Next-Gen CRM Sales and Lead Generation with AI

  IJCTT-book-cover
 
         
 
© 2023 by IJCTT Journal
Volume-71 Issue-3
Year of Publication : 2023
Authors : Anitha Peddireddy, Kiran Peddireddy
DOI :  10.14445/22312803/IJCTT-V71I3P104

How to Cite?

Anitha Peddireddy, Kiran Peddireddy, "Next-Gen CRM Sales and Lead Generation with AI," International Journal of Computer Trends and Technology, vol. 71, no. 3, pp. 21-26, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I3P104

Abstract
This paper presents an analysis of how artificial intelligence (AI) can be used to enhance the sales and lead generation capabilities of Salesforce CRM. As customer data continues to grow, it becomes increasingly challenging for businesses to effectively analyze and utilize this data to improve their sales and lead generation efforts. AI offers a solution to this problem by allowing businesses to automate and streamline their sales and lead generation processes. By incorporating AI into Salesforce CRM, businesses can more effectively identify and target sales leads, improve the efficiency and effectiveness of the sales process, and make more accurate sales forecasts. The study explores how AI can be used to analyze customer data and interactions within Salesforce CRM to identify potential sales leads and opportunities through natural language processing, machine learning, and predictive analytics. Furthermore, it explains how AI-based algorithms can be used to score the lead and predict the likelihood of closing the deal, which can help sales teams to focus on the most promising leads. Additionally, AI can also be used to optimize sales processes and forecasting by analyzing historical sales data and identifying patterns and trends that can be used to predict future sales and revenue. The paper concludes with the potential benefits of using AI in Salesforce CRM, such as increased revenue and improved customer satisfaction.

Keywords
Artificial Intelligence(AI), Salesforce CRM, Customer Relationship Management(CRM).

