Website Analytics |KPI|Bounce Rate|Engagement

Phase 1.2: Defining Key Metrics and Engagement for SankalandTech.com Analytics

Hello again. We just finished getting a good overview of our web analytics project for sankalandtech.com. Now it is time to get really specific. In this step we will talk about the numbers that truly matter. These are often called Key Performance Indicators or KPIs. Think of KPIs as the scoreboards for our website. They tell us if we are doing well or if we need to change our game plan.

For sankalandtech.com our main goal is to help people learn tech. So our KPIs will focus on understanding how well we are actually doing that.


What Are KPIs and Why Do We Care

KPIs are simply measurements that show how well we are meeting our goals. Instead of just saying lots of people visit we want to know if people are actually learning and finding value.

By tracking the right KPIs we can:

  • See what is working — Which tutorials are a big hit

  • Spot problems — Are people leaving a certain page too quickly

  • Make smart changes — How can we improve our content or website layout

Let us look at the specific KPIs and measurements that will be most useful for sankalandtech.com.


Infographic showing key website metrics and engagement indicators for SankalandTech.com

Our Key Measurements and What They Tell Us

We will gather various pieces of information. Then we will combine them to create these important measurements that show how well our site is performing.

1. Page Views

What it is: The total number of times any page on sankalandtech.com is loaded. If someone visits your C tutorial page twice that counts as two page views.

Why it matters: It shows which content is getting seen most often. More page views mean more people are finding and viewing that content.

2. Unique Visitors

What it is: This counts how many different people visit your site within a given time period. If one person visits five times they still count as one unique visitor.

Why it matters: It shows how many individual people are visiting sankalandtech.com. Are we attracting new users or mostly repeat visits

3. Sessions

What it is: A session includes all actions a visitor takes in one visit. It begins when they arrive and ends after a short time of inactivity usually around 30 minutes.

Why it matters: It helps us understand how often people visit and what they do each time they are here.

4. Average Session Duration

What it is: The average time someone spends on the site during a single session.

Why it matters: Longer session times often mean more engagement. If people stay longer they are likely reading and learning from our content.

5. Bounce Rate

What it is: The percentage of visitors who land on one page and leave without going anywhere else.

Why it matters: A high bounce rate might mean the content was not helpful or interesting or it could be a sign that users found what they needed very quickly. It could also point to design issues or unclear navigation. We will need to carefully review pages with high bounce rates.

6. Pages Per Session

What it is: The average number of pages a visitor looks at in one visit.

Why it matters: More pages per session often show that users are exploring the site. This is a good sign of interest and content flow.

7. Traffic Sources

What it is: This shows where visitors come from before arriving on sankalandtech.com. They might come from a search engine like Google a social media post or by typing the website directly.

Why it matters: Knowing our sources helps us understand what channels bring in users. This guides future content promotion strategies.

8. New versus Returning Visitors

What it is: This tracks how many people are visiting for the first time versus how many have been here before.

Why it matters: It helps us balance growth with retention. New users help us expand and returning users show that we are delivering ongoing value.


Understanding Engagement

Beyond just numbers we want to understand engagement. Engagement means how much a visitor interacts with and gains real value from your content. It is not just about whether they visited but how they visited.

For example

  • Someone who spends 10 minutes on a tutorial page and then clicks to the next part is very engaged

  • Someone who lands on a page and leaves in 5 seconds is not engaged at all

By tracking the measurements we talked about earlier and combining them we can get a much clearer picture of true engagement. This helps us make sankalandtech.com a truly valuable learning resource.

Now that we have defined these key measurements we are ready to think about the people behind the numbers our target audience


Previous Topic==> Website Analytics Project Overview ||  Next Topics==> Target User Pain Point


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Website Analytics Project: Phases and Action Steps

  • Home
  • 🟢 Live App: Web Analytics Simulator
  • Phase 0: Project Setup & Problem Definition
  • 0.1 Define Project Goals & Challenges
  • 0.2 Select Tools, Tech Stack & Data Sources
  • 0.3 Software Requirements & Installation
  • 0.4 Folder Structure & GitHub Repo
  • 0.5 Testing Project Locally
  • Phase 1: Planning for Analytics
  • 1.1 Website Analytics Project Overview
  • 1.2 Define KPIs, Bounce Rate, Engagement
  • 1.3 Identify Target Users & Pain Points
  • Phase 2: Data Collection
  • 2.1 Setup Google Analytics 4 (GA4)
  • 2.2 Export GA4 Data to BigQuery/CSV
  • 2.3 Design SQL Schema for Web Analytics
  • Phase 3: Data Cleaning & Feature Engineering
  • 3.1 Clean Website Data with Python & Pandas
  • 3.2 Create Custom Metrics (Session, Bounce, etc.)
  • Phase 4: Exploratory Data Analysis (EDA)
  • 4.1 Analyze Website Traffic Trends
  • 4.2 Behavior by Device, Source, Location
  • 4.3 Top Pages & High Bounce Pages
  • 4.4 Diagnose Low Traffic & User Drop
  • Phase 5: Business Insights
  • 5.1 Funnel Analysis & Drop-Off Points
  • 5.2 New vs Returning Users
  • 5.3 Time Spent & Scroll Depth
  • Phase 6: SQL for Business
  • 6.1 SQL for Business Insights
  • 6.2 Combine Web Data Using SQL
  • 6.3 Find Problematic Pages Using SQL
  • Phase 7: Machine Learning
  • 7.1 Segment Users with Clustering
  • 7.2 Predict Bounce Rate with ML
  • 7.3 Recommend Pages or Content
  • Phase 8: Dashboards & Visualization
  • 8.1 Dashboard with Streamlit
  • 8.2 Visualize KPIs with Python
  • 8.3 Page-Level Metrics & Drop Heatmaps
  • Phase 9: Final Analytics Story
  • 9.1 Summary Report & Findings
  • Phase 10: Hosting & Portfolio Presentation
  • 10.1 Host Website Project Online
  • 10.2 Add to GitHub with ReadMe
  • 10.3 Pitch Project in Interview
  • Other Topics
  • SQL Interview Questions
  • SQL Case Study: Account Management
  • Python Interview Questions
  • Why C Language

Get in touch

  • tech2dsm@gmail.com

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