywords and build a more conversion-focused landing page tailored to user intent.

Ads:

  • What copy was tested?
    One example used was: “Best online casino — play and win right now!”
    It turned out to be too generic and not specific enough, which only drove up the cost per targeted click.

Problem solution

  • What was optimized first?
    Keywords. Terms that were draining the budget without delivering results were removed and added a negative keyword list — something that hadn’t been used at all before.
  • How was the campaign structure changed?
    No changes.
  • Why was this decision made?
    As keywords were the key factor driving the high CPC.

Results and insights

  • Were there changes in CPA / ROI / CR?
    On average, traffic acquisition costs decreased by €70–90.
  • How quickly were results seen?
    The impact became noticeable within approximately 35–40 hours.
  • What had the biggest impact?
    Adding the negative keyword list delivered the desired outcome.
  • Main mistake at the start?
    Lack of experience. The partner was a newbie and wanted to scale profitable traffic as quickly as possible.
  • Is there scaling potential?
    After this optimisation, scaling the campaign is only a matter of time. The partner is already actively working on it.

Final FAQ on PPC Traffic

  • Who will benefit most from this case study: beginners or experienced teams, and why?

This case study is primarily useful for beginners. Experienced teams have usually already gone through these stages. For newcomers, it’s an opportunity to grasp the fundamentals faster, avoid common early mistakes, and not waste resources on the same pitfalls.

  • Which insights are the most universal and applicable across different traffic sources?

The key takeaway: speed does not equal quality. Being faster than competitors doesn’t mean better, just as higher spend doesn’t guarantee results. Regardless of the traffic source, analytics, testing, and proper preparation are critical.

CASE STUDY 3 (ASO traffic)

Context

  • GEO: DE
  • Platform (iOS / Android): Android
  • Brand: Lucky Hunter
  • Goal: Increase user return after registration and the first deposit

Initial problem (“Pain”)

  • What wasn’t working?
    Push notifications sent through the app were ineffective — users rarely returned to make their first or second deposit.
  • Where were users dropping off?
    The main drop-off point was right after registration.
  • Were there issues with ratings/reviews?
    Yes, but they were resolved quickly and ultimately had no impact on performance.

What did the analytics show?

  • Which metrics indicated the problem?
    The key indicator was retention.
  • What did the funnel look like?
    Unfortunately, the manager didn’t have full access to the funnel at that time, so the analysis relied mostly on available metrics and behavioral signals.
  • What was the main hypothesis?
    Initially, it seemed that the issue was low motivation for users to make their first deposit. There were also assumptions about possible misleading communication, which may have caused users to misunderstand the offer.

What exactly was tested?

Visual:

  • Visual component:
    Push notifications were sent without any visual support.

Texts:

  • Text example:
    Different variations of headlines, descriptions, and key messages were tested. For example:“Dein Bonus wartet auf dich 🎁 Hol dir +50% auf deine Einzahlung und versuche erneut dein Glück! Verpasse deine Chance nicht – das Angebot ist zeitlich begrenzt ⏳

    This was one of the push notification variants used by the partner to attract attention.

Problem solution

  • What exactly was changed in the store?
    Changes in the store were minimal — reviews were slightly updated and refreshed.
  • Which elements contributed the most?
    Push notification optimization and updated bonus information delivered the strongest impact.
  • Why was this approach chosen?
    A mismatch was identified: users were receiving outdated bonus information in communications, which directly affected their expectations and subsequent behavior.

Results and insights

  • How did performance metrics change (CVR / installs / organic)?
    The main growth came from first and second deposits. Within a week, Reg2Dep conversion increased from 14.77% to 31.17%.
  • How quickly were results achieved?
    The first improvements were noticeable within 1–2 days.
  • Which changes had the biggest impact?
    Adjustments to push communication and updating the bonus offer — these became the main drivers of conversion growth.
  • Is there scaling potential?
    Yes, these results are scalable. As long as the offer remains actual and communication stays consistent, the model shows stable performance.

Final FAQ on ASO Traffic

 

  1. What takeaway from this case study can be directly applied to other campaigns without losing effectiveness?
    The key takeaway is to always keep a bonus and offer information up-to-date and synchronised across all communication touchpoints. Even small discrepancies can significantly impact results.

 

  1. At what point did it become clear that the approach was working, and what supported the decision to scale?
    The first signals appeared after test push campaigns, showing improved engagement with first and second deposits. This confirmed the hypothesis, and subsequent results reinforced confidence in the approach.

All of these case studies show that growth in Facebook, PPC, and ASO traffic comes down to systematic work with analytics, creatives, and communication at every stage of the funnel. Any performance drop is an opportunity for optimisation that, when handled correctly, can quickly turn into profit.

Start working with N1 Partners — here you’ll get not just offers, but full-scale expertise and support to help you find winning setups faster and scale with confidence.

The post Pain Points in FB, PPC, ASO 3 Case Studies with Solutions by N1 Partners appeared first on Eastern European Gaming | Global iGaming & Tech Intelligence Hub.

