1.0.Introduction
Big data is a term that refers to the complex and vast data sets that supersede the capacity of traditional tools of data management. It is a combination of unstructured, semi-structured, and structured data collected by organizations and can be mined to provide relevant information that will influence decision-making (Oussous et al., 2018). This data-based technology has revolutionized the corporate landscape, more especially in marketing. The growth of big data utilization in the corporate world has partly been attributed to the Internet of Things (IoT); IoT is the growing network of connected devices that generates and exchanges data online (Khare & Totaro, 2019). Unlike the traditional tools by which organizations store data on-premises, the current large volume of data has necessitated an introduction of Analytics as a Service, enabling organizations to carry out analytics through a cloud-based subscription. One main advantage of big data is that it has reduced the cost related to on-site data storage and processing.
In marketing, Big Data continues to be the word for the day. As of the research done in 2021 by the Statista Research Department, 46% of the organizations surveyed agreed to use big data in marketing activities (Statista, 2021). The Global market for big data is around $240 billion; this value is projected to hit $650 billion by 2028 (Statista, 2021). The figure below shows how organizations have adopted Big data according to a survey by New Vantage Partners (Dilmegani, 2022).
Figure 1: Big Data Adoption by Business Functions in 2022
One of the industries that has utilized Big data is the fashion industry. A McKinsey & Co. report revealed that Bid Data is responsible for 30% of the increase in digital sales in the fashion Industry (Devillard et al., 2021). The same report also cites that the fashion companies that have utilized Big data have experienced increased sales ranging between 30% to 50% (Devillard et al., 2021). The fashion industry has adopted big data to provide insights into market dynamics such as customer preferences, behavior, and purchasing capacity. Louis Vuitton is one of the fashion companies that have utilized Big Data in their marketing strategies. This project report aims to elucidate how Big data can provide marketing insights in the fashion industry, analyzing its impact on marketing strategies. In keeping with this, the report also investigates how Big data techniques have impacted LV marketing strategies. Finally, the report also seeks to identify the different big data techniques that can be used in marketing.
2.0.Methodology
Since the success of any research depends on the effectiveness of the data techniques employed, this study primarily focuses on text-based analysis to collect data. Text-based analysis involves using natural language processing techniques to extract and interpret textual data (Baek, 2021). This method is significant in understanding the intricate relationship between big data and the fashion industry. This study achieved text-based analysis through the following methods:
Literature Review– Literature review involves an extensive review of past academic materials such as peer-reviewed articles and journals, books, and industry reports. This study’s literature review is based on reviewed literature materials obtained from Google Scholar. This technique was to establish an efficient theoretical framework that can be used to understand the role of Big Data in marketing strategies as applied in the fashion Industry.
Case Study Approach- The case study approach focuses on a specific case to obtain general insight into a subject matter. In this study, the case study approach focuses on Louis Vuitton’s adoption of Big data in its marketing strategies. This involved thoroughly examining the company’s reported and publicized initiatives and stories related to applying Big Data in marketing. LV’s official statements and reports, such as marketing materials, annual reports, and press releases, were comprehensively analyzed to achieve this.
3.0.Theoretical Framework
Customer Relationship Management Framework
This study will utilize the Customer Relationship Management (CRM) theory to understand the relationship between Big Data and the Fashion Industry’s marketing strategies. The CRM framework is a strategic approach that builds and fosters long-term customer relationships (DewnaraiN, 2019). This framework helps understand customers’ behavior, emotions, and preferences to enhance the profitability and performance of Business. The following are the ways through which this framework will be applied to achieve the objectives:
Segmentation– This will involve how the Fashion industry has used Big Data to understand the distribution of the market segments based on demographics, purchase history, customer behavior, preferences, and emotions.
Personalization– This will involve how the fashion industry has used Big Data to customize products to enhance the effectiveness of marketing strategies.
Predictive Analytics involves using Big Data to identify potential customer churn, estimate future sales, and anticipate market trends.
Social Media Analytics– This will involve the fashion industry’s use of Big data in monitoring and analyzing the social media activity around the Brand’s websites, posts, and mentions to identify new markets and customize campaigns.
