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Research topic

I want to find novel research and techniques that marketers are using to optimize email and push notification strategies, focusing on individual aspects of segmentation, personalization, and send frequency, to maximize specific downstream conversions like purchases or sign-ups.

Report

Novel techniques for optimizing email and push notification strategies focusing on segmentation, personalization, and send frequency have shown to significantly maximize downstream conversions like purchases or sign-ups, particularly using AI and machine learning methods [1, 3, 4, 6].

Details:

Segmentation:
- Behavioral Segmentation and Predictive Analytics: Techniques such as RFM analysis and weighted k-means clustering have been used for segmentation, showing improved targeting and personalization capabilities for online retailers [13]. Predictive analytics using machine learning further helps in segmenting users by predicting future behaviors like purchase likelihood [2].
Personalization:
- AI-Driven Personalization:
  - Dynamic Content Insertion: Real-time content adjustment based on user data has been demonstrated as effective for increasing engagement and purchase rates [6].
  - Deep Personalization: Data-driven personalization based on historical interactions and user value segmentation can greatly boost repeat customer revenue [7].
  - Hyper-Personalization: AI and machine learning techniques facilitate delivering personalized content based on real-time data and user context, leading to higher consumer engagement and conversion rates [6, 14, 19].
Send Frequency:
- Optimal Send Time Prediction: Models that dynamically predict the best send time for emails according to individual user behavior patterns have been shown to enhance engagement and repurchase rates [1, 3, 9].
- Frequency Capping: Managing email frequency to optimize the number of contacts and avoid user fatigue is critical for maintaining long-term profitability. Studies indicate a non-linear effect of send frequency on conversions [4, 11].
Downstream Conversion Optimization:
- Bayesian Inference and Decision Support Systems: Techniques utilizing Bayesian methods and machine learning for assessing email campaign effectiveness have shown improvements in purchase rates and overall marketing strategy efficiency [5, 16].
- Conversion Rate Optimization (CRO): AI-based CRO systems like Ascend by Evolv have demonstrated substantial improvements in conversion rates by optimizing web interface design, which can be translated into email marketing practices [20].
- Multi-Touch Attribution Models: Gauging the effectiveness of email and push notifications as part of a broader marketing mix can help allocate resources better and refine strategies [14].

Conclusion:

The integration of AI and machine learning into the domains of segmentation, personalization, and send frequency optimization in email and push notification marketing has demonstrated notable improvements in downstream conversions like purchases and sign-ups. Key findings from various studies underscore the importance of individualized approaches to achieve higher engagement and profitability [1, 3, 4, 6]. These methods show promise in enhancing the precision and impact of digital marketing campaigns.

References

Last 5 years

Last 2 years

> 1 citation per year

> 5 citations per year

Topic Match

Cit./Year

Year

Paper

Paper Relevance Summary

99.9%

1.3

2020

[1] Predicting the Optimal Date and Time to Send Personalized Marketing Messages to Repeat Buyers Alexandros Deligiannis, ..., and Dimitrios Kourtesis International Journal of Advanced Computer Science and Applications 2020 - 6 citations - Show abstract - Cite - PDF 99.9% topic match

Provides a model for optimizing send time for personalized marketing messages. Uses regression models with transactional and messaging data to predict optimal send times for repeat buyers in CPG. Focuses on send frequency and personalization, improving repurchase rates and engagement, which aligns with optimizing downstream conversions.

Provides a model for optimizing send time for personalized marketing messages. Uses regression models with transactional and messaging data to predict optimal send times for repeat buyers in CPG. Focuses on send frequency and personalization, improving repurchase rates and engagement, which aligns with optimizing downstream conversions.

99.6%

1.4

2019

[2] Data-driven marketing: how machine learning will improve decision-making for marketers Redouan Abakouy, ..., and Lotfi Elaachak Proceedings of the 4th International Conference on Smart City Applications 2019 - 7 citations - Show abstract - Cite 99.6% topic match

Provides a comparative study on machine learning methods for email marketing personalization. Focuses on predicting click and conversion rates based on email subject and sender lines. Highlights the relevance of subject-line optimization to improve downstream conversions, crucial for personalization strategies.

Provides a comparative study on machine learning methods for email marketing personalization. Focuses on predicting click and conversion rates based on email subject and sender lines. Highlights the relevance of subject-line optimization to improve downstream conversions, crucial for personalization strategies.

99.6%

0.0

2022

[3] Dynamic Best Send Time Prediction for Marketing Email Campaigns A. Pal, ..., and Pranjal Yadav 2022 International Joint Conference on Information and Communication Engineering (JCICE) 2022 - 0 citations - Show abstract - Cite Abstract: Email has been among the most popular mediums for sending out digital marketing campaigns since it provides an ease of engaging and keeping track of customers and prospects at an individual level. Being a cost effective medium, most marketing teams send out campaigns in bulk irrespective of the receiver's preference. This leads to a lower level of engagement and at times customers opting out from email subscriptions. Hence personalization that understands the receiver preferences is key to designing successful campaigns. One of the personalization is that of identification of the best time to send an email, where the objective is to identify the best time for email delivery where the likelihood of the email being visible and opened is high. Most of the existing solutions rely extensively on large receiver level behavioral information, leading to a cold start in most likely scenarios. Also, these solutions being static makes the prediction go stale over time. This work takes inspiration from studies which suggest that multiple attributes at senders and receivers level lead to differences in open behavior and propose a two-stage solution for the same. At first contacts/receivers are assigned to segments with similar attributes using a proprietary Decision Tree Algorithm, and further use Reinforcement Learning when predicting the best send time using the segment and receiver level behavioral information. Reinforcement Learning based prediction makes it possible for the predictions to be dynamic, whereupon receiving the feedback the predictions can be adjusted to a time that is closer to true behavior at each receiver level. The main novelty of the approach is that of being able to provide predictions for contacts with very little to no historical events, and a feedback mechanism to further improve upon the predictions in a live system. The system by design is simple and hence making it much more interpretable when trying to make sense of the predictions being made. 99.6% topic match

Provides a method for predicting the best send time for email campaigns. Utilizes personalization to align email send times with receiver preferences, enhancing engagement. Focuses on improving user engagement and reducing opt-outs, with implications for downstream conversions.

