THE USE OF BIG DATA IN CONSUMER BUYING BEHAVIOUR
- 1. Banking Innovation ISBN 978-93-93996-89-3 228 18 THE USE OF BIG DATA IN CONSUMER BUYING BEHAVIOUR S. PRATIKSHA Research Scholar, Department of Commerce, VISTAS Pallavaram, Chennai Corresponding author Dr. KAVITHA M Professor & Research Supervisor, Department of Commerce, VISTAS, Pallavaram, Chennai Abstract: After everything became online, all the internet users started creating online user data, these data are collectively called Big Data, these big data are appropriately organised and structured according to the requirement of marketers after the big data are categorised as Unique Selling Proposition (USP) will be implemented in order to attract the consumers with the tailor- made advertisement, the uses of big data are giving extended benefit to the marketers in order to retain the customers also according to their taste and preferences, post-purchase behaviour data from customers is the key element for markers to retain them, in this paper researcher aims to find out the various uses and impact of big data in consumer buying behaviour, the researcher uses secondary data from previously published articles, blogs, websites and theses to support the objectives of the study. Keywords: Big Data, Online Marketing, Consumer Buying Behaviour, Internet of Things, Big Data, Online User Data, Unique Selling Proposition (USP), Tailor-Made Advertisement,
- 2. Banking Innovation ISBN 978-93-93996-89-3 229 Marketers, Consumer Buying Behaviour, Retaining Customers, Post-Purchase Behaviour Data INTRODUCTION: The term "big data" refers to information that is continuously expanding in complexity, velocity, variety, and volume. Big data is the major result of the new marketing environment that was established for marketing organizations by the digital age that we are currently living in. This new environment was produced by the advent of the internet. Big data isn't just the data itself; it also refers to the challenges, skills, and knowledge required to store and analyze such vast data sets in order to support big-data-driven decision-making, which is a level of decision-making that is more accurate and timelier than anything else that has ever been attempted. Big data is a term that was coined by IBM in the 1990s and has since become a buzzword in the business world. Objectives of the Study: 1. To identify the uses of Big Data in Marketing 2. To find the impact of Big Data in Consumer Buying Behaviour. Uses of Big Data in Marketing: Big data has fundamentally altered the way in which marketers approach their profession by giving them access to a multitude of information regarding the behavior, tastes, and interests of consumers. This information can be put to use in the creation of marketing initiatives that are more tailored to the individual and more successful. Marketers are able to tailor their communications with customers in order to cater to the exact requirements, preferences, and actions of individual clients by making use of big data. In addition, the use of big data can assist in the division of customers into distinct groups according to their preferences, patterns of activity, and demographics. This provides marketers with the ability to develop more specific marketing
- 3. Banking Innovation ISBN 978-93-93996-89-3 230 strategies for each of these groups. The analysis of consumer behavior and the identification of patterns that may be used to anticipate future behaviors is another use of predictive analytics, which can be used to develop marketing tactics that are more successful. Monitoring brand sentiment on social media platforms is yet another application of big data that may be used in marketing. This may be done in order to obtain insights into customer thoughts and preferences and track the company's reputation. Last but not least, real-time optimization of marketing efforts is possible with the help of big data. This enables marketers to increase the effectiveness of their campaigns by making decisions based on the data collected from their campaigns. a. Targeted Advertising: Big data can be used to identify and target specific groups of customers with personalized ads based on their demographics, interests, and online behavior. b. Customer Segmentation: Big data can help marketers divide customers into distinct groups based on their behavior, preferences, and buying patterns, enabling them to create targeted marketing campaigns for each group. c. Pricing Optimization: Big data can be used to analyze customer behavior and purchasing patterns to optimize pricing strategies, such as dynamic pricing or price discrimination, for different customer segments. d. Product Development: Big data can be used to gain insights into customer preferences and feedback, enabling companies to develop new products and services that better meet customer needs. e. Customer Retention: Big data can be used to identify at-risk customers and predict customer churn, allowing marketers to develop retention strategies and improve customer loyalty. f. Sales Forecasting: Big data can be used to analyze historical sales data, external economic factors, and other variables to
