AI-Based Applications

Impact Analytics is helping a non-profit organization improve their content creating strategies and operational processes using advanced AI and ML-based applications that further enhance customer engagement

Big Picture

To democratize the education in the rural levels, the client distributed ‘smart-tablets’ with an inbuilt learning app for community-level interaction in a few villages. Initial insights into the grass root-level interaction lead them to expand their testing scenarios to the higher community level in rural areas. The client wanted to understand the content/subject consumption when students were given free access to learning. Information gathered about the content consumption would then help them develop better content ahead and improve student engagement.


The client could not zero-in on the exact groups of students who accessed the app on the tablet or even the content that worked well within these groups. Categorizing them into any specific cluster was not feasible without the help of deep analytical insights.
Data collected from each tablet was housed in a local server that would record the multiple variables needed to analyze the data. Various data sets such as the subjects, quizzes, time logged in, engagement levels, etc., were recorded to get an understanding of the overall effect of the smart-tablets distribution.
Voluminous data generated did not have any pattern for easy comprehension and implementation of new strategies to improve engagement. Impact Analytics performed extensive exploratory data analysis and clustering to understand trends in the data.


The data was clustered using K-means clustering on group level and content level. The group level clustering involved clustering segments/groups based on engagement with the tablet. Content level clustering was based on the content consumptions from the app. Before grouping, the data had to be cleaned and normalized. All the data obtained was normalized to one scale to carry out proper analysis and provide accurate results. Following variables were used for clustering:

Days in a month
  1.The average number of days the app was accessed in a month by a group.

  2.The metric was divided by 30 days to normalize the variable.

Resources accessed
The total number of resources accessed by the group in their history as a percentage of the total number of resources available to the group.

Time spent on the app
1.The total time spent on the app per day (in seconds)

2.The variable was normalized by dividing by 86400 (no. of seconds in a day).


Groups are clustered into high engagement, medium engagement, and low engagement to understand how the students are engaging with the content on the app. The objective was to understand the underlying metrics driving the engagement. The client wanted to know if the location of the student was the primary driving factor for participation or type of content.


Deep diving into the data provided by the client, IA was able to understand what type of content was preferred among students. Based on the results, IA is helping the NGO understand the metrics and the content consumption that enables them to drive the engagement amongst the students. In addition to this analysis, the NGO aims to understand the reasons behind the low/medium/high engagement clusters and the steps that can be taken to improve the learning capabilities of the rural students and design engaging content ahead.