Eventbrite Marketing Suite Turnaround: Packaging, Trials, and 40% ARPU Lift

I ran a tight learning agenda in partnership with product, market research, and design. Step one was re-examining existing data with fresh eyes: product analytics, cancel surveys, and the original market research that had justified the suite. That surfaced the frequent-versus-infrequent host split and the precise funnel drop-offs. Step two was ruthlessly prioritizing the biggest revenue leak—trial-to-paid conversion and packaging—so we could deliver a quick, confidence-building win. We reworked the trial to let people experience the full bundle instead of one feature at a time, defaulted them into subscription after trial, and redesigned packaging without touching list prices: we moved highly valued advanced features into the top tier and tiered email volume so customers could self-select. For the host-frequency problem we moved to fresh qualitative work (scrappy focus groups, sometimes recruited via LinkedIn or friends-and-family) before scaling to surveys, then ran lightweight operational tests—such as a backend-only one-time two-month non-renewing bundle—rather than rebuilding the whole system. A running “questions roadmap” kept the team focused on the single highest-priority unknown at each moment. We socialized insights, test plans, and revenue forecasts continuously with leadership, using funnel visuals and stakeholder maps so every pivot stayed visible and trusted. The product, engineering, and PMM trio operated as a three-legged stool meeting constantly, which let us ship the core packaging and trial changes in weeks.

We relied on product analytics and cancel-survey data, the original market-research reports, funnel and P&L analysis (segmented by host frequency and package tier), lightweight focus groups, follow-on surveys, backend-only operational tests, a living questions/experimentation roadmap, RACI-style collaboration charters with product and engineering, stakeholder maps, cohort and distribution dashboards (not just averages), and regular cross-functional metric rituals that tied every metric back to revenue impact.

Within two months of the packaging and trial release we lifted average revenue per customer (ARPU) 40%, multiplied trial starts by 5×, and doubled ARR. Package mix shifted dramatically as customers self-selected into the middle tier instead of clustering 90% in the bottom tier. The same learning system later showed that the one-time bundle idea had little traction for infrequent hosts; we pivoted instead to making subscriptions easy to cancel and rejoin so the product matched the seasonal behavior we already observed. Those early wins rebuilt internal confidence, unlocked continued iteration over the following year, and proved the marketing suite could become a meaningful growth lever for the platform.