Chapter 8 Impacts of Big Data on Mobile Marketing (Big Data and Mobile Marketing)

Harmanpreet Kaur

Introduction

The planet is filled with data, generated primarily by mobile devices, which can be used to create more useful services and ads for mobile users. CIBC, a Canadian bank, expects a 50-fold rise in information-generation growth over the next decade. Similarly, IDC, the industry consulting firm forecasted correctly that data volumes would increase 44 times between 2009 and 2020. Mobile plays an important part in driving this eruption (Figure 8.1).

Many mobile administrators are beginning to use mobile data to influence consumer retention and marketing decisions. Personalized strategies and hypertarget communications are crucial in mobile, personal, and private environments. And this can only be generated using big data. We describe big data; look at mobile access to it; evaluate its capacity, realistic uses, and falls; analyze how it is processed; and address six of the most often asked big data and smartphone questions.

Big data is usually defined as data sets that meet three attributes: “Vs.” However, “I want to remind you that there is a fourth V: meaning,” said Kipp Jones, Sky-product Hook’s vice president. To be useful, data must be collected and securely processed. Someone must then handle the results, interpret them, and derive meaning from them. Data, massive or not, does not add value if it has no value for anyone.

Mobile is suitable for a massive data lens in particular: Mobile big data is not just a function of smartphone adoption and consumer patterns. The data is often generated through background applications or other resources. Technically speaking, this is not so unlike conventional web-created results. The difference is to generate more consumers as we turn our activities into digital channels and leave a trail of information that records our actions and behaviors. And if we don’t use our phones ostensibly, we still build sources of info.

These specifics can be used to improve and customize the experience of smartphones: Mobile big data may be used for a variety of purposes but is often used to optimize, customize, and sell mobile services strategies. For example, Flurry’s analytics can be used by software developers to improve their applications. Retention is a key measure of success. In order to learn how they are stored and what they would have to do to improve their statistics, developers should match the consumer retention statistics for every other product and application with their own categories.

Source: I Intelligence Report 2019

Figure 8.1: Growth in average traffic per device.

To help speed up mobile advertising and explosion in marketing: Location data is a central component of mobile big data, possibly the most important source of data that differentiates between mobile and web-based large data. The marketing and mobile advertising industry are supposed to adjust location info. The opportunity to deliver hyperlocal, custom advertising in real time represents a potentially important development in the ad industry. The social network data can also be used along with location data to carry out personalized campaigns.

Key Areas of Mobile Marketing with Big Data

For business strategy and efficiency, big data is important. At a time where overpowering the amount of data allows us to make more data-driven choices and connectivity plays a crucial role in the data revolution, big data analysis is very likely to be the central process of mobile marketing. Any company knows the invincible role of mobile apps in the promotion and creation of a brand image. But to make the app discoverable, you probably need the latest data resources, big data analytics.

A Summary of Big Data’s Position in Sales and Marketing

What are big data analytics’ major sales and marketing sectors? In accordance with the Forbes survey, 48 percent of big data cases are used for market analysis, while only 21 percent of big data cases are used in corporate analytics. The same report also shows that big data use in fraud and enforcement is 12 percent. New product and business generation is 10 percent and the optimization of the industry data warehouse is 10 percent. According to this survey, many companies use big data for customer insights. Smartphone apps play a key role in the use of big data to promote sales and marketing.

Mobility and Big Data: A Connection

The link between mobility and big data is more mutual and promotes joint production. First, to understand the length of this relationship, one requires basic principles. The invention of smartphone apps has led to the exponential growth of the volume and range of digital data and, in turn, mobile device analysis has helped to better understand customers. The same user information created from device sensors and analytics provides more practical insights into analytics and policy making. Therefore, handheld applications for the whole big data trajectory are so awesome. The essence of perpetual mobility allowed companies to gain greater insight into consumer behavior, habits of usage and user data dependent on feedback. This huge reserve of mobile user data can be used further to refine mobile user interfaces, create, and encourage mobile traffic, promote market conversion, and cultivate further effort and user engagement.

