Locale.ai conducts geospatial data analysis to convert your location data as a means to power your hyperlocal decisions
Locale is trying to make location data analysis much easier and faster with its dashboard that ops managers can use any time
Whether it’s food, grocery, or medicines, city-dwellers in India have come to expect on-demand delivery on time. And with fierce competition among hyperlocal delivery players, any company that doesn’t provide an acceptable ETA and stick to it runs the risk of losing customer loyalty. This is where technology comes to their aid.
Google Maps and GPS have been enablers of on-demand delivery and mobility services. The key to such services lies in accessing the location of customers via apps installed on their smartphones. This is combined with tracking delivery personnel and drivers, so that they can be assigned tasks based on their availability and location.
Analytics on the location data then kick in. Routes can be optimized for efficiency and cost, ETA can be tracked for customers to be notified and, there can be constant improvement in how resources are deployed and bottlenecks removed.
The problem is the sheer volume of geospatial data on moving assets that bombards a hyperlocal delivery or mobility company. Startups in these spaces or small and medium enterprises involved with logistics often lack the analytical resources to derive the full benefits of insights from their massive location data.
This sets the stage for the entry of SaaS (software-as-a-service) startups whose plug-and-play products can provide insights to their clients from location intelligence. One of these is Bengaluru-based Locale.ai. A typical use case is analysing cancellation rate for a hyperlocal delivery company. The higher the rate, the bigger the drain on resources.
One of Locale’s clients had an average cancellation rate of 34% across Bengaluru. Drilling down on hexagonal heat maps pinpointing areas with the highest cancellation rates showed they were mostly on the outskirts of the city. The company could then focus on whether it made economic sense to improve operations or provide more incentives in those areas.
Location intelligence can drill down even to a store level to figure out demand-supply gaps that result in stocks running out and orders not being fulfilled. Delivery delays can be analysed down to the lap of the journey or time of day they’re happening the most in, so that operations can be modified.
“Is the delay happening in the first mile or the last mile? Is it happening at the restaurant or store? Is it happening in the evening during peak traffic or even at night which would indicate a systemic problem? Which are the areas where you’re continuously getting delays and in which categories? These kinds of insights would be what delay analysis would look like for a delivery company," says Aditi Sinha, co-founder of Locale.
Sinha felt the need for better location intelligence while working as a research analyst at SocialCops that helps governments and organizations use digital data to tackle social problems. She quit her job to launch Locale in early 2019 along with co-founder Rishabh Jain, who was a geographic information system expert at SocialCops.
The startup has developed different templates of its SaaS product for different use cases. For an e-commerce company, the requirement could be to analyse the performance of their third-party logistics providers in different areas. This helps to choose the best providers, adjust volumes of orders for a particular company, or negotiate prices based on performance.
Bengaluru-based bike-sharing company Bounce was an early adopter of Locale. One of the key metrics from location intelligence for mobility is to identify areas with a high churn rate, that is, where prospective users search for bikes but abandon the thought. One of the usual reasons for this is the distance to the nearest docking point.
Where to position new docks or remove less utilized ones can thus be done more efficiently by the operations team with quick insights from Locale. Bounce was able to reduce its churn rate by 9% soon after subscribing to the SaaS product.
Although India, and Bengaluru in particular, is the first test-bed for its product, Locale is targeting global customers. Vietnam logistics company AhaMove, for instance, is one of its clients.
innovation is key
Insights vary from location to location in different geographies, even if the first principles remain the same. For example, a mobility company in Europe saw on Locale that most of its users in some locations were students using the bikes to commute between their hostels and colleges. This led to a campaign to incentivize more students to use the ride-sharing bikes.
Locale has targeted hyperlocal delivery, e-commerce and mobility because these are domains where fast-growing companies are collecting location data from their customers as a regular part of their operations. But in principle any company with moving assets, including an app-based workforce like sales representatives, can use location analytics to improve performance.
A looming issue for location intelligence is a growing push back against intrusions on privacy, with users denying access to their location on mobile apps. That’s another reason Locale has focused on working with apps that rely on users providing access to their location, as in food delivery or e-commerce. But even there, the rules are shifting and they vary from geography to geography, with Europe being the most stringent on how user data is collected, stored and used.
“Just as with other SaaS companies in domains like customer analytics, we don’t collect personally identifiable information when we integrate our product with client apps. We work with user IDs," says Sinha. “We share our security practices and privacy policies with our clients."
One of the challenges the startup had to overcome was that clients had different schemas for recording location data, even if they were from the same industry. So it had to come up with user-friendly formats in which data could be ingested by clients and pushed out to Locale.
“Even before we built the product, when we were doing our research, companies would tell us, ‘If you’re going to take a lot of time to integrate this, forget it!’ So I’m glad that right now we have reached a point where it takes like eight quick steps to integrate the data," says Sinha.
The process of analysing location data is still largely an unsolved problem. That is why it usually involves a lot of back and forth between an analyst and a business user to zero in on problems and actionable insights. Locale is trying to make that much easier and faster with its dashboard that operations managers can use and not just analysts.
It’s still early days for the two-year-old startup with a team of 10 members, which includes six engineers. Geospatial data analytics is a nascent field, so analysts are mostly trained on the job. But Locale has received backing from angel investors like Fusion Charts founder Pallav Nadhani, Myntra co-founder Raveen Sastry and micro VC Better Capital, who see the potential for another SaaS success story out of India.
Sumit Chakraberty is a Consulting Editor with Mint. Write to him at firstname.lastname@example.org
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