EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials Head to Head Differences Tutorial Digital Analytics vs Digital Marketing
Secondary Sidebar
Head to Head Differences Tutorial
  • Differences Tutorial
    • Scikit Learn vs TensorFlow
    • Azure Functions vs Logic Apps
    • Azure Data Factory vs Databricks
    • SHA1 vs MD5
    • Azure SQL Database vs Managed Instance
    • Azure SQL Database vs SQL Server
    • PostgreSQL vs MySQL
    • PostgreSQL vs MySQL Benchmark
    • ArangoDB vs MongoDB
    • Cloud Computing vs Big Data Analytics
    • T-SQL vs SQL
    • PostgreSQL vs MariaDB
    • Spark vs Impala
    • Datadog vs Splunk
    • Domo vs Tableau
    • Data Scientist vs Data Engineer vs Statistician
    • Big Data Vs Machine Learning
    • Predictive Analytics vs Business Intelligence
    • AI vs Machine Learning vs Deep Learning
    • Business Intelligence vs Data Warehouse
    • Apache Kafka vs Flume
    • Data Science vs Machine Learning
    • Business Analytics Vs Predictive Analytics
    • Data mining vs Web mining
    • Data Science Vs Data Mining
    • Data Science Vs Business Analytics
    • Analyst vs Associate
    • Apache Hive vs Apache Spark SQL
    • Apache Nifi vs Apache Spark
    • Apache Spark vs Apache Flink
    • Apache Storm vs Kafka
    • Artificial Intelligence vs Business Intelligence
    • Artificial Intelligence vs Human Intelligence
    • Al vs ML vs Deep Learning
    • SQL vs SQLite
    • Assembly Language vs Machine Language
    • AWS vs AZURE
    • AWS vs Azure vs Google Cloud
    • Big Data vs Data Mining
    • Big Data vs Data Science
    • Big Data vs Data Warehouse
    • Blu-Ray vs DVD
    • Business Intelligence vs Big Data
    • Business Intelligence vs Business Analytics
    • Business Intelligence vs Data analytics
    • Business Intelligence VS Data Mining
    • Business Intelligence vs Machine Learning
    • Business Process Re-Engineering vs CI
    • Cassandra vs Elasticsearch
    • Cassandra vs Redis
    • Cloud Computing Public vs Private
    • Cloud Computing vs Fog Computing
    • Cloud Computing vs Grid Computing
    • Cloud Computing vs Hadoop
    • Computer Network vs Data Communication
    • Computer Science vs Data Science
    • Computer Scientist vs Data Scientist
    • Customer Analytics vs Web Analytics
    • Data Analyst vs Data Scientist
    • Data Analytics vs Business Analytics
    • Data Analytics vs Data Analysis
    • Data Analytics Vs Predictive Analytics
    • Data Lake vs Data Warehouse
    • Data Mining Vs Data Visualization
    • Data mining vs Machine learning
    • Data Mining Vs Statistics
    • Data Mining vs Text Mining
    • Data Science vs Artificial Intelligence
    • Data science vs Business intelligence
    • Data Science Vs Data Engineering
    • Data Science vs Data Visualization
    • Data Science vs Software Engineering
    • Data Scientist vs Big Data
    • Data Scientist vs Business Analyst
    • Data Scientist vs Data Engineer
    • Data Scientist vs Data Mining
    • Data Scientist vs Machine Learning
    • Data Scientist vs Software Engineer
    • Data visualisation vs Data analytics
    • Data vs Information
    • Data Warehouse vs Data Mart
    • Data Warehouse vs Database
    • Data Warehouse vs Hadoop
    • Data Warehousing VS Data Mining
    • DBMS vs RDBMS
    • Deep Learning vs Machine learning
    • Digital Analytics vs Digital Marketing
    • Digital Ocean vs AWS
    • DOS vs Windows
    • ETL vs ELT
