Key Takeaway
If you are a VP growth or strategy or operations at a micro-mobility company, this piece is for you. Read this blog to find out how you can use geo-spatial analytics to place your bikes in the right areas, locate your next parking junction, understand movement of power users and improve the overall health of your business.
Introduction
With urbanization on the rise and cities dealing with dangerous levels of pollution and gridlock cities, micro-mobility has become a default way in which people travel in the first-mile or last-mile of their journey nowadays.
Micro-mobility refers range of small, lightweight vehicles which operate at speed below 50km/hr and are ideal for travel upto 10kms. The number of micro-mobilty companies around the world has been on a rise for the past few years. Ofo Bikes and Mobike in China, Lime and Bird in the US, Beam in SEA and Vogo and Bounceshare in India are a few to name.
According to a report, Bird hit 10 million scooter rides within 12 months of first appearing on Southern California streets, while Lime users took 34 million trips In the first year itself. No doubt some of them have really stuck cords with the population because of their ease of use and low costs.
Though micro-mobility has brought a revolution, it is still fraught by challenges such a poor infrastructure, vandalism, congestion etc. and run into huge losses due to inefficient operations, improper fleet utilisation and lack of maintenance.
Going Hyperlocal in Decisions
Every micromobilty company collect three kinds of geolocation data broadly: user events that are geo-tagged, vehicle ping data collected from sensors as well as location of their parking stations. Despite collecting a huge amount of location data, it by far is one of the most under-utilized data point.
Analysis on location data helps in going super granular inside a city with our insights and therefore our strategies. This is extremely critical if you are a hyperlocal business. After all, just like you send targeted promotions to your users in different cohorts using your event and web analytics tools, similarly
With Locale, you can create area profiles based on their properties and how they behave. With these profiles, you can contextualize your decisions including that "context".
In the upcoming section, we will talk about how we can use geospatial insights that helps micro-mobiliity companies improve unit economics by improving fleet utilization/reducing idle time, proactive handling of cancellations and user churn and reducing demand-supply gap to ensure consistent flow of revenue.
(1) Vehicle Redistribution
Understanding and taking measures to get the right flow is crucial for micro-mobility companies. Which areas are going to experience a surge in demand next? Is there a sufficient supply of scooters nearby? If not, far how and what can be done to get the equilibrium right?
Demand
- Trip Searches: Where are your users searching for your vehicle and booking for a ride?
- Demand Velocity: How many request per hour are we getting in different areas?
- Unfulfiled Demand: How many requests we couldn't fulfil in different areas?
- Distance: How far were the users who we lost from the nearest idle vehicles?
Supply
- Total Available Supply: What is the total number of vehicles available in shape?
- Busy or Idle Vehicle: How many vehicles are idle or busy right now? (This is only useful in real-time)
- Idle time or Busy Time: Where do vehicles spend the most amount of idle time? Is there a time pattern when vehicles are ideal? Like on Sunday evenings near cafes and on Monday afternoons near tech parks. (This is useful historically)
(2) User Churn
Analyzing the funnel on the ground can be helpful to analyze which areas are users not converting and booking for a ride. The step with the maximum drop offs can help us understand how to strategize to reduce the churn.
- Ride Searches: Where are your users searching for your vehicle? Are they dropping off in this step because of large walking distances?
- Ride Selected: They would put their destination but churn out because maybe the price is very high?
- Vehicle Located: Are they not converting from this step because the vehicle is not in good shape and size? Maybe no helmet or low fuel?
- Ride Booked: What about this step? Are they dropping off in this step because they changed their mind or something external?
- Ride Cancelled: When and where do cancellations happen? How does the cancelation reason change with time and location?
(3) User Cohorts and Movement Patterns
Who are your most valuable or repeatable users? Where do they come from and where do they go? What are the characteristics of their trips? How do we lock them in? Having a deep understanding of your power user is crucial to understand what kind of user segment has the maximum utility of your service and around what use-case.
Cohorts:
Cohorts help us segment our users based on different behavior such as:
- Frequency: Who are the users who are repeat users of our service?
- Revenue: Who are the top 10% of customers who bring in the most revenue?
- Activation: Who are the users who used the service a couple of times but didn't return back?
- Demographics: What segment of age, income, gender, profession do they belong to (student or working professional)?
Trip Properties:
For each of these cohorts understanding their movement patterns in depth:
- Route: If they are a repeat user, they probably take similar routes. What percentage of routes are traveled repeatedly everyday?
- Distance: Do they take long distance trips or short distance trips?
- Time Taken: How much distance do they travel generally? What is their ride time on average?
- Utilization: What's the in-trip utilization of these users look like?
(4) Hyperlocal Marketing
Not all users are the same and hence the same marketing strategy might not work for everyone. In order to retain your customers and ensure a steady flow of revenue, it is important that targeted marketing/promotion is done. Go very targeted and hyperlocal in your marketing strategies.
