Details

NATALHA DIAS DE BARROS
Student number: 10541070

Analyses of Google Analytics

In the context of the Data and Digital Marketing Analytics module of the MSc in Digital Marketing, with the objective of learning about big data, five blog posts were published and to track the traffic of the blog it was used Google Analytics tool, the data generated will be analyse on this blog post.

Audience Overview:

Since the blog was created in 20^th of January until today (25^th of February), it has been accessed for 32 people that have visited the website 132 times, and from these sessions 43.9% of the audience are returning visitors. The above report also shows that the average of time that people spent on the blog was around 3 minutes, what is rated as adequate, considering the size of the content posted, plus it shows that 37.12% of users left the website immediately, what means that 62.88% of the audience spend some time on the website.

Acquisition Overview:

Checking the acquisition section is possible to understand from where the audience of the website come from, in the case of this blog the major accesses were via social, with 42.90%.

Since it was not promoted in any other social network the major traffic was all from Blogger.

Behavior Overview:

The home page was the one with more access, due the fact that all visitors come from direct, referral and social access, where it lands straight to the home page.

Through this project was possible to understand how Google Analytics works, how it is beneficial to understand customers behaviors and spot problems on websites.

AI-Powered Marketing

Artificial intelligence (AI) in marketing has become an indispensable tool that allows marketers to have a competitive advantage in the dynamic and aggressive market setting (Nagaraj, 2019, p.502). Especially in the era of Big Data, where a huge amount of different types of data are generated every second, with the use of AI it is possible to extract value from all this data in real time (Mitić, 2019), and obtain accurate insight into simple aspects that impact consumer’s behaviour. For that reason, AI has a direct and important implication on the approach of marketing strategies to engage with the target audience (Sterne, 2017).

The main pillar of AI is machine learning (ML), that consist of various mathematical models, such as for example, statistics, probabilistic, and neural networks, that are applied on huge datasets with a view to detect patterns in data, to learn from it or to predict valuable outputs (Mitić, 2019). Computers can do much more than what people tell them to do. Due their speed and efficiency, they find out what they should do, spot problems and come up with new solutions in a quickly and better way (Marr, 2016, p. 241). That systems support the process of decision-making in real-time, and these decisions already surpass the quality of human decisions (Gentsch, 2018).

Content creation, voice search, lead scoring, ad targeting, predictiveness analysis and dynamic pricing are the most popular applications of AI in marketing. Facebook and Google are great examples of the usage of AI and ML, they interpret users’ demographic information and interests with the aim of learning and identify the best audience for their brand. With all the information about their customers, marketers can focus on certain needs and build a long-term relationship with them (Mitić, 2019).

As the marketing scenario is full of competition, being able to directly target the main audience, provide value to customers and involve them in marketing campaigns is a unique tool.

REFERENCES

Gentsch, P., 2018. AI in marketing, sales and service: How marketers without a data science degree can use AI, big data and bots. Springer.

Nagaraj, S. (2019). AI enabled Marketing: What is it all about? International Journal of Research (p. 501-518)

Marr, B. (2016). Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary results. John Wiley & Sons.

Mitić, V., 2019. Benefits of Artificial Intelligence and Machine Learning in Marketing. In Sinteza 2019-International Scientific Conference on Information Technology and Data Related Research (pp. 472-477). Singidunum University.

Sterne, J. (2017). Artificial intelligence for marketing: practical applications. John Wiley & Sons.

Benefits and Challenges of using Customer Data Platform

Nowadays, how well a company understands their customers is what drives the business to success. However, according to Dan Springer, CEO of Responsys, consumers have changed. They have stopped reading newspapers, they fast forward through TV commercials, and they ignore unsolicited email, in fact they choose which marketing messages they want to receive, when, where and from whom. Consumers prefer companies that talk with them, not at them, and they want relevant interactive communication across digital channels as well (Minelli, Chambers and Dhiraj, 2013, p. 27).

Consumers leave a trail of information behind them, every time they communicate, shop, learn, relax, or interact online. This data reflects how customers spend their time, what they like, what is important for them, and even what they want (Arthur, 2013). All this information is defined as customer data, yet due its volume and also by the fact that it is isolated in silos for technical or organizational reasons, it is difficult for business to provide a good customer experience on diverse channels and devices (Tomas, 2019).

Thus, to extract meaning from all this data it is necessary to centralize the information, and this can be done on a Customer Data Platform (CDP), that is a system managed by the marketing department to build an unified and persistent database that can be accessed by other technologies. The CDP can store and consolidate different types of data, such as past purchases, social media interactions, websites visits, demographic details, etc (Earley, 2018). This type of platform offers a unique view of the customer, it allows companies to be more competitive, by using customer data to make better decisions, create marketing campaigns and provide better customers experiences, it also democratizes the data to all companies’ departments and provide a higher quality of interactions with partners and suppliers (Tomas, 2019).

