How India’s Top Micro-Mobility Player Used Locale.ai to Reduce User Churn by 9%
A step-by-step guide on how they used Locale.ai to set up their stations
An introduction to our Partner:
Locale recently worked with a micro-mobility company that helps users commute using their fleet of scooters and bikes. With their rapid growth, large user base, and wonderful reviews, they have become the face of the growing micro-mobility ecosystem in South Asia.
In 2019, they reached a significant milestone of 60,000 rides per day in Bengaluru, making it the fastest-growing bike-sharing start-up in the world. Let us take you through how the team used Locale.ai to open their stations, decrease user churn by 9%, and attain operational efficiency.
The Business Problem(s)
To make any important operational decision using geo-data, executives and decision-makers have to rely on the data provided by the engineering teams. The case was very similar to our partner too. Their business model was a docked model- where any user (like you or me) could pick up a bike from a station and drop it off to another station.
As they were rapidly expanding in new cities (pre-COVID), the business problems were to:
- Decide where to open new stations to service demand
- Close stations that were not performing well
The team wanted to ensure that they could capitalize on latent demand present in certain areas and expand their presence as well as minimize user churn by getting better insights into user behavior and making a strategy accordingly.
Meanwhile, they were also trying to ensure that the time and resources in building dashboards could be used in some other avenue so that they could grow more rapidly. That’s where they were looking for a tool to convert location data into insights that can aid business decisions.
Before we move on, a bit about Locale.ai
Locale is your one-stop destination for anything that involves analyzing hyperlocal operations. Imagine a tool built for city teams, ops teams & logistics teams empowering them to get answers to their questions without depending on any engineering or analyst bandwidth.
We ensure that a large chunk of location data collected from your users or your vehicle sensors, that might otherwise remain unused, can now be used to create meaningful insights that help business teams make quick, data-driven decisions.
The How’s & Why’s of the Solution
The questions that the team asked to make the following decisions:
Expansion & New Stations
- Which areas are users downloading the app or searching for bikes?
- Which areas are users churning out (searching but not booking)?
- What is the distance of areas with high churn density with current stations?
Shutting Down Stations
- Which stations are usually facing a high rate of cancellations?
- Which stations have a very high idle time for the bikes?
- Which stations are located near low demand areas?
Making Insights Actionable
At Locale, we consider ourselves successful only when we help companies take more precise and data-driven decisions using our product. So, we are always on the lookout for making these insights more actionable.
To read more on this, check this out:
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With our partner, for instance, the central ops team could just right click and get the lat-long of the prospective location. They could send a couple of these lat-longs to their individual city teams who would find the most optimal location on the ground, owing to the constraints.
With our commenting feature, they could coordinate internally on whether the station was opened in that location. If not, what were the possible reasons?
But, with all these decisions, what was the actual business impact and how did we move business metrics?
Since the team started using our product, we saw a 9% reduction in user churn and improvement in user satisfaction.
What this translates to is users who could previously not book a bike because of the unavailability of bikes, or bikes being far off can now hop on a nearby bike and start their rides, which resulted in an improvement in user delight.
The Use Cases of Locale in Micro-Mobility:
Analysts at McKinsey have evaluated the shared micro-mobility industry to cross over $300 Billion by 2030. But how can companies today reach there? What stops companies from realizing their potential? Inertia in expanding to newer locations? Problems with fleet management? Inaccuracy in gauging demand? A mixture of all these problems often cap the growth of a company.
Let us explore these problems one by one.
- Expansion: Metrics such as user bookings, cancellations, distribution of sales, and churn helps companies understand the spread of demand and supply across cities.
- Station Performance: Idle time of bikes, churn density around the stations, and cancellations help companies decide where to set up new stations and which stations to shut down.
- User Acquisition: It is important to understand the behavior of frequent users, which routes they travel, and how they can increase user acquisition along those routes via targeted offline and route-based campaigns.
- Fleet Management: Issues such as vandalism, incomplete drop-offs, and breakdowns need to be tracked in real-time and it helps companies to get immediate notifications for abnormal behavior of KPIs.
Often, companies search for tools that can be used to solve these problems for them, by using their location data. Luckily, that’s exactly what we love to do! If you work in the micro-mobility or ride-sharing industry, contact us to set up your Locale today.
To know more, get in touch with me on LinkedIn or Twitter.
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Originally published at https://blog.locale.ai on July 13, 2020.