Skip to main content

1.Google Analytics Home

In the last 28 Days, 105 Users have read this blog, creating 184 sessions with a bounce rate of 38.04% and session duration of 2m 17s.

FIG.1

1.1. Active Users

The below image gives an analysis of page views per minute. For instance, six active users are viewing four different blogs for the given minute.
FIG.2
1.2. How do you acquire users?  

There are three different ways to acquire users, namely:

1.2.1. Traffic Channel

The below histogram indicates maximum traffic is caused by direct and social channels.
FIG.3
1.2.2. Source/Medium

Views have been escalated through the use of Direct and Instagram.
FIG.4
1.2.3. Referral 

FIG.5
1.3. How are your active users trending over time?

The line graph displays the number of active users over 90 days. 
FIG.6
1.4. How well do you retain users?

The below statistic gives the percentage of returning users for 6 different weeks.
FIG.7
1.5. When do your users visit?

The below analysis shows that maximum users have visited the blog between 4pm-7pm.

FIG.8
1.6. Where are your users?

The below geographical map indicates that maximum users are from India followed by Ireland for the last 7 days.
FIG.9
1.7. What are your top devices?

The below pie chart indicates that 85% of users have visited the page using a mobile device and the rest using a desktop.
FIG.10
1.9. What pages do your users visit?

The below report indicated the number of page views per site.
FIG.11



2. Real-Time Overview
The below Dashboard gives detailed information about the active users.
FIG.12



3. Audience Overview
The Below dashboard gives a statistical view about users and their function on the Blog page.

FIG.13
3.1. Active Users
FIG.14


3.2. Demographics Overview
Below is the Density chart for age and pie chart for gender.
FIG.15



3.3. Geo



3.3.1. Language
Below report gives a comparative study of literature on the basis on acquisition, behaviour and conversion.
FIG.16

 3.3.2. Location
Below report gives a comparative study of location on the basis on acquisition, behaviour and conversion
FIG.17

3.4. Behaviour



3.4.1. New and Returning Users
The below chart gives a detailed report on the different types of users.
FIG.18
3.4.2. Frequency and Recency
This Dashboard gives a comparative study of pageviews created per session.

FIG.19
3.4.3. Engagement
The below report gives session durations on per sessions, causing the changes in the number of pageviews.
FIG.20
3.5. Technolgy



3.5.1. Browsers and OS
Below report gives a comparative study of different browsers on the basis on acquisition, behaviour and conversion.
FIG.21




3.5.2. Network
Below report gives a comparative study of the different network on the basis on acquisition, behaviour and conversion.

FIG.22




3.6. Mobile Overview
Below report gives a comparative study of different devices used to view the blog
FIG.23




3.6.1. Devices used in detail

FIG.24



4.1.Behaviour Overview

FIG.25




Comments

Popular posts from this blog

Value of Big Data in Marketing

Big data gives us eyes and ears on our marketing activities. It captures insights into our prospects and customers at a level of detail that has never been possible before. At the moment, we can respond to audience actions in real-time and drive customer behaviour. Big data is changing marketing and sales in areas that only a few years ago seemed unachievable. Types of Big Data for Marketers 1. Customer Data Data from consumers allow advertisers to consider their target audience. This type of apparent data is facts such as names, email addresses, purchase history, and web searches. Just as important are indications of the attitudes of your audience, which can be gathered from social media activity, surveys, and online communities. (Pearlman, 2019) 2. Financial Data Financial information allows us to assess and improve performance. The numbers, expenditures, and earnings of your company on sales and marketing fall into this category. Pricing can

3V's of Big Data

Businesses are generating massive amounts of data through its various data points and business process. Small companies can collect all the generated data into tools like excel sheets, accessing databases, and other devices. But in the case of huge businesses, the data which they generate cannot fit into such tools which cause human error instance to be increased drastically due to manual processing. 3V's of Big Data.   1.       Volume        The name Big Data itself has to do with a vast size. Data size plays a significant role in assessing meaning from the data. (Guru99.com, 2020) For Ex: Facebook has 2.37 billion users, Youtube has 2 billion users, Instagram has 1 billion users, and Twitter has 126 million users. All users of these social media share trillions of posts, images, videos, tweets, etc. Just think about the volume of data generated every single minute.  (Big Data Framework, 2019) 2.     Variety         In earlier days, s

Introduction to Big Data

Big data is more extensive, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able to tackle before.  (Oracle.com, 2014) Types of Big Data 1. Structured Data Any data that can be stored, accessed, and processed in a fixed format is called structured data. 2. Unstructured Data Any data with a form or structure unknown is labelled as unstructured data. A typical example of unstructured data is a heterogeneous source of data that contains a combination of simple text files, images, videos, etc. 3.  Semi-Structured data Semi-structured data may contain data in both types. We can see semi-structured data as structured in form, b ut it is not defined explicitly in relational DBMS with, e.g., a table description. Semi-structured data, for e