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.