A brief about Google Analytics data analyzed for Mani’s Blog.

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Google Analytics has many good features to talk about, below are the few main reason we have used Google analytics for the analysis data for Mani’s Blog.

Ø  We can study the real time overview of the website like location, traffic source etc.,
Ø  We can study behavior of audience overview like active users, demographics, geo etc.,
Ø  We can avail data of acquisition data like all traffic of the website, traffic through social media and other ad campaigns, etc.,
Ø  We can find the behavior of the website like site speed, site search etc.,
Ø  We can also has many other features like setting our own conversion goals etc.

Audience Overview:


In the above Audience overview screengrab we can find the metrics like
Ø  Number of users visited the blog
Ø  Number of time users visited the blog
Ø  Total number of time users visited different pages of the blog
Ø  Average time spent on the blog
Ø  Number of users who exited as soon as visited the website(Bounce rate)

Below screengrab shows the visitors location/region


Acquisition overview:



Behavior overview:
The behavior over screengrab show the metrics like overall pageview, unique pageviews, average time on page and bounce rate.
Further we can drill down about number of mobile users, desktop users, different visitors from different locations .







The data in the screengrab is very less due to the less duration. We can blindly say Google analytics data is very helpful in terms achieving the goals set for the new campaigns depending on the old campaign data’s


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Artificial Intelligence =  Intelligent Marketing

Artificial intelligence marketing is next generation marketing technique where AI and machine learning algorithms taking control of human decisions and making humans efforts more easy and convenient and improve customer journey.
There are there core elements of AI in marketing.




Machine learning: Machine learning helps in identifying the common occurrences to predict the common responses effectively.
Big Data: Big Data helps in aggregating and segmenting the large set of data with minimal manual work and reduced effort.
Powerful solutions: Provides insightful concepts and theme across huge data sets in shorter time.

Let’s discuss few major applications of AI in marketing

Leveraging Personalization: In this era personalization plays a major role in every business. Personalization is not just limited to ad targeting depending on the needs but also creating  a emotional connect with the customer and drive more engagement.AI helps the businesses to find the emotions and sentiments among the customer and helps in personalize the product/service .

Retargeting: Through AI machine learning and checking the past ads history of the customer and define the whether the ads were success or failure depending on the past data same customer would be targeted.

Customer support automation: AI is modifying the way customers look at the customer support provided by any business. Users opt for social-media , emails and other forums to raise their about a product/service. AI can be used to classify the data into different categories and allow relevant sources to respond back to such queries. By using intent analysis response to the queries can be automated which reduces the overall process and time.

 Adoption of AI in different sectors of business.

Ø  High tech and telecommunications (31%)
Ø  Automotive and assembly (29%)
Ø  Financial services (27.5%)
Ø  Energy and resources (27%)
Ø  Media and entertainment (22.5%)
Ø  Transportation and logistics (21%)
Ø  Consumer packaged goods (20%)
Ø  Retail (19%)
Ø  Education (16.5%)
Ø  Health care (16%)
Most used AI tools in market are virtual assistants which effectively impact the workflows streamlining and automating basic customer support and sales assistance. Few examples of virtual assistants are Alexa, Siri, Hey Google and any basic chatbots. All this reduces the human efforts, time and investments. Hence AI must be adopted by all the business for any services to increase the potential of the business.

References:
https://www.calpers.ca.gov/docs/board-agendas/201801/full/day1/06-technology-background.pdf
https://hackernoon.com/six-emerging-applications-of-ai-in-marketing-a6e7a78035e1
http://118.194.132.123:8088/upload/17/12/29/867cead8acfa4a2bbd069295428dfa25.pdf
https://econsultancy.com/15-examples-of-artificial-intelligence-in-marketing/
http://www.jamescannella.com/wp-content/uploads/2018/04/Cannella_J_Spring_2018.pdf




Benefits and Challenges of using Customer Data for marketing.