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game launches

Ten Square Games starts technical release for Medal Hunter ahead of global launch

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Ten Square Games has begun a phased rollout for Medal Hunter, a new mobile PvP shooter for iOS and Android. The title entered technical release on 4 May, with global availability planned around the turn of May and June, subject to further improvements.

The initial rollout covers Mexico, Vietnam, the Philippines and Poland. Ten Square Games said this stage is focused on verifying technical KPIs and performance stability, while the team fine-tunes gameplay parameters.

Around mid-May, Medal Hunter is expected to move into a broader soft launch, with gradual availability in Australia, Germany, the United Kingdom and the United States. The company said the focus will then shift to validating short-term retention and engagement.

Medal Hunter is set in combat environments inspired by different historical periods, with architecture and weapons “strongly influenced” by real references but stylized for mobile play. At launch, the game includes five locations, and players compete in short PvP rounds by eliminating moving targets including aircraft and naval units, using two different shooting models.

CEO Andrzej Ilczuk said the project builds on Ten Square Games’ development approach used for Trophy Hunter: “Medal Hunter is an example of how we are putting our growth strategy into practice. Trophy Hunter helped us build a new development model based on clear benchmarks, early validation and a better understanding of the signals that matter before scaling a product. Medal Hunter capitalizes on that experience and on the broader product knowledge we have built across our portfolio. By using proven gameplay mechanics and working in this model, we were able to bring a new title to market in less than a year from the start of development. This gives us earlier insight into a game’s potential, helps limit development risk and allows us to shape products more closely around what players actually respond to”.

The post Ten Square Games starts technical release for Medal Hunter ahead of global launch appeared first on Eastern European Gaming | Global iGaming & Tech Intelligence Hub.

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ASO 3

Pain Points in FB, PPC, ASO 3 Case Studies with Solutions by N1 Partners

Published

on

pain-points-in-fb,-ppc,-aso-3-case-studies-with-solutions-by-n1-partners

What mistakes do partners most often make at the start of ad campaigns? Why does scaling turn out to be harder than expected, and what stands on the way of getting faster profits?

The N1 Partners team presents the second article in the real case studies series (read the first one here), so you can apply the experience of N1 Partners affiliates in your own campaigns. In this section, you’ll get only practical knowledge and proven approaches from experts.

Read everything about ASO, FB, and PPC traffic in the article — no fluff, with real analytics and specific recommendations. Everything has been tested — take it and apply it!

CASE STUDY 1 (Facebook traffic)

Context

  • GEO: AU
  • Brand: N1 Bet
  • Goal: Increase conversion and reduce duplicate users
  • Bundle type: Creative + PWA App

Initial problem (“Pain”)

  • What exactly wasn’t working?
    Most incoming players were already registered. CTR was quite low, while Reg2Dep remained decent.
  • Where did the funnel break?
    At the creative viewing stage.

What did the analytics show?

  • Which metrics indicated the problem?
    Low CTR and a high number of duplicates.
  • What patterns were noticed (audience / timing / creatives)?
    Low CTR and a highly overlapping audience.
  • What was the main hypothesis?
    The creative had lost its efficiency: due to high audience coverage, new users were no longer interested.

What exactly was tested?

Creative:

  • Format: Video
  • Style: Standard dynamic video featuring a very popular slot

Message:

  • Main focus: Slot mechanics

Audience:

  • Peculiarities: None — broad standard targeting

Problem solution

  • What exactly was changed?
    The creative was replaced, made more unique, with a focus on a different slot.
  • How was the creative aligned with the product?
    Audience activity for slots within the product was analyzed, and a more engaging slot was selected.

Results and insights

  • Which metrics improved?
    CTR increased significantly. Reg2Dep remained stable. Duplicate users dropped substantially.
  • How quickly were the results visible?
    Immediately, CTR and audience stabilised right after the creative became unique.
  • Key insight:
    Don’t use top spy-service creatives without adapting them.
  • Main mistake at the start:
    Rushing for results without proper analysis and preparation.
  • How were campaigns scaled?
    By increasing the number of launched campaigns. Scaling was done quickly.

Final FAQ on Facebook traffic

  • Which mistake or underestimated factor had the biggest impact at the start?
    The biggest issue was rushing. The desire to launch campaigns quickly led to insufficient attention to creative uniqueness, reducing initial performance and requiring additional resource optimisation later.
  • If you were to relaunch this setup, what would you do differently?
    Focus more on creative uniqueness. It’s important not just to copy ideas but to refine presentation — keep the core message while experimenting with visuals, text, and triggers. This helps find more effective combinations faster.

CASE STUDY 2 (PPC traffic)

Context

  • GEO: CA 
  • Source: Google OfferWall
  • Brand: RollXO
  • Goal: Optimize FTD cost and increase conversion

Initial problem (“Pain”)

  • What wasn’t working?
    Traffic was too expensive. Costs needed optimization.
  • Which campaigns/keywords were problematic?
    There was a large number of irrelevant keywords.

What did the analytics show?

  • Which metrics signalled the issue?
    The key metric was CPC. It was 3× higher than the CPC of other partners using the same source.
  • Which keywords/segments performed the worst?
    Mainly keywords related to irrelevant slots and payment methods for the product.
  • What was the main hypothesis?
    The focus was placed on high-CPC keywords that were not aligned with the product.

