Applying advanced sentiment analysis for strategic marketing insights: A case study of BBVA using machine learning techniques
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Author(s)Luis Miguel Garay GallasteguiLink to ORCID Index: https://orcid.org/0000-0002-2119-4532
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Ricardo Reier ForradellasLink to ORCID Index: https://orcid.org/0000-0002-4790-0351
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Sergio Luis Náñez AlonsoLink to ORCID Index: https://orcid.org/0000-0001-5353-2017
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DOIhttp://dx.doi.org/10.21511/im.20(2).2024.09
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Article InfoVolume 20 2024, Issue #2, pp. 100-115
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281 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
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.
- Keywords
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JEL Classification (Paper profile tab)M31, G21
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References54
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Tables4
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Figures10
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- Figure 1. Sentiment analysis process
- Figure 2. Negative tweet feeling histogram
- Figure 3. Neutral tweet feeling histogram
- Figure 4. Positive tweet feeling histogram
- Figure 5. Confusion matrix classification vs feeling
- Figure 6. Histogram over 3,000 tweets with new properties
- Figure 7. SVM confusion matrix
- Figure 8. XGBoost confusion matrix
- Figure 9. Random Forest confusion matrix
- Figure 10. Confusion matrix neural networks
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- Table 1. Distribution of tweets based on the manual classification and automatic classification (CNL) of the feeling of the tweets
- Table 2. Comparison of precision between algorithms
- Table 3. Comparison of results between algorithms
- Table 4. Comparison improvement of results of the XGBoost algorithm
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Fintech in the eyes of Millennials and Generation Z (the financial behavior and Fintech perception)
Mohannad A. M. Abu Daqar ,
Samer Arqawi ,
Sharif Abu Karsh
doi: http://dx.doi.org/10.21511/bbs.15(3).2020.03
Banks and Bank Systems Volume 15, 2020 Issue #3 pp. 20-28 Views: 6424 Downloads: 2155 TO CITE АНОТАЦІЯThis study investigates the Millennials and Gen Z perception toward Fintech services, their usage intention, and their financial behavior. The study took place in the Palestinian context with a global comparison among these generations. The authors used the questionnaire-based technique to meet the study objective. West Bank respondents were selected for this purpose; the study instrument was distributed through different social media channels. The findings show that reliability/trust and ease of use are the main issues in using a financial service. Millennials are more aware (48%) of Fintech services than Gen Z (38%), which is different from the global view where Gen Z is the highest. The smartphone penetration rate is 100% among both generations, while the financial inclusion ratio in Palestine is around 36.4%; these clear indicators are the main Fintech drivers to promote Fintech services in Palestine, and these are global indicators for Fintech adoption intention. Both generations (84%) intend to use e-wallet services, Millennials (87%) and Gen Z is (70%) prefer using real-time services. Half of the respondents see that Fintech plays a complementary role with banks. The majority see that Fintech services are cheaper than bank services. Wealth management, and robot advisor services, and both generations are looking to acquire them in the long run. The authors revealed that 85% of respondents from both generations trust banks, so it is recommended that banks digitize their financial services to meet the customers’ needs, considering that 90% of respondents see that promotions are a key issue in adopting Fintech services. Promoting e-wallet services by banks is highly recommended due to the massive rivalry with Fintech parties.
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Embracing AI and Big Data in customer journey mapping: from literature review to a theoretical framework
Mario D'Arco ,
Letizia Lo Presti ,
Vittoria Marino ,
Riccardo Resciniti
doi: http://dx.doi.org/10.21511/im.15(4).2019.09
Innovative Marketing Volume 15, 2019 Issue #4 pp. 102-115 Views: 3007 Downloads: 1705 TO CITE АНОТАЦІЯNowadays, Big Data and Artificial Intelligence (AI) play an important role in different functional areas of marketing. Starting from this assumption, the main objective of this theoretical paper is to better understand the relationship between Big Data, AI, and customer journey mapping. For this purpose, the authors revised the extant literature on the impact of Big Data and AI on marketing practices to illustrate how such data analytics tools can increase the marketing performance and reduce the complexity of the pattern of consumer activity. The results of this research offer some interesting ideas for marketing managers. The proposed Big Data and AI framework to explore and manage the customer journey illustrates how the combined use of Big Data and AI analytics tools can offer effective support to decision-making systems and reduce the risk of bad marketing decision. Specifically, the authors suggest ten main areas of application of Big Data and AI technologies concerning the customer journey mapping. Each one supports a specific task, such as (1) customer profiling; (2) promotion strategy; (3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing strategy; (7) purchase history; (8) predictive analytics; (9) monitor consumer sentiments; and (10) customer relationship management (CRM) activities.
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The impact of artificial intelligence on commercial management
Renato Costa ,
Álvaro Dias 
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Leandro Pereira ,
José Santos ,
André Capelo
doi: http://dx.doi.org/10.21511/ppm.17(4).2019.36
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