Geospatial Analytics– This will involve analysis of how the fashion industry has used Big Data to identify product distribution based on demands to optimize the places with high demands.
4.0.Data Collection
4.1.Literature Review: Big Data in the Fashion Industry
Jain, S., Bruniaux, J., Zeng, X., & Bruniaux, P. (2017, October). Big data in the fashion industry. In IOP Conference Series: Materials Science and Engineering (Vol. 254, No. 15, p. 152005). IOP Publishing. https://iopscience.iop.org/article/10.1088/1757-899X/254/15/152005/pdf
Jain et al. (2017) is research done in 2017 and published during the 17th World Textile Conference. This study focuses on how Big Data can achieve Personalization in the fashion industry based on predictive analysis. It proposes a system that can apply Big Data in the fashion industry; this system has pre-existing information that can be used for customer behavior analysis, trend analysis, and forecasting. This system is fed vast information on materials, fashion design, body data, color, and technical designs. According to this study, a fashion company can easily gain insight into customer’s preferences, emotions, and preferences by the recommendations the system provides based on the existing data.
Silva, E. S., Hassani, H., & Madsen, D. Ø. (2020). Big Data in fashion: transforming the retail sector. Journal of Business Strategy, 41(4), 21-27. https://www.emerald.com/insight/content/doi/10.1108/JBS-04-2019-0062/full/html
Silva et al. (2020) analyze the impact of big data on the fashion industry. In this article, Silva et al., (2020) mention how Big data has revolutionized the fashion industry by shortening the supply chain, reducing wastage and trend forecasting, enhancing better quality control and counterfeits, and enhancing the consumer experience, engagement and marketing campaigns. In marketing strategies, Silva et al., (2020) mention how Big data has enhanced opinion mining of likes, comments, and shares on social media platforms to know consumer behavior and emotions in marketing campaigns. The article establishes that Big Data has contributed to Personalization by helping fashion companies identify and understand consumer needs.
Kumar, J., & Sikka, S. (2022). BIG DATA IN FASHION INDUSTRY. https://www.rajasthali.marudharacollege.ac.in/papers/Volume-1/Issue-3/03-23.pdf
Kumar & Sikka (2020) delve into how big data can be used in the fashion industry and the impact of big data in the fashion industry. Utilizing a conceptual research method, Kumar & Sikka (2020) identify how the fashion industry can utilize Big Data to identify customer reactions before modifying products. The article also mentions how big data can be used in optimizing product lifecycles, thus eliminating unnecessary inventory. In marketing, the article mentions how Big Data can be used in market segmentation and forecasting. According to Kumar & Sikka (2020), Big Data can identify and group markets into segments based on similar characteristics. On the impacts of big data in the fashion industry, the article mentions how Data science has transformed the industry from offer-based demand to demand-based offer. In addition, the article describes how Predictive analytics has helped the fashion industry to predict consumer trends and preferences.
Rejeb, A., Rejeb, K., & Keogh, J. G. (2020). Potential of big data for marketing: A literature review. Management Research and Practice, 12(3), 60-73. https://www.researchgate.net/profile/Abderahman-Rejeb/publication/339630258_Potential_of_Big_Data_for_Marketing_A_Literature_Review/links/5e5d67a24585152ce8010820/Potential-of-Big-Data-for-Marketing-A-Literature-Review.pdf
Rejeb et al., (2020) is a research based on a literature review that analyzes the big data techniques that can be used in marketing. This journal identifies four main Big Data techniques that can be adopted in marketing. These are social media analytics, Personalization, predictive analytics, and geospatial analytics. From analyzing various case studies, the authors found that social media analytics can be used for customer engagement, which provides valuable information while engineering marketing strategies. Additionally, the articles find that marketing can be optimized by adopting predictive analytics; this happens by using Big Data to foresee trends in customer behavior and aligning marketing to these trends. Furthermore, the article also finds that for all the companies studied, Big data was hugely used to personalize marketing strategies based on consumer tests and preferences.