Get the Edge of Big Data in Real Time Analysis

Recently, so-called management analysis has focused more on making it happen in real time. Mobile advertisers are now predisposed to their importance in real time. With the enormous volume of accurate data that analytics will track, real-time analytics offer businesses more advantages. In this respect, the promise of emerging big data technology would be critical. The Hardtop monitoring helps data get processed in real time. The corporations will now take fast decisions and adapt to shifts in immediate marketing campaigns through real-time monitoring. This real-time advantage allows an organization to respond to developments at the midst of every campaign.

Critical Elements of a Good Marketing Campaign based on Data

Now that data-driven marketing has led several companies to use big-data approaches and analytics, few important items must be remembered. First, a data-driven marketing approach must be cross-disciplinary, while also improving teamwork across different divisions and team members. Secondly, focus on the right key performance indicators (KPIs) and prioritize insights between lines rather than numbers. Not all numerical values will correctly lead you. Evaluate the customer from time to time based on evidence. Your company’s customer must be assessed, and more detail must be added if possible.

Customized Mobile Marketing with Big Data

Most administrators now use the words “big data” as a slogan. But unlike other hollow mottoes that make people sound intellectual, this word affects the way people do business. In reality, more facets of the market are now data-driven, including sales. After all, advanced big data entails identifying numerical development possibilities to make things more usable and competitive. Since revenues are the primary source of cash flow for businesses, investing in sophisticated analytics only helps to ensure that sales staff are at their best.

Coming up with the Best Price

In a 2014 survey, McKinsey found that 30 percent of price decision-makers struggle to provide the right price for all the available data. One of the key points emphasized in the study is that B2B businesses prefer to handle data, but do not use it in decision-making. In addition, the automation of their goods and price forecasting can help them distinguish visible and unobvious variables, including product tastes, the duration of their sales period, and the larger economic situation, to demonstrate what affects pricing for each client segment. The right price will significantly boost company sales up so it is something that companies cannot take for granted.

Better Customer Analysis

Aggregating data or what we all know as big data isn’t new. Companies have long sought to gather more information from customers to provide more insight into their conduct and where their desires lay. It is just that the “big data” today is so much greater than before. In an earlier post I wrote about the potential effects of mobile hitting big data. With mobile data, we get more customer knowledge in real time than ever before. More systems are being digitized with the growth in cloud communications, which means more data. For example, RingCentral business telecommunications system vendors can provide businesses with insights where the bulk of their calls come from simply extracting data from the telephone numbers of the organization. And if researchers can successfully gather mobile data as expected, it would have dramatic effects for how businesses advertise and distribute.

More Customized Selling and Marketing

Eric Tobias, vice president of Predictive and Online Products at Salesforce Marketing Cloud, addressed the prospect of hyperpersonalized marketing. The concept is that marketers would be able to offer customized touchpoints not only on the internet, but also in retail stores with more data to determine how customers respond in the most exact way possible. Big data will, in the (near) future, lead into a world where publicity, email, and smartphone deals are tailored for each customer and not only one sector of the population, rather than applying macro marketing and distribution tactics for a wider public.

Better Overall Product

When the product of the business is successful, it encourages the work of the sales staff. If it’s fantastic, it’s almost going to sell itself. But how do you go from decent to fantastic for your product or service? Simple, just listen to your clients. Feedback from clients is also data you can use. You get to know the pressure points and other areas for change by opening all available contact networks. Companies may grow their goods by understanding them. Data may lead to enhancing your product or service. Your business will present a stronger product or service with these changes. This means you will have more sales points and no pressure to justify your product or service, which is similar to more satisfied sales staff. This illustrates how big data will change the way businesses do things radically. It is just a matter of identifying and accurately using the right data.

Social Media and Big Data

Businesses prosper with the best possible perception of their clients. Therefore, tracking people’s online activity is vital for their progress. The company engages in the processing of big data analysis as a core component to track social media behaviors, in particular on platforms for social networking like Facebook, LinkedIn, & Twitter.

