Analysis of the Google Analytics Report for "SAMINDBS.BLOGSPOT.COM"

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The blog https://samindbs.blogspot.com/ started on 19-01-2020 as part of the Data and Digital Marketing Analytics Module CA 1 Assessment. Google Analytics linked to the blog on the same day.
There are a total of 6 blog posts on different topics including this post. Below is the list of blog posts including the number of comments and replies to those posts.
Real-time Traffic Analysis: We are going to skip this segment as we are going to see historical data for analysis.

Audience Overview:

The above Screenshot shows the traffic analysis from Jan 19,2020 to Feb 26,2020.
  • 50 users visited the blog.
  • The blog received 420 pageviews.
  • Avg Session duration is 2:58 seconds with a bounce rate of 39.74%.
  • 60.2% of New Visitors and 39.8% of Returning Visitors
Other Reports from Audience Segment:
The Above Screenshot Shows Location (Country) of Users coming from
The Above Screenshot Shows Device Category

Acquisition Overview: 

We can link Google Ads and Search Console accounts to Google Analytics and these reports can be viewed within this section of GA. We can analyze various parameters like source of traffic, which keywords the site or blog gets traffic from a search engine, etc. 

Behavior Overview:
The Behavior report shows Pageviews, Unique Pageviews, Avg Time on Page, Bounce Rate, User Exit Percentage, which page on the blog got how many page views and various other parameters.

Key learnings from the report
The blog is quite new but managed to maintain a good engagement. Need to work on gaining some organic traffic. Avg session duration and Bounce rate looks okay. Understood that GA provides various metrics to measure and improve the performance of the blog.

AI in Marketing

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Most people think of AI as a tool to automate important secondary tasks, such as scheduling, but it can have a much greater impact on businesses. Artificial intelligence is used to maximize the advantage of customer personalization in marketing. It’s the process of leveraging customer data using AI applications like machine learning and creating more personalized marketing and analytic systems.
Source: MagePlaza

A new Deloitte survey that includes 1,100 U.S. executives from organizations considered to be early AI adopters shows, 82 % received a good return on their investment for their AI projects.
The applications of AI in marketing are wide bound. A few such examples are

1. Customer segmentation

We anticipate the customer's next move based on the data available in the customer segmentation. The various data available from social media, emails and other platforms can be segregated easily. It’s easy to understand what the customer wants at each specific time from the large real-time data.

An example of this includes Facebook ads where customers provide social media data when they agree on the end-user agreement based on which customer profiles are made using data analytics of AI.

2. Future forecasts

AI can be used to forecast sales based on the data of current and future campaigns or ads.

Advantages of using artificial intelligence in marketing


Improved ROI (Return on investment)
It boosts the return on investment by analyzing data based on customer needs that in turn leads to an increase in returns.

Identification of trends
AI can help predict insights or responses from the customers and can be much help in product modification or improved product manufacturing.

Robust solutions
AI can identify and interpret concepts and themes across large data sets incredibly fast and accurate. It can interpret from any platform like emails, human emotions/ natural language, social media, customer feedback, etc.

There is no doubt that AI is going to penetrate various aspects of marketing, including social media, marketing automation, and even the creation of new products and services.

References:

  1. Deloitte Survey: Artificial Intelligence Delivers, but Missteps Can Yield 'Bridges to Nowhere' | Markets Insider (no date) Business Insider. Business Insider. Available at: https://markets.businessinsider.com/news/stocks/deloitte-survey-artificial-intelligence-delivers-but-missteps-can-yield-bridges-to-nowhere-1027638384 (Accessed: February 18, 2020).
  2. AI Marketing: What, Why and How to use Artificial Intelligence Marketing (no date) Mageplaza. Available at: https://www.mageplaza.com/blog/ai-marketing-what-why-how.html (Accessed: February 18, 2020).
  3. Tjepkema, L., Enochs, M., Donlan, K., Barlow, H. and Stewart, B. (2019) What Is Artificial Intelligence Marketing & Why Is It So Powerful?, Emarsys. Available at: https://emarsys.com/learn/blog/artificial-intelligence-marketing-solutions/ (Accessed: February 18, 2020).