Yoseph, F., Ahamed Hassain Malim, N. H., Heikkilä, M., Brezulianu, A., Geman, O., & Paskhal Rostam, N. A. (2020). The impact of big data market segmentation using data mining and clustering techniques. Journal of Intelligent & Fuzzy Systems, 38(5), 6159-6173. https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179698
Yoseph et al., (2020) focus on understanding the role of Big Data in market segmentation. The article describes how Big data can be used to categorize the market based on similar characteristics. The authors used data mining to get insight into the customer purchase behavior, thus grouping the market according to Customer Lifetime Value. Furthermore, the authors also used a performance clustering algorithm to produce accurate market segments. Their results revealed that the average lifetime of a customer is two years while the churn rate is 52%. The results were used to devise marketing strategies for the departmental stores surveyed. It was noted that those who adopted these results experienced a sales growth from 5% to 9%. This study reveals how effectively Big Data can be used to determine market segmentation and improve sales.
4.2.Case Study: Big Data in Louis Vuitton
Gan, Y. (2022). Luxury Marketing in the Age of Network Traffic: Taking Louis Vuitton as an Example. Frontiers in Business, Economics and Management, 5(2), 18-21. http://drpress.org/ojs/index.php/fbem/article/view/1636
Gan (2022) embarks on research to gain insight into luxury marketing in the age of network traffic; in doing so, he takes Louis Vuitton as an example. In this study, Gan (2020) examines how Louis Vuitton has utilized social media analytics to devise appropriate marketing strategies. The article reveals that Louis Vuitton utilizes mass information in network communications such as WeChat to deepen their brand image. LV utilizes online magazines based on customer reviews, comments, and shares to carry out promotional activities. Similarly, the article identifies LV’s use of the fan economy to propagate its marketing strategies. In China alone, LV’s partnership with Deli Reba to promote women’s summer wear gained more than 210000 forwarding of the fan economy.
ELIXIRR’s Case Study Report on Louis Vuitton’s Partnership
Elixirr is a tech-based group that specializes in Big Data Analytics solutions for companies. This report is a case study on how Louis Vuitton partnered with Elixirr to enhance their marketing experience through data and analytics. The report reveals that LV’s interest in improving multichannel customer engagement and experience landed them in Big Data solutions. According to the report, LV wanted to incorporate Big Data analytics in improving innovative retailing and Personalization. This partnership led to introduction of a data analytic solution that utilized customer data to improve brand loyalty and enter new markets.
ZIGURAT, Institute of Technology, Report on Digital Transformation in Luxury Retail
https://www.e-zigurat.com/en/blog/digital-transformation-success-cases-in-luxury-retail/
Ziggurat is a global institute offering innovative technology training through highly effective and valued hands-on experience. This report examines LV as one of the luxury industries that have successfully utilized Big Data. The report describes how Big Data has enabled LV to modernize and digitalize their brand identity through social media and predictive analytics. Through Big Data, LV has created a digital library that customizes customer’s purchase experience. In addition, this platform serves as a platform for digital marketing. Furthermore, the report mentions how Big Data has enabled LV to adopt an accelerator program that blends AI technology, Visual recognition-based predictive technology, robotic technology, and biometric wristwear to create customized clothes depending on the customer’s body and preferences.
Xiao, J. (2023, September). The Role of Social Media in Purchasing Luxury Goods–Taking Louis Vuitton as an Example. In 3rd International Conference on Economic Development and Business Culture (ICEDBC 2023) (pp. 213-220). Atlantis Press. https://www.atlantis-press.com/proceedings/icedbc-23/125991369
Xiao, (2023) is an Atlantic press publication that aimed to understand the role of social media in purchasing Luxury goods, taking LV as an example. The author discusses how Big Data through social media analytics has impacted LV in this article. The research reveals how, through social media, LV has utilized Big Data to reach a wider audience and enhance personal interaction and engagement. Xiao (2023) mentions how LV has utilized Big Data to achieve targeted advertising on social media platforms; through big data, social media networks can track the activities of online users, such as hobbies, purchase trends, and searches. Through social media analytics, LV has encouraged sharing user-generated content; through this content, it can monitor and predict customers’ behavior, emotions, and preferences. The report also discusses how Big data analytics has impacted LV’s pricing and distribution strategies via social media; social media has provided enough information for LV to do accurate market segmentation.