While Customer Data Platform leads to new opportunities and benefits for companies, marketing professionals should bear in mind the legal and ethical aspects of it. General Data Protection Regulation (GDPR) provides more control and transparency for the consumer, so for now on marketers have to step back and find a way to secure, reinforce and refresh a data-orientated connection with their customers, so they happily share their information (Wollen, no date). Therefore, the success of a company is related to redefining the communication with the customer, delivering consistent and transparent messages to create a clear relationship with them.

REFERENCES

Arthur, L., 2013. Big data marketing: engage your customers more effectively and drive value. John Wiley & Sons.

Earley, S., 2018. The Role of a Customer Data Platform. IT Professional, 20(1), pp.69-76.

Minelli, M., Chambers, M. and Dhiraj, A., 2013. Big data, big analytics: emerging business intelligence and analytic trends for today's businesses (Vol. 578). John Wiley & Sons.

Tomas, D. (2019) ‘What is a Customer Data Platform and its advantages in digital marketing?’, Cyberclick. Available at: https://www.cyberclick.es/numericalblogen/what-is-a-customer-data-platform-and-its-advantages-in-digital-marketing (Accessed: 17 Fev 202­­0).

Wollen, C. (no date) ‘Data Regulation Is A Marketing Issue’, CMO by Adobe. Available at: https://cmo.adobe.com/articles/2017/6/data-regulation-is-a-marketing-issue.html#gs.x7zqy1 (Accessed: 17 Fev 202­­0).

Big data driving value for marketing

As driving value and engaging customers more effectively is the center of Big Data, marketers must understand the implication of it in Marketing and realize that they can have that elusive 360-degree view of their customers. Converting this data into useful information allows business to deliver the right message, through the right channel, at the right time, to the right customer, because data drives exceptional insights and those insights drives better interactions (Arthur, 2013).

But first, it is important to know how to analyse the data to extract real value from it, the key to do that is to understand the different types of data analysis. Descriptive analysis is the simplest of all and with the most functional uses on business nowadays (Bhardwaj, 2019), it mines historical data to find similar patterns and correlations between outcomes, for example Google Analytics, a tool that shows how keywords positions have changed in the past, but it does not tell why that happened, that comes to be explained by diagnostic analysis, this type of analysis help to locate the root cause of an issue, to do that, the algorithms combine owned proprietary data and outside information to understand what happened and find a way to fix it (Karapalidis, 2018).

However, what is changing the analytics landscape is predictive and prescriptive analysis, the first one use the past data to predict future outcomes with a degree of probability, while the second takes the past information and uses it to straightforward future activities to obtain optimal or near optimal results (Minelli and Chambers and Dhiraj, 2013). In other words, predictive analysis makes business operations more efficient by cutting costs down, by optimizing marketing campaigns, and by promoting cross-sell opportunities, this type of analysis can also help companies engage, retain and maintain their most valued customers. On the other hand, prescriptive analysis help companies to answer the question “what should we do to reach the desired outcome?”, it takes into the insights of the past analyses to detect the best way to solve a problem or make a decision (Bhardwaj, 2019).

In conclusion, data mining business grows 10 percent a year as the quantity of data generated is booming (Mushtaq and Kanth, 2015), as well as more and more professionals are educated in the field, it will be seem higher number of companies joining the data-driven realm (Bhardwaj, 2019).

REFERENCES

Arthur, L., 2013. Big data marketing: engage your customers more effectively and drive value. John Wiley & Sons.

Minelli, M., Chambers, M. and Dhiraj, A., 2013. Big data, big analytics: emerging business intelligence and analytic trends for today's businesses (Vol. 578). John Wiley & Sons.

Mushtaq, A. and Kanth, H., 2015. Data mining for marketing. International Journal on Recent and Innovation Trends in Computing and Communication, 3(3), pp.985-991.

Bhardwaj, S. (2019) ‘Data Analysis and its Types’, Medium. Available at: https://medium.com/analytics-vidhya/data-analysis-and-its-types-88d001a9ea5a (Accessed: 12 Fev 202­­0).

Karapalidis, G. (2019) ‘Data Analysis and its Types’, Business 2 Community. Available at: https://www.business2community.com/big-data/data-science-for-marketers-part-2-descriptive-v-diagnostic-analytics-02144223 (Accessed: 12 Fev 202­­0).