Provides a method for predicting the best send time for email campaigns. Utilizes personalization to align email send times with receiver preferences, enhancing engagement. Focuses on improving user engagement and reducing opt-outs, with implications for downstream conversions.

99.6%

11.8

2017

[4] Dynamically Managing a Profitable Email Marketing Program Xi Zhang, ..., and Koray Cosguner Journal of Marketing Research 2017 - 81 citations - Show abstract - Cite 99.6% topic match

Shows optimal email send frequency effects on profitability. Uses data and modeling to determine impact of email frequency on purchases. Finds non-linear email effect, suggesting precise frequency improves both short- and long-term profitability.

Shows optimal email send frequency effects on profitability. Uses data and modeling to determine impact of email frequency on purchases. Finds non-linear email effect, suggesting precise frequency improves both short- and long-term profitability.

99.4%

2.9

2018

[5] Bayesian Inference for Assessing Effects of Email Marketing Campaigns Jiexing Wu, ..., and Jun S. Liu Journal of Business & Economic Statistics 2018 - 19 citations - Show abstract - Cite - PDF 99.4% topic match

Proposes a Bayesian method for assessing email marketing effectiveness. Evaluates how email offers and customer characteristics influence purchase rates short- and long-term. Highlights strong interactions between email offers and inactive customers, improving purchase rates.

Proposes a Bayesian method for assessing email marketing effectiveness. Evaluates how email offers and customer characteristics influence purchase rates short- and long-term. Highlights strong interactions between email offers and inactive customers, improving purchase rates.

99.3%

0.4

2022

[6] Hyper-Personalization Darshana Desai Advances in Marketing, Customer Relationship Management, and E-Services 2022 - 1 citations - Show abstract - Cite 99.3% topic match

Provides hyper-personalization strategies using ML and AI. Explores real-time analytics for segmentation, targeting, and positioning throughout the customer journey. Focuses on contextual needs and delivering timely, relevant information to individual customers, potentially impacting conversions.

Provides hyper-personalization strategies using ML and AI. Explores real-time analytics for segmentation, targeting, and positioning throughout the customer journey. Focuses on contextual needs and delivering timely, relevant information to individual customers, potentially impacting conversions.

96.4%

0.0

2020

[7] Deep Personalization For Better Human Connect And Optimization By Using Non-Conventional Mechanisms In The Modern Digital Systems Raghav Sehgal International Journal of Scientific and Research Publications (IJSRP) 2020 - 0 citations - Show abstract - Cite 96.4% topic match

Provides insight on advanced personalization and segmentation techniques for emails. Emphasizes dynamic content based on behavioral data, targeting, and user value segmentation to boost repeat customer revenue. While it touches on downstream conversions, it also includes broader marketing strategies like dynamic social media ads, which may be tangential.

Provides insight on advanced personalization and segmentation techniques for emails. Emphasizes dynamic content based on behavioral data, targeting, and user value segmentation to boost repeat customer revenue. While it touches on downstream conversions, it also includes broader marketing strategies like dynamic social media ads, which may be tangential.

95.4%

0.8

2018

[8] Improving Email Marketing Campaign Success Rate Using Personalization Gyanendra Singh, ..., and Sonika Shriwastav Advances in Analytics and Applications 2018 - 5 citations - Show abstract - Cite 95.4% topic match

94.1%

0.0

2021

[9] When to Message: Investigating User Response Prediction with Machine Learning for Advertisement Emails Christian Bitter, ..., and Philipp Meisen 2021 4th International Conference on Artificial Intelligence for Industries (AI4I) 2021 - 0 citations - Show abstract - Cite 94.1% topic match

Investigates machine learning for predicting email response behavior. Trains models to predict user interaction with emails, focusing on optimal send times. Relevant for send frequency optimization, but does not focus on segmentation or personalization.

Investigates machine learning for predicting email response behavior. Trains models to predict user interaction with emails, focusing on optimal send times. Relevant for send frequency optimization, but does not focus on segmentation or personalization.

92.2%

0.4

2015

[10] Clairvoyant-push: A real-time news personalized push notifier using topic modeling and social scoring for enhanced reader engagement Biying Tan, ..., and Giuseppe Manai 2015 IEEE International Conference on Big Data (Big Data) 2015 - 4 citations - Show abstract - Cite 92.2% topic match

Provides a novel Personalized Push Notification system using user segmentation and social scoring. Uses Latent Dirichlet Allocation (LDA) for topic modeling and A/B testing for validation. Shows a significant increase in engagement (opening rate), but lacks specific focus on downstream conversions like purchases or sign-ups.

Provides a novel Personalized Push Notification system using user segmentation and social scoring. Uses Latent Dirichlet Allocation (LDA) for topic modeling and A/B testing for validation. Shows a significant increase in engagement (opening rate), but lacks specific focus on downstream conversions like purchases or sign-ups.

87.2%

2.4

2018

[11] Notification Volume Control and Optimization System at Pinterest Bo Zhao, ..., and J. Egan Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 - 15 citations - Show abstract - Cite 87.2% topic match

Proposes a machine learning approach to optimize notification frequency. Focuses on optimizing notification volume for individual users to enhance long-term engagement. Improves click-through rates (CTR) and site engagement but lacks explicit mention of downstream conversions like purchases or sign-ups.

Proposes a machine learning approach to optimize notification frequency. Focuses on optimizing notification volume for individual users to enhance long-term engagement. Improves click-through rates (CTR) and site engagement but lacks explicit mention of downstream conversions like purchases or sign-ups.

86.3%

8.3

2016

[12] Email marketing in the era of the empowered consumer Mari Hartemo Journal of Research in Interactive Marketing 2016 - 68 citations - Show abstract - Cite 86.3% topic match

States the need for updated email marketing strategies. Discusses empowering consumers through personalized, relevant emails based on permissions. Lacks specificity on novel techniques or direct impact on downstream conversions.

States the need for updated email marketing strategies. Discusses empowering consumers through personalized, relevant emails based on permissions. Lacks specificity on novel techniques or direct impact on downstream conversions.

