Skip to content

Data Analytics Proposal

Jump to bottom
vighneshsridhar edited this page Nov 25, 2025 · 15 revisions

Introduction

Purpose

The purpose of this document is to give an overview of the required metrics that we will be analyzing and visualizing as part of the Trend Analysis for Saayam functionality task. This includes tracking the activities of beneficiaries, volunteers, voluntary organizations, and donors over periods of time or in terms of geographical location. It also discusses data fetching methodology.

Requests

Requests are the total number of all help requests we receive on Saayam for All.

Metrics to be tracked:

  • Help Request Count: Track the total number of help requests in the past 7 days, month, year or custom range.
  • Category Count: Track the number of help requests by category.
  • Geographical Distribution: Track the number of help requests by country or region.

Beneficiaries

Beneficiaries are the people who receive help from Saayam for All volunteers.

Metrics to be Tracked:

  • Beneficiary Count: Track the number of beneficiaries in the past 7 days, month, year or custom range
  • Geographical Distribution: Break down the number of beneficiaries by country and region

Volunteers

Volunteers carry out help requests for Saayam for All and we will be aggregating data to perform analysis on their activities.

Metrics to be Tracked:

  • Volunteer Count: Track the number of volunteers in the past 7 days, month, year or custom range
  • Geographical Distribution: Break down the number of volunteers by country and region
  • User Count: Count the number of Saayam users in the past 7 days, month, year or custom range
  • User Geographical Distribution: Break down the number of Saayam users by country and region

Organizations {For Profit, Non Profit}

  • Count the numbers of volunteers categorized by organization

Donors

Metrics to be Tracked

  • Donor Count Analysis: Track active donors over 7 days, month, year, or custom date ranges.
  • Geographic Distribution: Break down donor participation by country and region.
  • Top Donors by Contribution: Identify donors with the highest donation amounts in a given time period or region.
  • Average Donation per Donor: Calculate the average donation amount per donor across selected periods.
  • New Donor Trends : Monitor how many donors are new in a given time period.

Query Strategy Examples

  • Donor Count Analysis: Query donation records filtered by specified date ranges, aggregating distinct donor IDs by day, week, or month. Include status filters to ensure we are counting only successful donations.
  • Geographic Distribution Analysis: Join donor tables with location data to aggregate donor counts by country and region. This will involve cross-referencing donor location information with donation activity records.
  • Top Donors by Contribution: Aggregate donation amounts per donor and rank donors to highlight the top contributors in each region or time window.
  • Average Donation per Donor: Divide the total donation amount by the number of donors in a period to track donation behavior across regions.
  • New Donor Tracking: Identify the first successful donation date for each donor, then count donors whose first donation falls within the specified period.

Key Performance Indicators

We will use KPI's to analyze how quickly help requests are being resolved. Here is a breakdown of the aspects we will be considering:

  • Determine how fast we are resolving a help request
  • Include minimum time, maximum time, and average time
  • Show how we are improving on the average time

Data Fetching Strategy

Database Architecture We will utilize SQL queries to extract data from our RDBMS database, ensuring secure connections through proper authentication credentials. The data extraction process will focus on aggregating user activities across specified time ranges.

Data Processing

We will employ pandas DataFrames and Python dictionaries to structure and manipulate the fetched data:

  • Dictionaries: Store aggregated counts as key-value pairs (e.g., date-count mappings).
  • DataFrames: Convert dictionaries to pandas DataFrames for advanced data manipulation.
  • Data Cleaning: Implement data validation and cleaning procedures to ensure accuracy (e.g., remove duplicates, handle missing country/region data).

Visualization Framework:

Temporal Data Visualization:

  • Line Charts: Line charts can be used to display trends of request/user count over time.
  • Area Charts: Highlight cumulative engagement.
  • Time Series Plots: Display detailed temporal patterns with trend lines.

Geographical Data Visualization:

  • Regional Pie Charts: Pie charts can be used to show percentages of a whole. Therefore, regional pie charts can be used to show geographical distribution of users by country/region.
  • Bar Charts: Bar charts can be used to analyze a distribution of categorical data. Therefore, this can be used for visualizing counts by request category and by geographical location.
  • World Map Visualizations: Show global distribution with color-coded intensity.

Dashboard Summary Visualizations:

  • KPI Cards: Display key donor/request metrics with trend indicators and percentage changes.
  • Progress Bars: Track progress toward donor recruitment or fundraising goals.
  • Sparklines: Show mini trend lines within summary cards.

Clone this wiki locally