Why is Analytics Best in Google Than Any Other Cloud

What is Google Analytics?

Google Analytics is a cluster of business tools which helps the organization to determine the company’s website performance. Google Analytics helps the admin to find the source of their website’s traffic. The admin of the company will get to know from where the traffic is originating and what are their activities in his website by the analytics report.

 

Features of Google Analytics

 

• Collects traffic and user data from the website or mobile application.

• Analysis of audience’s behaviour to know about their liking on the site

• Creating custom funnels, variables, metrics to analyze customer’s purchase flow

• Integration with Google Adwords to understand Demographics

• Individual Ad creation by segmenting audience type

 

Why Google Analytics

 

The basic structure of Google Analytics has remained the same over the years. With the help of Google Analytics admins are able to determine the visitors, their behaviour, and preferences. Reports can be made by Google automatically or by the drag and drop interface by the admin of the organization. Data collected helps to determine whether the advertising campaigns are running well or the owner is wasting time. The company also comes to know about the real time visitors i.e. the number of visitors who are visiting the website currently. Google Analytics starts collecting data automatically once the code is written on the site. Admin can implement data from Google Analytics to other products like Google Adwords and Google Search Console. All the documentation and tutorials are provided by Google to ensure that users can learn before using.

 

A brief history of Google Analytics

 

Google Analytics was developed from Demand Software which was acquired by Google in 2005. The first version was released in November 2005. Further ideas about Measure Map which was developed by Adaptive path collaborated with Google Analytics in 2006. Earlier Analytics came into use by invitation after its release in August 2006.

                                                        

 

Why Analytics 5

 

Analytics is the perfect technology of Google to track traffic patterns in one’s website It is a free program which can integrate with other products of Google like Adsense. This comes to use when customers are using more Google’s products so as to analyze the data and traffic.
Custom Dashboards is one of the features in Analytics 5. Custom Dashboards can be created with preferable metrics and the way the information will be displayed such as a table or charts.

 

Some beta features of Analytics 4 have been used in Analytics 5 such as intelligence and Javascript interface. This makes it a smoother and professional product to use. Major change was the interface rather than the functionality. Breadcrumb navigation within reports and links to external sites have been removed which used to add extra steps. Percentage change in the metric chart was also removed. Due to the extra annoyance of these features, they were removed to make the interface more user-friendly.

 

 

Basic Interface

 

While accessing the report for the website, the user is first navigated to the visitors' overview screen. The difference was that; previously the user was navigated to a general overview screen which displayed information about traffic sources. At the top, a chart displays traffic pattern for the past months. The drop downs are used to change the date and rest of the reports which the user chooses to compare two date ranges.

 

 

Creation of annotations is chosen on the chart for specific days. Ex- there is a review of a product on a particular day. If the annotation is added on that day, then the user can look on that traffic pattern later to know what caused the spike. The vice versa is applicable for the drop in traffic too.

Left hand contains report navigation. Detailed reports of visitors and traffic sources, overviews, contents and conversions are shown here. Additional links to custom reports are also given.

 

 

Visitors

 

The visitor's overview is the default screen which gives the no. of visitors, page views and average pages per visit. It shows the user the average time spent on website, bounce rate, and the percentage of new visits by a pie chart to ensure how the website is doing. Basic demographics, system, and mobile reports are displayed at the bottom of the overview screen. The most useful becomes the demographic one in marketing sense while the system and mobile reports being used in design and programming sense.

More detailed view of demographic reports are displayed by clicking on the left navigation. Detailed information about your visitors’ locations, user defined and custom variables for more exact reporting are viewed there. This includes map overlay too.

 

                                          

The behaviour section determines user engagement and how frequently visitors are returning to the website. The owner can count on them to help in promoting a particular product. “Days since last visit” gives an idea of how often visitors are returning to the website.

 

                                     

Traffic Sources

 

Traffic sources determine the strength of SEO, incoming links, Adwords and other advertising campaigns. It shows where the weakness lies exactly. Keyword reports are also one of the important reports. A brief keyword is displayed on the overview page whereas detailed reports are found under ‘Search’ subreport. Search sub report tells the most preferred page and from the exact search engine. Paid traffic, organic traffic from RSS feed are viewed through campaigns.

If Adwords is used, detailed reports of traffic including campaigns, keywords, day parts, destinations, URLs and Tv ads are viewed. The reports show the no of visits generated, bounce rate, total goal completions and revenue from each source. A small percentage of traffic can also convert a large number of visitors. With prior information, only the ads should be disregarded otherwise important revenue might get lost.

 

Content

 

 

Here graph represents page views. A page view is loaded a single time page is refreshed or navigation to a different page. A list of AdSense revenue and $ Index for a given page is also listed. The user can review site content by page or page title at the bottom of the overview page. Exit pages should be taken into more consideration as they provide hints where visitors hung up.

Attention should be put onto landing pages too as they provide the necessary information that visitors actually view.

 

 

 

The speed of the site must be checked regularly. This is because slow pages can end up losing visitors added with an interruption in the conversion process. Google Site Search or AdSense are able to monitor these things.

 

How to Optimize Google Analytics

 

1. The owner must have a plan.

2. The goals needed to implement analytics should be defined initially.

3. Measuring trends (maybe Bounce Rate)

4. Campaign Tracking (newsletter/email)

5. Methodical and sequential analysis

 

 

     

Google Cloud Products for Analytics Tracking

 

1. Bigquery- Bigquery is a product of Google with fully managed interactive query service for big read-only datasets. It is based on Dremel which was a core technology of Google earlier. Bigquery was designed for data analysis by the order of thousands of rows using the similar syntax of SQL. It runs on GCP infrastructure and is accessed with REST API. Based on the comparison of Dremel and Bigquery, they employ columnar storage for fast scanning of data and tree architecture for query execution and collection of net results across huge clusters. Bigquery is basically used to track the data installed, creating of crash reports and spam analyzation. After it came into the cloud market, its features have continuously been improved. In 2013, the service was added with data join and time stamp. Stream data insert capability was included in this afterwards.

