Locale.ai Changelog
Latest updates and improvements to Locale.ai
Featured image for πŸš€ Locale Updates : Overlays

πŸ’‘Overlays

The overlays feature allows the user to upload an overlay which could be points or polygons containing the information regarding their static points and overlay them to visualize the desired POIs, Stores and so on. Through this feature, the user can compare demand patterns of different store locations or restaurants. 

For example:

  • Parking Bays

  • Restaurants 

  • Hubs

The user can hover over the point to view the id of the entity.

For example: What are the demand patterns of the restaurants in Brooklyn vs restaurants in Manhattan


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Featured image for πŸš€ Locale Updates : Data Export

πŸ’‘ Data Export

The data export feature enables the users to download the data from individual metrics to take action on them. The files are downloaded in a CSV format. This feature allows the user to share the data of problematic areas or users from a particular area across different teams to power your decisions and take necessary actions. You can put this data or the IDs into your campaign sending tool.

For example, The different types of data that can be exported are locations of the churned users, the bookings across a time interval, and so on, along with their unique ids.

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Featured image for πŸš€ Locale Updates : Time spent analysis

πŸ’‘ Time spent analysis 

This feature calculates the time between two events for your supply. The supply could be vehicles, driver fleet, or delivery personnel. Through this, you can measure the idle time of the driver, time spent in delivering an order, or idle time at the restaurants or warehouses. This can be used to measure the performance or productivity of your fleet and analyze the time they are not spending delivering your order. 

For example: What is the idle time of the delivery personnel in Brooklyn from 1 PM to 3 PM?


πŸ›  Other Changes

Updated MapStyles Picker

The Map Styles picker has been moved to the options tab in consoles.

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Featured image for πŸš€ Locale Updates : Polygons

πŸ’‘Polygons

Description: The polygon feature allows the user to analyze the performance of their zones or operational boundaries. You can simply upload these zones unique to your company by uploading their boundary definition of districts, counties, towns. You can drill down granularly into these zones and compare their performance with each other. 

For example: What are the cancellations in Winston as compared to Lauderdale?

πŸ›  Other Changes

Real-time Pipeline

The data will be updated at regular time intervals based on the desired update frequency by the user. It could range from 5-minute intervals to 24-hour intervals.

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Featured image for πŸš€   Locale Updates : Collaboration

πŸ’‘ Collaboration

This feature is designed to promote communication and collaboration across various teams (data scientists, analysts, product teams, and operations teams) for debugging and brainstorming. Once you tag a team member in a problematic area with a comment, they get an email notification. Upon getting the notification, they can resolve, reply and delete which enables them to adapt to real-time situations (just like Slack threads). It is designed to become a knowledge base of your insights and encourage problem-solving, keeping in mind the right context and being transparent makes for a great collaborative environment.

For example, @alex, why are cancellations so high in the eastern part of the city?

πŸ›  Other changes

Improved Visualization Picker

The grid selection picker is being changed to a visualization picker and is more detailed. Changing visualization and rendering data to polygons/boundaries can be done via the new Visualization picker.

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Featured image for πŸš€  Locale Updates : Impact Analysis

πŸ’‘ Impact Analysis

This feature enables the user to measure the direct impact of any strategy/campaign by simply comparing the metrics before and after the event. The data visualized is the percentage change between two time periods. The change can be measured across hours, days, weeks, or months. You can pick the time period most relevant to your use case and the metric you want to analyze. 

For example: How did this incentive campaign impact my key metrics? Which areas have been growing week on week?


πŸ›  Other Changes

Integration with AWS S3, Google Sheets, and GCP

You can now ingest your data from AWS S3 buckets, Google sheets, and Google Cloud Platform to Locale.ai

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