Robotic Framework For Customer Care and Digital Marketing
Author(s):
Samiksha Mukindrao Bandgar, Nikita Hemant Bankar, Shruti Jayant Lad, Rajiya Chirag Mulla, Prof. M. A. Rane
Keywords:
Digital Marketing, Data Analytics, Process Automation, Online Marketing, Data- Driven Decision-Making, Campaign Performance, Ad Placement, Content Creation, Customer Segmentation, Marketing Professionals, Marketing Analytics, Chatbots, Personalized Customer Interactions, Customer Experience, Operational Costs.
Abstract
The field of digital marketing has undergone profound transformation with the advent of automation and artificial intelligence technologies. This abstract introduces a cutting- edge robotic framework for digital marketing, offering a paradigm shift in the way businesses strategize, execute, and optimize their online marketing efforts. The robotic framework combines machine learning, data analytics, and process automation to create a powerful ecosystem that automates routine marketing tasks, enables data-driven decision-making, and enhances overall campaign performance. From automated ad placement and content creation to real-time performance analysis and customer segmentation, the framework empowers marketing professionals to achieve unprecedented levels of efficiency and precision. Key components of this framework include AI- driven marketing analytics tools, chatbots for customer engagement, and programmatic advertising solutions. These elements work in harmony to streamline marketing operations, drive personalized customer interactions, and adapt campaigns in real-time based on performance metrics. The transformative potential of implementing a robotic framework in digital marketing, resulting in reduced operational costs, improved targeting, increased ROI, and ultimately, a more satisfying customer experience. It emphasizes the need for businesses to embrace automation and data-driven decision-making in an era where consumer expectations and market dynamics continue to evolve rapidly.
Article Details
Unique Paper ID: 163418

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 2940 - 2945
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