Bloomberg Report: LV’s partnership with Google to Boost Sales
https://www.bloomberg.com/news/articles/2021-06-16/google-wins-lvmh-cloud-deal-amid-luxury-shopping-boom#xj4y7vzkg
This article by Bloomberg reveals the impact of Big Data on LV from its partnership with Google. According to this article, LV has benefited from its partnership with Google through advancing its Big Data and AI initiatives. This partnership was geared towards providing LV’s wealthy customers with personalized experiences in online shopping. The article mentions how LV would benefit from Google’s Big Data and AI solutions through enhanced demand forecasting and inventory management.
5.0.Results and Findings
Based on the text-based analysis done in the previous sector, most organizations are Big -data-conscious. In the fashion industry, Big Data is primarily used for social media analytics, predictive analytics, geospatial analytics, Personalization, and segmentation. However, as noted in multiple researches, the most commonly used purpose of Big Data in the Fashion Industry is personalization and social media analytics. Each of the literature acknowledges that Big Data has revolutionized the marketing experiences of the fashion industry by enabling accurate identification of customers’ preferences, emotions, and behavior, thus predicting trends based on the information accessed through data analytics such as social media analytics. LV has maximized Big Data to offer customized products to its customers. Much of LV’s financial investment went to the data center in 2022.
Figure 2: Recommended system on how Big Data can be used in Fashion Industry
Figure 3: Big Data and AI vs Traditional Tools Application in the Fashion Industry
Figure 4: Big Data Impact on Fashion Industry
Figure 5: Big Data Benefits in the Fashion Industry
Figure 6: Big Data Usage in Business by %
Figure 7: LV’s adoption of Big Data
6.0.Discussion
6.1.Big Data in the Fashion Industry
Big Data can be used to understand customer’s emotions, behavior, and preferences in several ways. Information about customer tests and preferences can be obtained through social media analytics. Jain et al. (2017) and Silva et al. (2020) confirm that social media analytics provides an opportunity to engage customers and predict their behavior based on comments, likes, or shares. Sentiment analysis of this feedback can provide tangible insight into customer’s behavior and emotions. Thus, through social media analytics, both sentiment analysis and predictive analysis can be done to identify consumer behavior and trends. Xiao, (2023) confirms these findings by his LV’s research. Big data through social media analytics can provide information on customers’ best colors, outfits, and purchase intentions, which can effectively be utilized to customize products. Therefore, Social media analytics is the most effective way of utilizing Big Data to understand customer feelings and behavior. Figure 2 presents a proposed system by which Big Data can use customers’ social media information to personalize marketing experiences and products.
6.2.Impact of Big Data Approaches on Marketing Strategies
In the fashion Industry, the impact of Big Data approaches on marketing strategies is enormous. Big Data has enabled accurate market segmentation, a very important thing in marketing. As Yoseph et al. (2020) and Rejeb et al. (2020) discover, Big Data has been a crucial tool in market segmentation. Through techniques such as geospatial analytics and predictive analytics, Big Data has enabled the fashion industry to analyze the geographical distribution of their products based on demand and offer demand-based offers instead of offer-based demands (Kumar & Sikka, (2020). Big Data has enabled fashion companies to group markets based on similar characteristics such as age, income, behavior, sex, and preferences. This has eliminated wastage, optimized the supply chain, and provided insight into customer’s needs, as figure 4 and 5 reveal. Big Data has also revolutionized marketing strategies by enabling organizations to make data-based marketing decisions. Kumar & Sikka (2020) describe how product lifecycles have been optimized through making data-based decisions. Unlike traditional tools, Big Data allows fashion companies to make personalized marketing strategies depending on customer’s needs, fostering loyalty among the fashion brands.
