Programmatic Digital Marketing

     Programmatic Digital Marketing is an advanced and automated digital marketing strategy designed to replace human negotiations with machine learning, which relies on automated and real-time bidding processes that allow companies to reach their target market with hyper-targeted and effective ads by advertising targeted customers in specific contexts. Businesses can reach the target audience who is more likely to be interested in their products or services with programmatic advertising through real-time auctions. Detecting, analysing, and reaching the optimized audience through behaviour-based advertising leads businesses to achieve a higher return on investment and expand their customer base. In 2021, 88% of display marketing in the United States is expected to transform programmatic advertising (Rask, 2021).

How Programmatic Digital Marketing Works?

 

The process starts when someone clicks on a page that is modified for programmatic marketing. After an auction among the advertisers is being put by the marketplace for an open advertising place, the advertiser who bids more for that advertising spot gains the right to display its ads. That allows the advertiser to increase sales and conversions since customers see that ad and make purchases. Although it may seem complex, it is an easy and fast process that takes only milliseconds which means the winner of the advertisement auction can be determined even before the user’s page uploads. Besides, there are several points that need to be considered while embracing programmatic digital marketing:

 

·      Companies should have the understanding and knowledge of not only consumer data but also customer behaviour to achieve successful programmatic advertising.

·      Companies should rely on not both website analytics and survey data that reflects more natural indications while designing programmatic work.

·      Companies should use insights to identify the most effective channels and devices.

·      Companies should use existing customer data to deliver hyper-personalized messages.

·      Companies should track and test the effectiveness of advertisement to make sure where to focus on, considering business objectives and brand metrics.

 

Benefits of Programmatic Digital Marketing 

    Programmatic digital marketing allows companies to focus more on their target market and audience at the right time and in proper context. Programmatic digital marketing enables businesses to save on costs since it helps set the frequency, time, and price of ads and requires payment for only relevant impressions. In addition, it helps companies keep track of their advertisement budget while achieving their set goals since it allows them to have control over ads by providing more flexible options. For example, a telecom company in Qatar, Ooredoo achieved to reduce the acquisition cost to reach their target audience 88% by using programmatic advertising, compared to traditional channels (Fayez, 2016). Lastly, it enables companies to provide a better user experience by promoting tailored advertisements and advanced targeting. There numerous brands that achieved building successful ads by embracing programmatic campaigns based on consumer data, such as O2, The Economist, Turner Sports, Audi, ScS, and Amanda Foundation (Keane, 2018). Regardless of the industry type, sector, or size of the company, programmatic digital marketing can be embraced to increase sales, conversions or drive awareness.

Buket Bostanci

Keywords: programmatic digital marketing, programmatic advertising, target market, target audience, personalised advertisement

References & Sources

 

Al Fayez, N., 2016. What is Programmatic and Why is Everyone Talking about it? [online] Think with Google. Available at: https://www.thinkwithgoogle.com/intl/en-145/marketing-strategies/programmatic/what-programmatic-and-why-everyone-talking-about-it/ [Accessed 13 April 2021].

 

Rask, O., 2021. What is Programmatic Advertising? The Must-Have 2021 Guide. [online] The Match2One Blog. Available at: https://www.match2one.com/blog/what-is-programmatic-advertising/ [Accessed 13 April 2021].

 

Keane, L., 2018. Programmatic Ad Buying: 6 Examples of Brands Getting it Right. [online] GWI. Available at: https://blog.globalwebindex.com/marketing/programmatic-ad-buying/ [Accessed 13 April 2021].

Emerging Technologies

    Emerging technologies can be considered the type of technologies that are still in the development stage or the new technologies existing out in front.  Although all new technologies may not pass the testing or development stage, the ones that can pass may transform the lives of people and, finally the world (Williams, 2017). Evolving technologies not only bring new opportunities and practical applications for tech firms in terms of generating new business, revenue, and improving customer experience, but also improve the quality of people’s lives in numerous aspects such as efficiency, safety, health, environmental conservation, and so on.

The Emerging Technology Community has released the list of Top 10 Emerging Technologies for 2020, considering the research on the technology landscape and the marketplace’s assessment (Fitzgerald, 2020).  The list includes:

 

1.     Artificial Intelligence

2.     5G

3.     Internet of Things

4.     Serverless Computing

5.     Biometrics

6.     Augmented Reality / Virtual Reality

7.     Blockchain

8.     Robotics

9.     Natural Language Processing

10.  Quantum Computing

 

What are The Emerging Technologies?

 

Artificial Intelligence: Being used in the daily lives of many people and was adopted by numerous industries, AI refers to systems, machines, and algorithms that mimic behaviour associated with human intelligence such as perception, learning, planning, reasoning, and decision-making.

5G: Fifth-generation wireless (5G) refers to the cellular technology designed to increase the speed of wireless networks and provide connectivity of multiple devices.