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Customer Data is of various types collected from various channels. Marketing with Customer data is a boon to achieve the targets, conversions, profit etc. But there are challenges as well if the customer is been collected without the customer knowledge later data itself would become a curse for the business. In this blog  let’s discuss benefits and challenges of using Customer data in Marketing.




Benefits of Customer Data in Marketing:
Customer Profiling: Customer profiling is also known as customer behaviour analysis. It’s the collection of data about internet users behaviour across the different platforms, depending on the demographics, using all the available customer data we would be able to create a unique profile for the customer and target them based on their interest.
Right Audience Targeting: It was a great in business to find who are potential customer, now a days using customer data platform “Right Audience Targeting” has become quite easy and beneficial for the businesses. Businesses analyse the database to find out what are the customer interests, their past purchase and what page they spend more time etc and use all this data and target the right customer on the right time.
Customer Segmentation: Customer segmentation is a great thing to be adopted in marketing Customer analytics research show 70% of the companies use customer segmentation technique to dividing their customer category depending on the common characteristics between the groups of customers.
Customer Engagement: In this segment we would keep the existing customer engaged and this would be happening with the existing customer who has been purchased or subscribing to newsletters etc. This is possible by sending promotional email, push notifications depending on their past purchase or interests.
Challenges of Customer data in Marketing:
It is very obvious when the usage of customer data in marketing has many benefits likewise many challenges also would be around. Recently data privacy policies have been updated. Thus, GDPR guidelines has become very hard to ensure data protection becomes key challenge for the companies. Recently many GDPR complaints has been raised from the customer there are many rules been implemented in terms collection of personalised data’s of any customer without the concern of the customer. Under the data protection law EU General Data Protection Regulation have imposed huge fines and penalties for many companies.

References:
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/capturing-value-from-your-customer-data 
https://sites.tcs.com/big-data-study/marketing-big-data-benefits-challenges/
https://www.cyberclick.es/numericalblogen/what-is-a-customer-data-platform-and-its-advantages-in-digital-marketing
https://diligent.com/en-gb/blog/cost-of-a-data-breach-ponemon-institute-report/
https://www.enforcementtracker.com/

BIG DATA – Bigger Market| Bigger Customer| Bigger Profit.

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Marketing plays a major role for any business to succeed, for the success business should plan for a strong marketing strategy to understand the market, competitors, consumer behaviors etc. All this is possible only when we have good amount of datasets. Below are few things achieved using Big Data.  

 “Marketing without data is like driving with your eyes closed.“ - Dan Zarrella




 Decision on Target Audience: In this hard competition, all the marketer has to make an extra effort to stand by their goals and achieve the same. By analyzing the big data we can the potential of customers and their requirements and later we can decide on successful campaigns. Any company can decide who would be their potential target customer’s using the data available about customer behavior, time spent in searching a particular product etc.

Planning for Better Marketing campaigns: Campaigns that use big data are more effective and result oriented. We can take out the guess work and plan for a better campaign using the past data available like customer interest, purchasing patterns, background etc. Research says women’s more likely to fall for email marketing campaigns where they can use the coupon and get some better deals.

Displaying appropriate Web contents: It is possible to display appropriate web contents to the users using the data available in Big Data. Here we can take example of Netflix. We might be wondering how Netflix shows us individualized recommendations, it does all the using the previous history of movies or series watched a person. Just by considering the “time spent on a page” data we would be able to determine the interest of a visitor to a website and depending the data we would be able to display appropriate content/ad.

Personalized customer experience: As every business started moving towards content marketing there is enormous increase in content ,due to this high demand it has become difficult for users to find the right information as per their needs. To compete this demand companies like Facebook, Google, Netflix use Big Data analysis to enable marketers to display ads targeted customers depending on individual interest and behavior and organizations can also undertake study at individual level too.

Better Pricing Optimization: In the past companies used to price the product and services depending on the basic information like manufacture cost, competitor pricing, consumer demand in the market etc., But these days using Big Data companies started using many other factors to price a product/service. For example data from completed deals, incentives and performance based data pricing decisions has become as granular as possible.

