Introduction to Digital Marketing

Digital marketing has changed the structure of markets today in the modern era of digitalization, which has also resulted in the fourth industrial revolution. Digital marketing is a component of marketing technique that uses technology to influence consumer behavior. It utilizes the internet to create an online platform for communication services (Avery et al., 2012, 96–111). Digital marketing also relies upon digital social computing platforms to advertise products and gauge customer reactions. The major revolution in digital marketing came in the 1990s and 2000s (Mahajan, 2020). Today, the rise of internet enabled marketing applications has led to the advent of a digital ecosystem. Digital marketing has connected various market services across the world. It has also helped in transforming user behavior and habitats (Naimi et al., 2014, 1143–1144).

Digital marketing has changed today’s market scenario. In contrast to traditional marketing, digital marketing has led to major growth of the industry in terms of profitability and use (Tiago et al., 2014, 703–708). Today, even third world countries like India, Bangladesh, Sri Lanka, etc., have become supporters of digital marketing strategies due to their advantages. Some of the major advantages of digital marketing are:

1. Creation of an online community and 24/7 services: Digital marketing has led to creation of a close-knit online community. Consumers, companies, as well as suppliers work together rationally (Tiago et al., 2014, 703–708). Most internet communities today provide a place for customers to keep up to date with the companies regarding their goods or services. One can easily visit company websites to get customer support, give feedback, purchase products, and maintain the purchase and return details. In other words, they get comprehensive information with regard to various products or services (Karp, 2014). Digital marketing has allowed 24-hour service to people wherein prices are transparent (Eri et al., 2011).

2. Monitoring and measuring of results: Digital marketing has enabled companies to monitor their internet marketing with the help of web analytics and other online metric tools. This makes it easy to determine how effectiveness of the brand campaigns can also help in extracting extensive information about how customers interact with products and company advertising.

3. Improved return of investments: The popularity of digital marketing has led to a substantial research interest among various data scientists and scholars (Kannan et al., 2017, 22–45). Many researchers (D. Rogers et al., 2012, 1–17) have tried to identify and understand the methods to improve market profitability. They have researched ways to increase ROI (Return of Investment) in digital marketing.

4. Precise targeting: Traditional marketing employs the spray and pray strategy, in which an ad is spread across a large platform in the hopes that a few individuals who like what they see, hear, or read will take action. Marketing via digital platforms, on the other hand, enables targeted marketing where advertisements are offered to clients based on their preferences or first action. Consider transactional emails that will only be sent to customers after they have taken action with the company. In other words, clients only get what they request.

5. Search engine optimization: Search Engine Optimization (SEO) which is one of the most important digital marketing techniques also uses data to operate. SEO is a technique wherein search results from the popular search engines are optimized to increase company reach. This strategy not only helps in programmatic advertising or search engine marketing but also provides a way to get a company known across the world.

Although digital marketing has various advantages, most of the techniques depend upon collection, analysis, and usage of valuable data. In the context of digital marketing, valuable data refers to the data collected in relation to a customer’s preferences and other relevant details. Thus, improved data management has the potential to change and influence the digital market. Hence, a major change in research strategy came when people began giving importance to data. Scholars like Kumar et al. (2014) have successfully analyzed the influence of data in digital marketing. Data is also one of the major metrics to measure the efficiency of companies. This metric comparison to calculate company efficiency has been done by Mahajan (2020). Big data analytics has resulted in the identification of valuable knowledge, as well as the promotion of market-leading activities. It has allowed market transformation at both local and global levels.

The further sections of this chapter describe the importance of big data and its application in digital marketing. Section 2 analyzes the growth of digital marketing in India while Section 3 introduces the concept of data and big data in digital marketing in more detail. The last section defines the role and application of big data in digital marketing.

Growth of Digital Marketing and Popular Options

Digital marketing has witnessed a major increase over the past few years. According to Gandhi (2020) a 26 percent increase in digital advertising alone was witnessed from 2018 to 2019. One of the major factors for the growth of digital marketing is the increase in the number of internet users. In 2020, India reported approximately 700 million internet users.

The number is further expected to increase in 2021. According to internetworldstats.com, India is among the top 4 global internet users with at least 391.2 million active internet users from a total of 4.21 billion users (Kemp, 2021). Although the internet has been a primary factor for the growth of digital marketing, Indian digital marketing has flourished with an increase in the number of mobile users.

Another factor related to the rise in digital marketing is the emergence of social computing. Social computing is the new computing paradigm that starts with the study of human behavior and their interaction with the environment and computational systems, that is, human-computer interaction (HCI). It was developed with the goal of creation or recreation of social conventions and social context using computer science fields like networking, data analysis and social software technologies like blog posts, social media sites, instant messaging applications, and many more. Hence, social computing has provided a large platform for digital marketing and advertising. An increase in the number of internet and social media users from the 2010 to 2021 is depicted in Figure 13.1 (Dean, 2021).

