Embracing AI and Big Data in customer journey mapping: from literature review to a theoretical framework

Understanding marketing channel performance is a crucial and complex task for the mobile financial technology segment of the mobile industry in emerging markets. However, poor techniques and capabilities for channel optimization of the mobile money users across available channels by the service providers often undermine the performance of these channels. The research aims to develop a target selection and campaign optimization framework for mobile money customers along two channels of transactions. It is complemented by mapping the appropriate campaign techniques across digital and non-digital channels of mobile money transactions. The key analytical method is the combination of fuzzy c-means clustering and RFM algorithm for the target selection development through the usage logs of customers (n = 300) of a mobile service provider. The results indicated that fuzzy c-means clustering and RFM algorithm are efficient for target selection. Also, the mapping of clusters with the appropriate channel of transactions revealed that mobile money users’ transactions could be optimized along the digital channel. The analytic model’s output enables appropriate cross-selling and up-selling campaigns that optimize the service provider revenue from existing and new mobile money users within the customer base. The channel evaluation revealed mobile application channels to be a promising and future channel for mobile money transactions as smartphone penetration continues to grow in emerging mobile markets. That is a positive sign of the digital channel’s future potential for mobile money transactions in developing markets.

Acknowledgment
The authors wish to thank the University of Pecs under the Higher Education Institution Excellence Program of the Ministry of Innovation and Technology in Hungary within the framework of the 4th Thematic Program – “Enhancing the Role of Domestic Companies in the Re-industrialization of Hungary.”

Notwithstanding the perceived global potentiality, how big data enhances decision-making quality prompts an intriguing inquiry, especially in an increasingly competitive banking environment in developing economies. Building on an industry data-driven framework, this study strives to understand the state of implementing big data in the Indonesian banking sector. A deductively organized descriptive method employing in-depth interviews was conducted with subject matter experts representing Indonesian banking-related areas. The result and the following analysis show the modest status of big data implementation across three major banks and two complementary companies, as indicated by many elements of the framework phases that were found during the early adoption stage. This denotes a steady buy-in across banking business processes as particularly reflected in the framework’s four phases – continuing push to meet the variety aspect (intelligence), structured data architecture domination (design), limited choice of performance indicator for big data value (choice), and customer–corporate vision decoupling (implementation). While Indonesian banks have evidently initiated the big data implementation, further improvement remains imperative for the decision-making process. Accordingly, big data should be tightly coupled with a strong data-driven vision that drives decision-making across intra-firm actors. Handling data omnipresence shall be viewed as the embodiment of a data-driven vision.

In the digital era, understanding public sentiment toward brands on social media is essential for crafting effective marketing strategies. This study applies sentiment analysis on Banco Bilbao Vizcaya Argentaria (BBVA) tweets using advanced machine learning techniques, particularly the eXtreme Gradient Boosting (XGBoost) algorithm, which showed remarkable precision (91.2%) in sentiment classification. This process involved a systematic approach to data collection, cleaning, and preprocessing. The precision of XGBoost highlights its effectiveness in analyzing social media conversations about banking. Additionally, this paper achieved improvements in neutral tweet classification, with accuracy rates at 87-88% and a reduced misclassification rate, enhancing the analysis reliability. The findings not only uncover general sentiments toward BBVA but also provide insight into how these sentiments shift in response to marketing activities and global events. This gives marketers a valuable tool for real-time assessment of campaign effectiveness and brand perception. Ultimately, employing the XGBoost algorithm for sentiment analysis offers BBVA a strategic advantage in understanding and engaging its online audience, demonstrating the significant benefits of using sophisticated machine learning in banking. The study emphasizes the crucial role of data-driven sentiment analysis in developing informed business strategies and improving customer relationships in the banking industry’s competitive landscape.