Insights and use cases

This page is all about how shape filters can help your business and the exciting use cases it offers.

One may have a question here, don’t shape filters sound very similar to area filters? Well, no. Area filters don’t provide the freedom to select the area of your choice. These are bifurcations made by the system according to the inputs provided by the user. On the other hand shape filters allow the user to draw and analyze specific portions of areas, manually. This allows greater flexibility to study only those areas that you wish to.

Building upon the already intuitive and amazing data visualization that we offer, shape filters will help managers and operations strategists to gather insights on different regions, analyze them appropriately and take decisions accordingly. This tool would be super helpful for those with a knack for studying maps and data and finding out patterns, or for those with experience and expertise in this domain. Let us take an example of different industries:


A key factor apart from revenue, would be churn rate and driver idle time. With the use of shape filters, now you can visualize different areas on the map, ranked and color-coded, and can draw custom shapes to select and study areas of choice. This will help you to figure out pain points and regions needing attention. For example, it will be super easy for you to now see

  • Which areas are your drivers most engaged in, at what times of the day, and can you map out your strategies accordingly

  • Where do you see maximum ROI for the number of drivers


Moving things may seem easy but is a gargantuan task with primary variables being delays, cancellation, and churn. The novelty of shape filters combined with experience in operation will help you minimize all three. Shape filters will help you:

  • Identify and isolate regions of maximum delays, cancellation, and churn

  • Easily identify if the different variables things correlate

  • Find out problematic areas and the reasons behind them

Hyperlocal Delivery:

Procuring and distributing items fast is very critical to the hyperlocal delivery space. With shape filters, you can now focus on areas where you see problems or growth, look:

  • At regions with more driver idle time and figure out reasons behind that

  • Easily look at the supply and demand gap in different regions.

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