83.0%

0.7

2023

[13] Customer analytics for online retailers using weighted k-means and RFM analysis Ahmed Mohamed Ahmed Serwah, ..., and Alhamzah Alnoor Data Analytics and Applied Mathematics (DAAM) 2023 - 1 citations - Show abstract - Cite - PDF 83.0% topic match

Provides a novel segmentation approach using RFM analysis and weighted k-means. Demonstrates improved targeting for online retailers with a higher silhouette score of 0.40. Relevant to segmentation and personalization, but does not explicitly address email/push notifications or conversion metrics.

Provides a novel segmentation approach using RFM analysis and weighted k-means. Demonstrates improved targeting for online retailers with a higher silhouette score of 0.40. Relevant to segmentation and personalization, but does not explicitly address email/push notifications or conversion metrics.

81.3%

9.0

2023

[14] Mobile Marketing: Exploring the Efficacy of User-Centric Strategies for Enhanced Consumer Engagement and Conversion Rates Mohammad Khalaf Daoud, ..., and J. Al-Gasawneh International Journal of Membrane Science and Technology 2023 - 12 citations - Show abstract - Cite - PDF 81.3% topic match

Shows effectiveness of user-centric strategies in mobile marketing. Emphasizes personalized content, push notifications, and tailored user experiences to boost engagement and conversion rates. Findings affirm the role of personalization in driving consumer behavior but lack specifics on segmentation and send frequency for email or push strategies aimed at conversions.

Shows effectiveness of user-centric strategies in mobile marketing. Emphasizes personalized content, push notifications, and tailored user experiences to boost engagement and conversion rates. Findings affirm the role of personalization in driving consumer behavior but lack specifics on segmentation and send frequency for email or push strategies aimed at conversions.

77.3%

2.4

2021

[15] Conversions on the rise – modernizing e-mail marketing practices by utilizing volunteered data Mari Hartemo Journal of Research in Interactive Marketing 2021 - 7 citations - Show abstract - Cite - PDF 77.3% topic match

Shows how utilizing volunteered data can enhance email marketing. Emphasizes consumer empowerment and its role in increasing response rates. Focuses on consumer advocacy rather than novel segmentation or personalization techniques specific to conversions.

Shows how utilizing volunteered data can enhance email marketing. Emphasizes consumer empowerment and its role in increasing response rates. Focuses on consumer advocacy rather than novel segmentation or personalization techniques specific to conversions.

70.1%

5.8

2022

[16] Email Marketing as a Tool for Strategic Persuasion Jacquelyn S. Thomas, ..., and D. Iacobucci Journal of Interactive Marketing 2022 - 14 citations - Show abstract - Cite 70.1% topic match

Shows how different types of emails impact consumer response. Analyzes effectiveness on open rates, spending, and shopping cart abandonment. Lacks specific focus on segmentation, personalization, or AI-driven strategies.

Shows how different types of emails impact consumer response. Analyzes effectiveness on open rates, spending, and shopping cart abandonment. Lacks specific focus on segmentation, personalization, or AI-driven strategies.

65.0%

0.0

2019

[17] Effective email marketing: an empirical study of the impact of personalized communication on customer engagement and purchase decisions Hanna Kiselova Journal Not Provided 2019 - 0 citations - Show abstract - Cite 65.0% topic match

59.9%

0.9

2019

[18] Decision Tree Analysis to Improve e-mail Marketing Campaigns Hamzah Qabbaah, ..., and T. Munjishvili Journal Not Provided 2019 - 5 citations - Show abstract - Cite 59.9% topic match

Provides novel techniques for improving response rates in email campaigns using decision tree analysis. Predicts customer loyalty and segments customers by analyzing open and click-through rates using CHAID, CART, and QUIST. Focus is on optimizing response rates (open/click-through) rather than downstream conversions like purchases or sign-ups.

Provides novel techniques for improving response rates in email campaigns using decision tree analysis. Predicts customer loyalty and segments customers by analyzing open and click-through rates using CHAID, CART, and QUIST. Focus is on optimizing response rates (open/click-through) rather than downstream conversions like purchases or sign-ups.

55.1%

28.6

2024

[19] THE ROLE OF AI IN MARKETING PERSONALIZATION: A THEORETICAL EXPLORATION OF CONSUMER ENGAGEMENT STRATEGIES Sodiq Odetunde Babatunde, ..., and Damilola Oluwaseun Ogundipe International Journal of Management & Entrepreneurship Research 2024 - 16 citations - Show abstract - Cite - PDF Abstract: This paper explores the transformative potential of Artificial Intelligence (AI) in personalizing marketing strategies. It delves into the theoretical underpinnings of consumer engagement sand investigates how AI can be leveraged to develop targeted and relevant marketing experiences. AI can personalize messages based on consumer behavior and demographics, influencing the processing route and maximizing engagement. This theory explores the use of game mechanics to motivate and engage users. AI can personalize gamified marketing experiences, tailoring rewards and challenges to individual consumer preferences, driving deeper engagement. Algorithms can analyze vast amounts of customer data to predict individual preferences and behaviors. This allows for targeted advertising, product recommendations, and content that resonates with specific consumer segments. Natural Language Processing (NLP), AI-powered NLP tools analyze customer reviews, social media conversations, and other forms of unstructured data. This allows brands to understand customer sentiment and personalize communication styles for optimal engagement AI-powered chatbots and virtual assistants can provide personalized customer support and product recommendations in real-time, fostering a more interactive and engaging brand experience. Potential Benefits and Considerations Personalized marketing messages and experiences cater to individual needs and preferences, leading to higher satisfaction and loyalty. By tailoring content and offerings to specific consumer segments, brands can establish a more relevant and relatable image. Improved Conversion Rates, Personalized marketing campaigns can be highly targeted and effective, leading to increased conversions and sales. Balancing personalization with data privacy concerns is crucial. Transparency and user control over data collection practices are essential. AI algorithms can perpetuate biases present in training data. Ensuring fairness and inclusivity in AI-powered marketing is paramount. AI is revolutionizing marketing personalization. By leveraging AI's analytical capabilities and understanding the theoretical aspects of consumer engagement, brands can develop targeted and relevant marketing strategies that foster deeper customer connections and drive business growth. Keywords: AI Personalization, Consumer Engagement, Marketing Strategy, Theoretical Exploration, Data Privacy, Algorithmic Bias. 55.1% topic match

Explores AI-driven personalized marketing. Discusses improved conversions through tailored content and offerings to consumer segments. Mention of personalization; lacks detail on email or push notifications specifically.