Source: Dean, 2021

As shown in Figure 13.1, the number of social media users has been steadily increasing every year in relation to internet users. While in 2010, only 970 million users out of the 2,035 million total internet users were active on social media platforms, the number of social media users became almost proportional in 2021 with 4,200 million active social media users out of the 4,600 million internet users (Dean, 2021).

The scope of digital marketing in India has become even more broad and prominent due to the advent of COVID-19. The digital marketing scope for the Indian industry is expected to grow by 160 billion dollars by 2025 (Dash, 2019). This is approximately three times the current value. Due to the pandemic, a work from home culture has started which has led to people shifting toward freelancing and online business ventures. Over time, many such business ideas emerged, which helped promote digital marketing among the masses. Some of the popular digital marketing options that have gained huge popularity among digital marketing amateurs as well as professionals are:

1. Influencing customers through social media: With the rise of social media, it has now become a major platform for brand promotions and customer reviews. Many digital marketing professionals today use social media platforms to influence customers. Companies provide funding to influence on various platforms like YouTube and Instagram to promote their products via strategic advertisements. Blogging is also one of the ways where people affiliate with companies to generate income. Due to its success, professional bloggers can now earn 1,000 dollars a month, while celebrity bloggers even earn more than 10,000 dollars per month (Sam, 2021).

2. Freelancing: The concept of freelancing has become a major source of employment today. Freelancing has further led to an increase in digital marketing. It provides a way for people to choose their product and decide its price according to the quality and quantity. Moreover, since these services are online, they can be accessed by people across the country.

3. Augmented and virtual reality: This is a fairly new technology that is starting to change the digital market strategy in 2021. It can help in increasing brand awareness and can also help to satisfy customer demand. Many multinational corporations, including Nivea, Starbucks, and Volkswagen, have already launched successful AR and VR campaigns.

4. Omni-channel marketing: While the present purchaser anticipates that every business should, at any rate, have an online presence, utilizing an assortment of media to draw in your objective market in a consistent way is ideal. Omnichannel computerized promoting is characterized as giving consistent client experience across all channels. These incorporate both web-based promoting channels like web-based media, versatile publicizing, online business sites, and disconnected channels like print-based ads, sends, customer facing facade, bulletins, actual connections, and so on.

5. Data analytics: Digital analytics or data analytics is one of the primary aspects of marketing. It refers to the use of tools and techniques to analyze and report on marketing data. Companies employ data analysts to work on the data collected through digital channels and social media forums. This not only helps to evaluate brand presence but also helps identify flaws and strengths. Thus, to summarize, it helps in

   1. understanding the customers

   2. analyzing and tracking market behavior

   3. making data-driven predictions

   4. optimizing search results

Since the amount of data is huge, data analytics takes the help of data science techniques like big data to help with systematic information extraction and analysis. The current chapter focuses on the role and application of big data in digital marketing data management.

Big Data Evolution and Parameters for Data Evaluation

Today, most digital marketing strategies are dependent upon analysis and collection of data. This data can range from consumers and their behaviors in the market system to recent trends and customer feedback. The increase in the number of devices has indirectly resulted in an increase in the size and the number of data sets. Today, these data sets are collected using not only mobile devices but many other remote and online information devices, that is, Internet of Things enabled applications, sensor devices, software logs, RFID readers, CCTV’s, and wireless sensor networks (Jia et al., 2012, 1282–1285). As per IDC report prediction, the rise in global data volume has substantially increased from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. As a result, they predict that by 2025, the world will have collected at least 163 zettabytes of data (Seagate, 2017). Since most of digital marketing depends upon data analytics, relational database management systems prove efficient to meet the demands of these companies. Processing and analysis of this huge and complex dataset may need high performance parallel software that runs across many servers (Jacobs, 2009, 10–19).

Big data has emerged because of an increase in the amount of data. Although the notion of big data is relatively new, the concept of handling large datasets began in the 1960s and 1970s. This was the time when the world of data was just getting started with the first data centers and the invention of the relational database. In 1990, a computer scientist named John Mashey officially introduced the notion of big data in the United States, igniting the interest of several marketing experts. Due to this, he is even referred to as the creator of the term “big data” (Lohr, 2013). Although several definitions of big data have emerged over the years, big data primarily refers to data that is so massive, fast, or complicated that traditional methods are difficult or impossible to process.