What exactly was tested?

Keywords:

  • How did the approach change?
    The team added negative keywords and build a more conversion-focused landing page tailored to user intent.

Ads:

  • What copy was tested?
    One example used was: “Best online casino — play and win right now!”
    It turned out to be too generic and not specific enough, which only drove up the cost per targeted click.

Problem solution

  • What was optimized first?
    Keywords. Terms that were draining the budget without delivering results were removed and added a negative keyword list — something that hadn’t been used at all before.
  • How was the campaign structure changed?
    No changes.
  • Why was this decision made?
    As keywords were the key factor driving the high CPC.

Results and insights

  • Were there changes in CPA / ROI / CR?
    On average, traffic acquisition costs decreased by €70–90.
  • How quickly were results seen?
    The impact became noticeable within approximately 35–40 hours.
  • What had the biggest impact?
    Adding the negative keyword list delivered the desired outcome.
  • Main mistake at the start?
    Lack of experience. The partner was a newbie and wanted to scale profitable traffic as quickly as possible.
  • Is there scaling potential?
    After this optimisation, scaling the campaign is only a matter of time. The partner is already actively working on it.

Final FAQ on PPC Traffic

  • Who will benefit most from this case study: beginners or experienced teams, and why?

This case study is primarily useful for beginners. Experienced teams have usually already gone through these stages. For newcomers, it’s an opportunity to grasp the fundamentals faster, avoid common early mistakes, and not waste resources on the same pitfalls.

  • Which insights are the most universal and applicable across different traffic sources?

The key takeaway: speed does not equal quality. Being faster than competitors doesn’t mean better, just as higher spend doesn’t guarantee results. Regardless of the traffic source, analytics, testing, and proper preparation are critical.

CASE STUDY 3 (ASO traffic)

Context

  • GEO: DE
  • Platform (iOS / Android): Android
  • Brand: Lucky Hunter
  • Goal: Increase user return after registration and the first deposit

Initial problem (“Pain”)

  • What wasn’t working?
    Push notifications sent through the app were ineffective — users rarely returned to make their first or second deposit.
  • Where were users dropping off?
    The main drop-off point was right after registration.
  • Were there issues with ratings/reviews?
    Yes, but they were resolved quickly and ultimately had no impact on performance.

What did the analytics show?

  • Which metrics indicated the problem?
    The key indicator was retention.
  • What did the funnel look like?
    Unfortunately, the manager didn’t have full access to the funnel at that time, so the analysis relied mostly on available metrics and behavioral signals.
  • What was the main hypothesis?
    Initially, it seemed that the issue was low motivation for users to make their first deposit. There were also assumptions about possible misleading communication, which may have caused users to misunderstand the offer.

What exactly was tested?

Visual:

  • Visual component:
    Push notifications were sent without any visual support.

Texts:

  • Text example:
    Different variations of headlines, descriptions, and key messages were tested. For example:“Dein Bonus wartet auf dich 🎁 Hol dir +50% auf deine Einzahlung und versuche erneut dein Glück! Verpasse deine Chance nicht – das Angebot ist zeitlich begrenzt ⏳

    This was one of the push notification variants used by the partner to attract attention.

Problem solution

  • What exactly was changed in the store?
    Changes in the store were minimal — reviews were slightly updated and refreshed.
  • Which elements contributed the most?
    Push notification optimization and updated bonus information delivered the strongest impact.
  • Why was this approach chosen?
    A mismatch was identified: users were receiving outdated bonus information in communications, which directly affected their expectations and subsequent behavior.

Results and insights

  • How did performance metrics change (CVR / installs / organic)?
    The main growth came from first and second deposits. Within a week, Reg2Dep conversion increased from 14.77% to 31.17%.
  • How quickly were results achieved?
    The first improvements were noticeable within 1–2 days.
  • Which changes had the biggest impact?
    Adjustments to push communication and updating the bonus offer — these became the main drivers of conversion growth.
  • Is there scaling potential?
    Yes, these results are scalable. As long as the offer remains actual and communication stays consistent, the model shows stable performance.

Final FAQ on ASO Traffic

  1. What takeaway from this case study can be directly applied to other campaigns without losing effectiveness?
    The key takeaway is to always keep a bonus and offer information up-to-date and synchronised across all communication touchpoints. Even small discrepancies can significantly impact results.

 

  1. At what point did it become clear that the approach was working, and what supported the decision to scale?
    The first signals appeared after test push campaigns, showing improved engagement with first and second deposits. This confirmed the hypothesis, and subsequent results reinforced confidence in the approach.

All of these case studies show that growth in Facebook, PPC, and ASO traffic comes down to systematic work with analytics, creatives, and communication at every stage of the funnel. Any performance drop is an opportunity for optimisation that, when handled correctly, can quickly turn into profit.

Start working with N1 Partners — here you’ll get not just offers, but full-scale expertise and support to help you find winning setups faster and scale with confidence.

The post Pain Points in FB, PPC, ASO 3 Case Studies with Solutions by N1 Partners appeared first on Americas iGaming & Sports Betting News.

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