Social networking analysis was the synthesis of the behavior of internet users. The provision of statistics on online browsing, internet buying habits, user feedback, and social networks, and marketing research enables businesses to receive accurate and detailed customer details. Therefore, companies should target their business analysis efforts on various targets, including advertisement and product launch, advertising and market strategy, consumer engagement marketing, customer tailored services, market dynamics, and rivals keeping track of risk minimization, cost reduction, and overall organizational growth.

Tools and Metrics

Website Ranking

Web pages may be categorized to estimate the success of any website for a given period compared to all other websites (for instance, six months or one year). Rankings are provided by tools such as www.ranking.com and www.alexa.com. The lower the ranking, the better the website (for instance, the rank of Google.com is 1 followed by Facebook.com and YouTube.com). The rankings can be used both by businesses and their competitors to assess the success of their websites in general.

Analytics of Online Traffic

Online devices such as Google Analytics and www.alexa.com have website traffic metrics in tables and dynamic graphs that can be adapted to the needs of consumers. Some resources provide data collected in a tablet that can be used by businesses to generate their own graphs. Any of the measurements given are the total number of website visits over a certain period, the number of single visitors, the total number of visited spaces, the average number of website pages used during a visit, the average time and pace of visiting a website (i.e., visits in which a user left the website from the first page without continuing to view other pages within the site).

Social Media Mentoring

On social media sites like Facebook, Twitter, LinkedIn, YouTube and blogs, organizations meet their customers every day. Organization like Yammer or a private social network that encourages team, local or industry collaboration may even connect with staff and other stakeholders (e.g., Students, clients, external advisors).

Engagement of Customer Via Mobile Marketing by using Big Data

Companies have often focused on what their clients think to enhance their offerings and goods. Today, corporations don’t have to wait until their clients talk to them directly. The use of broad data will let your consumers decide what they want before they know what they want, and understanding what the buyers need now is not enough. You also need to see what they are going to need in the future. Big data would allow advertisers to understand how consumers feel, who they are and whether they want to communicate with themselves. There is a problem where so much data is available that it cannot be handled. Data experts should encourage data collection, processing, and use.

Improved Customer Engagement

The American Express and its loyalty partner Acxiom are an example of a company exploiting big data. This device boosted sales, improved brand awareness and fostered loyalty. Passengers are given points by using their American Express card and then can purchase flight tickets. The scheme required sophisticated technology and, while American Express initially planned to operate internally, the dynamic system had to be externalized. Pace and security are two of the most important problems in system management. Both companies use the amount of information present in the system to help understand their customers’ tastes and lifestyles. The result is a dynamic loyalty program that matches.

Customer Records must be Maintained and Often Updated

Customer data were quickly stalled. This means that you can’t contact or customize your clients and not dedicated customers.

Use the Desired Contact form of a Client

Customers should be able to use their feedback page for their preferences. You respect their wishes and just communicate in this way. It’s needed. First, if American Express wishes to keep its members up to date, it will contact them by email, text message, or telephone as it pleases. If not, you can hit them through these three means, interrupt, and possibly losing your customers.

Using Omnichannel

It is essential to your customers when they view your website, smartphone application, or print advertisements with the same brand experience. Similarly, anyone who has flights browsed on your platform must be able to pick up simultaneously using the app. If you have the information to let you know how people like to connect with your brand, you will be glad and loyal.

Emphasize the Importance of Security

Confidence is part of the relationship between brands and their customers. All this big data must be kept private, and the customers must be assured that their information is safe when registering to handle their customer relations.

Issues and Challenges faced during Mobile Marketing by using Big Data Customized Decisions

Issues of Big Data in Mobile Marketing

  1. Issue of scalability and storage: The data development rate is far greater than the new processing systems. Such data cannot be contained enough by storage devices (Chen et al., 2014; Li & Lu, 2014; Kaisler et al., 2013). A processing system that meets not only today’s needs but also future needs must be developed.

