Feature Overview: Location Based Clustering – Dista Insight

Clustering is a method by which you can perform surface-level analysis that helps in automatically identifying patterns and structures in data sets. It focuses on essential features of data that can be enhanced with deeper analytics. Businesses need to perform clustering as part of a broader data discovery strategy to make the most out of it.

Here’s all you need to know about location-based data clustering.

Location based clustering

What is Clustering?

Clustering algorithms, also popularly called “clustering,” facilitates natural groupings of people and objects into large data sets. It is a method that makes it easier to read and understand larger data sets. The main aim of clustering is to reduce the amount of data by categorizing similar data items together.

How Does Dista Insight Help in Location-based Clustering?

Dista Insight, our geospatial analytics software, performs clustering analysis based on various data parameters (organization, industry, and point of interest (POIs), among others). It uploads geotagged customers, prospective clients, visits, deliveries, and sales data at a city or a specific geographic level. Our system enables you to define various clustering parameters like (proximity, points, distance, etc.). This helps determine the maximum distance of a polygon to distribute the area into the most optimal clusters. Our advanced clustering algorithm leverages the above data to create polygons and recommend centroids in a logistically optimal manner. Moreover, our software offers manual grid creation and territory management.

Dista Insight facilitates polygon creation for service qualification, sales team allocation, sales coverage, delivery area mapping, and beat planning. Our intelligent system can retrieve prospects’ outlets / POIs at a polygon level as per industry or outlets and adjust clusters based on business criteria and other market data.

With Dista Insight You Can

  • Create clusters of the plotted location
  • Define minimum/maximum points for clustering
  • Cluster by radius
  • Cluster by proximity
  • View distance between points within or between clusters
  • View centroid of the cluster

Benefits of Geotagged Clustering Analysis

Clustering analysis enables you to identify and define patterns between data elements. It can help in customer segmentation, better data classification, and structuring of your datasets. A robust Geographical Information System (GIS) software like Dista Insight identifies patterns that may be otherwise overlooked by finding common traits and transforming that data into a more usable format.

Clustering Analysis use Cases for Industries

Here are some of the key clustering use cases

1) Banks

Banks can leverage our spatial analytics platform to identify positive and negative areas in a specific location regarding the creditworthiness of their prospective clients. It could also help in mitigating risks for loan disbursals and management. It helps them understand the credit risk at an address level which is crucial to improve their credit risk management.

2) Insurance Providers

Insurance providers can leverage Dista Insight to identify areas that have higher fraudulent cases. They can isolate new claims based on their proximity to clusters that indicate fraudulent cases. With clustering analysis, insurance companies can check the number of claims reported in a particular region. This enables them to understand the reasons driving the rise in claims.

3) Marketing

Clustering algorithms can group people and develop customer segments by clubbing them with similar traits and likelihood. This helps the marketing team create tailored promotional campaigns for customers and offer special discounts. By leveraging clustering analysis, organizations optimize the quality of the promotional messages to increase footfall and customer retention. It helps in strengthening customer relations and boosting sales.

Generic Use Cases

  • Dividing areas for optimal operation management
  • Hyperlocal analytics across industries like FMCG, courier and logistics services, quick commerce, and e-commerce.
  • Resource planning, forecasting, and allocation across multiple industries depending on several parameters like a footfall.

Final Thoughts

Cluster analysis saves a significant amount of time while working with large and unstructured datasets, as it identifies a pattern or structures implicit in the data. Leverage Dista Insight to quickly identify the driving factors that contribute to each group and help explore the correct parts of your dataset to look out for promising clusters.

Get in touch with us to know more.