    • Small Data Vs Big Data
    • Apache Hadoop vs Apache Storm
    • Hadoop vs HBase
    • Between Data Science vs Web Development
    • Hadoop vs MapReduce
    • Hadoop Vs SQL
    • Google Analytics vs Mixpanel
    • Google Analytics Vs Piwik
    • Google Cloud vs AWS
    • Hadoop vs Apache Spark
    • Hadoop vs Cassandra
    • Hadoop vs Elasticsearch
    • Hadoop vs Hive
    • Hadoop vs MongoDB
    • HADOOP vs RDBMS
    • Hadoop vs Spark
    • Hadoop vs Splunk
    • Hadoop vs SQL Performance
    • Hadoop vs Teradata
    • HBase vs HDFS
    • Hive VS HUE
    • Hive vs Impala
    • JDBC vs ODBC
    • Kafka vs Kinesis
    • Kafka vs Spark
    • Cloud Computing vs Data Analytics
    • Data Mining Vs Data Analysis
    • Data Science vs Statistics
    • Big Data Vs Predictive Analytics
    • MapReduce vs Yarn
    • Hadoop vs Redshift
    • Looker vs Tableau
    • Machine Learning vs Artificial Intelligence
    • Machine Learning vs Neural Network
    • Machine Learning vs Predictive Analytics
    • Machine Learning vs Predictive Modelling
    • Machine Learning vs Statistics
    • MariaDB vs MySQL
    • Mathematica vs Matlab
    • Matlab vs Octave
    • MATLAB vs R
    • MongoDB vs Cassandra
    • MongoDB vs DynamoDB
    • MongoDB vs HBase
    • MongoDB vs Oracle
    • MongoDB vs Postgres
    • MongoDB vs PostgreSQL
    • MongoDB vs SQL
    • MongoDB vs SQL server
    • MS SQL vs MYSQL
    • MySQL vs MongoDB
    • MySQL vs MySQLi
    • MySQL vs NoSQL
    • MySQL vs SQL Server
    • MySQL vs SQLite
    • Neural Networks vs Deep Learning
    • PIG vs MapReduce
    • Pig vs Spark
    • PL SQL vs SQL
    • Power BI Dashboard vs Report
    • Power BI vs Excel
    • Power BI vs QlikView
    • Power BI vs SSRS
    • Power BI vs Tableau
    • Power BI vs Tableau vs Qlik
    • PowerShell vs Bash
    • PowerShell vs CMD
    • PowerShell vs Command Prompt
    • PowerShell vs Python
    • Predictive Analysis vs Forecasting
    • Predictive Analytics vs Data Mining
    • Predictive Analytics vs Data Science
    • Predictive Analytics vs Descriptive Analytics
    • Predictive Analytics vs Statistics
    • Predictive Modeling vs Predictive Analytics
    • Private Cloud vs Public Cloud
    • Regression vs ANOVA
    • Regression vs Classification
    • ROLAP vs MOLAP
    • ROLAP vs MOLAP vs HOLAP
    • Spark SQL vs Presto
    • Splunk vs Elastic Search
    • Splunk vs Nagios
    • Splunk vs Spark
    • Splunk vs Tableau
    • Spring Cloud vs Spring Boot
    • Spring vs Hibernate
    • Spring vs Spring Boot
    • Spring vs Struts
    • SQL Server vs PostgreSQL
    • Sqoop vs Flume
    • Statistics vs Machine learning
    • Supervised Learning vs Deep Learning
    • Supervised Learning vs Reinforcement Learning
    • Supervised Learning vs Unsupervised Learning
    • Tableau vs Domo
    • Tableau vs Microstrategy
    • Tableau vs Power BI vs QlikView
    • Tableau vs QlikView
    • Tableau vs Spotfire
    • Talend Vs Informatica PowerCenter
    • Talend vs Mulesoft
    • Talend vs Pentaho
    • Talend vs SSIS
    • TensorFlow vs Caffe
    • Tensorflow vs Pytorch
    • TensorFlow vs Spark
    • TeraData vs Oracle
    • Text Mining vs Natural Language Processing
    • Text Mining vs Text Analytics
    • Cloud Computing vs Virtualization
    • Unit Test vs Integration Test?
    • Universal analytics vs Google Analytics
    • Visual Analytics vs Tableau
    • R vs Python
    • R vs SPSS
    • Star Schema vs Snowflake Schema
    • DDL vs DML