Insights of Customer emotions: By using sentiment analysis companies are able to understand the emotion behind a text by analysis, also using this technique communication regarding a brand on social media platforms, blogs and review sites to understand the collective market opinion and revert back appropriately depending the consumer emotions.It becomes clear that advanced applications in big data offer new avenues for solving critical communication imperatives. Big data insights aim to transform mainstream ads and create a clearer picture of customer buying behavior. The study of large data sets helps advertisers to fine-tune marketing efforts, and to develop superior approaches for customer engagement.

It’s clear that advanced applications in big data offer new avenues for solving critical communication imperatives. Big data insights aim to transform mainstream ads and create a clearer picture of customer buying behavior. The study of large data sets helps advertisers to fine-tune marketing efforts, and to develop superior approaches for customer engagement.


Reference:

https://www.forbes.com/sites/theyec/2018/11/08/how-can-big-data-contribute-to-digital-marketing-success/
https://www.newgenapps.com/blog/5-practical-uses-of-big-data-in-marketing-and-sales
https://www.inc.com/james-paine/5-ways-big-data-is-changing-marketing.html
https://www.singlegrain.com/digital-marketing-analytics/how-to-use-predictive-analytics-for-better-marketing-performance/
https://medium.com/digital-reflections/big-data-in-digital-marketing-fcf04445d2a6

We are the V’s of Big Data - Volume|Velocity|Variety

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In my previous Blog I had written about Introduction to Big Data, hope it was helpful and thanks everyone for reading the blog and sharing your valuable comments. Following the Introduction of Big Data ,In this blog little in depth its about 3V’s in Big Data. Kindly read the blog and pass on your comments.
Thanks in advance
           
We are the V’s of Big Data - Volume|Velocity|Variety


          

Volume : We will go first with volume. It’s obvious our understanding to Big Data is 
“Big = Volume” .There is huge volume of data collected every day in various formats from various sources. We will take example of Facebook. Their are 2.5 billion monthly active users on Facebook, all there data is been collected and processed by Facebook, it could be image, video, gif’s etc. This is just example of Facebook. Just Imagine how much data would be processed in different social media’s on a daily basis all over the world. We can’t even imagine the amount data collected and processed on a daily basis. We are aware of KB, MB, GB & TB but there are exabyte (EB) is 1018  bytes . A zettabyte (ZB) is 1021 bytes and  yottabyte (YB) is 1024 bytes of data is been collected and processed.

Variety: We spoke about Volume and now wondering what kind of data are collected. There are different variety of Data’s are been collected from different platforms. Let’s take an example of email, just think how many email do we get in our inbox on a daily basis 10?, 50?, 100?. There are 2.9 billion email users worldwide send and receive about 269 billion each day. All the email sent will not be same like another, every email will consist of sender’s ,receiver’s email address ,a time stamp and human-written text also possibly attachments of images, videos, email attachment ,documents ,audio recording etc. All this data makes the variety element of Big Data.

Velocity : What is velocity? Why velocity comes in Big Data? We have collected huge volume of data and different variety of data but did we think about the velocity of the data processed .We will take example of Facebook, 900 million photos a day been uploaded by users. Just imagine Facebook has to ingest it, process it, file it and later should be able to retrieve .Let’s all this process has to be taken place in few seconds of time and also it has to be hassle free, here comes the importance of velocity. Velocity will be like the sailor of the ship. Hence velocity becomes the key requirement of 3V’s of Big Data.
There are many things plays important role in Big Data, but these 3V’s play major role in Big Data like 3 important vital organs of human body.


References:
https://www.campaignmonitor.com/blog/email-marketing/2019/05/shocking-truth-about-how-many-emails-sent/
https://kb.iu.edu/d/ackw
https://zephoria.com/top-15-valuable-facebook-statistics/
https://bigdataldn.com/intelligence/big-data-the-3-vs-explained/
https://www.forbes.com/sites/brentdykes/2017/06/28/big-data-forget-volume-and-variety-focus-on-velocity/