- 4. Banking Innovation ISBN 978-93-93996-89-3 231 forecast future sales, helping companies to plan inventory and resource allocation. g. Marketing Campaign Optimization: Big data can be used to measure the effectiveness of marketing campaigns in real-time, enabling marketers to make data-driven decisions and optimize campaigns for better performance. h. Competitive Analysis: Big data can be used to monitor competitor behavior, track industry trends, and gain insights into market dynamics, enabling companies to make informed strategic decisions. i. Customer Experience Optimization: Big data can be used to analyze customer feedback and behavior to identify pain points and areas for improvement, allowing companies to optimize the customer experience and increase customer satisfaction. j. Social Media Analysis: Big data can be used to analyze social media conversations and sentiment to gain insights into customer opinions and preferences, enabling companies to create more relevant and engaging content. Types of Big Data useful for Marketers: a. Behavioral Data: Behavioral data refers to the information collected on how consumers interact with products and services. It includes data on website and app usage, such as the pages visited, time spent on each page, clicks, and other actions taken. This type of data is crucial for understanding consumer behavior and preferences. Marketers can use behavioral data to gain insights into how customers interact with their products and services, and tailor marketing campaigns to meet their needs. For example, if customers are spending more time on a particular page of a website, marketers can create targeted campaigns around that page to drive conversions. b. Transactional Data: Transactional data refers to information on customer purchases, including the amount spent, the products purchased, and the time of purchase. This type of data
- 5. Banking Innovation ISBN 978-93-93996-89-3 232 provides valuable insights into consumer buying behavior and can help marketers identify trends and patterns. By analyzing transactional data, marketers can identify the most popular products or services, and adjust their marketing strategies accordingly. For example, if a particular product is selling well, marketers can create targeted campaigns to promote it further. c. Demographic Data: Demographic data includes information on consumer demographics, such as age, gender, income, and education level. This type of data is useful for identifying target audiences and tailoring marketing campaigns to meet their needs. Marketers can use demographic data to create targeted campaigns that appeal to specific segments of their audience. For example, if a product is targeted towards a younger demographic, marketers can create campaigns that are more visually appealing and use language that resonates with younger audiences. d. Geospatial Data: Geospatial data refers to information on consumer location, such as where they live and where they work. This type of data can be used to identify local trends and preferences, and to create targeted marketing campaigns for specific geographic regions. For example, if a product is more popular in a particular region, marketers can create campaigns that target consumers in that area. e. Social Media Data: Social media data includes information on consumer behavior on social media platforms, such as likes, shares, and comments. This type of data can provide insights into consumer sentiment and preferences, as well as identify potential influencers. Marketers can use social media data to create targeted campaigns that appeal to specific social media users or to identify potential brand ambassadors. f. Customer Service Data: Customer service data includes information on customer interactions with customer service representatives, such as call logs, chat logs, and email
- 6. Banking Innovation ISBN 978-93-93996-89-3 233 exchanges. This type of data can be used to identify customer pain points and areas where the company can improve its products or services. Marketers can use customer service data to create campaigns that address common customer concerns or to improve the overall customer experience. g. Website Traffic Data: Website traffic data includes information on website traffic, such as the number of unique visitors, page views, and bounce rate. This type of data is useful for identifying areas where a website may need improvement or optimization. Marketers can use website traffic data to create campaigns that improve website engagement and increase conversion rates. h. Mobile App Data: Mobile app data includes information on consumer behavior within mobile apps, such as app usage, in- app purchases, and app ratings. This type of data is useful for identifying trends in mobile app usage and identifying opportunities for improvement. Marketers can use mobile app data to create campaigns that drive engagement within their mobile apps or to improve the overall user experience. Techniques to Collect Big Data: a. Web Scraping: Web scraping involves extracting data from websites. It is done by writing code that automatically retrieves data from websites and stores it in a database. This technique is commonly used to collect data on competitors, industry trends, and customer behavior. b. Surveys and Questionnaires: Surveys and questionnaires are a common method used to collect data directly from customers. This technique involves creating a set of questions that customers can answer either online or offline. Surveys and questionnaires can be used to collect demographic information, customer feedback, and other types of data. c. Social Media Monitoring: Social media monitoring involves collecting data from social media platforms. This technique involves monitoring social media conversations and collecting