    • R vs R Squared
    • ActiveMQ vs Kafka
    • TDM vs FDM
    • Linear Regression vs Logistic Regression
    • Slf4j vs Log4j
    • Redis vs Kafka
    • Travis vs Jenkins
    • Fact Table vs Dimension Table
    • OLTP vs OLAP
    • Openstack vs Virtualization
    • Cluster v/s Factor analysis
    • Informatica vs Datastage
    • CCBA vs CBAP
    • SPSS vs EXCEL
    • Excel vs Tableau
    • Cassandra vs MySQL
    • RabbitMQ vs Kafka
    • SAAS vs Cloud
    • RabbitMQ vs Redis
    • AMQP vs MQTT
    • Forward Chaining vs Backward Chaining
    • Google Data Studio vs Tableau
    • ActiveMQ vs RabbitMQ
    • Cloud vs Data Center
    • Cores vs Threads
    • Inner Join vs Outer Join
    • ZeroMQ vs Kafka
    • Mxnet vs TensorFlow
    • Redis vs Memcached
    • RDBMS vs NoSQL
    • AWS Direct Connect vs VPN
    • Cassandra vs Couchbase
    • Elegoo vs Arduino
    • Redis vs MongoDB
    • Chef vs Puppet
    • GSM vs GPRS
    • Keras vs TensorFlow vs PyTorch
    • Cloudflare vs CloudFront
    • Bitmap vs Vector
    • Left Join vs Right Join
    • IaaS vs PaaS
    • Blue Prism vs UiPath
    • GNSS vs GPS
    • Cloudflare vs Akamai
    • GCP vs AWS vs Azure
    • Arduino Mega vs Uno
    • Qualitative vs Quantitative Data
    • Arduino Micro vs Nano
    • PIC vs Arduino
    • PRTG vs Solarwinds
    • PostgreSQL vs SQLite
    • Metabase vs Tableau
    • Arduino Leonardo vs Uno
    • Arduino Due vs Mega
    • ETL Vs Database Testing
    • DBMS vs File System
    • CouchDB vs MongoDB
    • Arduino Nano vs Mini
    • IaaS vs PaaS vs SaaS
    • On-premise vs off-premise
    • Couchbase vs CouchDB
    • Tableau Dimension vs Measure
    • Cognos vs Tableau
    • Data vs Metadata
    • RethinkDB vs MongoDB
    • Cloudera vs Snowflake
    • HBase vs Cassandra
    • Business Analytics vs Business Intelligence
    • R Programming vs Python
    • MongoDB vs Hadoop
    • MySQL vs Oracle
    • OData vs GraphQL
    • Soft Computing vs Hard Computing
    • Binary Tree vs Binary Search Tree
    • Datadog vs CloudWatch
    • B tree vs Binary tree
    • Cloudera vs Hortonworks
    • DevSecOps vs DevOps
    • PostgreSQL Varchar vs Text
    • PostgreSQL Database vs schema
    • MapReduce vs spark
    • Hypervisor vs Docker
    • SciLab vs Octave
    • DocumentDB vs DynamoDB
    • PostgreSQL union vs union all
    • OrientDB vs Neo4j
    • Data visualization vs Business Intelligence
    • QlikView vs Qlik Sense
    • Neo4j vs MongoDB
    • Postgres Schema vs Database
    • Mxnet vs Pytorch
    • Naive Bayes vs Logistic Regression
    • Random Forest vs Decision Tree
    • Random Forest vs XGBoost
    • DynamoDB vs Cassandra
    • Looker vs Power BI
    • PostgreSQL vs RedShift
    • Presto vs Hive
    • Random forest vs Gradient boosting
    • Gradient boosting vs AdaBoost
    • Amazon rds vs Redshift
    • Bigquery vs Bigtable
    • Data Architect vs Data Engineer
    • DataSet vs DataTable
    • dataset vs dataframe
    • Dataset vs Database
    • New Relic vs Splunk
    • Data Architect and Management Designer
    • Data Engineer vs Data Analyst
    • Grafana vs Tableau
    • MySQL text vs Varchar
    • Relational Database vs Flat File
    • Datadog vs Prometheus
    • Neo4j vs Neptune
    • Data Mining vs Data warehousing
    • DocumentDB vs MongoDB
    • PostScript vs PCL
    • QRadar vs Splunk
    • Qlik Sense vs Tableau
    • DigitalOcean vs Google Cloud
    • PostgreSQL vs Elasticsearch
    • Redshift vs blueshift
    • Gitlab vs Azure DevOps