In other words, big data is a field of computing which works with large data sets that are not only complex but also difficult to handle using traditional techniques (Elgendy, et al., 2014, 214–227). Big data hence finds ways to analyze and extract information systematically from these large and complex data sets. It classifies data based on two main parameters, which are the number of fields and complexity. Both go hand in hand. While data with multiple fields generates a lot of statistical power, it complicates data analysis. This can result in a higher false discovery rate (Rouam, 2013). Big data analysis challenges often deal with gathering data and its conversion to valuable information. It includes a wide range of steps, from searching and capturing data to data visualization, storing and analysis, data querying, information privacy and updating etc.

The concept of big data became even more prominent in the early 2000s when industry analyst Doug Laney defined big data in terms of three factors referred to as the 3 Vs of big data. The three key concepts were variety, volume, and velocity. Furthermore, many researchers have hence redefined big data. While researchers like Camacho et al. (2014, 500–505) defined big data in terms of 4 Vs (variety, veracity, volume, and velocity), some scholars (Andreu-Perez, et al., 2015, 1193–1208) even defined 6 Vs of big data as value, volume, velocity, variety, veracity, and variability. In addition, 10 Vs of big data (Borne, 2014) has been discussed in context in Figure 13.2.

1. Variety: Before the computation can take place, heterogeneous data sources must be identified and homogenized. Because it includes all the prework required to set up the high-performance computation, this procedure is frequently the most labor-intensive. The great majority of data mistakes and faults are discovered here, and this is the primary bottleneck in high-performance computing.

2. Volume: The measure of information waiting to be prepared at a given time. This can show either a sum over the long run or a sum that should be handled at one time; finding the most popular Twitter hashtags, for example, or categorizing customer opinions about a product via various social media platforms.

3. Value: This term is characterized as whatever is critical to the client. Another approach to characterize esteem is the expulsion of deterrents in their way to permit them to get to their expressed objective.

4. Veracity: This is one of the unfavorable aspects of big data. The veracity (belief or trust in the data) decreases as any or all the above qualities increase. This is comparable to, but not identical to, the concepts of validity and volatility. The term “veracity” refers to the data source’s provenance or dependability, as well as its context and how relevant it is to the study based on it.

5. Visualization: Due to the constraints of in-memory technologies, as well as insufficient scalability, functionality, and reaction time, current big data visualization solutions suffer from technological hurdles.

6. Viscosity: The ease or difficulty with which data can be flowed to various use cases that might benefit from its adaptability is referred to as viscosity. Internal friction in highly viscous data stems from custom, albeit preferably internally consistent, representations that, at the very least, necessitate high-touch interpretation, transformation, and integration.

7. Velocity: Similar to volume, this has to do with the speed of the information coming in and the speed of the changed information leaving the figure. An illustration of a high-speed prerequisite is telemetry that should be investigated continuously for a self-driving vehicle.

8. Variability: In the context of big data, the term “variability” can apply to a variety of things. One is the number of data discrepancies. Anomaly and outlier detection methods must be used to find these before any useful analytics can be performed.

9. Vocabulary: Data science provides a lexicon for tackling a wide range of issues. Different modeling approaches are used to tackle different problem domains, and different validation procedures are used to harden these approaches in various applications.

10. Vagueness: Regardless of how much data is accessible, the significance of found data is frequently obscure.

Big data’s contribution to the market toward trade, operation, and data can prove to be valuable and diverse. It can not only help in discovering hidden relationships but also help in identifying new opportunities for business. Valuable data captured through big data can help companies to redefine their organizational structure and make changes in their way of communication (Sharma, et al., 2014, 433–441). Thus, it improves both the performance and competitiveness of the digital market and helps the market to respond to customer demands in a much faster and more efficient way.

Role of Big Data in Digital Marketing

As seen in the previous section, big data deals with handling large and complex data. Hence, it plays an important role in the field of digital marketing, which is highly demand dependent. It helps the market analyzers determine customer behaviors and business insights. It provides all the required information related to a market. It can range from customer preferences, their highs and lows to business and their profits and losses in particular situations.

Big data is something that all digital marketing companies are interested in and look up to. The goal of digital marketing is to increase revenues and attract as many clients as possible. It aids marketers in growing traffic by generating fresh strategies and ideas for enticing customers. And none of this would be feasible without the use of big data.

Because of their objectives, big data and digital marketing are inextricably linked (Writer, 2020). By focusing on the proper people, big data helps to increase traffic. It also aids in the planning of marketing for the intended demographic, allowing them to come and stay. The same may be said for digital marketing. The fundamental goal of digital marketing is to use innovative marketing techniques and methods to boost revenues and the number of customers. Both are linked by their goals, which are primarily to engage as many people as possible.

Application of big data in digital marketing and big data techniques used in digital marketing is discussed in the following sections:

Application of Big Data in Digital Marketing

The application of big data for digital marketing is briefly explained in the following points as shown in Figure 13.3.

