Product Marketing
Launching Eventbrite's first Pricing Recommendation
Overview
Eventbrite is an event-technology company that allows anyone to create an event and sell tickets online. While at Eventbrite, I Program Managed an experimental feature called "Pricing Recommendations."
Objective
Eventbrite sits on a wealth of event-specific data such as location, date, time, ticket price, ticket sales, and so on. Our objective was to use this data to help event creators make more strategic decisions when planning their events, in particular, how to price it.
Phase 1: Experimentation & Validation
Goals:
Keep things lean - use a marketing channel to deliver the insight. Saved resources in product and engineering.
Validate the hypothesis: "Creators need help pricing their tickets."
Result:
40% open rate (baseline: 25%)
3% click-thru rate to Tickets (baseline: 2.25%)
Approximately 100% of users who clicked took the recommendation (no baseline)
6 out of 8 interviewees spoke positively about the pricing recommendations they received
Goals:
Expose creators to an improved user experience, and learn how creators engage with a pricing insight.
Integrate a machine learning model into the product for the very first time
Result:
131k Events received a pricing insight
Engagement: 3.3% clicked the recommendation
Adoption: 50% adoption rate (for those who clicked, 50% adopted)
Successfully integrated a machine learning model, creating an ML Service pathway for new models to be productionized more quickly