Internet of Things: IoT refers to the significant number of connected physical devices that collect and share data over wireless networks.

Serverless Computing: Serverless refers to a cloud-based execution model that eliminates the need for managing servers to run applications.

Biometrics: It refers to the process in which unique physical traits of people are being detected and used to verify their identity.

 

Benefits of Emerging Technologies in The Supply Chain 

 Integrating emerging technologies like artificial intelligence, the internet of things, virtual reality, blockchain, etc., in supply chains and embracing the transformations is crucial for businesses’ futures and values since they allow companies to save on time and costs by improving business processes. There are several examples of how emerging technologies may benefit the supply chain in preparing companies for the future. (Chorley, 2018).

 

·      Contributing to the real-time problem-solving performance of companies by predicting and preventing potential problems and malfunctions.

·      Improving the customer service and reducing the cost to serve by connecting the digital thread by leveraging information gathered from emerging technologies like the Internet of Things or Big Data.

·      Allowing companies to monitor and interfere with the processes, changes, and the supply chain not only remotely and globally by the use of cloud-based emerging technologies but also autonomously by the use of artificial intelligence in decision making.

·      Training and preparing supply chain workers using Virtual Reality to provide better practices, realistic experience, and remote training.

·      Helping companies manage supply chain related crises easily by tracing failures, offering correct balances, and providing complete transparency in the product information.

·      Transform the way that supply chains are being operated by the use of IoT-based inventory, asset, or order management systems that consider sustainability and green initiatives.

Buket Bostanci

 

Keywords: emerging technologies, artificial intelligence, machine learning, digital transformation, supply chain, IoT

References & Sources

 

Williams, O., 2017. The Advantages of Emerging Technology. [online] bizfluent. Available at: https://bizfluent.com/info-8427727-advantages-emerging-technology.html [Accessed 3 April 2021].

Chorley, J., 2018. 6 Benefits of Emerging Technologies in the Modern Supply Chain. [online] EBN. Available at: https://www.ebnonline.com/6-benefits-of-emerging-technologies-in-the-modern-supply-chain/ [Accessed 3 April 2021]

Fitzgerald, L., 2020. 10 Emerging Technologies Making an Impact in 2020. [online] Available at: https://connect.comptia.org/blog/emerging-technologies-impact-2020 [Accessed 3 April 2021]

What is GDPR? What are the Seven Principles of GDPR?

As a result of public concern over privacy, the GDPR was adopted by the European Parliament to protect the privacy and regulate the exportation of personal data of EU Citizens.  Collecting and processing data of European Union countries’ citizens, companies must comply with strict rules that protect customer data (Nadeau, 2020).

 

According to Data Protection Commission Ireland, General Data Protection Regulation (GDPR) is a standardized data protection law applied across European Union that came into effect in 2018. Article 5 of the regulation identifies the seven main principles of GDPR about how personal data should be gathered and processed by organizations. The seven principles can be explained as below (Kulakova, no date):

 

     1. Lawfulness, fairness, and transparency mean all personal data should be processed, referring to these standards. Lawfulness requires a legal basis within the GDPR when a company wants to use personal data; fairness tells how the organization must be fair to data subject during the data processing by not being harmful or tricky. Transparency means the users should be informed clearly by the organizations with straightforward explanations about the purpose of asking for personal information before the collection and processing of data.

2. Purpose Limitation means all personal data should be gathered for significant and legal purposes and should be limited to any future processing which is irrelevant to current goals. On the other hand, personal data could be processed for public interest, such as creating statistics if it is related to the original purpose. This principle aims to let organizations explain their intentions from the beginning about why they will be processing the personal data and for which purposes they will use it for.

 3. Data minimization principle allows organizations to gather only compatible and necessary information related to the specific purpose of the data processing. This principle and Purpose Limitation could be considered as complementary principles in terms of their aims. The nature of data minimization helps organizations collect up-to-date and recent personal data, and the principal protects the secrecy and entirety of data by avoiding any possible hijacking. As a result of this, it is recommended that companies should regularly check personal data by the criteria of compatibility and adequacy and should erase the unnecessary data. 

 4. According to the accuracy principle, all personal data collected and processed by organizations has to be precise, and they should be updated or deleted immediately if anything changes or turns into inaccurate information. In addition to this, companies should also be aware of their responsibilities considering individuals' rights, such as providing correction or completion of inaccurate and missing data. 

5. Storage limitation means organizations are able to keep personal data only within the time limit of the required period, which also has to be related to the purpose of data processing. As mentioned before, personal data may be kept for later for public interest considering and ensuring the regulation rules as a whole. The unnecessary data has to be erased immediately if it doesn’t serve the initial purpose. GDPR leaves it to the organizations how they identify which data is no longer necessary or not and encourage companies to inform their data subject about any changes. Besides, companies may anonymize personal data if the data subject cannot be recognized anymore, considering the compatibility to be considered anonymous. 

