Estimate infrastructure or support load created by creator analytics

Problem Statement Description

You are asked to estimate the infrastructure and customer support load created by a creator analytics product used by small businesses. The product helps creators or small business owners understand audience growth, content performance, sales attribution, engagement trends, and campaign outcomes so they can make better business decisions.

Assume the analytics experience includes common workflows such as opening a dashboard, filtering by date or channel, viewing charts, exporting reports, receiving automated insights, and asking for help when numbers look incorrect or confusing. Your estimate should cover both technical load, such as data processing, storage, dashboard queries, and scheduled reporting, and operational load, such as support tickets, escalations, and education needs.

The estimate should consider:

- The scope of the analytics product: dashboard views, reports, exports, alerts, benchmarks, and possible AI-generated insights

- The relevant user population: small business creators, active accounts, team members per account, and geographic or time-zone distribution

- Usage frequency: daily, weekly, monthly, and peak-period behavior around campaigns, launches, payouts, or content drops

- Infrastructure units: events ingested, records stored, queries run, reports generated, compute cycles, bandwidth, and data retention needs

- Support units: ticket volume, contact rate, issue categories, resolution effort, self-serve deflection, and escalation rate

- Adoption assumptions: percentage of eligible creators using analytics, power-user behavior, and growth over time

- Sensitivity drivers: dashboard complexity, freshness requirements, historical lookback windows, export size, and data accuracy expectations

- Sanity checks: comparison to creator activity volume, content publishing frequency, account size, and support benchmarks for SaaS-like tools

The goal is to produce a structured, defensible estimate that helps a product or operations team understand the scale of investment required to support creator analytics reliably, while also identifying the assumptions that most affect capacity planning and support readiness.