Reference

[1] Anuradha Manchar, and Ankit Chouhan, "Salesforce CRM: A New Way of Managing Customer Relationship in Cloud Environment," 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1-4, 2017. [CrossRef] [Google Scholar] [Publisher link]
[2] Anitha Peddireddy, and Kiran Peddireddy, Digital Transformation using Salesforce CRM, 2023. [Publisher link]
[3] Hiba Thanduparakkal, P. Shahad, and C. G. Raji, “Using Salesforce to Build Real Time Covid 19 Tracker with Cloud Computing Technology,” 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 942-948, 2022. [CrossRef] [Google Scholar] [Publisher link]
[4] Kiran Peddireddy, Transforming Product Lifecycle Management with AI and Machine Learning, 2023. [Publisher link]
[5] N. Narkhede, J. Rao, and S. Neha, “Kafka: A Distributed Streaming Platform,” Confluent., 2017.
[6] Nathan Marz, and James Warren, Big Data: Principles and Best Practices of Scalable Real-Time Data Systems, Manning Publications, 2015. [Google Scholar] [Publisher link]
[7] Jay Kreps, Neha Narkhede, and Jun Rao, “Kafka: A Distributed Messaging System for Log Processing,” Proceedings of the NetDB Workshop, 2011. [Google Scholar] [Publisher link]
[8] K. Grolinger et al., “Challenges for the Adoption of Big Data Technologies: A Systematic Review of Literature,” Journal of Big Data, vol. 5, no. 1, 2018.
[9] W. Ahmad, and J. Seong, “Machine Learning for Customer Complaint Management: A Review,” Journal of Big Data, vol. 7, no. 1, p. 57, 2020.
[10] Kiran Peddireddy, Transforming Product Lifecycle Management with AI and Machine Learning, 2023. [Publisher link]
[11] Priya Parameswarappa, Taral Shah, and Govinda Rajulu Lanke, “A Machine Learning-Based Approach for Anomaly Detection for Secure Cloud Computing Environments,” 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), pp. 931-940, 2023. [CrossRef] [Google Scholar] [Publisher link]
[12] Kamaljit Kaur, Tajinder Singh Sahdev, and Anmoldeep Singh Sahdev, “Emerging Trends In Cloud Computing: Is It A Friend Or Foe For The Economic Development And New Age Of Globalisation: A Case Study Approach,” International Journal of Computer Trends and Technology, vol. 67, no. 5, 2019. [Publisher link]
[13] G.Devi, and M.Pramod Kumar, “IJCTT - Cloud Computing: A CRM Service Based on a Separate Encryption and Decryption using Blowfish Algorithm, International Journal of Computer Trends and Technology, vol. 3, no. 4, pp. 1011-1016, 2012. [Publisher link]
[14] Paul Goodey, Salesforce CRM: The Definitive Admin Handbook (2nd edition), Packt Publishing, 2013. [Google Scholar] [Publisher link]
[15] Paul Greenberg, “CRM at the Speed of Light, Essential Customer Strategies for the 21st Century (3rd edition), Manassas, VA: McGraw Hill Professional, 2004. [Google Scholar] [Publisher link]
[16] V.V.Narendra Kumar, and T. Satish Kumar, “Smarter Artificial Intelligence with Deep Learning,” SSRG International Journal of Computer Science and Engineering, vol. 5, no. 6, pp. 10-16, 2018. [CrossRef] [Google Scholar] [Publisher link]
[17] R.R. Karthikeyan, and B. Raghu, “Simple Analytics in Retail Sales Data using Hadoop,” International Journal of Computer Trends and Technology, vol. 58, no. 1, 2018. [Google Scholar] [Publisher link]
[18] Yohanes Suhari et al., “Social CRM Framework For University,” International Journal of Computer Trends and Technology, vol. 69, no. 12, pp. 1-4, 2021. [CrossRef] [Publisher link]
[19] X. Wang, L. Chen, and P. Li, “An AI-powered Customer Complaint Handling System Based on NLP and Sentiment Analysis,”
[20] Pradeep Kumar Dhoopati, “Enhancing Enterprise Application Integration through Artificial Intelligence and Machine Learning,” International Journal of Computer Trends and Technology, vol. 71, no. 2, pp. 54-60, 2023. [CrossRef] [Publisher link]
[21] Wen Bin et al., “Text Sentiment Classification Research Based on Semantic Comprehension,” Computer Science, vol. 37, no. 6, pp. 261-264. 2010. [Google Scholar] [Publisher link]
[22] Dishant Banga, and Kiran Peddireddy, “Artificial Intelligence for Customer Complaint Management,” International Journal of Computer Trends and Technology, vol. 71, no. 3, pp. 1-6, 2023. [CrossRef] [Publisher link]
[23] Sirajul Huque, and V. Kiran Kumar, “A Study on Sentiment Analysis of Movie Reviews using ML Algorithms,” International Journal of Computer Trends and Technology, vol. 70, no. 9, pp. 33-37, 2022. [CrossRef] [Publisher link]
[24] Kiran Peddireddy, Enterprise Data Integration and Streaming Using Kafka, ActiveMQ, and AWS Kinesis, 2023. [Publisher link]
[25] M. Sakthivadivu, and P. Suresh Babu, “Analytical and Empirical Survival Study on Natural Image Compression and Classification using Machine Learning Techniques,” International Journal of Computer Trends and Technology, vol. 70, no. 8, pp. 21-29, 2022. [CrossRef] [Publisher link]
[26] Xiancheng Xiahou, and Yoshio Harada, “K-Medoids Clustering Techniques in Predicting Customers Churn: A Case Study in the ECommerce Industry,” International Journal of Computer Trends and Technology, vol. 70, no. 2, pp. 22-28, 2022. [CrossRef] [Publisher link]
[27] Arnav Ghosh, “Analytics and Project Management in Investment Banks,” International Journal of Computer Trends and Technology, vol. 68, no. 12, pp. 44-46, 2020. [CrossRef] [Publisher link]