Launching Locale.ai: Operational Analytics for Hyperlocal Decisions
Locale converts location data into localized insights for ground operations
“Back in the bad old days of traveling circuses, elephant trainers would shackle baby elephants to stakes using large chains. At first, the elephant would pull and pull, but would eventually give up. Over time, the trainers were able to reduce the chain to a rope, and eventually to an even smaller rope. The elephants, believing they couldn’t escape, never tested if they could break the bonds.” — Lloyd Tabb, CEO Looker
TL;DR
Today, we launch and present to you: Locale.ai! Use this link, you choose your industry and your job role and experience what Locale has to offer for your business.
Locale’s Premise: I Locate, therefore I Operate
In 2009, when Mixpanel launched, their pitch was — In this user-centric era, the most successful companies are the ones who understand how their users behave, what they interact with the most, why they come back, and why they leave.
This pitch definitely worked because it led to a massive change in behavior. A decade later, marketers and growth teams in every web or mobile app company, take data-driven decisions and keep experimenting to personalize the user experience.
But what about the city and central ops teams in companies that have ground operations?
Having access to localized insights on user movement, fleet performance, delays, and shipping costs should be as easy as it is analyzing user behavior on apps. However, that’s not how it works today!
Because the real world is far more fickle and chaotic, it becomes even more challenging to deliver to users as soon as possible, utilizing assets on the ground in the most optimum manner and maximizing the efficiency of your workforce.
At Locale, we are going long on these companies helping them decipher this world. Thankfully, with the help of GPS tech, companies are collecting a lot of location data than ever before! But geo location is still a very under-utilized asset today in all companies. After all, only ~7% of the location data gets used for analytics!
Why Go Hyperlocal?
If you are a company with hyperlocal supply chain operations, its imperative for you to reduce user churn, improve unit economics and become operationally efficient.
However, it’s quite impossible to improve these metrics without going very granular within your city across all your verticals- demand, operations and supply. Going granular helps you debug bottlenecks in your operations and find the right areas and opportunities to focus on.
Just like you create user cohorts depending on their behavior and provide personalized experiences, promotions and messaging to each cohort- Similarly, areas also have properties. Depending on the properties that areas show, you can experiment with customized strategies and see what works in different areas.
The Status Quo: Everyone Ends up Losing!
“We can’t spare developer time right now.”
That’s what you’ll hear if you ask companies why they don’t have visibility into their supply-demand gaps, delays or why they are not tracking how different strategies.
The current process ends up being extremely painful — Data would be imported from the warehouse or analytics engine like Clevertap, transformed in R or Python, downloaded in the form of Excel Sheets and visualized on Kepler.gl!
Upon speaking, interviewing, and working with a countless number of companies, here is what we learned:
- For almost every vertical in a company, there exist SAAS products (HR, Marketing, Accounting, Sales) but nothing much really for operations or logistics teams!
- Most of the operations (even in the leading tech companies) still use Excel Sheets to analyze metrics at city levels and have a “mental map” of how areas are distributed in the city!
- Product managers and decision makers depend on an analyst to get values of a metric which takes anywhere between 24 to 48 hours!
- Live internal dashboards for product managers often take a sprint of developer’s bandwidth and they are often built as a one-off use case.
- We have spoken to data scientists who felt more like a “reporting team” because 90% of their job was about calculating geospatial metrics!
- Due to all these reasons and the internal tools not being well-maintained, a lot of operational decisions end up powered by the intuition of ops and city teams.
The geospatial industry’s innovation cycle simply hasn’t kept up with the rest of the data and tech space. Where are the tools for the new generation of tech-savvy geospatial data experts? Why has an innovation cycle been missed?
Hence, we built Locale.ai!
Over the past 15 months, Locale was specially built for companies that have supply chain and hyperlocal operations. Our target industries include ride-sharing, hyperlocal delivery, logistics and supply chain and app-based workforce.
We get in all your location data that is segregated across your marketing data (user events), operations data (supply pings) as well as trips data (transactions) into one place and give you precise insights at scale. Our aim has always been to empower decision makers and CXOs who don’t have time to write code or don’t prefer writing SQL with localized insights.
With Locale, you can drill down from “city” to “a single order” in two clicks without depending on any engineering favors. Whether you need real-time insights for your tactical decisions or want to go back in time for historical analysis, we have got you covered!
“Locale enables us to look at our business in a completely new way. We now have visibility on our demand, supply and operations from the city down to each building. We love the depth of insights Locale provides.”- Co-founder, Hydrop
Use Cases
Several rounds of iterations led us to this version of the product that we are proud of! Fun fact, you can read more about our journey here:
Here are some of the ways in which we can help different teams with their decisions:
Marketing and Growth
- Acquisition: Finding out the right areas to acquire users based on already existing latent demand.
- Conversion: Debugging the reasons for user drop-off right before booking across different areas and incresing conversions.
- Retention: Doing very targeted hyperlocal promotions using user order patterns or doing route-based promotions for mobility users.
- Impact Analysis: Measuring the conversion via different offline advertisement avenues in different areas.
City and Central Ops
- Monitoring Operations: Monitoring key metrics in real time and getting alerted in case of anomalies. Also measuring business health across all verticals and areas.
- Demand-Supply Gaps: Evaluting the gaps in supply re-distribution to ensure high booking fulfilment.
- Revenue vs Cost: Improve topline by analyzing the revenue vs cost (time taken or distance traveled to rech destination) in different areas.
- Inventory Analysis: Analyzing inventory shelf time, damaged or returned at different warehouses.
- Cancellations: Analyzing cancellations patterns and reasons across partners, couriers and users in different areas.
Strategy and CXOs
- Movement Patterns: Finding out how your power users move in the city and where they go.
- Cost of Shipping: Readjusting the price or delivery feesMeasuring the true cost of shipping to users in different locations.
- Delay and SLAs: Understanding which deliveries, routes, partners or couriers cause delays and due to whcih lap of the journey.
- Productivity: Measuring fleet performance and rewarding the top performers or incentivizing the bottom ones.
- Courier Performance: Analyzing the performance of all 3rd party logistics companies across SKUs in different areas.
If any of these use cases resonate with you, you can log into the product, select your industry and your job roles and experience all that Locale has to offer- and understand how Locale helps in solving for these problems by showing very precise, localized insights.
Access the demo link here.
So, why give us a shot?
The most successful companies that have some kind of ground operations have a deep understanding of their business health on ground and that makes all the difference. They know everything from how much demand is going to come from this location ( Uber) and allocate supply accordingly, or price stays not only as per user’s destination but also the place of booking ( Airbnb).
“We were silently losing money on shipping but were not capturing that data because we lacked precise visibility into our true cost of shipping i.e. fuel adjustments and post-shipping accessorial charges. After Locale, we updated our shipping charges in real-time, so we can maintain our margins and best serve our customers.”- Praful Mathur, Co-founder, VNDR
We have worked very hard to ensure that we make your experience of working with maps and taking hard operational decisions delightful. We want to take all the unpleasant grunt work away while you work on your core business challenges.
We have seen some impactful results with the companies we have been working with. Here’s a case study with India’s largest scooter sharing company for you to delve further:
Locale is the fastest route to attain operational excellence using your location data. There’s a lot more to it and we encourage you to try us out as there’s so much under the hood. Our aim is to support you in what you do best — delivering magic to your customers irrespective of the location, time, day or weather!
Originally published at https://blog.locale.ai on August 26, 2020.
