Optimizing Customer Touchpoints through Data Analytics in Multi-Channel Engagement
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DOI:
https://doi.org/10.67228/30713498/IJADSMC-2018PII0M5QPublished 12-05-2018
Customer Touchpoints, Multi-Channel Engagement, Data Analytics, Customer Experience Optimization, Predictive Analytics, Personalized Marketing, Cross-Channel Integration, Customer Journey, Omnichannel Strategy, Artificial Intelligence In Customer Engagement, Data-Driven Decision Making, Customer Data Platforms (CDP) Issue
Section
ArticlesHow to Cite
[1]P. Sharma, “Optimizing Customer Touchpoints through Data Analytics in Multi-Channel Engagement”, IJADSMC, vol. 1, no. 2, pp. 01–13, Dec. 2018, doi: 10.67228/30713498/IJADSMC-2018PII0M5Q.Abstract
In today’s rapidly evolving digital landscape, businesses face the challenge of effectively engaging customers across a variety of touchpoints. Multi-channel engagement spanning online platforms, physical stores, social media, email, and mobile apps presents both opportunities and complexities. Leveraging data analytics is key to optimizing these touchpoints by enabling businesses to understand customer behaviors, preferences, and pain points. This paper explores the role of data analytics in improving customer engagement through multi-channel touchpoints, emphasizing the importance of personalized interactions, predictive insights, and real-time decision-making. By integrating and analyzing customer data across multiple channels, businesses can enhance the customer experience, increase satisfaction, and drive loyalty. The paper also discusses emerging trends such as artificial intelligence and machine learning in multi-channel strategies, alongside the challenges related to privacy and data integration. Ultimately, it offers strategic recommendations for businesses seeking to create seamless, data-driven customer engagement.
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How to Cite
[1]P. Sharma, “Optimizing Customer Touchpoints through Data Analytics in Multi-Channel Engagement”, IJADSMC, vol. 1, no. 2, pp. 01–13, Dec. 2018, doi: 10.67228/30713498/IJADSMC-2018PII0M5Q.