  2. Timeliness of analysis: The value of the data decreases with time. Many applications, such as telecoms, insurance, and financial crime, require transaction data to be interpreted in actual or nearly real-time (Chen et al., 2014; Li & Lu, 2014).

  3. Heterogeneous data representation: Data gathered from different sources are designed heterogeneously. Unstructured data (e.g., photographs, videos, social media) are not captured or evaluated using conventional techniques like SQL.

  4. Missing pool of talent: Talent is needed to increase the amount of (structured and unstructured) data generated. Demand for people with good analytical expertise in big data is growing. The analysis states the need for additional big data experts between 140,000 and 190,000 by 2018 (Brown et al., 2011).

  5. Privacy and security: The connectivity and storing of information for the purpose of research was provided by modern devices and technologies such as cloud computing. This incorporation of IT architectures would improve data protection and intellectual property risks. Links to sensitive information such as purchasing habits and calling data would raise privacy issues (Kaisler et al., 2013; Benjamins, 2014). Researchers have technological facilities for accessing data from any source, even social networking platforms, while users do not know what benefits may be obtained from information they share (Boyd & Crawford, 2012). The distinction between privacy and ease cannot be understand by big data researchers.

  6. Not necessarily better data: Researchers have been drawn by social media mining. Twitter has been a common new source of knowledge. The world population is not measured by Twitter users. Big data researchers should understand the difference between large data and whole data. Pornography and spam comparisons tweets are removed, and the topical frequency is unreliable. Amount of Twitter users and Twitter profiles have several human and multiple users generated one-account redundancies.

  7. Out of context: Data reduction is a common way of fitting into a mathematical model. During data abstraction, it is essential to keep the context. Focus information loses relevance and significance. The introduction of social networking sites as a “social graph” is fascinating. Big data introduces two types of social networks: “articulated networks” and “enforcement networks.” Contacts via mediation technologies are the product of articulated networks.

  8. Digital divide: Big data access is one of the main restrictions. Data vendors and social media platforms have access to broad social data. Few organizations determine who and to what degree will access info. Few offer the right of view at high fees, although some researchers have a range of information sets. This leads to “internet division” in big data: rich big data and poor big data. There are three classes of people and organizations in this field of big data.

  9. Data errors: The development of information technology produces vast volumes of data. Big data can be used for data storage and recovery by introducing cloud computing. Big internet data sets are vulnerable to and therefore inconsistent to errors and losses. The data source should be understood to minimize errors caused using various data sets. Before the analysis, the properties and deficiencies of the data set should be understood to avoid or explain the data representation distortion (Boyd & Crawford, 2011). Imagine, for example, reviewing social media websites from first parties and third parties, where the content of the first party accounts is examined while the other is not (Kaisler et al., 2013).

Opportunities of Big Data in Mobile Marketing

Big data analytics are now attracting too much popularity, but there are still a lot of analysis issues to be solved:

  1. Photos, audio, and video storage and recovery: Multidimensional data above and over large minimized data can be applied in computing to investigate in-memory model arrays. Multidimensional data models integrating big data calls for changes in HiveQL query language by multidimensional extensions (Cuzzocrea et al., 2011). The creation of images, audio, and videos is unprecedented as smartphones appear. However, storing, compiling, and analyzing these unstructured data requires tremendous research in each aspect.

  2. Data lifecycle: Most applications include big data analytics to run in real time. In order to make the computational process real time, the life cycle of the data, its significance, and the computation process must be defined (Chen et al., 2014). Big data is not always better, but appropriate filtering techniques should be developed to ensure data coherence (Boyd & Crawford, 2012). The availability of full and usable data is another important issue.

  3. High-dimensional data visualization: At any point of data processing, visualization enables decision analysis. The complexities of visualization remain a part of data processing and online analytic processing (OLAP) science. There is space for large-scale data visualization tools.

  4. Algorithms for real time computing: The pace at which the data is produced, and the optimal time delay is not achieved would not fulfill the criteria for these algorithms.

  5. Efficient storage equipment: The need for digital data storage is rising. The purchase and use of available storage devices cannot meet this demand. Research in the development of suitable storage devices that will remove the need for fault-tolerant Hadoop Distributed File Systems (HDFS) can increase data processing and substitute the requirement for software layers.