6. Integrity and confidentiality principles aim to secure all personal data processed by organizations and keep it confidential to avoid any serious harm or loss. Therefore, it is recommended that companies should benefit from security measures and control them regularly to ensure an entire security approach. 

     7. Lastly, the accountability principle, which is the newest one among others, means that organizations must take their responsibility to fulfill GDPR principles in coherence and prove them with appropriate data processing demonstrations. These demonstrations may include adopting internal policies, privacy policies, reporting any data issues, and updating the security measures for the organizations in the digital environment. 

Buket Bostanci

Keywords: GDPR, GDPR principles, data protection, privacy, personal data

References & Sources

Nadeau, M.,2020. General Data Protection Regulation (GDPR): What You Need to Know to Stay Compliant.[online] CSO. Available at: https://www.csoonline.com/article/3202771/general-data-protection-regulation-gdpr-requirements-deadlines-and-facts.html [Accessed 13 March 2021].

 

Kulakova, G., No date. 7 Principles of the GDPR and What They Mean [online] Amara. Available at: https://www.amara-marketing.com/travel-blog/7-principles-of-the-gdpr-and-what-they-mean [Accessed 13 March 2021]. 

Velocity – Data Processing

What is Big Data?

Big Data is a modern subfield of data science that requires the use of several tools, methodologies and techniques to explore, analyse and process complex data sets in order to be provided insights and information systematically. 

The Five V’s of Big Data 

Big Data had broken into four dimensions by IBM data scientists. Volume, Velocity, Variety, Veracity and Value are known as the five V’s which can be referred as the characteristics or the key elements that define Big Data. These characteristics are crucial and must be considered by companies that want to run operations successfully. Volume refers to the size of big data while velocity can be defined as the pace in which the data is getting accumulated. Variety stands for the complexity of the data while Veracity can be considered to assure the accuracy of the data. Lastly, the term Value was added, representing the usefulness of accumulated data.
 
 
Velocity – Analysis of Streaming Data 
 
The speed at which data from IoT, mobile data and social media is generated, accumulated, distributed and collected can be defined as Velocity. The rate of velocity parallels with the speed in with the data is acquired and processed. Velocity-oriented databases provide users real-time analytics, information and insights which enable companies to make valuable business decisions at the right time. In addition; acquiring, analysing and processing data in real-time allows companies to solve upcoming problems before facing complex issues, making real-time decisions and catching frauds as well. Besides, Processing information quickly into data allows users to have flexibility in their queries and reports.
 
On the other hand, the systems which analyse the data should be compatible with the task which means high velocity data may require distributed processing techniques due to the speed it is being generated. Some organizations came up with several solutions for streaming data such as; Apache’s Kafka and Spark, Amazon’s Kinesis, Google’s Cloud Functions and other streaming applications that process information in almost real-time to handle the high velocity of data (Reca, 2020).
 

Every day 2.5 quintillion bytes of data is created to be managed, secured (Marr, 2018). Processing that streaming data instantaneously requires strong data handling processes. High velocity allows users to react high volume of information within a short time. Twitter or Facebook messages, social media posts, Facebook status updates, online transactions, fraud checking, credit card swipes, compliance mechanisms, GPS signals, purchase transaction records and live transportation data can be shown as high velocity data examples. For instance, Facebook needs to ingest, process and file all of the photos uploaded by the users in a short time (Gewirtz, 2018). Also, one TB of trade information can be captured by The New York Stock Exchange during each trading session. In addition, velocity is essential for packet analysis for cybersecurity since the massive flow of data should be analysed and investigated quickly in order to detect anomalies and prevent risks. Thus, data should be gathered, processed and presented near real-time to achieve successful business results (Tudor, 2020). 
 
Buket Bostanci

Keywords: big data, data velocity, velocity, data flow, data volume

References & Sources

Gewirtz, D., 2018. Volume, Velocity, and Variety: Understanding the three V’s of Big Data.  [online] ZDNet. Available at: https://www.zdnet.com/article/volume-velocity-and-variety-understanding-the-three-vs-of-big-data/[Accessed 21 February 2021].
 
Reca, M., 2020. The 5 V’s of Big Data. [online] FlyData. Available at: https://www.flydata.com/blog/5-vs-of-big-data [Accessed 21 February 2021].
 
Marr, B., 2018. How Much Data do we Create Every Day? The Mind-Blowing Stats Everyone Should Read. [online] Forbes. Available at: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/?sh=6da11b3560ba [Accessed 21 February 2021].
 
Tudor, N., 2020. Understanding the 5Vs of Big Data: Volume, Velocity, Variety, Veracity & Value. [online] BornFight. Available at: https://bornfight.com/blog/understanding-the-5-vs-of-big-data-volume-velocity-variety-veracity-value/ [Accessed 21 February 2021].