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Digitalization and its impact on contemporary marketing strategies and practices

• Guest Editorial
• Published: 20 April 2022
• Volume 10 , pages 103–105, ( 2022 )

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• Tat-Huei Cham 1 ,
• Jun-Hwa Cheah 2 ,
• Mumtaz Ali Memon 3 ,
• Kim-Shyan Fam 4 &
• Józsa László 5  

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Since its inception, technology has transformed the way businesses operate and the consumption of goods and services. (Matarazzo et al. 2021 ; Sestino et al. 2020 ). For example, technology has revolutionized the way companies promote their products and services, perform their business activities, communicate/exchange information, and manage resources. On the consumer side, technology has significantly changed consumption patterns and empowered them to be part of the product acquisition process (Cham et al. 2020 , 2022 ; Cheah et al. 2022 ; Lim et al. 2022 ). In every aspect of business, the emergence of new technologies such as artificial intelligence, big data, blockchain, virtual reality, and robots have created a new paradigm shift and promoted innovation in the area of marketing research and practices (Grewal et al. 2020 ; Lim et al. 2020; Steinhoff and Palmatier 2021 ). Such transformation has become a marketing catalyst, perpetuating new marketing trends and archetypes in digital marketing and marketing analytics.

In recent years, the progression of digital marketing via social media has expanded beyond its original purpose as a platform for social networking. Instead, it has evolved into a platform that enables businesses to communicate with their customers almost instantly and be directly involved in developing marketing strategies (Cham et al. 2021 ; Iankova et al. 2019 ). Specifically in digital marketing, customers can collaborate with companies as co-creators in almost every aspect of the business process including product/service development, value creation, and marketing strategy development (Li et al. 2021 ; Olson et al. 2021 ). By incorporating user-generated content into digital marketing, consumers can assume the role of “broadcasters,” they no longer listen to the marketers, just like what happened in the past (Cham et al. 2022 ; Cheung et al. 2021 ). Undoubtedly, the benefits of digital marketing and the potential of high ROI have made this channel one of the marketers’ most preferred choices (digitalthirdcoast.com 2022 ).

In addition, the emphasis on digitization and data-driven practice among businesses nowadays has made marketing lean towards science-based and provides marketers unlimited access to valuable insights into their company performance, customers, and opportunities (Ritter and Pedersen, 2020 ). In essence, marketing analysis is seen as identifying patterns of data that help marketers in marketing decisions. With the availability of marketing and data analysis tools (e.g., Google Analytics, Phyton, Heap Analytics, Optimizely, Klipfolio, etc.), the importance of how data can explain market trends and better understand consumer preferences are clearly spelled out (Petrescu and Krishen 2020 ; Yu et al. 2019 ). More importantly, marketing analytics help businesses and marketers optimize their marketing campaigns, segment their market, and reduce costs associated with marketing activities, providing business organizations with a sustainable approach in the long term.

Despite particular research conducted on the issues related to digital marketing and marketing analytics, additional attention is needed to study the revolution and potentially disruptive nature of these domains (Petrescu and Krishen 2021 , 2022 ). Considering the substantial impact of digital marketing and marketing analytics in the current competitive and demanding business landscape, the special issue editors hope that this issue lays a foundation in the academic perspective of these domains. We would like to recommend that more research be conducted to challenge the existing status quo and raise awareness of these domains in the near future; especially in the contemporary environment that requires more than just the knowledge brought from traditional marketing. For instance, there is room to explore further how biological technology (i.e., facial recognition payment), livestreaming, virtual influencer, neuromarketing, blockchain technology, metaverse, gamification, and omnichannel platform could be used for the marketing and analytic purposes.

Lastly, we would like to take this opportunity to thank all the authors who have submitted their work to this special issue, “ Digitalization and Its Impact on Contemporary Marketing Strategies and Practices, ” of the Journal of Marketing Analytics, and we are grateful to all reviewers who have rendered their service and expertise to ensure the quality of the publications. We want to extend our appreciation to the editors, Anjala S. Krishen and Maria Petrescu, for their endless support and for entrusting us with this task.

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Cham, T.H., B.L. Cheng, and C.K.Y. Ng. 2021. Cruising down millennials’ fashion runway: A cross-functional study beyond Pacific borders. Young Consumers 22 (1): 28–67.

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Cham, T.H., Y.M. Lim, and M. Sigala. 2022. Marketing and social influences, hospital branding, and medical tourists’ behavioural intention: Before-and after-service consumption perspective. International Journal of Tourism Research 24 (1): 140–157.

Cheah, J.H., X.J. Lim, H. Ting, Y. Liu, and S. Quach. 2022. Are privacy concerns still relevant? Revisiting consumer behaviour in omnichannel retailing. Journal of Retailing and Consumer Services 65: 102242.

Cheung, M.L., W.K. Leung, J.H. Cheah, and H. Ting. 2021. Exploring the effectiveness of emotional and rational user-generated contents in digital tourism platforms. Journal of Vacation Marketing 13567667211030675.

digitalthirdcoast.com. 2022. Digital Marketing Statistics for 2021, https://www.digitalthirdcoast.com/blog/digital-marketing-statistics-for-2020 . Assessed 31 March 2022.