Explores AI-driven personalized marketing. Discusses improved conversions through tailored content and offerings to consumer segments. Mention of personalization; lacks detail on email or push notifications specifically.

49.4%

1.3

2020

[20] Ascend by Evolv: AI-Based Massively Multivariate Conversion Rate Optimization R. Miikkulainen, ..., and Aaron Shagrin AI Mag. 2020 - 6 citations - Show abstract - Cite - PDF 49.4% topic match

Describes AI-driven CRO for web interfaces. Details an evolutionary search method to optimize web design for conversions through real user feedback. Relevant as it uses advanced AI techniques for conversion optimization but focuses on web interfaces, not emails or push notifications.

Describes AI-driven CRO for web interfaces. Details an evolutionary search method to optimize web design for conversions through real user feedback. Relevant as it uses advanced AI techniques for conversion optimization but focuses on web interfaces, not emails or push notifications.

37.8%

19.3

2016

[21] Personalization in Email Marketing: The Role of Non-Informative Advertising Content Navdeep S. Sahni, ..., and Pradeep Chintagunta Stanford Graduate School of Business Research Paper Series 2016 - 154 citations - Show abstract - Cite 37.8% topic match

35.6%

2.3

2020

[22] Modelling e-mail marketing effectiveness – An approach based on the theory of hierarchy-of-effects Ángel José Lorente Páramo, ..., and Julián Chaparro Peláez https://doi.org/10.5295/cdg.191094ah 2020 - 9 citations - Show abstract - Cite - PDF 35.6% topic match

34.2%

0.0

2022

[23] Navigating the Landscape of Hyper Personalization in Financial Services: Challenges, Mitigations and Strategies Ashok Reddy Annaram Journal of Economics & Management Research 2022 - 0 citations - Show abstract - Cite - PDF 34.2% topic match

Highlights hyper personalization strategies in financial services. Employs advanced data analytics for precise, individual customer messaging. Lacks direct focus on email/push notifications and downstream conversions like purchases or sign-ups.

Highlights hyper personalization strategies in financial services. Employs advanced data analytics for precise, individual customer messaging. Lacks direct focus on email/push notifications and downstream conversions like purchases or sign-ups.

30.1%

0.0

2024

[24] Data-driven strategies for enhancing user engagement in digital platforms Chioma Susan Nwaimo, ..., and Mayokun Daniel Adegbola International Journal of Management & Entrepreneurship Research 2024 - 0 citations - Show abstract - Cite - PDF 30.1% topic match

Explores data-driven strategies for enhancing user engagement. Details techniques like A/B testing, gamification, and predictive analytics for personalizing user experiences and optimizing interactions. Lacks focus on segmentation, send frequency, or downstream conversions specific to email and push notifications.

Explores data-driven strategies for enhancing user engagement. Details techniques like A/B testing, gamification, and predictive analytics for personalizing user experiences and optimizing interactions. Lacks focus on segmentation, send frequency, or downstream conversions specific to email and push notifications.

29.4%

0.0

2023

[25] PERSONALIZATION AT SCALE: DATA-DRIVEN APPROACHES FOR HYPER-TARGETED DIGITAL MARKETING - A CASE STUDY OF AMAZON Sankul Seth Journal Not Provided 2023 - 0 citations - Show abstract - Cite 29.4% topic match

Examines scaled personalization in hyper-targeted digital marketing. Details customer segmentation, predictive analytics, and AI-powered personalization for enhanced engagement. Primarily focuses on customer engagement and loyalty, suggesting relevance but lacking explicit conversion optimization.

Examines scaled personalization in hyper-targeted digital marketing. Details customer segmentation, predictive analytics, and AI-powered personalization for enhanced engagement. Primarily focuses on customer engagement and loyalty, suggesting relevance but lacking explicit conversion optimization.

25.0%

24.8

2003

[26] E-Customization Asim Ansari and Carl F. Mela Journal of Marketing Research 2003 - 533 citations - Show abstract - Cite 25.0% topic match

Provides a statistical and optimization approach for e-mail customization. Uses clickstream data to tailor email design and content, increasing click-through rates by 62%. Focuses on customization for click-through rates, not directly on downstream conversions like purchases or sign-ups.

Provides a statistical and optimization approach for e-mail customization. Uses clickstream data to tailor email design and content, increasing click-through rates by 62%. Focuses on customization for click-through rates, not directly on downstream conversions like purchases or sign-ups.

20.5%

1.2

2023

[27] Estimating promotion effects in email marketing using a large-scale cross-classified Bayesian joint model for nested imbalanced data S. Mukhopadhyay, ..., and Gourab Mukherjee The Annals of Applied Statistics 2023 - 2 citations - Show abstract - Cite 20.5% topic match

17.3%

2.1

2023

[28] Demographic and Psychographic Customer Segmentation for Ecommerce Applications Ch. Sai Vamsee, ..., and C. Bharathi 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) 2023 - 3 citations - Show abstract - Cite 17.3% topic match

16.2%

None

[29] HOW MARKETERS CAN INCREASE THE RELEVANCE OF EMAIL MARKETING CAMPAIGNS: DATA ANALYSIS WITH MACHINE LEARNING METHODS Redouan Abakouy and E. En-naimi Journal Not Provided None - 1 citations - Show abstract - Cite 16.2% topic match

Utilizes machine learning for email campaign relevance prediction. Analyzes main factors to improve email open rates using classification algorithms. Focuses on open rates rather than downstream conversions like purchases or sign-ups.

Utilizes machine learning for email campaign relevance prediction. Analyzes main factors to improve email open rates using classification algorithms. Focuses on open rates rather than downstream conversions like purchases or sign-ups.

15.7%

0.6

2017

[30] Exploring the Inherent Growth of e-Tailing via e-Personalization and Technological Innovations Alan D. Smith Int. J. Innov. Digit. Econ. 2017 - 5 citations - Show abstract - Cite 15.7% topic match

Provides insights into e-personalization and technological innovations in e-tailing. Shows how big data mining and analytics enhance personalized communications and customer journey optimization. Relevant to segmentation and personalization but lacks specifics on email/push notifications and their impact on downstream conversions.