Related Courses

Online Data Science Course

Online Tableau Training

Azure Training Course

Hadoop Certification Course

Data Visualization Courses

All in One Data Science Course

Digital Analytics vs Digital Marketing

By Priya PedamkarPriya Pedamkar

digital analytics vs digital marketing

Introduction to Digital Analytics vs Digital Marketing

The world is continuously evolving in such a rapid manner that it is today a hectic jungle of technology and mass of information that is vast and complicated. A decade ago, the world might have seemed to be a simple and less complex place, but that is not the story any longer. That is why nations and economies will have to adapt to the changing times with the intention of surviving this digital era which is definitely here to stay.

Since its inception in 1983, the internet has completely transformed the manner in which human being live and conduct their activities in almost all aspects of their lives. In fact, the internet today has a powerful impact on the lives of people and businesses around the world. It has made the flow of information easier, not just on the national level but on the global level as well. It provides information of all kinds to not just brands, but also to organizations, individuals, educational institutions, and others. It also helps us to connect with our friends and relatives, thereby helping us develop our relationships and keep in touch with people that matter.

Today, brands need to create a powerful presence in the online world in order to be successful, otherwise, they will be left out and become redundant brands. Besides impressive growth, the audience reach in the digital world is almost limitless. This is because a brand can reach millions of customers on the net, not just within their country but also around the globe. The internet is, therefore, a great medium through which brands can ensure that they remain important contributors to the lives of their clients, customers, and stakeholders.

Being present in the digital medium is therefore of vital importance for brands and digital marketing companies around the world. As people consume more and more digital content, almost every second of their life especially through mediums like smartphones, laptops, and desktops, companies need to invest in smart solutions that can help them make use of the immense potential of the internet and its related fields. Today, almost six billion people around the globe use smartphones to access information/services/goods of different kinds and that is why ignoring digital communication is one of the biggest mistakes that any brand can make in the current time.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Digital Analytics

Digital marketing – The future of marketing

Digital marketing is today continuously evolving and growing, almost every single day. As it is an evolving field, digital marketing is not just the present but the future of brands and digital marketing companies. In fact, this field is growing so rapidly, that it might very soon be traditional forms of advertising like print, radio, and television. Brands and companies need to embrace this brave world of digital consumption so that they can create campaigns that are faster, versatile, streamlined, and practical.

With so much potential in the digital medium, brands and companies who adopt this medium will see a lot of positive results in the coming years. Investing in digital medium communication, is, therefore, one of the biggest investments that can yield positive results in the future days. Some of the key forms of digital marketing include SEO content, websites, blogs, banner ads on the web, online videos, PPC advertising, email advertising, advertisements on social media that include Facebook, Twitter, LinkedIn, Instagram among others, and mobile marketing.

