Locale.ai Leverages Geoinformatics To Fix Hyperlocal, Mobility Data Blindspots

“We saw that data visibility and visualisation is a  repetitive problem that almost every company has. They have a lot of location data that they are collecting but they don’t have the right tools to solve those problems,” —  Aditi Sinha, cofounder of Locale.ai. 

Founded in March 2019 by data scientist Aditi Sinha and geospatial information engineer Rishabh Jain, logistics tech startup Locale.ai is looking to empower businesses and decision makers to take calls based on real-world data around consumer behaviour combined with geolocation and geoinformation data. 

Having worked together at data analytics startup SocialCops and seeing how businesses visualise location data to solve various business problems, the duo set out to launch their own tech platform for the same. Essentially, Locale.AI is a SaaS offering that can be utilised by any business looking to glean insights from its location data. 

The Bengaluru startup’s product offers a dashboard that uses geospatial analytics to crunch data to provide insights to businesses. This kind of analysis is done on the basis of data from location data sources such as GPS, social media and smartphones that businesses collect from consumers. “Our product helps companies improve unit economics, increase user conversions and reduce cost per delivery by showcasing how their business performs on the ground and pinpointing where the problems lie,” Sinha told Inc42.

Geospatial Tech Comes To The Fore

Geospatial analytics uses data from GPS and location sensors, social media content, mobile devices, aerial imagery and more to build data visualizations which help businesses identify trends, patterns and relationship trees between the people and places they serve. Geospatial analytics also leverages geographic information system (GIS) data and imagery including GPS and satellite photographs. While naturally such geospatial data will be more useful for companies that have a presence in the physical world, it can be applied to all kinds of businesses, depending on the problem to be solved. 

For instance, a food delivery company or restaurant could zero in on what’s causing user drop offs in a particular neighbourhood — it could be because of a roadblock or bottleneck that has delayed orders or longer delivery times — or a ride-hailing company could price routes lower in a route where the frequency of bookings may be high.

Locale.ai’s dashboard can help figure out the areas where there are more users dropping off from the order chain and the company can take action on that basis. Besides getting users back, companies can also use the data to simplify cohort analysis and segmentation of users based on specific behaviour rather than the standard MAUs and DAUs.

The first version of Locale’s product was launched in September last year — the minimum viable product was deployed in a few companies as Locale looked to see how its product was being used and then iterated on it. 

Bengaluru based watertech company Hydrop and a well known logistics company funded by a top ecommerce player were among the company’s first few customers. But even this did not solve a major problem — user education.

 “We took the product to the companies, but it was not very clear to them — the UX was completely broken and that’s when we realised that we need another iteration,” Sinha recollected.

When the Covid pandemic struck, the startup had to again alter its launch plans. “Mobility was a huge target segment for us and the entire industry went in shut down mode because of the pandemic..that’s when we pivoted really quickly and started targeting ecommerce and logistics companies as well,” she added.

Differentiating The SaaS Dashboard  

While any company in the location analytics space needs to keep an eye on how it will deal with tech behemoths such as Amazon or Google, a startup in this space also needs to consider the strengths and weaknesses of its peers. In the Indian context, logistics tech has been a crucial sector for the growth of ecommerce and hyperlocal deliveries as well as the rise of D2C brands. Bengaluru-based Locus and Mumbai-based LogiNext are among the startups that are working to improve location intelligence for businesses that need logistics solutions or geoinformation data. 

Both these rivals for Locale are also well-funded — Locus has raised $28.8 Mn in funding and counts brands such as Nestle, Unilever and BigBasket among its customers, whereas LogiNext has raised over $50 Mn with Airtel, Paytm and Samsung as its clients. Sinha claims that though the two peers operate in the location analytics space, these two don’t offer the same solutions as Locale.ai. 

While Locus has solved the problem of how to route deliveries amongst delivery agents efficiently, its focus is primarily on the logistics and delivery market. LogiNext, on the other hand, focuses on optimising deliveries through route planning and analytics as compared to Locale which is focused on geospatial analytics, according to Sinha. “We are specialised in handling large scale and high-frequency location data and are built for more tech-heavy companies.” 

How Locale Is Diversifying Location Analytics 

Another important marker that sets Locale.ai apart from its peers is its use case scenario for marketing. Marketing teams can use the insights gleaned from its dashboard to customise their promotions according to data regarding when people order, where people order, what people order. 

The marketing use case can be extended to a mobility company too — Locale.ai has worked with a mobility company who launched route based promotions. If a lot of their users in a particular route are students who commute between colleges to hostels the pricing can be set at a lower point to attract more customers, according to the cofounder.

Sinha says India’s largest two-wheeler rental startup, which she can’t name, used Locale.ai’s insights to figure out what would be the right location to set up their stations in about seven cities  which led to a reduction of their user drop off by 9%.

Similarly, fintech startups who want to do a lot of location-specific strategies are also showing interest in the product according to the founder. While Locale’s team is still trying to figure out all the ways that the product could be used by financial services companies, two possible use cases could be the productivity of sales agents and location-related loan defaults data.

The Locale.ai dashboard can help monitor the movement of on-ground workforce in real-time as well as get a historical timeline of their movement patterns.  “What makes this monitoring extremely powerful is the anomaly detection models we have built-in in the system. This means, we study data historically and alert a company with a severity score in case a metric behaves abnormally in a particular area,” Sinha wrote in a blog post.

Another use case of the analytics product is providing ecommerce companies with insights on the actual cost of shipping to a particular area. To solve the shipping cost problem, Locale.ai mainly uses data regarding the base fares of using a route from warehouse to delivery and how certain overhead costs stack up in an area such as delays, fuel prices, cancellations etc. 

Sinha says that the use case has already been tried out by an ecommerce company called VNDR in the US and it has helped increase their margins by 2.7%. 

One of the biggest challenges ecommerce companies face is increasing the productivity of their delivery agents and drivers, who may sit idle when the fleet’s distribution is not optimised to cater to demand at all times. Locale.ai uses geospatial heatmaps — where the location of the entire fleet is accurately depicted — so that riders and drivers can be distributed across geographical locations in the best possible manner.

Hitting The Ground Running

Similar to a lot of SaaS companies in India, Locale.ai aims to focus more on the US and European markets than India. After going live with the dashboard in the first week of September, the company has started focussing on sales. 

“Pricing depends on the amount of data that we ingest,” said Sinha. For very small businesses that have operations in just one city the pricing could be as low as $200 per month. But for a company that is doing upwards of 1 million orders per month, her estimate of the fee is $1,000-1,500 per month. 

The founder says that Locale.ai is also attracting interest for funding which is very important to fuel its expansion plans in the international market. In April, the startup raised a round of seed funding from Better Capital, a VC firm focussed on early stage startups that has made bets on companies such as digital ledger Khatabook, agri trading commodity platform Bijak and SME neobank Open.

The biggest hindrance for the founders when approaching VCs was that they did not have concrete answers to a lot of their questions.  Moreover, Sinha said they were looking for an investor who would believe in the importance of solving the location analytics problem as well as a mentor who could guide them.

“The challenge in creating a new category of products is that not everyone is going to believe in you, but you need to find the select few who do,” says Sinha.