Provides insights into e-personalization and technological innovations in e-tailing. Shows how big data mining and analytics enhance personalized communications and customer journey optimization. Relevant to segmentation and personalization but lacks specifics on email/push notifications and their impact on downstream conversions.

12.8%

8.0

2004

[31] The impact of e‐mail marketing on brand loyalty Marko Merisavo and M. Raulas Journal of Product & Brand Management 2004 - 159 citations - Show abstract - Cite 12.8% topic match

12.2%

0.0

2023

[32] RetroMailer- An Email Marketing Campaign using Amazon SES Sridevi Saralaya, ..., and Merrill Fernandes 2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) 2023 - 0 citations - Show abstract - Cite 12.2% topic match

Develops an inexpensive email marketing service using Amazon SES. Allows businesses to send and track bulk emails reliably and cost-effectively. Does not specifically address segmentation, personalization, or optimization for downstream conversions.

Develops an inexpensive email marketing service using Amazon SES. Allows businesses to send and track bulk emails reliably and cost-effectively. Does not specifically address segmentation, personalization, or optimization for downstream conversions.

10.9%

0.8

2016

[33] Theoretical Approaches on Successful Email Marketing Campaigns Camelia Budac Ovidius University Annals: Economic Sciences Series 2016 - 7 citations - Show abstract - Cite 10.9% topic match

10.5%

0.3

2010

[34] Applying Instant Business Intelligence in Marketing Campaign Automation Chan Gaik Yee, ..., and S. Hasan 2010 Second International Conference on Computer Research and Development 2010 - 5 citations - Show abstract - Cite 10.5% topic match

10.1%

0.0

2023

[35] Enhancing Digital User Experiences through Personalized Interfaces and Predictive Modelling Arun Chandramouli Journal of Artificial Intelligence & Cloud Computing 2023 - 0 citations - Show abstract - Cite 10.1% topic match

9.4%

2.5

2014

[36] Learning to predict subject-line opens for large-scale email marketing Raju Balakrishnan and R. Parekh 2014 IEEE International Conference on Big Data (Big Data) 2014 - 25 citations - Show abstract - Cite - PDF 9.4% topic match

9.1%

9.0

2019

[37] A Machine Learning Based Method for Customer Behavior Prediction Jing Li, ..., and Xin Zhu Tehnicki vjesnik - Technical Gazette 2019 - 44 citations - Show abstract - Cite - PDF 9.1% topic match

8.7%

0.0

2023

[38] Smart Notifications – An ML-based Framework to Boost User Engagement Victor M. Magalhães Pinto, ..., and Dimas S. Lima Proceedings of the 29th Brazilian Symposium on Multimedia and the Web 2023 - 0 citations - Show abstract - Cite 8.7% topic match

8.5%

0.2

2019

[39] Data-driven Direct Marketing via Approximate Dynamic Programming J. Slik and S. Bhulai Journal Not Provided 2019 - 1 citations - Show abstract - Cite 8.5% topic match

8.1%

1.9

2023

[40] A Novel Approach for Enhancing Customer Retention Using Machine Learning Techniques in Email Marketing Application Dharmveer Yadav, ..., and Gunjan Chhabra 2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS) 2023 - 3 citations - Show abstract - Cite 8.1% topic match

8.0%

0.0

2018

[41] Direct Marketing Optimization Optimizing the email marketing strategy of an airline using data modeling : a literature study Mathijs Koopman Journal Not Provided 2018 - 0 citations - Show abstract - Cite 8.0% topic match

7.4%

0.6

2020

[42] Building a Cloud-based Regression Model to Predict Click-through Rate in Business Messaging Campaigns Alexandros Deligiannis, ..., and Dimitrios Kourtesis International Journal of Modeling and Optimization 2020 - 3 citations - Show abstract - Cite 7.4% topic match

7.3%

0.7

2020

[43] Data‐Driven Approaches to Targeting Promotion E‐mails: The Case of Delayed Incentives Bharadwaj Kadiyala, ..., and A. S. Şimşek Production and Operations Management 2020 - 3 citations - Show abstract - Cite 7.3% topic match

7.2%

3.0

2016

[44] Simple Dynamic Emission Strategies for Microblog Filtering Luchen Tan, ..., and Jimmy J. Lin Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval 2016 - 25 citations - Show abstract - Cite 7.2% topic match

7.0%

3.5

2012

[45] Uplift Modeling in Direct Marketing P. Rzepakowski and S. Jaroszewicz Journal of Telecommunications and Information Technology 2012 - 43 citations - Show abstract - Cite - PDF 7.0% topic match

6.9%

0.0

2023

[46] Ngram-LSTM Open Rate Prediction Model (NLORP) and Error_accuracy@C metric: Simple effective, and easy to implement approach to predict open rates for marketing email Shubhamkar Joshi and Indra Banerjee ArXiv 2023 - 0 citations - Show abstract - Cite - PDF 6.9% topic match

6.6%

0.0

2024

[47] Study on Personalized Positioning and Prediction Model of Consumer Behavior in Digital Marketing Zhongjie Wang Academic Journal of Business & Management 2024 - 0 citations - Show abstract - Cite - PDF 6.6% topic match

6.6%

0.0

2016

[48] Web based Email Marketing based Recommendation Dhanashree Dombe, ..., and Jyoti Kshirsagar Journal Not Provided 2016 - 0 citations - Show abstract - Cite 6.6% topic match

6.1%

0.7

2017

[49] Personalization in mobile commerce Aalaa Albadarneh and A. Qusef 2017 8th International Conference on Information Technology (ICIT) 2017 - 5 citations - Show abstract - Cite 6.1% topic match

5.9%

0.0

2021

[50] Hyper personalization in e-commerce sector GAGANA. V. Murali and Anila Jose Journal Not Provided 2021 - 0 citations - Show abstract - Cite 5.9% topic match

5.8%

0.4

2013

[51] The analysis of push technology based on iphone operating system Wenlan Guo and Hong Liu Proceedings of 2013 2nd International Conference on Measurement, Information and Control 2013 - 5 citations - Show abstract - Cite 5.8% topic match

5.7%

0.6

2014

[52] A Study to Improve the Response in Email Campaigning by Comparing Data Mining Segmentation Approaches in Aditi Technologies P. Theerthaana and S. Sharad International Journal of Management and Business Research 2014 - 6 citations - Show abstract - Cite 5.7% topic match

