Case Studies

Real projects,
real decisions.

Each project on this page began with a specific monetization problem. What follows is an honest account of what was diagnosed, what changed, and what the numbers looked like afterward.

Game monetization strategy session at Dremkoval
Monetization strategy in practice

At a glance — across all projects

38+

Games audited and restructured for sustainable revenue

6mo

Average engagement period per project from audit to implementation

4×

Typical difference between initial and final ARPU after model restructure

91%

Of clients returned for a second engagement within 18 months

Selected project cases

Three projects, three different starting conditions. Each one required a different monetization diagnosis and a different path forward.

Mobile RPG monetization audit project
Mobile RPG

A mid-core RPG with strong retention but flat revenue

The studio behind this title had built solid daily engagement — players returned consistently, session lengths were healthy, and reviews were positive. Revenue was not matching any of it. The monetization layer was almost entirely cosmetic, with no friction-reducing offers and no progression-based purchase moments.

After a full model audit, we introduced a layered offer system tied to natural progression bottlenecks, adjusted ad placement to non-intrusive moments, and created a seasonal battle pass structure aligned with the existing lore. The studio's team handled implementation; we handled the logic architecture and pricing calibration.

+340% ARPU in 5 months
-8% Churn change (acceptable)
14 wk From audit to live
Casual puzzle game monetization restructure
Casual Puzzle

Ad revenue dependency with no in-app purchase path

A casual puzzle game with over 400 levels and a loyal player base was generating income almost entirely from interstitial ads. When ad network CPMs dropped by 35% across a quarter, the revenue drop was immediate and severe. The studio had never built an IAP flow and was unsure whether their audience would pay for anything.

We ran a targeted segmentation study using their existing analytics data, identified the top 12% of engaged players as viable IAP candidates, and designed a minimal purchase offering — three tiers, no subscriptions, focused on removing the most annoying friction points in late-game progression. The rollout was gradual, starting with 20% of the player base.

+180% Total monthly revenue
7.4% Conversion rate, top segment
8 wk Rollout period

How each engagement runs

Every project follows the same five-stage structure. The content at each stage differs depending on the game's category, audience, and existing data quality — but the sequence does not change. This keeps scope controlled and expectations clear from day one.

"The audit revealed three things we had never thought to measure. One of them turned out to be the actual root cause. Without that step we would have spent months optimizing the wrong thing."

— Vasyl Hnatiuk, product lead, mobile studio client
Step 01 Revenue model audit

Review of all existing monetization touchpoints — ad placement logic, IAP structure, pricing, offer timing, and player segmentation quality. Typically takes 5–7 days depending on data availability.

Step 02 Behavioural data review

Analysis of session data, drop-off points, and purchase event clusters. We identify where players leave, what they do before they leave, and what the top-spending cohort does differently.

Step 03 Strategy design

A written monetization plan covering recommended model type, offer architecture, pricing structure, and ad strategy — formatted for both executive sign-off and direct developer handoff.

Step 04 Implementation support

We stay involved during the build phase — reviewing builds, responding to scope questions, and adjusting parameters when testing reveals unexpected player responses. Not a handoff and disappear model.

Step 05 Post-launch measurement

A 60-day monitoring window after go-live, with a structured review at day 30 and a final written summary at day 60. Metrics are compared against the baseline from Step 01.