All in One Data Science Bundle(360+ Courses, 50+ projects)
Python TutorialMachine LearningAWSArtificial Intelligence
TableauR ProgrammingPowerBIDeep Learning
Price
View Courses
360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (86,527 ratings)

In addition, there are many other forms of digital content that are being discovered every single day and that is why staying relevant in this industry and keeping yourself updated about all the trends takes on prime importance.

Note: Become a Full-Stack Digital Marketer
Learn how to drive online traffic to the company website. Develop and manage digital marketing campaigns. Practice with hands-on, essential training in Google Analytics.

Why is digital marketing so important?

Digital marketing is undoubtedly the future of all brands and companies. Very soon they will become extremely affordable and a cost-effective medium through which brands can reach their target audience in a powerful yet engaging manner. For example, an email or social media campaign can have far-reaching consequences that can transmit information and generate brand awareness at less cost than a television ad or print campaign on one hand and reach a wider audience on the other hand.

Another major benefit of digital analytics marketing is that the impact and reach of each campaign can be easily tracked and monitored at every stage. This enables brands to understand the effectiveness of their campaigns without spending millions of money on customer research. In the digital medium, brands can have access to multiple tools that can allow them to track customer response rates and measure the success/failure of each campaign in real-time and at the same time also help companies to plan better and more successful marketing campaigns in the future. One of the strongest reasons for incorporating digital marketing into the overall marketing campaigns is that traditional marketing is quickly overtaking digital communication in a rapid and fast manner.  Today, almost three billion people use the internet and the bottom line is that unless brands adapt to the changing marketing scenario, they will lose their brand loyalty and connect at a rapid pace.

What is digital analytics & how does it impact digital marketing?

One of the main areas in which brands have failed to implement their digital analytics solutions. According to a recent study, there is a huge talent and hiring gap in the field of digital analytics arena where many digital companies said that they were in dire need of professionals who had expertise in the field of digital analytics. This means that if you are in the field of online marketing content, then professionals need to gain a lot of knowledge and digital analytics skills in the field of data analytics as this is what will help them take their campaigns and policies to the next stage. Data professionals, therefore, need to upgrade their digital analytics skills and strategically plan their policies so that they can improve their overall content strategy and policy.

The Harvard Business Review declared the job of a data scientist as the sexiest job of the century. In the next two years, according to Gartner, there will be more than four million big data opportunities and only a third will be successfully filled, which is a great challenge for the industry. All the digital analytics firms in the world are rapidly moving towards big data and that is why there are new updates in the field of mobile data, performance data, campaign data, product data, and even the manner in which data is being tracked by the data scientists. All these changes mean that there are two major implications for this change.

The first is that brands and digital analytics companies across sectors can strengthen their digital plans by investing in good content marketers and SEO professionals. Data skills are therefore a very important aspect of the future of digital analytics. The second aspect of big data growth is that as campaigns grow bigger and more complex, data analytics and technological capabilities will also have to grow in a rapid manner to adjust to this demand.

Data capabilities will need to become sophisticated and capable so that larger campaigns can be implemented in a successful fashion. That is why companies and organizations need to evaluate the digital marketing plans of the company in a strategic manner. In short, creating and implementing a digital analytics program needs four steps which include defining the digital analytics metrics and developing a plan, collection of data, development of reporting features and capabilities, and lastly ongoing analysis and implementation.

Digital analytics is therefore one of the prime focus areas for all brands and companies, especially in the coming years. The term digital marketing first appeared on the scene in the year 2011 but it reeky gained a lot of prominence in the year 2013. According to the latest digital analytics trends, Google anticipates that digital analytics will gain even more traction and popularity in the coming years as well.

Things needed to create a successful digital analytics plan

One of the first things needed by any brand that wishes to employ a data analytics tool is the employment of people who are really passionate about analytics and gaining insights from data. Many brands and companies, especially at the mid-level are still unaware of the huge potential and opportunities that digital analytics big data can provide them, especially in terms of digital growth and expansion. Sometimes, even big organizations fail to understand the growing need to employ data analytics solutions and that is why they continue to invest in old techniques and methods.