6.3.Big Data’s impact on LV’s marketing strategies
Louis Vuitton is a perfect example of a fashion company that has experienced the benefits of adopting Big Data in marketing activities. LV has adopted different Big Data techniques to enhance its marketing activities and customer loyalty. A report by Elirr, Bloomberg, and Zigurat confirms that through Big Data, Louis Vuitton has successfully gained insight into consumers’ needs via predictive analytics. The reports confirm LV’s efforts towards utilizing Big Data in understanding marketing dynamics such as pricing and distribution, customer preferences, market trends, and personalized marketing. LV has also enhanced customer experience by blending AI technology, Visual recognition-based predictive technology, and robotic technology to create virtual outfit experiences for online shoppers. As Gan (2022) also confirms, LV has leveraged social-media analytics of mass communication platforms such as Facebook and Instagram to enhance its promotional activities. Xiao (2023) also points out Big Data’s impact on LV’s social media analytics. Both Gan, (2022) and Xiao, (2023) discover how LV has used Big data to track online users’ activities such as hobbies, purchase trends, and searches, thus, customizing marketing and products to fit customer’s preferences. Through Big Data, LV has also utilized online customer reviews from the fan economy, leading to a massive following on social media platforms.
6.4.Big Data Techniques in Marketing
This study identifies five major techniques for using Big Data in marketing activities. First and foremost, Social media analytics: this technique involves collecting, processing, and analyzing the voluptuous data on social media platforms to extract insightful data about customer preferences and market trends (Gan, (2022). Organizations can monitor customer’s needs and emotions through this technique and predict trends. Secondly, Predictive Analytics involves harnessing data from different sources to forecast future trends, outcomes, or behaviors. Through this technique, businesses can optimize their device and marketing strategies to effectively fit customer’s foreseen needs (Kumar & Sikka, (2020). Thirdly, Personalization: this technique involves customizing marketing strategies and products based on individual behaviors and preferences; through this technique, businesses can leverage the large amount of data available to create personalized promotional activities and products for their customers (Jain et al., (2017) Fourthly, Segmentation: In the context of big data segmentation is a technique that leverages on the big data analytics to categorize the market into homogeneous groups. This helps businesses to tailor their marketing strategies based on these market groups (Silva et al., 2020). Finally, Geospatial Analytics involves analyzing location-based or geographic data using Big Data techniques and tools. In marketing strategies, organizations utilize this technique to understand spatial behavior and patterns of customers to optimize location-based promotions (Kashyap, R. (2019).
7.0.Limitations
As much as this study was based on the well-reviewed previous literature and reports, it only focuses on those reports that address the positive impact of Big Data. Despite this limitation, this study offers a comprehensive analysis of the impact of Big Data in the fashion Industry; researchers and marketers can use this information to gain more insight into the role of Big Data in marketing activities. This limitation also leaves a window for further research into the negatives of Big Data.
8.0.Conclusion
In conclusion, the era of Big Data has revolutionized the marketing landscape across various industries. Big Data has enabled organizations to make data-based decisions in marketing strategies. The fashion industry is one of the industries in which the advent of Big Data has transformed. With access to large volumes of data, Fashion companies can now understand customers’ demands and preferences and tailor their marketing activities to fit customer expectations. Big Data has also enabled the fashion industry, with an example of LV, to utilize large amounts of data obtained from social media platforms to predict consumer behavior and patterns. This research has identified five main marketing techniques through which organizations can utilize Big Data. These are: Personalization, Segmentation, Social media analytics, Predictive analytics, and geospatial analytics.
9.0.Recommendations
As a wide field, Big Data’s capabilities go beyond marketing and promotional activities. Fashion companies like LV should explore other fields of Big Data, such as AI, machine learning, and Augmented realities, to optimize their operational activities. One such solution to the fashion industry from these technologies in the virtual fitting room is an integration of robotics, artificial intelligence, and Big Data (Jain et al., (2017). This will automate processes and improve decision-making processes. In addition, fashion companies can leverage these technologies in marketing to allow customers to tailor their products per their tests and preferences.
