November 18, 2022

Deployment Efficiency in Micromobility

How do ride-sharing services think about propelling customer behavior? It is more complex than just dropping a bunch of vehicles on the corner. Building a product that customers naturally use habitually will create a significant competitive advantage for the business.

The "Hook Model" presents a great product case study that can significantly impact ride revenue for micro-mobility companies. 

Ride-sharing apps that utilize this four-phase hook model will encourage better customer habits and allow users to feel more connected to the service provider's solution.  

The four phases:

  1. Triggers (Internal & External)
  2. Action to satisfy the trigger
  3. The reward for the action
  4. An investment

Let's dive into each of them.

1) The Trigger to cue an action.

Triggers to use a ride-sharing app come from both intern and external cues.

The internal trigger comes from the thought or needs to travel from one destination to another.
"How will I get to work today?"
"I need to pick up lunch; what transportation should I use?"

This internal trigger often results in users opening up a ride-share app.   Which app do they choose? Well, that responsibility falls on the ride-sharing app itself. What are they doing to differentiate themselves from the rest?

The external trigger is where 3rd parties services, like Zoba comes in to reinforce. Vehicles placed in specific high-demand areas will enable the motivation to use the ride-sharing service. Physically seeing the vehicle can ultimately trigger the user to travel by this vehicle over any other mode of transportation.  

As a user walks to lunch, they notice a scooter on the sidewalk. The trigger to use the scooter to ride to lunch has been transmitted.

Users will only be hit by these triggers where there are already vehicles, so supply heavily influences utilization. Zoba will forecast demand and increase external triggers. Zoba uses highly intelligent, predictive data to capture demand and maximize market performance to recommend where fleet operators position their fleets on certain days and times.

2. Action to satisfy the trigger

The action here is starting a trip but quickly finding a vehicle is the starting point. Internal triggers may not work in your favor, depending on where the closest vehicle is placed. Zoba's demand data will elevate the opportunity to capitalize on both internal and external triggers to start a trip using your ride service.

3. A Reward for the Action.

The reward is basically the problem solved using your service. In this case, it could be arriving at the destination.

4. The investment: An action that improves the product or service.

The investment here can be a variety of options. It may be safely placing the vehicle in a designated scooter parking area, but it could even be bringing the vehicle into a high-demand location. This type of investment ultimately makes the vehicle more valuable for the following user, who happens to be in this high-demand area.

Zoba's demand forecasting could amplify the investment phase by strengthening the external trigger, creating a more desirable reward.  

How does Zoba amplify the investment? Zoba can help shape user behavior with dynamic ride prices that induce rides. Zoba price produces recommendations for vehicles to discount based on demand, where the vehicle will likely end up after a ride, and battery level.

Deployment efficiency and competition are the most significant concern for ride-sharing services. Zoba is here to improve your deployment efficiency and partner with you to encourage better customer habits and allow users to feel more connected to the service provider's solution.

Still waiting for that iPhone moment.

During an interview with the legendary Horace Dediu and Bird founder Travis VanderZanden, they discussed how micro-mobility has yet to see that "iPhone moment" That moment, or that ride-share experience that changes the industry and the world of transportation.

VanderZanden compared Scooter Sharing to the Motorola Razor, the phone that everyone loved for a long time, but once the iPhone came out, the loyalty towards the razor quickly disappeared.  

Zoba has the potential to enable ride-share services with a 'digestible' data-driven experience for the Motorola Razor. But when the iPhone moment hits, the need for Zoba's mobility demand forecasting data and operational infrastructure driven by decision-making automation will be right there in position.


Fleet Optimization Case Study:

Every hour that a Zipcar vehicle spends blocked is lost revenue from being unavailable to members.

There is no "magic button" that we can press to reduce this percentage, but there are vital workflows that I began to break down to address areas that can be improved to bring down blocked percentages and directly impact revenue opportunities.

Managing vehicles issues is a task that requires work and cooperation from each role on a fleet team. Drive down time per blockBuild a product that will drive down blocked vehicles by 5% - 380 vehicles x $X.00 times 12 months = $X.00M vehicle costs

Zipcar Fleet Management Design Thinking