Unit 2: Network fundamentals
Network Fundamentals
A social network is a structure made up of individuals (nodes) connected by relationships (ties). This concept helps in analyzing how people, groups, or organizations interact with each other.
Nodes, Ties, and Influencers
A. Nodes (Actors)
Nodes represent the individuals, groups, or entities in a network. Each node can be a person, an organization, or even a webpage.
🔹 Example: In a workplace social network, nodes can be employees. , In an online social media network, nodes are users on platforms like Facebook, LinkedIn, or Twitter.
B. Ties (Connections)
Ties represent relationships between nodes. These relationships can be strong ties (close friends, family) or weak ties (acquaintances, colleagues).
🔹 Example: A strong tie is a friendship between two close colleagues. , A weak tie is the connection between two professionals who met at a conference.
C. Influencers
Influencers are nodes with high levels of connections or influence within a network. They help spread information, trends, or behaviors.
🔹 Example: A celebrity on Instagram with millions of followers who influences people’s buying decisions. , A senior manager in a company who guides and influences the decision-making process.
Social Network, Web Data, and Methods
A. Social Network
A social network is a digital or real-world representation of relationships among individuals or groups. It can be formal (business networks) or informal (friendships).
🔹 Example: A company's internal network connecting employees for collaboration. , LinkedIn, which connects professionals worldwide.
B. Web Data
Web data refers to information collected from social media, websites, and other online platforms. This data helps in understanding network interactions, trends, and influences.
🔹 Example: Twitter API collects tweets to analyze trending topics., Facebook tracks user interactions to suggest friends or advertisements.
C. Methods of Social Network Analysis (SNA)
Social Network Analysis (SNA) involves using data analytics, mathematical models, and visualization techniques to study relationships in a network.
🔹 Key Methods
- Degree Centrality: Measures the number of direct connections a node has. Example: A LinkedIn user with 500+ connections has higher centrality than one with 50.
- Betweenness Centrality: Identifies key nodes that act as bridges between clusters. Example: A recruiter who connects job seekers with hiring managers.
- Clustering Coefficient: Determines how tightly nodes are connected in a group. Example: A close-knit friend circle in WhatsApp groups.
- PageRank Algorithm: Used by Google to rank webpages based on links and importance. Example: Websites with many inbound links rank higher on Google.
Data Collection and Web Analytics Fundamentals
Web analytics is the process of collecting, measuring, and analyzing web data to understand and optimize website usage. Various techniques are used to capture data from users’ online activities. Below are some fundamental methods:
Capturing Data Techniques
Explanation of Each Technique
A. Web Logs
- Web logs store user activity directly on a web server. They include information such as:
- IP Address (user’s location)
- Timestamp (date and time of access)
- Requested Page/Resource
- HTTP Status Codes (e.g., 404 error, 200 success)
🔹 Example: An e-commerce website analyzes web logs to see which product pages have the most visits and identify peak traffic hours.
B. Web Beacons (Pixel Tags)
- Web beacons are 1x1 pixel transparent images placed in emails or web pages.
- They work with cookies and help track whether an email was opened or which webpage was viewed.
- They do not require user interaction to collect data.
🔹 Example: An online store sends promotional emails. Web beacons track how many users open the email and click on product links.
C. JavaScript Tags
- JavaScript tags are small code snippets embedded in web pages to track user actions.
- They help collect real-time data, such as:
- Clicks
- Page views
- Time spent on a page
- Scroll behavior
🔹 Example: Google Analytics uses JavaScript tags to track visitor behavior, such as how long users stay on a page.
D. Packet Sniffing
- Packet sniffing captures data packets traveling over a network.
- It provides detailed insights into network traffic, security vulnerabilities, and user activity.
- Used by network administrators and cybersecurity teams.
🔹 Example: A company’s IT team uses packet sniffing to detect unauthorized access attempts on their website.
Key Differences
These data collection methods are essential for web analytics, marketing, cybersecurity, and user behavior tracking. Choosing the right method depends on business needs, whether it's tracking web traffic, analyzing security threats, or optimizing digital marketing efforts.
Outcome Data in Digital Marketing and Web Analytics
Outcome data refers to the measurable results obtained from various digital marketing efforts. It helps businesses understand how well their online strategies are performing. The four main types of outcome data are:
- E-commerce
- Lead Generation
- Brand/Advocacy
- Support
E-Commerce Outcome Data
E-commerce outcome data tracks online transactions, sales performance, and customer behavior. It includes:
Key Metrics
- Revenue (Total sales generated)
- Conversion Rate (Percentage of visitors who made a purchase)
- Average Order Value (AOV) (Total revenue ÷ number of orders)
- Cart Abandonment Rate (Percentage of users who add items to the cart but don’t complete the purchase)
Example: A fashion e-commerce website uses Google Analytics to track
- Total revenue of ₹5,00,000 in a month.
- Cart abandonment rate of 40%, leading to retargeting ads.
- Best-selling product based on sales data.
- By analyzing this data, the business optimizes pricing, promotions, and product recommendations.
Lead Generation Outcome Data
Lead generation outcome data helps businesses track potential customers (leads) who show interest but haven’t purchased yet. It is commonly used in service-based industries, B2B businesses, and online education platforms.