Benefits and challenges of using customer data for Marketing

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Customer data is the communication between the marketer and the customer which includes phone calls, messages, emails, promotional letters, magazines, websites and many more. The main goal is to collect the data accurately and analyze it to make future business decisions and attract customers. Many companies collect and store customer data but don’t effectively use data for their business needs.

There are also equal challenges in interpreting data obtained from the customers and the benefits of using the customer data effectively in marketing.

Benefits of collecting customer data

Source: Retailnext

Catering for customer interests and needs
It’s all about the customer's voice, the more you hear, the more you gain. It’s a way of saying we care. Customer data collection leads to an understanding of customer needs and interests. Interpretation of customer data positions a company to present the right product to the right customer and increases the business of an organization.

Better product development
The organization can develop better products based on consumer feedback. Artificial intelligence and technology make it easy to interpret the data from larger customer databases.

Expansion of the market
The simple email communication to the new marketing trends the marketing has come a long way. Data-driven advertising will ensure the message reaches every person intended because people believe in testimonies rather than a simple ad.

Challenges in using consumer data

Investments
One of the primary drawbacks of using customer data is the marketing costs. It requires software, hardware and third-party auditors for data collection.

Privacy disclosure
To secure the data of the customer is the key. The marketers need to convey their intention of data usage. The data should not be exploited. Taking preventive measures to avoid data exploration and maintaining accountability with the customers.

Having quality data and skilled people along with technology can ideally satisfy the customer and can increase productivity.

References:

  1. Using Customer Data for Marketing: The Good, Bad & Ugly (2018) Aberdeen. Available at: https://www.aberdeen.com/cmo-essentials/good-bad-ugly-using-customer-data-for-marketing/ (Accessed: February 17, 2020).
  2. Data-Driven Marketing: Benefits, Challenges, And Examples (2020) Infotanks Media. Available at: https://www.infotanksmedia.com/blog/data-driven-marketing-benefits-challenges-and-examples/ (Accessed: February 17, 2020).
  3. Sharma, B. (2019) How to Collect Customer Data and Improve Shopper Experience, RetailNext. Available at: https://retailnext.net/en/blog/how-to-collect-customer-data-and-improve-shopper-experience/ (Accessed: February 24, 2020).

3 Vs - Volume, Velocity & Variety

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In the last post, I have discussed what Big Data is and also mentioned the 4vs of big Data. Today in this post I want to discuss more details about 3vs of big Data. So to understand this 3vs concept, I want to remind you once again that Big Data is a huge collection of data say in exabytes. 
Source: Richestsoft

Where does this data come from?
It's the data generated by us when we use internet services like email, social media, ads, blogs etc. 
Example: Facebook, YouTube, Twitter, WhatsApp all these services generate huge data which is big Data. 

 Characteristics of Big Data 3Vs
Source: SlideShare

 The data is classified as 
  • Volume (Data Volume)
  • Velocity (Data Velocity)
  • Variety (Data Veracity)
Rd-alliance.org
What is Data VOLUME?
Volume nothing but quantity is the amount of data that we are going to store analyse and use. The size of the data is the volume of the Data.
Example: Every time we watch a video, upload a video on YouTube, the data is generated that's huge volume.

What is Data VELOCITY?
Velocity is nothing but speed is the time taken to store or retrieve the data. It's basically the speed of data accessed. Data velocity is the speed at which data is generated.

What is Data VARIETY?
Variety is nothing but various forms of data. The data is usually in different formats, like Structured data and unstructured data.