5.5%

0.0

2022

[53] Determination of Channel Effects on User Conversion Rate in Online Advertising by Deep Learning O˘guz Kahraman, ..., and Mühendisli˘gi Bölümü 2022 30th Signal Processing and Communications Applications Conference (SIU) 2022 - 0 citations - Show abstract - Cite 5.5% topic match

5.2%

0.0

2016

[54] Web based Email Marketing based Recommendation D. Milosevic and Marjan Milosevic Journal Not Provided 2016 - 0 citations - Show abstract - Cite 5.2% topic match

4.9%

0.0

2018

[55] PROMOTING E-MAILS ON THE WEB Dr. Ravi Kumar Goriparthi and Dr. KarunakerChiluka Journal Not Provided 2018 - 0 citations - Show abstract - Cite 4.9% topic match

4.7%

0.0

2014

[56] Servicio para la mejora de resultados en campañas de email marketing J. Blanco Journal Not Provided 2014 - 0 citations - Show abstract - Cite 4.7% topic match

4.6%

0.0

2015

[57] Prediction of return in online shopping Ismail Bilgen and Ö. Saraç 2015 23nd Signal Processing and Communications Applications Conference (SIU) 2015 - 0 citations - Show abstract - Cite 4.6% topic match

4.4%

0.0

2023

[58] Identifying the Base Sales Contribution for MTA Model Sandnya Dalvie INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 2023 - 0 citations - Show abstract - Cite - PDF 4.4% topic match

4.3%

1.7

2023

[59] Data analytics in digital marketing for tracking the effectiveness of campaigns and inform strategy Ahmad Samed Al Adwan, ..., and A. Khattak International Journal of Data and Network Science 2023 - 3 citations - Show abstract - Cite 4.3% topic match

4.1%

0.3

2017

[60] Analysis of Customer Behavior in Online Retail Marketplace Using Hadoop G. Shrivastava and Shailesh Shrivastava https://doi.org/10.21276/IJIRCST.2017.5.5.3 2017 - 2 citations - Show abstract - Cite - PDF 4.1% topic match

4.1%

0.0

2003

[61] Behavioral Segmentation for e-Tail Personalization D. MacLachlan Journal Not Provided 2003 - 1 citations - Show abstract - Cite Abstract: A multichannel retailer desires to develop a personalization strategy for customers who agree to receive e-mail communications containing imbedded Internet purchase appeals. Using customers' transactions history with the firm, four behavioral segments emerged from factor and cluster analyses. The segments were also distinguishable on the basis of attitudes toward shopping, determined from an attitude survey. A CART analysis provided an algorithm for classifying customers into the behavioral segments, based simply on their purchase history information. The segments were validated in a replication, which involved a survey with a new sample. Finally, different appeals were used in personalized messages tailored to two of the segments in an I-media experiment. The appeals resulted in higher sales response for one of the segments. 3 Although hyped as the eCommerce " Holy Grail, " true personalization at the individual customer level remains far in the future for most retailers. Certainly a degree of 1-to-1 communications between retailers and customers exists today, but customers rather than retailers typically initiate the exchange, and the actual personalization involved is fairly minimal (Peppers, Rogers, and Dorf, 1999). So-called " personalized " retailer communications are by and large reactive, rather than proactive or promotional. Reaching each customer with individualized promotional messages is largely infeasible for most retailers, despite the new information economics driven by the digital revolution (Peppard, 2000). Indeed, some have questioned whether personalization is even appropriate (Nunes and Kambil, 2001). Those customers who agree to receive persuasive communications through I-media, however, represent a potential source of new revenues for retailers having the desire and technology to address them. What can a retailer do to achieve a degree of personalization of promotional messages, without communicating individually with all customers? The answer would seem to lie in behavioral segmentation; that is, grouping customers with similar purchase behavior into segments for the purpose of targeting them with persuasive messages that are personalized at the segment level. Retailers potentially have in their databases vast amounts of cumulative information on the transactions of many of their customers. Not only can those databases contain transaction monetary value of customer transactions), but they may also record what was purchased on each occasion, including such data as styles, colors, and other item-specific information, 4 whether the item was purchased on promotion, method by which the purchase was made (credit card, check, cash), channel used (store, catalog, internet), and so forth. These are the " behaviors … 4.1% topic match

3.7%

1.9

2018

[62] Near Real-time Optimization of Activity-based Notifications Yan Gao, ..., and S. Chatterjee Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 - 12 citations - Show abstract - Cite 3.7% topic match

3.5%

0.2

2019

[63] Trends in mobile customer journeys: Are you ready for mobile customer decision-making? Elise Schurink, ..., and S. Vries Journal Not Provided 2019 - 1 citations - Show abstract - Cite 3.5% topic match

3.5%

1.3

2024

[64] In My Veins. William Moody JAMA 2024 - 1 citations - Show abstract - Cite 3.5% topic match

3.4%

0.6

2008

[65] Investigating the Impact of Mobile Marketing in the Current Indian Scenario and Proposing CUSTOMERIZATION as a Solution S. N. Tripathi Journal Not Provided 2008 - 10 citations - Show abstract - Cite Abstract: Key Words: consumer responsiveness, mobile marketing/advertising, purchase decision, customization, Customerization Innovation creates marketing opportunities and challenges. Mobile advertising, an area of mobile commerce, is a form of advertising that targets users of handheld wireless devices such as mobile phones and PDAs. It can reach the target customers anywhere anytime. In order to promote the selling of products or services, all the activities required to communicate with the customers are transferred through mobile devices. Combining with the customer's user profile and context situation, advertising companies can provide the target customers exactly the advertisement information they desire, not just " spam " them with irrelevant advertisements. Previous studies have investigated dimensions of consumer acceptance of mobile marketing/ advertising. In contrast the current study attempts to study consumer responsiveness to mobile marketing, in terms of its impact on purchase decision making. The primary objective being to gain an insight into the perception of mobile users, towards mobile marketing/advertising and their utility value in terms of impact on the purchase decision. Hence we are attempting to explore consumers' responsiveness to mobile marketing, taking into cognizance the impact of demographic factors. The study also aims to concretize some features enhancing the acceptability/ utility of mobile marketing/advertising and suggests an appropriate strategic initiative for the same. The major findings reveal that the perception of consumers towards mobile marketing can be broadly categorized into three factors: 1) Lack of contextualization and personalization of mobile ads 2) Disruptive nature of mobile ads 3) Perceived usefulness of mobile ads. Further results indicate that mobile marketing/ advertising (in its current format) does not have a significant impact on the purchase/brand decision of consumers. Mobile marketing is relatively at a nascent stage in India. Distinct preferences were expressed by customers regarding the desirable content of such messages. Customers are looking for customization of mobile marketing messages as per their individual requirements, tastes and preferences. Hence the need of the hour appears to be Customerization. Customerization combines operationally driven mass customization with customized marketing in a way that the company is able to respond to individual customers by customizing its products, services, and messages on a one-to-one basis. Marketers could harness the complete potential of mobile advertising by deploying Intelligent Mobile Software Agents, which enable firms to completely customize mobile marketing messages to individual customer preferences. Artificial intelligence is harnessed, which enables software agents to learn, optimize, and individualize information dissemination to … 3.4% topic match

