Over the past decade, web analytics has emerged in the practice-driven work environment, spanning across diverse business verticals and organizations. Web analytics aim to retrieve, extract and evaluate information and actionable insights for knowledge discovery from web site content about how to improve user engagement. The landscape is dominated by plethora of tools that primarily use structured data, collected by web logs or JavaScript tags. Traditional web analytics applications (i.e., web analytics 2.0) provide mainly descriptive-type of services for the analysis of qualitative and quantitative data from website (e.g., Internet, Intranet, B2B market place) to drive a continual improvement of the users online experience.
However, these tools are constrained in respect to acquiring, managing and the analysis of web-based data as a multi-V model to express data sets that are characterized by a massive volume, a variety of features and high velocity. Such analytical features refers to big data analytics techniques that have the ability to process data with an immense volume (from gigabytes to terabytes), variety (from semi-structured to unstructured) and velocity (from batch to streaming) via unique data storage management, analysis and visualization technologies. Thus, the differences in analytical capability between big data architecture and traditional web analytics architecture, are that the former has a unique ability to analyze unstructured textual data, to parallel process large data volumes, and to parse data in real time or near real time.
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Moreover, the current development of machine learning algorithms, NLP-based text mining, data mining and graph mining also suggest a new direction of web analytics which more is geared toward predictive and prescriptive analytics as opposed to descriptive analytics. A major emerging component in this new era of web analytics applications is the development of cloud computing platforms and services delivered as services (i.e., SaaS) over the Internet. Cloud services for web analytics have become crucial given that communication costs often dominate computation costs, and thus it is essential to move the analytics capabilities ‘closer’ to the data where it is collected and stored.
Adding additional insights into the business impact of state-of-the-art web analytic and the evolution to digital intelligence propel a major shift from website-centricity tactics toward extended practices i.e. – to a more strategic (Big) Data analytics framework.
This framework aims to provide a complete solution in this space of emerging digital intelligence, in diverse analytical applications such as social network analysis (e.g., SNA, organizational entities analysis), text analytics (e.g., information extraction, sentiment analysis), advanced visualization techniques (e.g., co-occurrences networks, words cloud), clustering algorithms, anomaly detection mechanisms , trend analysis, pattern discovery and predictive modeling methods (e.g. decision trees, random forest, logistic regression, SVM).
This approach to web analytics brings a set of extended requirements, augmented metrics, and optimization practices to the current base of web analytics and will be covered more deeply in the forthcoming series of posts on this topic.
Dr. Elan Sasson. Intlock LTD
FAQs about Web Analytics
What is web analytics, and how does it benefit organizations?
Web analytics involves collecting, analyzing, and interpreting data from websites to improve user engagement and enhance online experiences. By leveraging web analytics, organizations can refine their digital strategies, optimize user interactions, and drive business growth through data-driven decisions.
➡️ Learn more about web analytics and how it impacts user engagement.
How is traditional web analytics different from big data analytics?
Traditional web analytics focuses on descriptive analysis of structured data, typically collected from web logs or JavaScript tags. In contrast, big data analytics processes massive volumes of data, including unstructured and high-velocity data, allowing for more comprehensive, real-time insights. This shift enables organizations to uncover deeper, actionable insights that go beyond basic metrics.
What are the main challenges with traditional web analytics tools?
Traditional web analytics tools often struggle to handle large-scale data and process unstructured data in real time. These limitations include difficulty processing high-velocity data streams and offering advanced analysis, which are essential for modern business applications.
What is predictive analytics, and how does it differ from descriptive analytics?
Predictive analytics uses machine learning and statistical models to forecast future trends and behaviors, helping organizations anticipate outcomes and make proactive decisions. Descriptive analytics, on the other hand, focuses on past data to understand historical patterns. Together, these techniques provide organizations with a complete picture of their data, from understanding what happened to predicting what will happen next.
What are common techniques used in predictive analytics?
Predictive analytics involves several key techniques, including:
- Regression Analysis: Identifies relationships between variables to predict continuous data trends.
- Decision Trees: Categorizes data into distinct groups based on specific variables for easy interpretation.
- Neural Networks: Recognizes complex patterns and non-linear relationships in large datasets, improving prediction accuracy.
How do organizations apply predictive analytics in different industries?
Predictive analytics is widely used across industries to improve operations and outcomes, including:
- Fraud Detection: Identifying anomalies in real-time to prevent financial fraud.
- Customer Segmentation: Tailoring marketing campaigns to specific audience groups for higher conversion rates.
- Risk Management: Evaluating credit scores and insurance claims to mitigate potential risks.
- Operational Efficiency: Forecasting inventory needs and optimizing resource allocation to reduce costs and waste.
What are the emerging trends in web analytics?
The field of web analytics is evolving, moving beyond descriptive analytics to more advanced approaches like predictive and prescriptive analytics. Key trends include:
- Big Data Integration: Incorporating large, complex data sets to uncover more valuable insights.
- Cloud-based Analytics Solutions: Providing scalable, secure analytics platforms with easier access to data.
- Machine Learning for Real-Time Insights: Leveraging AI to offer predictive insights and make smarter business decisions faster.
These advancements help organizations remain competitive in today’s data-driven world by making better-informed decisions and optimizing digital experiences.
➡️ Explore trends and innovations in web analytics.
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