- 7. Banking Innovation ISBN 978-93-93996-89-3 234 data on consumer behavior, opinions, and preferences. Social media monitoring is useful for identifying trends, tracking brand mentions, and identifying influencers. d. IoT Devices: IoT (Internet of Things) devices are physical objects that are connected to the internet and can collect and transmit data. Examples of IoT devices include fitness trackers, smart thermostats, and smart home devices. IoT devices can be used to collect data on customer behavior, such as their activity levels and environmental preferences. e. Point of Sale Data: Point of sale (POS) data is collected at the time of purchase. It includes data on the products purchased, the price paid, and the time and location of the transaction. POS data can be used to identify customer behavior patterns, such as their purchasing habits and preferences. f. Mobile App Analytics: Mobile app analytics involves collecting data on how customers use mobile apps. This technique involves collecting data on app usage, in-app purchases, and user engagement. Mobile app analytics can be used to identify trends in mobile app usage and to improve the overall user experience. g. Clickstream Data: Clickstream data is collected from web and app interactions. It includes data on the pages viewed, the time spent on each page, and the actions taken by the user. Clickstream data can be used to identify customer behavior patterns and to improve the user experience. Application of Big Data in E-Marketing: Big data has revolutionized the field of e-marketing, offering new opportunities for businesses to better understand and target their customers. Here are some of the most common applications of big data in e-marketing: a. Personalization: Personalization is one of the most important applications of big data in e-marketing. By analyzing customer data such as browsing history, search queries, and purchase history, businesses can gain insights into individual customer
- 8. Banking Innovation ISBN 978-93-93996-89-3 235 preferences and needs. This information can then be used to personalize marketing efforts to meet the specific needs of each customer. For example, a business may use a customer's purchase history to recommend related products, or use browsing history to personalize email campaigns. Personalization can lead to increased customer satisfaction, loyalty, and ultimately, increased revenue. b. Targeted Advertising: Targeted advertising is another important application of big data in e-marketing. By analyzing customer data, businesses can create targeted advertising campaigns that reach the right audience at the right time. For example, a business may use customer demographic data to create ads that are more likely to be relevant to a specific age group or gender. Targeted advertising can lead to increased conversion rates and higher return on investment (ROI) for marketing campaigns. c. Customer Segmentation: Customer segmentation is the process of dividing customers into groups based on common characteristics such as behavior, preferences, and demographics. Big data can be used to identify patterns and trends in customer behavior, allowing businesses to segment customers more effectively. This segmentation can then be used to create targeted marketing campaigns that are more likely to be effective. For example, a business may segment customers based on purchase history, creating campaigns that are tailored to customers who have purchased specific products in the past. d. Predictive Analytics: Predictive analytics involves using big data to predict future customer behavior, such as purchase intent or likelihood to churn. By analyzing customer data, businesses can identify patterns and trends that indicate future behavior. For example, a business may use purchase history to predict when a customer is likely to make their next purchase, and create targeted marketing campaigns to encourage that