3.3%

0.0

2017

[66] The effects of personalized email communication within loyalty programs for businesses without possibilities for e-commerce Alexis Tubulekas Journal Not Provided 2017 - 0 citations - Show abstract - Cite 3.3% topic match

3.2%

0.0

2015

[67] Adaptive Modeling for Real Time Analytics: The Case of "Big Data" in Mobile Advertising D. Kridel, ..., and David G. Castillo 2015 48th Hawaii International Conference on System Sciences 2015 - 0 citations - Show abstract - Cite 3.2% topic match

2.9%

1.5

2015

[68] Designing Email Marketing Campaigns - A Data Mining Approach Based On Consumer Preferences Radu Mogos and C. Acatrinei https://doi.org/10.29302/oeconomica.2015.17.1.1 2015 - 15 citations - Show abstract - Cite 2.9% topic match

2.7%

2.1

2022

[69] PickMail: A Serious Game for Email Phishing Awareness Training G. Jayakrishnan, ..., and S. Lodha Proceedings 2022 Symposium on Usable Security 2022 - 6 citations - Show abstract - Cite 2.7% topic match

2.6%

3.3

2019

[70] Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting Tereza Semerádová and P. Weinlich IEEE Access 2019 - 19 citations - Show abstract - Cite - PDF 2.6% topic match

2.4%

0.0

2019

[71] Metrics based content layouting Balaji Vasan Srinivasan, ..., and Niyati Chhaya Companion Proceedings of the 24th International Conference on Intelligent User Interfaces 2019 - 0 citations - Show abstract - Cite 2.4% topic match

2.3%

0.3

2012

[72] Mobile Advertising Using Location Based Services Deema Adeeb Al Shoaibi and I. Rassan https://doi.org/10.1109/ICIOS.2012.15 2012 - 4 citations - Show abstract - Cite 2.3% topic match

2.3%

2.1

2019

[73] A State Transition Model for Mobile Notifications via Survival Analysis Yiping Yuan, ..., and Rómer Rosales Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining 2019 - 12 citations - Show abstract - Cite - PDF 2.3% topic match

2.0%

0.7

2019

[74] A 190 GHz VCO with Transformer-Based Push–Push Frequency Doubler in 40 nm CMOS Yibo Liu, ..., and B. Chi Circuits, Systems, and Signal Processing 2019 - 4 citations - Show abstract - Cite 2.0% topic match

2.0%

3.4

2013

[75] Automatic selection of social media responses to news Tadej Štajner, ..., and A. Jaimes Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining 2013 - 38 citations - Show abstract - Cite 2.0% topic match

1.7%

0.6

2013

[76] Push vs. Pull: An Energy Perspective Daniel Burgstahler, ..., and R. Steinmetz Journal Not Provided 2013 - 6 citations - Show abstract - Cite 1.7% topic match

1.7%

0.0

2011

[77] Social Networks , Personalized Advertising , and Privacy Controls Catherine Tucker Journal Not Provided 2011 - 0 citations - Show abstract - Cite 1.7% topic match

1.6%

32.2

2005

[78] Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective K. Tam and S. Y. Ho Inf. Syst. Res. 2005 - 616 citations - Show abstract - Cite 1.6% topic match

1.4%

0.3

2012

[79] EMAIL MARKETING: A PARADIGM SHIFT TO MARKETING Prof K. Venugopal, ..., and Dr. D. Vishnu Murthy Journal Not Provided 2012 - 4 citations - Show abstract - Cite 1.4% topic match

1.4%

0.0

2023

[80] Exploration of Effective Methodologies for Web Personalization Elena K. Slavkova Postmodernism Problems 2023 - 0 citations - Show abstract - Cite - PDF 1.4% topic match

1.3%

0.3

2009

[81] Email Users Churn Analysis Based on PMCLP and Decision Tree Ai-hua Li and Zefu Lin 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery 2009 - 4 citations - Show abstract - Cite 1.3% topic match

1.3%

13.0

2016

[82] Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage P. Gangi and M. Wasko J. Organ. End User Comput. 2016 - 111 citations - Show abstract - Cite 1.3% topic match

1.0%

0.0

2000

[83] How to use e-mail marketing to build customer relationships Delmar and J. Joseph Journal Not Provided 2000 - 0 citations - Show abstract - Cite 1.0% topic match

1.0%

1.1

2005

[84] Exploring Service Marketing Aspects of E-Personalization and Its Impact on Online Consumer Behavior Alan D. Smith Services Marketing Quarterly 2005 - 21 citations - Show abstract - Cite 1.0% topic match

1.0%

0.0

2016

[85] Viral marketing 2.0 L. Lakshmanan Proceedings of the 1st ACM SIGMOD Workshop on Network Data Analytics 2016 - 0 citations - Show abstract - Cite 1.0% topic match

1.0%

0.8

2012

[86] Mobile Advertising Using Location Based Services D. A. Al Shoaibi and I. Al Rassan 2012 IEEE First International Conference on Internet Operating Systems 2012 - 10 citations - Show abstract - Cite 1.0% topic match

0.8%

19.0

2006

[87] The Influence of Personalization in Affecting Consumer Attitudes toward Mobile Advertising in China D. Xu Journal of Computer Information Systems 2006 - 340 citations - Show abstract - Cite 0.8% topic match