Another big step needed for the implementation of data analytics is a strong and detailed budget. Many companies are guilty of investing less money in this field as they feel other departments require investment and not digital analytics. While this might be true in the short run, data analytics has huge returns, especially in the long run. While data analytics requires heavy investment in the beginning, this investment is extremely important as it helps brands to get ahead of their competition and also make use of the immense opportunities available on the digital analytics platforms.

digital analytics

The next step in data analytics is the creation of key performance indicators or KPIs which will help you create strategies that are strong across issues including customer engagement, reach, and conversion. This is why brands need to invest in individuals who have intense and comprehensive knowledge about the various aspects of digital analytics marketing. At the same time, it is important to create a balance between all the aspects of the metrics. If there is too much focus on one part of the metrics, it can lead to the completion of only a few goals. Brands and companies need to keep the big picture in mind while creating and deciding the key metrics so that add even greater value and influence in the market.

While deciding on digital analytics, it is important that brands overview their digital analytics strategy at regular intervals so they can find out how effective their campaigns and solutions are. It is extremely important that brands evaluate their success on different social platforms as well because different social media platforms can have different levels of success. Analysts need to know which campaign can work in which platform and implement the said campaign on that platform which can create the maximum effect and reach.

One of the most important and essential parts of data analytics is data visualization. Visualizing data in an effective manner is extremely important as it is only then data analysts can make sense of the data. Unless companies can make sense of data in a successful manner, there is no tangible benefits of big data. In short, data visualization is the most critical and important part of the collection of data as raw data is of no use to anyone. Visualizing data in the form of information, you can valuable insights and data analysts are the map that can help brands to make sense of the vast amounts of data present in the company. Data visualization allows companies to interpret data and through this method, data analysts can identify hurdles in a process successfully and fix them before they result in a full blown crisis that can damage the growth of the company.

For example, many e-commerce sites can have GPS installed in their vehicles for tracking the progress of the goods. By assigning different colors to the trucks, the company can assign colors like green for timely delivery, yellow for a possible delay, and red for like delays. In this manner, the company is completely updated about the delivery mechanism, enabling them to provide better customer support and information. Also if their vehicles run into problems like traffic and weather, they can quickly send replacements and this would not damage relationships between the consumer and brand. While many companies, employ digital analytics, they fail to use the said data to create insights that can be used in future courses of action. It is important to uncover new digital analytics trends in the data so that marketing managers can create better and more effective plans in the future.

In digital Analytics vs Digital Marketing, we have seen both digital analytics and digital marketing are the two pillars on which brands will continue to build their growth story in the company years. Ignoring this medium of communication can prove to be fatal and that is why brands must continue to pool their resources to create strong and powerful solutions that revolve around the digital analytics platforms.

Recommended Articles

This has been a guide to Digital Analytics vs Digital Marketing. Here we have discussed the basic concept, why is it important along with the future of digital marketing. You may also have a look at the following articles to learn more –

  1. Digital Marketing Tips
  2. Different Domains in Digital Marketing
  3. Various Types of Digital Marketing Processes
  4. Digital Marketing Examples
Popular Course in this category
Business Analytics Training (14 Courses, 8+ Projects)
  14 Online Courses |  8 Hands-on Projects |  88+ Hours |  Verifiable Certificate of Completion
4.5
Price

View Course

Related Courses

Data Scientist Training (85 Courses, 67+ Projects)4.9
Tableau Training (8 Courses, 8+ Projects)4.8
Azure Training (6 Courses, 5 Projects, 4 Quizzes)4.7
Hadoop Training Program (20 Courses, 14+ Projects, 4 Quizzes)4.7
Data Visualization Training (15 Courses, 5+ Projects)4.7
All in One Data Science Bundle (360+ Courses, 50+ projects)4.7
14 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more