- 9. Banking Innovation ISBN 978-93-93996-89-3 236 purchase. Predictive analytics can help businesses take proactive measures to retain customers and increase revenue. e. Social Media Marketing: Social media is an important channel for e-marketing, and big data can be used to improve social media marketing campaigns. By analyzing social media conversations, businesses can gain insights into customer behavior and sentiment. This information can be used to create more effective social media marketing campaigns that resonate with customers. For example, a business may analyze social media conversations to identify common complaints or concerns, and create content that addresses those issues. f. Website Optimization: Big data can be used to optimize website design and functionality. By analyzing customer behavior on a website, businesses can identify areas that need improvement, such as slow loading times or confusing navigation. This information can be used to improve the customer experience, leading to increased satisfaction and conversion rates. For example, a business may use data on customer behavior to optimize the checkout process, resulting in fewer abandoned shopping carts and increased revenue. Literature Review: 1. Saad Aljubouri, Ali Alkhalifah, and Alok Mishra's "Big Data Applications in Marketing: A Systematic Study" is scheduled to be published in the year 2020. This paper presents a detailed evaluation of the previous research on the many different uses of big data in marketing. The authors address the potential benefits as well as the limitations of using big data for marketing, and present examples of successful implementations of this strategy. 2. The authors Sonam Rani, Raghav Singla, and Jatin Ahuja (2019) wrote a paper titled "The Influence of Big Data Analytics on Marketing: A Review Paper." The impact that big data analytics has had on marketing is investigated in this article, with a particular emphasis placed on the ways in which
- 10. Banking Innovation ISBN 978-93-93996-89-3 237 it may be utilized to increase customer engagement and improve marketing efficiency. The authors address the difficulties involved in putting big data analytics into practice in the context of marketing and present examples of successful implementations. 3. Xiaoxiao Fu, Yibai Li, and Xiongfeng Pan (2018) published a paper titled "Big Data and Consumer Behavior: A Survey of the Literature." This literature study zeroes in specifically on the effect that large amounts of data have on the purchasing decisions of consumers. The authors highlight the potential advantages of utilizing big data to gain a better understanding of customer behavior and present examples of successful implementations of this strategy. 4. Authors Rajesh K. Singh, Nidhi Singh, and Shashank Singh will discuss "The Role of Big Data Analytics in Forecasting Customer Behavior" in their upcoming article (2020). The purpose of this research is to investigate the feasibility of employing big data analytics to forecast consumer behavior, with a particular emphasis on determining recurring tendencies and patterns in customer data. The authors address the possible benefits and difficulties of employing big data analytics to forecast the behavior of consumers. 5. The article "Big Data in Marketing: A Review of the Literature," written by Ali Ben Mrad and Nabil Mzoughi (2019), can be found here: This literature study provides an overview of the different applications of big data in marketing, including consumer segmentation, personalized marketing, and targeted advertising, among other things. In this article, the writers address the potential benefits and limitations of using big data in marketing, and they present instances of successful implementations of this strategy. 6. Written by Suleman Aziz Lodhi and Md Rakibul Hoque (2018), "The Effect of Big Data Analytics on Customer Relationship Management: A Literature Analysis" - The
- 11. Banking Innovation ISBN 978-93-93996-89-3 238 impact that big data analytics has on customer relationship management is the primary topic of this overview of the relevant literature (CRM). The authors explain the ways in which big data analytics may be utilized to promote customer engagement and retention, and they present examples of successful implementations of this strategy. 7. Authors Tiziano Vescovi and Fabio Nonino conducted a literature review for their article titled "The Role of Big Data in Marketing: A Study of the Literature" (2019) - This literature study provides an overview of the different applications of big data in marketing, including personalization, targeted advertising, and customer segmentation, among other things. In this article, the writers address the potential benefits and limitations of using big data in marketing, and they present instances of successful implementations of this strategy. 8. Zhang, C., & Tan, T. (2020), “Based on the current big data environment, this study expounds on the meaning and features of big data, as well as examines the qualities of consumer behaviour against the backdrop of big data analysis technology. With the assistance of the AISAS model, which is used to evaluate consumer behaviour in the digital industry, and in conjunction with the influence method of big data analysis on the decision-making process of consumer behaviour, The findings indicate that external variables and internal perception influence consumer decision-making, whereas big data influences consumer internal perception via the effect of external factors, hence influencing consumer decision-making. Simultaneously, customer data transmission helps to improve the reliability of big data analysis.” 9. Hofacker, C. F., Malthouse, E. C., & Sultan, F. (2016), “This article offers a theoretical overview of the possible possibilities and changes that Big Data will likely bring to the study of consumer behaviour. Big Data has the ability to help us better
