Influencer marketing for games · Lab

A mock-data prototype of one place for a studio's influencer marketing: find creators who fit the game, send keys, and see who drove installs; I wrote the pitch and Barış Ülgen built the prototype.

What it is

FIG. 1The discovery flow, and what was still to come. Dashed boxes were planned and not built. Scores move when the chosen comparable games change.
Source: wishlift README.md and src/lib/fit.ts, HEAD 3e7c43e

Studios mostly run influencer campaigns by hand. Finding creators, contacting them, sending game keys and tracking the results happen in separate tools, and nobody sees clearly which creator actually brought players. Wishlift puts those steps in one place.

In the prototype only the first module works. You describe your game, an agent finds comparable games, you pick the closest ones, and creators are ranked by how well they fit those picks: genre, tags and engagement. Change the picks and the scores change. The CRM with key sending and the install attribution sit in the sidebar as "Soon". There is no backend; the research steps are paced animations over mock data.

What happened

I wrote the pitch, and Barış Ülgen built the prototype in July 2026; all four commits are his. The pitch named four questions that no ready library answers: how a fit score relates to real returns, how to tell fake signals in game content, how to match one creator across platforms when data is missing, and how to credit an install to the right creator in a noisy funnel with many channels. The plan was to answer them in pilot campaigns. We filed it as an R&D project under the Wishlift name in July 2026; no decision is on record here.