10.0. References
Baek, S., Jung, W., & Han, S. H. (2021). A critical review of text-based research in construction: Data source, analysis method, and implications. Automation in Construction, 132, 103915. https://www.sciencedirect.com/science/article/abs/pii/S0926580521003666
Bloomberg, (2021). Louis Vuitton Owner Embraces Google’s AI to Boost Sales. Bloomberg.com. https://www.bloomberg.com/news/articles/2021-06-16/google-wins-lvmh-cloud-deal-amid-luxury-shopping-boom#xj4y7vzkg
Devillard, S., Harreis, H., Landry, N., & Sanchez Altable, C. (2021, October 14). Jumpstarting value creation with data and analytics in fashion and luxury | McKinsey. Www.mckinsey.com. https://www.mckinsey.com/industries/retail/our-insights/jumpstarting-value-creation-with-data-and-analytics-in-fashion-and-luxury
Dewnarain, S., Ramkissoon, H., & Mavondo, F. (2019). Social customer relationship management: An integrated conceptual framework. Journal of Hospitality Marketing & Management, 28(2), 172-188. https://www.tandfonline.com/doi/abs/10.1080/19368623.2018.1516588
Dilmegani. C (2022). Top 50 Big Data Statistics: Market Size, Importance & Benefits. Research.aimultiple.com. https://research.aimultiple.com/big-data-stats/
Elixirr, (n.d.( Moët Hennessy Louis Vuitton: designing data-driven, multichannel experiences. Elixirr. https://www.elixirr.com/en-gb/case-study/louis-vuitton-moet-hennessey-lvmh-data-accelerator/#:~:text=LVMH%20engaged%20one%20of%20its
Gan, Y. (2022). Luxury Marketing in the Age of Network Traffic: Taking Louis Vuitton as an Example. Frontiers in Business, Economics and Management, 5(2), 18-21. http://drpress.org/ojs/index.php/fbem/article/view/1636
Jain, S., Bruniaux, J., Zeng, X., & Bruniaux, P. (2017, October). Big data in fashion industry. In IOP Conference Series: Materials Science and Engineering (Vol. 254, No. 15, p. 152005). IOP Publishing. https://iopscience.iop.org/article/10.1088/1757-899X/254/15/152005/pdf
Kashyap, R. (2019). Geospatial Big Data, analytics, and IoT: Challenges, applications and potential. Cloud Computing for Geospatial Big Data Analytics: Intelligent Edge, Fog and Mist Computing, 191-213. https://link.springer.com/chapter/10.1007/978-3-030-03359-0_9
Khare, S., & Totaro, M. (2019, July). Big data in IoT. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. https://ieeexplore.ieee.org/abstract/document/8944495/
Kumar, J., & Sikka, S. (2022). BIG DATA IN FASHION INDUSTRY. https://www.rajasthali.marudharacollege.ac.in/papers/Volume-1/Issue-3/03-23.pdf
Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4), 431-448. https://www.sciencedirect.com/science/article/pii/S1319157817300034
Rejeb, A., Rejeb, K., & Keogh, J. G. (2020). Potential of big data for marketing: A literature review. Management Research and Practice, 12(3), 60-73. https://www.researchgate.net/profile/Abderahman-Rejeb/publication/339630258_Potential_of_Big_Data_for_Marketing_A_Literature_Review/links/5e5d67a24585152ce8010820/Potential-of-Big-Data-for-Marketing-A-Literature-Review.pdf
Silva, E. S., Hassani, H., & Madsen, D. Ø. (2020). Big Data in fashion: transforming the retail sector. Journal of Business Strategy, 41(4), 21-27. https://www.emerald.com/insight/content/doi/10.1108/JBS-04-2019-0062/full/html
Xiao, J. (2023, September). The Role of Social Media in Purchasing Luxury Goods–Taking Louis Vuitton as an Example. In 3rd International Conference on Economic Development and Business Culture (ICEDBC 2023) (pp. 213-220). Atlantis Press. https://www.atlantis-press.com/proceedings/icedbc-23/125991369
Yoseph, F., Ahamed Hassain Malim, N. H., Heikkilä, M., Brezulianu, A., Geman, O., & Paskhal Rostam, N. A. (2020). The impact of big data market segmentation using data mining and clustering techniques. Journal of Intelligent & Fuzzy Systems, 38(5), 6159-6173. https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179698
Zigurat, (2019). Digital Transformation Success Cases in Luxury Retail https://www.e-zigurat.com/en/blog/digital-transformation-success-cases-in-luxury-retail/
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