Areas:
Promotions can be done area wise depending on the properties of users inside them:
- Bookings: What's the distribution of bookings for different kinds of user cohorts? Where do people book two wheelers vs four wheelers? Bounceshare, a micro-mobility company operating in Indian cities uses telematics to generate more demand in the location of supply through a digital outreach program. [Source]
- Installs: Where should we focus on user acquisition based on areas that have latent demand in terms of installs or searches?
Routes:
A new trend in the micro-mobility world to retain and convert users is through doing route-based promotions.
- Repeatability: What are the routes of users who are most frequent users?
- Flow: How does the city moves at different times of the day or even historically?
- Revenue: Which trips and routes bring you the most revenue and are profitable? Double down on them!
Impact:
Running promotions or campaigns is not enough. We need to quantify its effectiveness in different areas using metrics such as installs, searches, bookings.
(5) Station Performance
Quite a few micro-mobility companies work on a docked model, which means they have stations where bikes are kept. In that case analyzing how these stations perform becomes very important so as to know which stations are not being profitable to you and where you should open up new stations in order to expand your business .
Opening Up Stations:
Opening up new stations involves finding which areas have latent demand and expanding your presence to those areas while minimizing user churn by getting better insights into user behavior .
- Latent Demand: Which areas are users downloading the app or searching for bikes?
- Churn: Which areas are your users churning out mainly (searching but not booking a ride)?
- Proximity to current stations: What is the distance of areas with high churn density with the current stations?
Shutting Down Stations:
- Bookings: In which has the number of booking decreased over time ?
- Searches: Which areas are your user not looking for a ride/bike ? This basically boils down to finding areas with low demand
- Cancellations: Which stations are usually facing a high rate of cancellation ?
- Idle Time: Which stations have unusually high idle time for bikes
- Demand within 3 kms: How does the demand density look within 3kms radius of the station ? Are there any POIs or tech parks nearby ?
- Revenue: Which stations have the lowest revenue generation but incur a high amount of maintenance/service cost ?
(6) Business Health
User demand for a micro-mobility company is highly en-tropic and can easily shift to a competition in case ETA is high or the ride is expensive. If you are a decision maker, you need to ensure proper utilization of your fleet to keep the efficiency level high along with ensuring outstanding user experience. Focusing on the right areas for this reason will come in handy.
- Revenue: How is revenue distributed in a city? What are the lowest revenue but the highest driver cost areas?
- Vehicle Utilization: How is vehicle utilization spread across city and how does that change with different times of the day?
- Total Kms Driven: How many kilometers have been driven? How is that spread across unique users and trips?
- Requests per hour: How many booking requests do we get per hour in different areas?What’s the conversion rate of those requests?
- Cancellations Rate: How is cancellation rate distributed between users, partners as well as restaurants?
Real-time Monitoring
Monitoring helps to know what’s happening on the ground right now and be proactive about it. The real world is very fickle and chaotic and your model always cannot accommodate these sudden changes.
- Accidents: Analyzing the historical patterns in accidents can help us become more cautious when there are lots of trips happening in an accident-prone area.
- Vandalism: Theft and vandalism has been a problem in most of the cities where these micro-mobility services operate. They cause loss of revenues and setbacks to the overall operation.
- Outliers: Micro-mobility companies have strict boundaries which users can;t cross. Example, cases of people driving to the outskirts or crossing state borders aren't new.
- Bad Parking: Since most of the micro-mobility vehicles are "pick anywhere and drop anywhere", it isn't a rare scene to find these bikes ill-parked in some garage or clumsy lane or along the footpath.
- No Helmet/No Fuel: Visualizing which bikes don't have to sufficient petrol or don't have a helmet can help you match users to more suitable bikes.
Why Locale.ai?
Locale is an analytics platform which helps you take hyperlocal decisions using operational analytics leveraging location data. In a matter of few clicks, Locale empowers teams to drill down from "city" to the smallest "granular" level and makes the process of taking operational decisions delightful with geo-location data
But the question remains why should you leverage our platform to track and analyze your ground operations when you could build internal tools?
Because building it internally would take 6 months of time with a dedicated team of designers, engineers and geo-spatial analysts putting all their efforts to build such a analytics platform along with the added costs of maintaining the system.
Locale's analytics console is built in a way that it can:
- It can handle large scale data (close to 55 million pings in production)
- Can be integrated to your system in a day (allows integration with close to 14 different data sources)
- Can help you deep dive to the tiniest granular level and take crucial business decisions using both real-time as well as historical data.
- Our pre-built consoles makes it easy for you to edit and customize them according to your needs so that you can visualise your most important metric in a matter of few clicks without writing a single query.
It can be extremely detrimental if you are a hyperlocal business but are not looking at your business at a granular level to get to sustainable growth and operational efficiency. With Locale you can focus on your core business operations while we take care of the grunt work.
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