A venture-backed live audio social platform needed product and finance decisions backed by analysis rigorous enough to act on — not another dashboard. RTS Labs embedded senior data science across the data warehouse, delivering a reconciled revenue model, isolated metric mechanisms, and honest forecasts across eight high-stakes workstreams.
a Live Audio Social Platform
Product & Finance Data Science
BigQuery
Mode Analytics
Python
SQL
Our engineers will map your workflow and define a ship date in a 2-week Discovery Sprint.
A venture-backed live audio social platform was making product and finance decisions on questions where a plausible-sounding answer was not good enough. Its North Star engagement metric had stalled after a holiday weekend with no confirmed cause. Active users were softening across a multi-month window with no identified driver. The product team needed defensible forecasts to set roadmap commitments, and finance needed revenue from a complex in-app virtual-currency economy recognized correctly enough to stand behind.
The stakes were concrete. A misread metric meant a misdirected roadmap. An unreconciled revenue model meant misstated books. A forecast presented with false confidence meant commitments the data could not support. What the team needed was not another dashboard but rigor: ruling out data artifacts before behavioral stories, reconciling every figure against a governing baseline, and separating where a loss showed up from what actually caused it.
The goal was simple to state and hard to meet: answer these high-stakes questions with analysis defensible enough for product and finance leadership to act on, and say plainly when the data could not yet support a conclusion.
The North Star engagement metric failed to recover after a holiday weekend, and active users kept softening — with no identified driver. A misread metric meant a misdirected roadmap.
Revenue from the in-app virtual-currency economy is a chain of definitional calls — any one wrong misstates the books. It had to be earned against the ledger, not assumed.
The product team needed roadmap forecasts built on a thin data window — few observations, all metrics trending together — where the temptation is to over-model.
RTS Labs embedded senior data science directly against the warehouse and ran the work as a disciplined loop, not a set of one-off queries: draft and execute read-only SQL, analyze and chart in notebooks, then package findings into a stakeholder-ready readout. Every derived figure was reconciled against the reference baseline before any external claim, every number carried its denominator, and findings that were compositional were labeled as compositional rather than dressed up as causal.
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