0.8%

0.0

2020

[88] USER ADDICTION AS A DIGITAL MEDIA DESIGN STRATEGY O. Sytnyk PARADIGMATIC VIEW ON THE CONCEPT OF WORLD SCIENCE - VOLUME 1 2020 - 0 citations - Show abstract - Cite 0.8% topic match

0.7%

0.0

2014

[89] Commerce E - Marketing - Challenges and Opportunities G. N. Mills Journal Not Provided 2014 - 0 citations - Show abstract - Cite 0.7% topic match

0.7%

10.5

2011

[90] Managing Consumer Privacy Concerns in Personalization: A Strategic Analysis of Privacy Protection Dong-Joo Lee, ..., and Youngsok Bang MIS Q. 2011 - 141 citations - Show abstract - Cite 0.7% topic match

0.6%

0.0

2016

[91] THE ATTITUDES OF GAUTENG USERS TOWARDS SMS ADVERTISING Michael Humbani Journal Not Provided 2016 - 0 citations - Show abstract - Cite 0.6% topic match

0.5%

6.7

2024

[92] Neuro Computing-Based Models of Digital Marketing as a Business Strategy for Bangalore's Startup Founders T. Ilakkiya, ..., and P. Venkatesh 2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) 2024 - 4 citations - Show abstract - Cite 0.5% topic match

0.4%

0.5

2022

[93] Impact of Content, Context and Creator on User Engagement on Instagram Angel Manthanam -, ..., and Varsha Agarwal - International Journal For Multidisciplinary Research 2022 - 1 citations - Show abstract - Cite - PDF 0.4% topic match

0.3%

17.3

2013

[94] Addressing cold-start in app recommendation: latent user models constructed from twitter followers Jovian Lin, ..., and Tat-Seng Chua Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval 2013 - 194 citations - Show abstract - Cite 0.3% topic match

0.3%

1.5

2017

[95] Understanding and Modeling Success in Email Search Jin Young Kim, ..., and Fang Liu Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval 2017 - 11 citations - Show abstract - Cite 0.3% topic match

0.2%

1.1

2020

[96] MARKETING AND ADVERTISING STRATEGY IN ACHIEVING THE TOP OF MIND (SAMSUNG BRAND CASE STUDY) Andhita Vidya Putri and Eriyanto https://doi.org/10.14421/PJK.V13I2.1935 2020 - 4 citations - Show abstract - Cite 0.2% topic match

0.1%

0.0

2008

[97] INFORMATION CONGESTION Simon P. Anderson and André de Palma Journal Not Provided 2008 - 0 citations - Show abstract - Cite 0.1% topic match

0.1%

1.0

2015

[98] Improving Email Response in an Email Management System Using Natural Language Processing Based Probabilistic Methods A. Al-Alwani J. Comput. Sci. 2015 - 10 citations - Show abstract - Cite - PDF 0.1% topic match

0.1%

0.0

2018

[99] Big Data in Performance Measurement: : Towards a Framework for Performance Measurement in a Digital and Dynamic Business Climate Karin Knobel and Lovisa Laestadius Journal Not Provided 2018 - 0 citations - Show abstract - Cite 0.1% topic match

0.0%

0.2

2007

[100] 'Internet Based Viral Marketing for Global Competition: The Road Ahead' Naresh K. Patel Journal Not Provided 2007 - 4 citations - Show abstract - Cite Abstract: We work in an age when a sweeping revolution brought about by the convergence of telephone and computer technologies is irrevocably changing the commercial landscape. Today, companies in every category are ready to strike out in the new directions. “THIS CHANGES everything” or “we will never be the same” are expressions often associated with the major news events of the day. They are equally relevant when discussing technological innovations in business. Every day, another technology driven announcement can be found in the pages of a variety of trade publications. Newsletters, magazines, and newspapers are frequently reporting on a new way to meet customer needs or communicate with target audiences. The Internet has become the new battlefield for marketing competitors. No longer an experimental media vehicle for computer geeks, it is mainstream and turning into the jumping-off point of creative exploration in marketing. (Larry, 2003)At one time, mass-media advertising primarily did brand building, and the advertising agency was often the dominant player, but now a day’s mass-media advertising is ineffective and costly. Fragmentation and rising costs are already inhibiting marketing through traditional mass media like television. The new communication channels are giving option to bypass mass media advertising for building strong and differentiated brand. (Aaker, Joachimsthaler, 1997).The fragmentation of media is making communication a very expensive affair and the advent of Internet has put the power of information in customer’s hand. A combination of these factors is putting the marketing function in a poorer light as more and more investment is being pumped into marketing for no palpable reason, much to the irritation of other functions. Therefore, in the light of companies aiming for sustainable competition, marketing function finds itself inadequately equipped.In the past decade, the advent of the Internet has embarked businesses on a revolution and given consumers the power to control information. Marketing has hence harnessed the Internet to sell, communicate and diffuse products and services. Therefore using “Viral Marketing” techniques on the Internet to ‘spread the word’ and accelerate growth at exponential rates is fast becoming popular.Viral marketing is an advertisement that is in some way tied to an e-mail. It can be attached to the e-mail or placed in the body of the e-mail. “Viral Marketing” is a technique whereby information related to products, services or companies are “seeded” strategically. Viral marketing is a form of advocacy or word-of-mouth endorsement marketing. One customer passes along the message to other potential buyers. The name “viral” is derived from the image of a person being infected with the marketing message, then spreading it to friends like a virus. The major difference, however, is that the customer voluntarily sends the message to others. It does not occur automatically. Viral marketing messages may be hyper-linked promotions that take someone immediately to a Web site, online newsletters, or various games. Statistics indicate that 81 percent of recipients who receive a viral marketing message pass it along to at least one other person. Almost 50 percent pass it along to two or more people. The marketing message can be more deliberate, similar to an individual recommending something to a friend. A viral message can also be transmitted passively by being attached to an e-mail. Viral marketing allows a firm to gain rapid product awareness at a low cost.The paper focuses on two issue of viral marketing. It gives idea about Viral marketing from a theoretical perspective and explains how businessmen can use it as competitive tool. This is an era of internet, so it demonstrate how these strategies can be practiced in the country like India, and get benefited to the businessmen to achieve sustainable growth with global competition, are discussed. 0.0% topic match

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