 

2. Cloud Dataflow - Cloud Dataflow is a data processing service of Google cloud for application of both batch and real-time data streaming. Using these developers can set up processing pipelines for integration, preparation, and analysis of large datasets. Dataflow is based on an earlier project named MapReduce originating from the same company. Dataflow has begun its competition with Amazon Kinesis, Apache Storm, Apache Spark and Facebook Flux. Cloud Dataflow takes up data from Cloud Pub/Sub middleware feeds or in batch mode from any database system. It generally handle data of variable sizes by the format PCollections. Dataflow includes library of PTransforms allowing high level programming of repeated tasks using basic templates also supporting developer customization of data transformations. Dataflow ultimately optimizes processing tasks by reducing innumerable tasks into a single execution.

 

3. Cloud Dataproc - A Google’s product as a managed service for processing large sheets of data used in Big data. It is used for processing, transforming and understanding vast quantities of data. Users can create managed clusters which scales up nodes from three to thousands. Clusters are created on demand, used for the duration of processing and then gets turned off when the task is completed. Clusters are also created based on workload type, budget limitation, performance preferences and existing resources. Scaling up clusters are possible dynamically whereas users only pay for the computing resources that are consumed.

4. Cloud Datalab - Cloud Datalab is Google's product for exploration, analyzation, and visualization of data with some simple steps. This product is mainly used by the developers to get insights from raw data to explore, share and publish reports in an easy and cost-effective way. Using these developers process the data that needs to be used in Google Bigquery, Compute Engine, and Cloud Storage. At the starting, Cloud Datalab is deployed as an App Engine application, where the cost comes into consideration after the beta period is over. After this the service contains pre-installed notebooks before getting started.

 

5. Cloud Dataprep - Cloud Dataprep is Google’s product of intelligent data service to visually explore, clean and prepare structured and unstructured data to analyze them. It is a serverless and auto-scalable product. Dataprep is managed by another company Trifecta in collaboration with Google to provide user experience which eliminates software installation or operational overhead. Dataprep has the ability to predict the next transformation of data so that the user does not have to write the code. Once the user defines a data preparation flow, the sample is exported in free of cost or as a job, which charges pricing later on.
   
   

6. Cloud Pub/Sub - Cloud Pub/Sub is a Google’s product which ingests live streaming from any location in a scalable, simple and reliable manner. It syndicates data across projects through on-premise to cloud, cloud to cloud, or through any other applications in the cloud using efficient client libraries, open REST/HTTP and open source Apache Kafka.
   Pub/Subscales up to millions of messages and user needs to pay only for the resources that are being used. Data is continuously reciprocated across different zones to make sure customers can get the messages immediately. There is an end to end encryption to
   ensure extra security to the pipeline.
   
   

7. Cloud Composer - Cloud Composer is managed service of Google which creates workflows and processes automation on the GCP platform. It is basically an Apache Airflow service that helps to orchestrate workflows irrespective of where the server is residing i.e. on-premise or any cloud. Cloud composer authorizes, schedules and monitors enterprise workflows. Workflows made are portable so that if the user wants to migrate it, it can be done easily. Another feature Cloud Identity-Aware Proxy offers access to Airflow web UI along with Airflow runtime and environment configuration with plugin support. After IAM, DAG and PyPi package management Composer also provides Stackdriver logging and monitoring in the initial beta release part.

 

8. Genomics - It Is the product of Google which ensures the life science community to organize the genomic information from all over the world and process it in an effective manner. Data which was in petabyte size is now growing towards exabytes. The similar technology through these extensions is applied to Google Search and Google Maps to store, explore and share large complex datasheets. In collaboration with Global Alliance for Genomics and Health, Google Genomics is implemented across multiple genome repositories with a backup in Bigtable and Spanner.
   

 

9. Google Data Studio - Google Data studio is a product of Google which allows the user to create branded reports with data visualizations for sharing with the clients It is a part of Analytics 360 suite - the high-end Analytics enterprise package. The reports generated are easy to read, share and customizable to each client. It is usually represented by bar graphs, pie charts, line graphs etc. Being dynamic if any update is made in the data source, updated information is automatically shown t in the report. The user can share the reports and grant other people permission to allow changes in it.

                             

 Use Cases of Google Analytics

 

• Building, tracking and improving the goals set by the owner for the client.

• Innovative ideas for SEO and marketing campaigns like newsletters, press, and others.

• Utilizing the data repository for monthly reports

How Google Analytics helps in increasing the business  

 

Demographics - A data point used to develop an effective campaign is Demographics. Google Analytics helps to know more about the people who are actively using the website.

Blog post engagement - Data point to determine which content is more popular.

Traffic channels - Data point to determine which traffic sources are the most effective for strategy.

Performance by platform - Google Analytics gives information about the possible UX issues by comparing your performance by platform. Mobile users are more likely to abandon a task if the website is not suitable for mobile.GA see the audience’s behavior on each platform compared to the site average.

Content groups - If the blog has a lot of content, it is difficult to know which type of content has the biggest impact. But if content groups are created, performance can be easily assessed based on content categories, tags or any other rules which are set to analyze the content.

Conversion goals - A goal is created that reflects the desired behavior, like viewing the “Thank you” page after a purchase is done. Once some goals have been set up, the Goal Flow report is used to visualize the journey the traffic is taking through your site to complete the goal.

 

The content which played a powerful role in helping the traffic convert is seen by GA.