The three V's of Big Data

There is an explosive growth of data and it has changed the way that services are provided, the engagement on business, and also the association measurement of value and profitability (Krishnan, 2013). The saying “You cannot manage what you do not measure”, attributed to W. Edward Deming and Peter Drucker, explains why this digital data explosion is so meaningful (McAfee, Brynjolfsson, et al., 2012). To explain the concept of Big Data better, specialists break it down in the form of three V’s:

Data Volume is described by the amount of data that is produced continuously, and it can be different types, such as blog text, voice calls, videos files, machine logs, etc, and it come in diverse sizes, for example, kilobytes, megabytes, gigabytes, etc (Krishnan, 2013). Thinking from a social media viewpoint, as it represents a massive impact on data, since 2016, there are over 250 billion pictures and 2 trillion posts uploaded (Hansen, 2019). More data is generated across the internet in every second than were stored in the whole internet 20 years ago (McAfee, Brynjolfsson, et al., 2012).

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However, Big Data is not just huge, it is also growing very fast. For some applications, the velocity that data is generated is even more relevant than the volume. Getting data on real-time or nearly real-time gives companies competitive advantage over competitors (McAfee, Brynjolfsson, et al., 2012). An example is Facebook statistics, where 293,000 statuses are updated, 136,000 photos are uploaded and 500,000 comments are posted every minute on the platform (Hansen, 2019).

Besides big and fast, data is still extremely diverse. Variety in Big Data, is about the capacity to label the incoming data into different categories, the data can be structured or unstructured, and be generated either by humans or by machines (Whishworks, 2017).

Simplifying, due to Big Data, managers can know more about their business and directly use that knowledge to improve their decisions and performance, just because, now, they can measure it (McAfee, Brynjolfsson, et al., 2012).

References

Krishnan, K., 2013. Data warehousing in the age of big data. Newnes.

McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J. and Barton, D., 2012. Big data: the management revolution. Harvard business review, 90(10), pp.60-68.

Hansen, S. (2019) ‘The 3 V’s of Big Data Analytics’, Medium. Available at: https://medium.com/hackernoon/the-3-vs-of-big-data-analytics-1afd59692adb (Accessed: 05 Fev 202­­0).

‘Understanding the 3 Vs of Big Data - Volume, Velocity and Variety’ (2017) Whishworks. Available at: https://www.whishworks.com/blog/big-data/understanding-the-3-vs-of-big-data-volume-velocity-and-variety (Accessed: 06 Fev 2020).

What is Big Data?

Big data point the beginning of a major transformation, what challenges the way we live and interact with the world. The term refers to a huge volume of diverse, complex and fast-changing data that come from new data sources. Due to its large volume, it is difficult to manage, store and process this data using the traditional computing approach within a given time frame.

When it comes to which amount of data can be termed Big Data, either gigabytes, terabytes, petabytes, exabytes or anything larger than this is considered as Big Data, therefore it is important to analyse the context in which it is being used, because even small amounts of data can be considered Big Data.

It is possible to classify Big Data into three categories:

• Structured: refers to any data which can be stored, processed, and accessed in a fixed format. The way to obtain value out of it and the format of this type of data is previously known. For example, information that is present in any database software. 
• Unstructured: describes the data that does not have an specific format or structure, for example, mix of text files, videos, images and social media content. Its size is massive and it can be considered as untouchable due the fact of not meet conventional norms. Moreover, it is not easy to derive value from this type of data. 
• Semi-structured: refers to any data that is stored in non-relational databases or even XML file, for example. This type of data does not have a proper structure associated with it.

In the early 2000’s the concept of Big Data in the form of the three V’s was articulated by Doug Laney, they are volume, which refers to the number of data that is getting generated; velocity, that is the speed at which data is being created; and variety, that refers to the different types of data that are being made. With the power of analyzing this large scale of data is possible to extract new insights and generate unique forms of value in ways to transform markets, organizations, the bond between government and citizens, and so on.

References

Mayer-Schönberger, V. and Cukier, K., 2013. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.

Sagiroglu, S. and Sinanc, D., 2013, May. Big data: A review. In 2013 international conference on collaboration technologies and systems (CTS) (pp. 42-47). IEEE.

Ray, R. (2018) ‘The Complete Beginner’s Guide To Big Data in 2018’, Medium. Available at: https://medium.com/swlh/the-complete-beginners-guide-to-big-data-in-2018-82ed7a396ba3 (Accessed: 27 Jan 202­­0).

Shahzan (2019) ‘Big Data Explained in Plain and Simple English’, Medium. Available at: https://medium.com/swlh/big-data-explained-38656c70d15d (Accessed: 27 Jan 202­­0).