Web Analytics: Boon for New Age Entrepreneurship

Web Analytics: Boon for New Age Entrepreneurship

Himani Singal (Birla Institute of Technology, India) and Shruti Kohli (Birla Institute of Technology, India)
Copyright: © 2016 |Pages: 20
DOI: 10.4018/978-1-4666-9764-5.ch012
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Abstract

There is a remarkable association between an organization's analytics intricacy and its competitive enactment. The biggest problem to adopting analytics is the lack of knowledge of using it to improve business performance. A website is believed and considered as ‘face' of the company. In present era, there are more than 200 million people who buy goods online across the globe. Business Analytics helps companies to find the most profitable customer and allows them to justify their marketing effort, especially when the competition is very high. Predictive analytics helps organizations to predict churn, default in loan payment, brand switch, insurance loss and even the outcome in a football match. There is ample evidence from the corporate world that the ability to make better decisions (by management executives) improves with analytical skills. This chapter will provide an in-depth knowledge of business analytic techniques and their applications in improving business processes and decision-making.
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Introduction

Analytics is the application of computer expertise, domain knowledge and statistics to solve problems in business and industry; to aid efficient and effective decision-making. It is simply a scientific process of converting raw data into knowledge to support decision making by finding patterns in data. The goal of Analytics is to improve business, society or personal performance by gaining knowledge from data. It encourages well informed decision-making driven by data rather using gut-feeling or guessing estimates. It is reported that data is growing at 40% compound annual rate which will reach to 45 ZB (Zeta Bytes) by 2020. About 2.5 Quintillion bytes of data is created each year, out of which 90% data was created in last 2 years itself. With such huge amount of data, decisions of any kind cannot be taken randomly. Proper analysis is required to undermine any pinning information which can increase customer base or revenues; to which insightful analytics is the solution. Analytics can be classified broadly along four dimensions as depicted in Figure 1.

Figure 1.

Dimensions of Analytics

Each of these dimensions of Analytics gives answer or insightful measures to a specific type of problem. These problems can be best described in the form of questions as follows:

  • 1.

    Descriptive Analytics – What happened or happening in the business?

  • 2.

    Inquisitive Analytics – Why did it happen?

  • 3.

    Predictive Analytics – What is likely to happen based on historical information?

  • 4.

    Prescriptive Analytics – What action should be taken?

Web analytics (WA) is the measurement, collection, analysis and reporting of internet data for purpose of understanding and optimizing web usage. It is a tool for measuring web traffic, business and market research by assessing and improving the effectiveness of a website. For instance, it helps to measure change of traffic to a website after the launch of a new advertising campaign, number of visitors on the website and number of pages viewed by the visitors. WA helps in measuring both on-site and off-site data (read site as website). Here, on-site data refers to the website data or direct data which is collected by tracking the visitors’ behavior directly, whereas off-site data refers to the indirect data which cannot be determined online directly. The kind of data which Web Analytics helps to measure both on-site and off-site is depicted in Figure 2.

Figure 2.

Web Analytics measures

To sum up in simple words, ‘systematic evaluation based on correct and precise data is known as analysis or analytics and if data is related to Internet (particularly websites), it is known as Web Analytics’. At conceptual level, Web Analytics is composed of different kinds of analysis activities, according to which it is categorized as depicted in Figure 3.

Figure 3.

Composition of Web Analytics

  • 1.

    Statistical Analysis – Why is this happening?

  • 2.

    Forecasting – What if these trends continues?

  • 3.

    Predictive Modeling – What will happen next?

  • 4.

    Optimization – What’s the best that can happen?

These four components collectively help to measure five Ws and one H; which is fundamental for existence of any website.

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