LTV&ARPU Tracker

What the company appears to be: A self-reported tool for mobile game developers to automate tracking of Return on Ad Spend (ROAS) and Average Revenue Per User (ARPU) cohorts. The project was submitted as a hackathon entry by one individual.

What changed: No evidence of prior version or evolution; this is a new submission.

Single most important open question: Is there any evidence of actual use, traction, or revenue from this tool? The description provides no indication that it has been deployed or adopted beyond its submission to a hackathon.

The description states: “LTV&ARPU Tracker” is a tool designed to automate ROAS and ARPU cohorts tracking for mobile games. It aims to replace manual processes such as downloading CSVs and analyzing performance metrics.

Evidence: The project name, tagline, and submission context (Devpost hackathon) are the only sources of information.

Confidence: Low — this is a self-reported product description with no technical or functional details.

The author states that the tool automates ROAS and ARPU tracking for mobile games. It positions itself as a solution to manual data analysis workflows, aiming to reduce time spent on CSV downloads and performance metric analysis.

Evidence: Tagline and self-description only.

Confidence: Low — no indication of prior positioning or evolution in claims.

The description states that the tool is for mobile game developers. It is implied that these are users who track ROAS and ARPU metrics, but there is no further segmentation or customer profile.

Evidence: The tagline implies targeting mobile game developers; nothing more.

Confidence: Very low — no evidence of customer personas, ICP definition, or buyer intent.

There is no evidence in the description of a business model or pricing structure. The submission does not indicate whether this is a freemium, SaaS, or one-time tool, nor how it would be monetized.

Evidence: Not evidenced.

Confidence: None — no mention of revenue, pricing, or monetization strategy.

The project was submitted to the OpenAI 2026 hackathon on Devpost. The author is listed as a student (강민수, 학부재학 / 컴퓨터학과), suggesting this may be an academic or experimental effort.

Evidence: Submission context and team member details.

Confidence: Low — no technical architecture, delivery method, or implementation details provided.

There is no evidence of traction, adoption, or product maturity. The project was submitted as a hackathon entry, and the team size is listed as one person.

Evidence: Submission to Devpost hackathon; single-member team.

Confidence: Very low — no signs of user base, revenue, or product development beyond initial concept.

No evidence of competitive analysis or context. The description does not mention existing tools in the ROAS/ARPU tracking space for mobile games.

Evidence: Not evidenced.

Confidence: None — no indication of market awareness or competitive positioning.

• Unproven traction: No evidence of adoption, revenue, or user base.
• Single-person team: Limited capacity to build, iterate, or scale.
• Hackathon project: Likely experimental or prototype-level, not a commercial product.
• No pricing or monetization model: Unclear path to revenue.

Evidence: Submission context, team size, and lack of business details.

Confidence: Medium — these are inferences from the limited evidence provided.

1. What is the actual use case for this tool? Is it being used by any mobile game developers?
2. How does it differ from existing tools or manual processes?
3. Are there any users or customers currently engaged with the product?
4. What is the plan for monetization or scaling beyond the hackathon?
5. Has the tool been tested in real-world conditions?

Evidence: These are questions that would be needed to assess commercial viability, not present in the description.