Key Metrics
- Lead Conversion Rate (Percentage of visitors who fill out a form or subscribe)
- Cost per Lead (CPL) (Total marketing cost ÷ number of leads)
- Customer Acquisition Cost (CAC) (Total spend to acquire a new customer)
- Click-Through Rate (CTR) (Percentage of users who click on an ad or link)
- Example: A SaaS company offering CRM software runs a Google Ads campaign. The company tracks:
- 500 new leads generated in a month.
- Lead conversion rate of 10%, meaning 50 leads became paying customers.
- CPL of ₹200, helping the company decide budget allocation.
By analyzing lead data, the company improves targeting and refines the sales funnel.
Brand/Advocacy Outcome Data
Brand awareness and advocacy data measure how well customers recognize, engage with, and promote a brand. It is crucial for long-term brand growth and customer loyalty.
Key Metrics
- Brand Mentions (How often the brand is mentioned online)
- Social Media Engagement (Likes, shares, comments, retweets)
- Net Promoter Score (NPS) (Measures customer satisfaction and likelihood to recommend)
- Customer Reviews & Ratings (Feedback on platforms like Google, Amazon, or Yelp)
- Example: A smartphone company launches a new model and tracks:
- 10,000 social media mentions in the first week.
- 4.5-star average rating on e-commerce platforms.
- NPS score of 75, showing high customer satisfaction.
By leveraging brand data, the company refines its marketing and customer engagement strategies.
Support Outcome Data
Support outcome data evaluates customer service performance and helps improve user experience. Businesses use it to reduce complaints and increase customer retention.
Key Metrics
- Customer Satisfaction Score (CSAT) (Survey ratings on support quality)
- Average Response Time (Time taken to reply to a query)
- Resolution Rate (Percentage of issues resolved in the first response)
- Support Ticket Volume (Number of complaints received)
- Example: A telecom company’s customer support center tracks:
- CSAT score of 85%, indicating good service.
- Average response time of 2 minutes via live chat.
- First-time resolution rate of 90%, reducing repeated complaints.
By analyzing support data, the company improves customer experience and reduces churn.
Comparison Table: Outcome Data Types
In Short, Understanding outcome data helps businesses optimize marketing, improve customer service, and enhance brand presence. By using the right metrics, companies can make data-driven decisions to grow their online presence and sales.
Competitive Data in Web Analytics
Competitive data helps businesses analyze their market position, competitor performance, and industry trends. It provides valuable insights into customer behavior and online traffic.
The key competitive data sources are:
- Panel-Based Measurement
- ISP-Based Measurement
- Search Engine Data
- Organizational Structure
Panel-Based Measurement
Panel-based measurement collects data from a pre-selected group of users (panelists) who agree to share their browsing behavior. This method provides insights into audience preferences and market trends.
How It Works:
- Users install tracking software or browser extensions.
- The tool collects data on website visits, time spent, and engagement levels.
- Companies analyze the data to understand market trends.
Key Metrics:
- Audience Demographics (Age, gender, location)
- Website Traffic Share (Which competitor gets more visits)
- User Engagement (Time spent on sites, bounce rates)
Example: Nielsen and Comscore use panel-based measurement to track:
- Which streaming platform is most popular?
- How many users visit a competitor’s website?
📌 E-commerce Example: Amazon uses panel-based data to compare its traffic with Flipkart.
ISP-Based Measurement
ISP-based measurement collects web traffic data directly from Internet Service Providers (ISPs). It provides real-time, large-scale data on user behavior without requiring direct participation.
How It Works:
- ISPs track all internet activity of users connected to their networks.
- The data is anonymized and aggregated.
- Businesses use it to analyze trends and competitor performance.
Key Metrics:
- Total web traffic of a website
- Geographic distribution of users
- Internet speed and service quality data
Example: A telecom company like Jio or Airtel collects ISP data to:
- See which websites get the most traffic.
- Identify peak browsing hours.
📌 Marketing Example: An advertising agency uses ISP data to check which websites are most visited in India before running digital ads.
Search Engine Data
Search engine data provides insights into what users search for and how websites rank in search results. It helps businesses optimize SEO and understand competitor performance.
How It Works:
- Google, Bing, and Yahoo track search queries.
- Businesses analyze keywords, rankings, and user intent.
- SEO tools (Google Search Console, SEMrush, Ahrefs) extract this data.
Key Metrics:
- Search Volume (How often a keyword is searched)
- Keyword Rankings (Where a website ranks for a keyword)
- Click-Through Rate (CTR) (How many users click a search result)
Example: A travel website analyzes Google Search Console data to:
- Find that “best places to visit in 2025” is a trending keyword.
- Optimize content for higher ranking in search results.
📌 SEO Example: Zomato and Swiggy monitor search engine data to rank higher for “best restaurants near me.”
Organizational Structure in Competitive Data Analysis
The organizational structure of competitive data analysis refers to how companies manage and utilize web analytics data. It involves various teams, tools, and processes.
Key Teams in Competitive Data Management:
Comparison Table: Competitive Data Methods
Competitive data helps businesses track industry trends, competitor strategies, and customer behavior. By combining panel data, ISP insights, and search engine data, organizations make better marketing and business decisions.