Locale.ai Changelog
Latest updates and improvements to Locale.ai

We improvised 2 highly used features on Locale this week -

Adding cluster percentage calculation

Now whenever you make clusters you can see the contribution of these clusters inside the metric in comparison to the original value of the metric.

Calendar UX Improvements

Our calendar was not very easy to use and it being one of the most important parts of the product we have improved the experience of using and navigating through it really intuitive now.

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Featured image for 🏹 Feature Release: Clusters

With clusters, you can now monitor and take actions in your focus areas filtered on multiple metrics.

What are clusters?

Clusters are areas that share common qualities, patterns, or behavior that we want to analyze over a certain time period.

These areas can be

  • Problematic areas

  • Well-performing

  • Areas with important KPIs

  • Areas bringing in a lot of revenue or bookings

Creating clusters on Locale

How to create and save a cluster?

For example:
 To create a cluster comprising areas

  • High idle time and

  • Low bookings

  1. Select the idle time metric in the metric studio, filter to the areas with high idle time, and click the APPLY FILTERS button.

  2. Repeat the previous steps to apply the area filters for low bookings

  3. Click on SAVE CLUSTER.

Where can I see my clusters?

All the clusters are listed under the β€œArea Cluster” section on the homepage of Locale.

To know more about the feature:

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Featured image for ⚑ Locale is Now 100x Faster & comes with a New and Improved UI! πŸ’„

We at Locale are thrilled to announce a major product update. Our product is now 100x faster than it used to be. You heard it right: 100x FASTER!!! ⚑⚑⚑

πŸš€ We’ve completely revamped the infrastructure behind the product. Our data pipelines have been overhauled, the backend has been rewritten and migrated to a new set of databases with more optimized storage query and storage engine.

✨ Our timeline view has been redesigned to make it easier for our users to visualize their data based on time filters of their choice, with most used durations as presets. πŸ“…

πŸ› Major Bug Fix: Polygons are now stable and usable for all users as queries can now be processed in a really fast and efficient manner.

πŸŽ‰ What does all this mean to you as a user? You can do much faster analysis now and reduce your time to generate insights and take decisions.

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Featured image for πŸš€ Locale Updates : Computed Metrics

πŸ’‘Computed Metrics

With the computed metric feature, the users can perform mathematical operations on two or more existing metrics (also known as the base metrics) to derive compound metrics. These metrics are denoted by Ξ£ in the metric section. The operations included are addition, subtraction, multiplication, division, and so on.

Users can also create computed metrics using metrics of users, bikes, and so on, i.e. metrics across various entities. For example, if you want to find out revenue per bike, trips per bike, and so on.

For example: If you want to create a metric for churn, the user can use the computed metric option to subtract searches from the bookings.

i.e. Churn =bookings - searches

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Featured image for πŸš€ Locale Updates : Intent Count Metrics

This feature is particularly useful when a user performs the same action multiple times, before moving on to the next. 

For example, if a user searches for a cab multiple times between 10:00 AM to 10:05 AM, before booking, then you can add your interval as 5 minutes. These multiple events are grouped and counted as one event.

With the Intent count metric, the users can group events with a unique intent. One can choose the time intervals (ranging from 1 minute to 60 minutes) to group them.

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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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