Structured data is data that has a proper structure.
Example : 
  • Phone numbers
  • ZIP codes
  • Customer names
  • Product inventories
Unstructured data is data that does not have a proper structure.
Example :
  • Media files like images, videos
  • Text files like docs and PPT
  • Social media data
  • Chat data
References:

  1. Big Data - Definition, Importance, Examples & Tools (2019) RDA. Available at: https://www.rd-alliance.org/group/big-data-ig-data-development-ig/wiki/big-data-definition-importance-examples-tools (Accessed: February 6, 2020).
  2. 3 Vs of Big Data and How They Sum Up The Whole Big Data Schematic, RichestSoft. Available at: http://richestsoft.com/blog/3-vs-of-big-data-and-how-they-sum-up-the-whole-big-data-schematic/ (Accessed: February 6, 2020).
  3. Saif, H. (2017) What is Big Data, LinkedIn SlideShare. Available at: https://www.slideshare.net/HaniSaif/what-is-big-data-73865078 (Accessed: February 6, 2020).

What is Big Data?

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In this Digital Era, data sharing on Digital platforms is increasing rapidly. We generate data whenever we go online, when we carry our GPS-equipped smartphones, when we communicate with our friends through social media or chat applications, and when we shop.
Source: smartdatacollective.com
Organizations collect, store and analyze this massive amount of data and use it for various purposes. This massive amount of data is nothing but Big Data.
Big Data is such a powerful asset that it could predict the hurricane's landfall five days in advance.
The picture below shows how much data is being generated every minute on the internet.
Source: YouTube Simplilearn
  • 2.1 Million Snaps are shared on Snapshot
  • 3.8 Million Search Queries are made on Google
  • 1 Million logged on Facebook
  • 4.5 Million Videos Watched on YouTube

Concept of 4 V's

  1. Volume
  2. Velocity
  3. Variety
  4. Veracity
Volume: The volume of data helps to identify whether or not the data is Big Data.
Velocity: Velocity is nothing but how fast the Data is retrieved or stored.
Variety: Variety is nothing but Various types of data such as
  • Structured Data: Any form of data that is in a fixed format is known as Structured data. Example: Excel Sheets 
  • Unstructured Data: Any data that is without any structure or unknown form is known as Unstructured data.
    Example: Google search results page
  • Semi-Structured Data: This data can contain both structured and unstructured forms of data. Example: XML File
Veracity: Accuracy and trustworthiness of the data are known as veracity.

How Big Data helps in Digital Marketing Strategies 

Source: gecdesigns.com

  • Create Personalized Marketing Plans
  • Increase sales by reaching the target audience with the data available
  • Improve the performance quality of the campaigns
  • Optimize the Budget
  • Measure the Results
  • Understanding the competition 

Top 5 Big Data Analytics Tools

Source: Apacbusinessheadlines.com

  • Hadoop
  • CDH
  • Cassandra
  • MongoDB
  • Storm
References:
  1. Warner, J. (2019) Why Marketers Should be Focused on Big Data, business.com. business.com. Available at: https://www.business.com/articles/big-data-marketing/ (Accessed: January 20, 2020).
  2. Elichai, A. (2018) How Big Data Can Help in Disaster Response, Scientific American Blog Network. Scientific American. Available at: https://blogs.scientificamerican.com/observations/how-big-data-can-help-in-disaster-response/ (Accessed: January 20, 2020).
  3. Simplilearn. (2019) Big Data In 5 Minutes | What Is Big Data?| Introduction To Big Data |Big Data Explained |Simplilearn. [Online Video]. Available at: https://www.youtube.com/watch?v=bAyrObl7TYE (Accessed: January 20, 2020).
  4. Big data Tools: What suits best for your company? (no date) The Technology Headlines. Available at: https://www.apacbusinessheadlines.com/Big-data-Tools/ (Accessed: January 20, 2020).
  5. Gec (2019) Role of Big data in digital marketing - The Future, GEC Designs. GEC Designs. Available at: https://gecdesigns.com/blog/role-of-big-data-in-digital-marketing (Accessed: January 20, 2020).