1. Improving competitiveness: When it comes to gaining a technological competitive advantage, big data adoption allows businesses to create and capture value (Zeng & Glaister, 2017, 105–140). By extracting value insights from data, it allows businesses to make better decisions, improve processes, and improve goods (Kitchen, et al., 2018, 540–574). Businesses can gain in-depth knowledge about their customer segments and their perceptions of product and service quality by exploiting consumer reviews. Big data capabilities include the creation of new business models in which customers receive a free service in exchange for delivering a revenue-generating data stream (Trabucchi & Buganza, 2019, 23–40). By providing dynamic pricing, competitive options, and better campaign management based on the customer’s previous spending and preferences, big data even helps businesses stay competitive. The use of technology for price optimization will go to a new level, with analysis of data at a finer resolution based on pricing and sales (Sanders, 2016, 26–48).

2. Enhanced website navigation: Navigation and browsing experience are critical elements for any online company. These have a direct impact on sales while also directing customers to the checkout. With millions of visits, big data is the only method to quantify all interactions. Every day, data is being generated all around the world at a rate of 40 percent each year, and each single piece of this data is critical for organizations (Schultz, 2019). Even though it may appear to be a nuisance, big data has been developed to make things more relevant and convert analytics into a treasure of knowledge. The sooner organizations utilize big data, the better their chances of competing in this increasingly competitive market.

3. Calculation of rate of rate of interest: According to certain surveys, half of B2B marketing executives find it challenging to properly correlate marketing efforts to revenue results to justify budgets. Big data solves this challenge by accounting for all marketing channels, activities, and investments and performing a cost-benefit analysis on each one. This makes it nearly impossible to misinterpret your marketing actions and budget.

4. Sales increase: Several firms around the world have already been transformed by big data. Big data technology has aided marketing and sales professionals in better defining products and services as well as managing sales networks. According to Forbes, big data has had a 48 percent influence on new age consumer analytics, a 21 percent impact on operational analytics, a 12 percent impact on compliance/fraud, a 10 percent impact on new product/service innovation, and a 10 percent impact on corporate data (Columbus, 2016). Furthermore, it has been a key instrument in influencing how marketing managers assess customer value analytics (CVA), allowing them to provide a highly consistent and better customer experience across all channels.

5. Price optimization: Big data provides the best options for product pricing. It also assists businesses in determining their profit and loss. It helps marketers choose rates for their items and suggests pricing that will increase their profit. According to a study, new and innovative pricing has created more than a quarter of firm sales (Sneader & Singhal, 2021). Pricing flexibility has the potential to improve sales, and big data has been a critical instrument in the development and implementation of innovative pricing strategies. Developing the optimum pricing plan is an analytical process, especially for large corporations and brands. Big data aids in the automation of price analysis and product development. Uber is a wonderful example of this, as it has been developing different pricing tactics in real-time to maximize impact.

6. Service customization: Because customer self-service and product modification are potential sources of customer data, the capabilities of big data and business analytics enable and facilitate product or service personalization (Huang & Rust, 2013, 251–258). The development of strategic marketing goals, as well as the design of individualized products and contextual messages, begins with a more personalized customers and the market. Firms can obtain real-time data on their customers’ perceptions, product assessments, and recommendations (Xu et al., 2016, 1562–1566.). Big data applications improve the firm’s ability to access consumer demands and perspectives, raise performance, and ultimately improve customer service (Richey et al., 2016, 710–739). Furthermore, a company that uses big data to capture real-time consumer data might have a better grasp of any unmet consumer needs. Firms can then turn these insights into actions, increasing the efficacy of digital advertising while also improving the organization’s dynamic capability (Erevelles, 2016, 897–904). Big data enables retailers to deploy marketing efforts through targeted marketing interventions and get higher returns on marketing investments (Bradlow et al., 2017, 79–95). Big data leads to the creation of well-focused organizations that focus on the delivery of individualized products and marketing equipment and increase their capital performance by investing enough in customization techniques.

Conclusion

Digital marketing is a marketing technique that uses technology to influence consumer behavior. Over the last few years, there has been a significant surge in digital marketing. With the introduction of COVID-19, the scope of digital marketing in India has expanded and grown more significant. Because most of digital marketing relies on data analytics, with the rise of digital marketing, the amount of data generated has also increased. Big data emerged because of an increase in data volume. Big data analysis problems frequently include obtaining data and converting it into useful knowledge. The present chapter discussed the concept of big data and evaluated data based on 10 Vs. The role of big data in improving digital marketing, especially in developing countries like India, Bangladesh, etc., was also discussed. This chapter reviewed some of the recent applications of big data in digital marketing and some of the most popular big data techniques. Due to the advantages provided using big data in digital marketing, especially in the times where work from home culture has been popularized, big data holds a vast scope for changing the marketing world.

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