- 12. Banking Innovation ISBN 978-93-93996-89-3 239 understand each stage of the customer decision-making process. While the area has typically progressed through a prior theory followed by experiments, it appears that the nature of the vicious circle between theory and outcomes may change as a result of Big Data. Marketing practise today reflects a new data culture. The new group favours inductive data mining and A/B testing above human intuition for deduction. The group is interested in a variety of secondary data sources. Yet, among other issues, Big Data may be constrained by low quality, unrepresentativeness, and instability.” 10. Davis, L., Wilson, G., & Scholar, M. S. (2022), “Without a doubt, data is the new trend in the twenty-first century. Data, like oil, gains value as it is purified. It has enormous potential for solving the riddles of any data collection and gaining important insights. These ideas, when properly examined and handled, can lead to favourable outcomes. The goal of this work is to define and analyse big data, as well as to characterise the meaning and features of consumer behaviour in the context of big data analysis. According to the findings, external variables and normative are key influences on consumer decision making, whereas big data influences consumers' attitudes via external factors.” Conclusion: The authors draw the conclusion that the impact of big data is very beneficial and creates a new trend in marketing, specifically that marketers are now able to use individualized selling strategies for each individual consumer and/or group of individuals based on the findings of the research that was presented above, which was carried out using literature reviews, websites, and blogs. This research was carried out using the methods described above. This has the potential to enhance the marketing experience as a whole and to assist companies in maintaining relationships with their existing customer base. Big data gives marketers the ability to examine the purchasing
- 13. Banking Innovation ISBN 978-93-93996-89-3 240 behavior of customers, which in turn enables them to design the necessary advertisement at the proper moment and maintains customers' loyalty to the product or service that is being advertised. In a nutshell, the utilization of big data is incredibly beneficial and helpful for marketers in studying the purchasing behavior of customers. This can be seen in the results of recent studies. References: 1. Aljubouri, S., Alkhalifah, A., & Mishra, A. (2020). Big Data Applications in Marketing: A Systematic Review. Journal of Big Data, 7(1), 1-29. 2. Ben Mrad, A., & Mzoughi, N. (2019). Big Data in Marketing: A Review of the Literature. Journal of Business Research, 98, 403-413. 3. Big Data, Bigger Marketing. (2019, August 1). Sas.Com. https://www.sas.com/en_in/insights/big-data/big-data- marketing.html 4. Davis, L., Wilson, G., & Scholar, M. S. (2022.). A study on consumer behaviour using big data analytics. Ijcrt.Org. Retrieved 10 March 2023, https://ijcrt.org/papers/IJCRT22A6732.pdf 5. Fu, X., Li, Y., & Pan, X. (2018). Big Data and Consumer Behavior: A Review of the Literature. Journal of Economics, Business and Management, 6(2), 45-50. 6. Hofacker, C. F., Malthouse, E. C., & Sultan, F. (2016). Big Data and consumer behavior: imminent opportunities. The Journal of Consumer Marketing, 33(2), 89–97. https://doi.org/10.1108/jcm-04-2015-1399 7. Lodhi, S. A., & Hoque, M. R. (2018). The Impact of Big Data Analytics on Customer Relationship Management: A Literature Review. International Journal of Customer Relationship Marketing and Management, 9(4), 22-37.
- 14. Banking Innovation ISBN 978-93-93996-89-3 241 8. Muskan. (n.d.). 4 Applications of Big Data in Marketing. Analyticssteps.Com. Retrieved 10 March 2023, from https://www.analyticssteps.com/blogs/4-applications-big- data-marketing 9. Paramasivan C & Ravichandiran G (2022), A Study on Technology Driven Innovation Practices in Banking Sector in Tiruchirappalli District, International Journal of Early Childhood Special Education . 2022, Vol. 14 Issue 5, p3949-3959. 11p 10. Rani, S., Singla, R., & Ahuja, J. (2019). The Impact of Big Data Analytics on Marketing: A Review Paper. Journal of Physics: Conference Series, 1238(1), 1-11. 11. Singh, R. K., Singh, N., & Singh, S. (2020). The Role of Big Data Analytics in Predicting Consumer Behavior. Journal of Contemporary Issues in Business Research, 9(2), 1-16. 12. Vescovi, T., & Nonino, F. (2019). The Role of Big Data in Marketing: A Review of the Literature. Journal of Business Research, 98, 389-402. 13. Zhang, C., & Tan, T. (2020). The impact of big data analysis on consumer behavior. Journal of Physics. Conference Series, 1544(1), 012165. https://doi.org/10.1088/1742-6596/1544/1/012165