In the wake of social distancing becoming the new normal with COVID-19, there has been a natural move away from public transport towards micro-mobility as people are starting to look for affordable and safe ways of making their daily commutes.
A McKinsey consumer survey demonstrated that the number of respondents willing to use shared micromobility on a regular basis has increased by 12% compared to the pre-pandemic numbers. Experts say that this boom is going to stay its course and even amplify further post-pandemic given the shift towards sustainability and increasing congestion in cities.
The future of micromobility looks very promising, but companies in the sector can boost profitability by reducing inefficiencies in operation, fleet management and maintenance. The scenario seems to be changing with many micromobility companies actively looking for new tools and services that can help them leverage data to improve their asset utilisation, cut costs and turn profitable as a result. Zwings is one such company.
Introduction to Zwings
Zwings is one of the UK's leading operators for e-scooter and e-bike rental schemes. Earlier a B2B e-bike share company selling only to businesses, it pivoted to its existing model as soon as the pandemic underlined the need for safe, sustainable and affordable modes of commute accessible across urban areas and campuses in the UK. The best part about the company is its community-first approach.
They work very closely with city officials, community and business leaders in order to ensure smooth deployments, public acceptance and better planning. Zwings works on a docked model where users can pick a Zwings e-scooter or e-bike from one of its parking bays and drop it off to another one.
Turning Location Data into Actionable Insights
The executive team at Zwings felt the need for advanced dashboards that could give them actionable insights to increase their asset utilisation and cut down their costs. As a rapidly growing company focused on expanding and improving user experience, Zwings didn’t want to hire engineers and analysts and dedicated its resources to build merely usable dashboards.
This is when they came across Locale.ai on Wunder Mobility’s platform. Once we started conversations with Zwings, it became apparent that there was a very clear need and use-case for Locale.ai in the company’s operations.
Idle Time & Asset Underutilisation
For micromobility companies, assets not being in the right area at the right time is the biggest reason for low profitability. E-scooters at Zwings could be more efficiently utilised and some of them were sitting idle at parking bays for too long. A huge part of its operations continued to be a blackbox for the company. There was no visibility on how many e-scooters were idle and for how long or how the different bays and areas were performing.
Locale set up idle time dashboards that helped bring a location perspective to the e-scooter deployment and relocation process at Zwings giving the team clear visibility on asset utilisation. Now, the operations team at Zwings could observe the supply-demand gap in real time and work to move the e-scooters from low-demand areas to high-demand ones. The marketing team at Zwings leveraged hex heatmaps to understand which parking bays were not being used as much as the other ones and designed offers targeting the same.
Before battery swapping came into the picture, e-scooters had to be physically taken to the charging stations. It resulted in increased transportation costs and meant that the e-scooters remained idle all through the time they were being charged. Decoupling the batteries from the e-scooters ensured quicker re-fueling and increased bike uptime.
The operational battery swap teams at Zwings take care of this whole process. However, a little while ago, this wasn’t being done as efficiently as it could be. The field technicians would have to filter all the data manually in order to figure out the bikes in their areas that were low on battery and then charting their own routes for the day. This whole process took a lot of time and needed optimization.
In order to optimise battery swapping at Zwings, we created a battery level metric and dashboards on battery level for each city that the company operates in. Field Managers at Zwings now directly feed the data from their Locale.ai dashboards to a route optimization software that charts the most efficient routes for their field technicians to go on and swap batteries.
Understanding User Behavior and Coordinating with City Officials
Zwings works closely with the city authorities in order to integrate its micro-mobility solutions into the wider urban transportation system for the cities it is present in.
However, before presenting to the city authorities each time, the team at Zwings had to go through the painstaking and time consuming process of extracting their data from MS-Excel and then using a third party web-browser service to generate presentable heat maps out of it.
The consoles on movement patterns and route analysis equipped the team at Zwings to get fresh insights into user behavior that had remained uncovered before Locale came into the picture. This ability to generate heat maps and route analysis in a matter of a few clicks means that the team at Zwings is always ready for a presentation and can better liaise with community leaders and city officials.
The automation of the battery swapping process at Zwings using Locale.ai has resulted in significant time savings for the company’s workforce. Fleet managers do not have to manually plan the routes and the Fleet Technicians do not have to go on figuring out e-scooters for which the batteries have to be swapped. The time needed to plan, travel as well as do the battery swapping task have seen significant reductions.
The fleet managers save ~50% time by using Locale.ai to retrieve the data on required battery swaps while the fleet technicians now save approximately 36 minutes per shift. As a result, the number of parking bays visited per shift has increased by 10%.
With the time that is saved, the workforce at Zwings is relocating the bikes to high demand areas that the company has a visibility on, thanks to the Locale dashboard on supply-demand gap. Zwings estimates that the day cost saving for the company on their fleet managers is 6% and on fleet technicians, it is 10%. Zwings has just started relocating bikes based on user demand and that has also shown early signs of significant increase in revenue for the company.
At the executive level, Locale has enabled better planning by equipping the team with real time visibility on operational KPIs like number of active and idle vehicles at a time or within a duration, revenue per day, trips per day, vehicles used per day etc. helping them to plan their operations in a more agile and efficient manner.
Stephen Bee Chief Operating Officer, Zwings
“Locale.ai enables Zwings to do in seconds what an analyst with the right skills would take hours to do. The geo-spatial heatmaps, origin destination maps, and supply/demand maps enable us to quickly understand on a daily basis variations in our trip data. Data is ingested directly via API without any preparation by our team at all. It has provided significant insights of the supply/demand profiles of the e-scooter fleets in each of our locations. Operational efficiencies have been captured due to the geo-spatial analysis dashboards provided by Locale.”
Locale is a one-of-its-kind location analytics platform. We help micro mobility companies like Zwings take hyperlocal decisions by leveraging their own data. At Locale, we solve the problem of engineering and analyst dependency faced by operations teams by empowering them to build their own consoles and metric templates, perform various analyses, create workflows, run experiments, measure performance, and collaborate all on one platform for location-powered decision-making.
If you are a micromobility company with similar problems to solve, contact us to get started with Locale or follow us on LinkedIn and Twitter. Don’t forget to check out the demo here: demo.locale.ai