Design and prepare a go-to-market strategy for a cab scheduling feature for Lyft.

What? Cab scheduling feature in Lyft upto 30 days in advance with guaranteed on time pick up

Who? People who have confirmed plans of going somewhere at a future data and time

Why? Pre planned event in future, airport ride, station ride, etc. ensuring no last minute hassle in getting a cab before the event

Value proposition? Booking confirmation in advance; Zero cancellation because of advance booking; Cheaper prices; Getting timely rides; No difficulty in getting a cab

Market research - Competitors like Uber already has this feature of scheduled rides live in the market, so users are aware of this feature

User segment - People who take airport rides freuquently; Office goers who have  a fixed time to commute; sinior citizens who have periodic clinical appointments

UI personalization - Since the on demand ride hailing feature already exists, the scheduling feature should be coupled with the same feature with mutlpe options for the users. There can be a help video regarding demo of this feature. AI bot can be built supporting the users in scheduling a ride. Loyalty points for usage of scheduling feature, automated reminders for upcoming scheduled rides

User onboarding - For existing users, in-app notifications, sms, push notifications with announcement of this feature. For new users, through ads in the form of google ads, meta ads, billboards, etc.

Driver onboarding - No real changes for the drivers. Driver allocation will still happen some time before the ride. so, we can assume no specific need to onboard new drivers specially for scheduled rides. But, for existing drivers, higher incentives to minimize cancellations, higher predicatbility in terms of future rides, etc.

Monetization - At the beginning no additional cahrges for scheduling a ride. Based on popularity of the feature, in future we can have a prior scheduling fee added to the overall fare

Metrics -

• % users scheduling a ride
• % users completing a scheduled ride
• % rides with delayed pick up
• % users cancelling a scheduled ride
• % driver cancellations on scheduled rides
• DAU/ WAU/ MAU
• D7/ D30 returning users
• Avg. time duration to find a driver for scheduled rides
  • % times when there is a DNF for scheduled rides
• User NPS
• Driver NPS

• Pre launch
  • Alignment on the rollout plan with all stakeholders [MVP scope, gradual scale up plan]
  • Alignment on the marketing modes and baner content with the marketing team
  • Alignment with the ops/ support team on objection handling
  • User manual and training for all the ops/ support team
  • Alignment with the engineering team on deployment prerequisites
  • Early access in the form of invite only mode for high frequency users [creating exclusivity and getting early feedback]
  • Beta testing with selected users
  • KPI monitoring dashboard readiness
• Launch
  • Production deployment for all users
  • Production sanity
  • Stakeholder communication
  • SMS, Push notifs, in app notifs to users
  • Closely monitoring the launch
• Post launch
  • Metrics monitoring
  • User feedback
  • Post mortem and continuous improvement