AI-Enabled Processes: The Age of Artificial Intelligence and Big Data

Beheshti, Amin; Benatallah, Boualem; Sheng, Quan Z.; Casati, Fabio; Nezhad, Hamid-Reza Motahari; Yang, Jian; Ghose, Aditya

doi:10.1007/978-3-031-14135-5_29

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…These methods employ complex algorithms, often rooted in deep learning structures, to learn and replicate patterns from existing data. They mostly generate new and diverse outputs across different data analysis domains such as text, images, and processes [16,52]. GAI has a remarkable ability to create realistic and innovative outcomes in various fields.…”

Section: Gai and Feature Engineeringmentioning

confidence: 99%

“…As GAI continues to evolve, its potential impact on technology and creativity becomes increasingly important. DALL-E, VALL-E, ProcessGPT, and ChatGPT are some examples of leveraging the power of GAI in different domains [16]. ChatGPT 2 is a complex AI language model introduced by OpenAI in 2018.…”

Section: Gai and Feature Engineeringmentioning

confidence: 99%

“…Applying ProcessGPT involves training a generative pre-trained transformer model on extensive business process data, followed by fine-tuning on specific process domains. This refined model can then produce process flows and decisions by context and user input, offering potential enhancements to decision-making in complex processes [16]. Motivated by our previous study [14] as well as the widespread popularity of GAI power in revolutionizing different aspects of industries (i.e., domain-specific tasks and processes) especially the education sectors [68,70,71,75,76], we come up with the idea of leveraging ChatGPT on improving and facilitating feature engineering task for customer churn problems.…”

Section: Gai and Feature Engineeringmentioning

confidence: 99%

“…Beyond what was previously considered achievable, businesses now have additional opportunities to use generative AI to add value to their operations [15]. This can be seen in the way that generative AI is being used to create content, which is transforming fields like journalism, visual arts, and customer service [16]. As our first contribution to this study, we propose a customer churn-related knowledge base and leverage GAI to enhance its structure.…”

Section: Introductionmentioning

confidence: 99%

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Generative AI-enabled Knowledge Base Fine-tuning: Enhancing Feature Engineering for Customer Churn

Shahabikargar,

Beheshti,

Mansoor

et al. 2024

Preprint

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Customers are the most critical component in a business’s success regardless of the industry or product. Companies make significant efforts to acquire and, more importantly, retain their existing customers. Customer churn is a significant challenge for businesses, leading to financial losses. To address this challenge, understanding customer’s cognitive status, behaviors, and early signs of churn is crucial. However, predictive and ML-based analysis, being fed with proper features that are indicative of a customer’s cognitive status or behavior, is extremely helpful in addressing this challenge. Having practical ML-based analysis relies on a well-developed feature engineering process. Previous churn analytical studies mainly applied feature engineering approaches that leveraged demographic, product usage, and revenue features alone, and there is a lack of research on leveraging the information-rich content from interactions between customers and companies. Considering the effectiveness of applying domain knowledge and human expertise in feature engineering, and motivated by our previous work, we propose a Customer Churn-related Knowledge Base (ChurnKB) to enhance the feature engineering process. In the ChurnKB, we leverage textual data mining techniques for extracting churn-related features from texts created by customers, e.g., emails or chat logs with company agents, reviews on the company’s website, and feedback on social media. We use Generative AI (GAI) to enhance and enrich the structure of the ChurnKB regarding features related to customer churn-related cognitive status, feelings, and behaviors. We also leveraged feedback loops and crowdsourcing to enhance and approve the validity of the proposed ChurnKB and apply it to develop a classifier for customer churn problems.

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Section: Gai and Feature Engineeringmentioning

confidence: 99%

Section: Gai and Feature Engineeringmentioning

confidence: 99%