  6. Dimensions of social perspectives: It is important to remember, though, that any technology can provide faster efficiency, and policy makers must do it with experience. This research may have various social and cultural implications and cynically contribute to online forums. There are few concerns as to whether large search data can contribute to improved resources and services, or invasive privacy and advertisements will increase; whether it is used to track protests and limit free speech or whether data analytics can understand online behavior, communities, and political movements (Boyd & Crawford, 2012).

Impacts of Big Data on Mobile Marketing

Millions of bytes of data that people use daily can be used by smartphone app developers to create and produce better applications. Users need prompt insights into their mobile life and times that a customer will order, settle on fuel, and deliver their brands or services in real time and context through a range of devices. However, developers need comprehensive knowledge from several sources to drive software growth by incorporating research and big data.

  1. Using and understanding of big data: Big data is just larger than life itself, so it provides a complete rendering to the customer. The volume of data generated by users overtook the petabyte level; several zeta bytes of raw information or data have been clocked and this number is increasing exponentially. In the next few years, the volume of data stored globally will hit the yotta byte mark. The total amount of data collected years ago is lower than the unstructured data that were given before the data were produced. Therefore, it is only by using high-level analytics that the vast amount of these data can be minimized and translated into useful information. It’s a lot to do, but it’s worth it.

  2. Making customer-driven mobile apps: Quick, bug-free, attractive, and critical of all, it should be easy to use and must be able to meet users’ needs as much as possible. A comprehensive analysis of user engagement of big data analytics will also give enough insights into the development of broader, more open apps, and provides knowledge about what users want the applications to do. Moreover, user interface is the main source of the best possible concepts for making fresh and great applications.

  3. User experience analytics for big data fuels: As previously stated, for app creation, a thorough review of user interactions is needed. Big data explains the full activity descriptions of users that can highlight vivid points when taking user experience into account in software development. The overall desires and expectations of users are then communicated by evaluating their cumulative actions in relation to the software. This will also allow the production of the latest app. Mobile app creators will learn fresh ideas for creating new applications by seeing how much the consumers like the app by the study of big data behind apps identical to the ones they make.

  4. New marketing age: Business analytics and big data lead to the awareness-based mobile app design in which product marketers strive to connect email networks to mobile apps. In addition, other well-known advertisers include the Cloud Email Studio sales team, the Agility Harmony Network of Epsilon, the Powerful Selligent, and Cheetah Mail. For companies to address customers at a technological level, the ability of mobile apps to use big data analysis is critical. From business analysis to corporate intelligence and marketing, anything can be useful.

  5. Future app’s main feature: The mobile app market reached $189 billion by 2020, shattering the $100 billion quota thanks to many customers, who have almost fully converted to tablets and smartphones. Thus, the future of emerging technologies is simply the creation of better accessible smartphone applications. Mobile devices are much more volatile than computer applications. Thanks to their features and easy view, they are considered simpler to use. Users who display considerable interest in these unique characteristics are necessary. Big data processing is the most effective way to collect knowledge and to make it a big expense for companies.

Conclusion

This chapter offered an outline of big data and explored different big data methods and approaches in mobile marketing. We have also attempted to compare various frameworks for the management of large-scale data collection, big data management tools, various libraries, and bundles. This chapter has summarized the effect of big data on mobile marketing along with different big data opportunities and threats in mobile marketing.

References

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Boyd, D. and Crawford, K., (2011). Six provocations for big data. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society. →

Brown, B. Chui, M. and Manyika, J. (2011). Are you ready for the era of big data? McKinsey Quarterly, http://www.mckinsey.com/insights/strategy/areyoureadyfortheeraofbigdata. Accessed on: Jan 20, 2021. →

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Sivarajah, U. Irani, Z and Gupta, S. (2020). Role of big data and social media analytics for business-to-business sustainability: A participatory web context. Industrial Marketing Management. 86: 163–179. 

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