Grewal, D., J. Hulland, P.K. Kopalle, and E. Karahanna. 2020. The future of technology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science 48 (1): 1–8.

Iankova, S., I. Davies, C. Archer-Brown, B. Marder, and A. Yau. 2019. A comparison of social media marketing between B2B, B2C and mixed business models. Industrial Marketing Management 81: 169–179.

Li, F., J. Larimo, and L.C. Leonidou. 2021. Social media marketing strategy: Definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science 49 (1): 51–70.

Lim, X.J., J.H. Cheah, Y.K. Dwivedi, and J.E. Richard. 2022. Does retail type matter? Consumer responses to channel integration in omni-channel retailing. Journal of Retailing and Consumer Services 67: 102922.

Lim, X.J., J.H. Cheah, S.I. Ng, N.K. Basha, and G. Soutar. 2021. The effects of anthropomorphism presence and the marketing mix have on retail app continuance use intention. Technological Forecasting and Social Change 168: 120763.

Matarazzo, M., L. Penco, G. Profumo, and R. Quaglia. 2021. Digital transformation and customer value creation in Made in Italy SMEs: A dynamic capabilities perspective. Journal of Business Research 123: 642–656.

Olson, E.M., K.M. Olson, A.J. Czaplewski, and T.M. Key. 2021. Business strategy and the management of digital marketing. Business Horizons 64 (2): 285–293.

Petrescu, M., and A.S. Krishen. 2020. The importance of high-quality data and analytics during the pandemic. Journal of Marketing Analytics 8 (2): 43–44.

Petrescu, M., and A.S. Krishen. 2021. Focusing on the quality and performance implications of marketing analytics. Journal of Marketing Analytics 9 (3): 155–156.

Petrescu, M., and A.S. Krishen. 2022. Co-creating transformative value in marketing analytics. Journal of Marketing Analytics 10 (1): 1–2.

Ritter, T., and C.L. Pedersen. 2020. Digitization capability and the digitalization of business models in business-to-business firms: Past, present, and future. Industrial Marketing Management 86: 180–190.

Sestino, A., M.I. Prete, L. Piper, and G. Guido. 2020. Internet of Things and Big Data as enablers for business digitalization strategies. Technovation 98: 102173.

Steinhoff, L., and R.W. Palmatier. 2021. Commentary: Opportunities and challenges of technology in relationship marketing. Australasian Marketing Journal 29 (2): 111–117.

Yu, L., Y. Zhao, L. Tang, and Z. Yang. 2019. Online big data-driven oil consumption forecasting with Google trends. International Journal of Forecasting 35 (1): 213–223.

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UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia

Tat-Huei Cham

School of Business and Economics, Universiti Putra Malaysia (UPM), Serdang, Selangor, Malaysia

Jun-Hwa Cheah

NUST Business School, National University of Sciences and Technology, Islamabad, Pakistan

Mumtaz Ali Memon

School of Management, Harbin University of Commerce, Harbin, China

Kim-Shyan Fam

Széchenyi István University, Győr, Hungary

Józsa László

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Cham, TH., Cheah, JH., Memon, M.A. et al. Digitalization and its impact on contemporary marketing strategies and practices. J Market Anal 10 , 103–105 (2022). https://doi.org/10.1057/s41270-022-00167-6

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Accepted : 11 April 2022

Published : 20 April 2022

Issue Date : June 2022

DOI : https://doi.org/10.1057/s41270-022-00167-6

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Fatal Traffic Risks With a Total Solar Eclipse in the US

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A total solar eclipse occurs when the moon temporarily obscures the sun and casts a dark shadow across the earth. This astronomical spectacle has been described for more than 3 millennia and can be predicted with high precision. Eclipse-related solar retinopathy (vision loss from staring at the sun) is an established medical complication; however, other medical outcomes have received little attention. 1

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Redelmeier DA , Staples JA. Fatal Traffic Risks With a Total Solar Eclipse in the US. JAMA Intern Med. Published online March 25, 2024. doi:10.1001/jamainternmed.2023.5234

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Computer Science > Machine Learning

Title: megalodon: efficient llm pretraining and inference with unlimited context length.

Abstract: The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and state space models exist, they empirically underperform Transformers in pretraining efficiency and downstream task accuracy. We introduce Megalodon, a neural architecture for efficient sequence modeling with unlimited context length. Megalodon inherits the architecture of Mega (exponential moving average with gated attention), and further introduces multiple technical components to improve its capability and stability, including complex exponential moving average (CEMA), timestep normalization layer, normalized attention mechanism and pre-norm with two-hop residual configuration. In a controlled head-to-head comparison with Llama2, Megalodon achieves better efficiency than Transformer in the scale of 7 billion parameters and 2 trillion training tokens. Megalodon reaches a training loss of 1.70, landing mid-way between Llama2-7B (1.75) and 13B (1.67). Code: this https URL

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