Monday, 16th May 2022
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Network analysis to improve navigation within digital products

There are a wide variety of networks around us even though we might be oblivious to them.

There are a wide variety of networks around us even though we might be oblivious to them. A network is a structure that represents a group of entities/objects/people and the relation among them. For instance, social media platforms such as Twitter are a network of individuals who follow and communicate with one another. A Basketball team that passes the ball to one another can be construed as a network as well.

There is a tremendous amount of information that can be obtained by analyzing the networks. One such innovative use case involves analyzing the navigation pattern of users in mobile applications. Harish Srigiriraju is an expert in product analytics and machine learning models. His work revolves around drawing insights into user behavior and developing new features with help of various analytical models. He developed an innovative approach to map navigation in mobile applications through Network Analysis. Harish managed to significantly improve the user engagement for millions of users with his innovation.

In mobile applications, one of the most important metrics is user engagement. As product leaders continue to add new features to digital products, there is a high possibility that new features don’t add any substantial value to the users, a phenomenon commonly known as “Feature Creep”. Ease of navigation in an application ultimately leads to better user engagement.

Before Network Analysis can be performed, the data needs to be collected and prepared in order to convert the sequence of events into a network-based dataset. This dataset can then be ingested into tools such as Gephi to visualize the network.


Network Visualization of Navigation in the App using Gephi

The above figure is a visual sample of the network developed by Harish that maps navigation patterns in one of the mobile applications. Each node represents a feature and the lines represent the path from one feature to another. In addition, the thickness of the line represents the number of times, the path was traversed. So, the thicker lines represent the most common paths of navigation for the users.

This visualization can help in identifying features that are difficult to discover and paths that lead to dead ends. These insights can be used to significantly improve feature adoption and overall user engagement. Product leaders around the world should push themselves to investigate their applications using Network Analysis to uncover critical user behavior.