Bank Marketing Using Intelligent Targeting (original) (raw)

Enhancing Bank Direct Marketing through Data Mining

The financial crisis created pressure on banks due to credit restriction, increasing competition for deposits retention and demanding efficiency improvements of direct marketing campaigns. Our research conducted a data mining project on direct marketing campaigns for deposits subscriptions by using recent data of a Portuguese retail bank. We used the Support Vector Machine (SVM) data mining technique for modeling and evaluated it through a sensitive analysis. The findings revealed previously unknown valuable knowledge, such as the best months for campaigns to occur, and optimal call duration. Such knowledge can be used to improve campaign efficiency.

IJERT-Comparison on Banking Dataset-Marketing Targets using Power BI

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/comparison-on-banking-dataset-marketing-targets-using-power-bi https://www.ijert.org/research/comparison-on-banking-dataset-marketing-targets-using-power-bi-IJERTV10IS070341.pdf Nowadays, data is the king. Use it's anything but a possible way and it's anything but an immense effect on your business, don't use it and you will be abandoned in this quickly moving world instantly. Also, one of the manners in which an organization can work on its presentation in the market is to catch and proficiently check client information to further develop the client experience. The dataset is initially gathered from the UCI Machine learning repository and the Kaggle site. The information is identified with bank marketing efforts of banking establishments dependent on call. In this work, Power BI is utilized. The primary explanation of utilizing Power BI is to assemble a Graphs and BI report. The principal objective of building the Graph is to think about testing and preparing whether the client has decided on terms of deposit. The bank should focus on the likely client with an extensive measure of time reacting to the calls.

A Data-Driven Approach to Predict the Success of Bank Telemarketing

Decision Support Systems (Elsevier), 2014

We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features. We also compared four DM models: logistic regression, decision trees (DT), neural network (NN) and support vector machine. Using two metrics, area of the receiver operating characteristic curve (AUC) and area of the LIFT cumulative curve (ALIFT), the four models were tested on an evaluation phase, using the most recent data (after July 2012) and a rolling windows scheme. The NN presented the best results (AUC = 0.8 and ALIFT = 0.7), allowing to reach 79% of the subscribers by selecting the half better classified clients. Also, two knowledge extraction methods, a sensitivity analysis and a DT, were applied to the NN model and revealed several key attributes (e.g., Euribor rate, direction of the call and bank agent experience). Such knowledge extraction confirmed the obtained model as credible and valuable for telemarketing campaign managers.

Hybrid Datamining Approaches to Predict Success of Bank Telemarketing

IJCSMC, 2019

Telemarketing is a kind of straightforward marketing in which salesman requests the consumer either face to face or telephone request and influence him to purchase the product. Telemarketing achieves most prevalence in the 20th century and still increasing it. Now, the phone has been broadly accepted. It is valued efficient and holds the consumers up to date. In the Banking area, marketing is the backbone to exchange its goods or service. Business promotion and marketing is frequently based on an exhaustive understanding of actual information about the market and the real client demands for the productive bank manner. We recommend a data mining (DM) method to foretell the achievement of telemarketing requests for contracting long-term bank deposits. A local Portuguese bank was labeled, with data gathered from 2011 to 2016, thus involving the effects of the current economic crisis. We examined a comprehensive set of 11 features associated with bank consumer, goods and social-economic characteristics. We also discuss four DM forms with the hybrid model: Naïve Bayes (NB), Decision Trees (DTs), Perceptron Neural Network (NN) and Support Vector Machine (SVM). The four types were tested and compared with proposed hybrid classification methods (Perceptron Neural Network + Decision Tree) on an evaluation set, and we are splitting data into training and testing sets using cross-validation method. The proposed hybrid classification technique presented the best results (Precision 99% and ROC = 97%).

‘Next product to offer’ for bank marketers

Journal of Database Marketing, 2003

His research interests include customer relationship management (CRM), data mining, MIS and marketing research. In the past few years, he obtained funding of more than US$1.2m in CRM research from the government and the banking industry and served as CRM consultant in five leading banks in Hong Kong.

Mining a Marketing Campaigns Data of Bank

IJCSMC, 2019

In this paper, we propose a data mining approach to predict the success of telemarketing. We are applying the algorithms for the first time on the dataset. The dataset obtained from UCI, which contain the most common machine learning datasets. The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. The number of the instance is 45212 with 15 input variables and the output variable. Classification is a data mining techniques used to predict group membership for a data instance. we present the comparison of different classification techniques in open source data mining software which consists of a One-R algorithm methods and Naïve-Bayes algorithm The experiment results show are a bout classification sensitivity, specificity, accuracy. The results on bank marketing data discovered that the One-R algorithm is better in classifying the data comparing with the Naïve-Bayes algorithm; where the error rate is lower.

Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology

The increasingly vast number of marketing campaigns over time has reduced its effect on the general public. Furthermore, economical pressures and competition has led marketing managers to invest on directed campaigns with a strict and rigorous selection of contacts. Such direct campaigns can be enhanced through the use of Business Intelligence (BI) and Data Mining (DM) techniques. This paper describes an implementation of a DM project based on the CRISP-DM methodology. Real-world data were collected from a Portuguese marketing campaign related with bank deposit subscription. The business goal is to find a model that can explain success of a contact, i.e. if the client subscribes the deposit. Such model can increase campaign efficiency by identifying the main characteristics that affect success, helping in a better management of the available resources (e.g. human effort, phone calls, time) and selection of a high quality and affordable set of potential buying customers.

Evaluating the Role of Artificial Intelligence and Big Data Analytics in Indian Bank Marketing

Tuijin Jishu/Journal of Propulsion Technology, 2023

The banking landscape in India is experiencing a profound transformation, driven by the adoption of artificial intelligence (AI) and big data analytics. This research paper delves into the evolving role of AI and big data analytics in Indian bank marketing, offering a comprehensive evaluation of the impact and implications of these advanced technologies. With an expanding customer base and the growing importance of providing personalized and efficient services, Indian banks are increasingly turning to AI and big data analytics to gain a competitive edge in a dynamic market.This study begins with an overview of the Indian banking industry and the motivations for this research, emphasizing the significance of staying relevant and customer-centric in an era of rapid technological change. The objectives of this research encompass exploring the applications, benefits, challenges, and future prospects of AI and big data analytics in the marketing domain of Indian banks. big.The research also addresses the challenges and limitations of AI and big data implementation, including concerns related to data privacy, skill gaps, and regulatory compliance. It calls attention to the need for effective integration of these technologies with legacy systems and underscores the importance of ethical considerations in the ever-evolving landscape of bank marketing.Ultimately, this paper offers insights into the future prospects of AI and big data in Indian bank marketing, projecting a trajectory towards hyper-personalization, advanced fraud detection, real-time analytics, and strategic collaborations. The findings and recommendations of this study are poised to inform stakeholders, industry practitioners, and policymakers, guiding their efforts in navigating the transformative journey of Indian bank marketing powered by AI and big data analytics.

Certain Investigations on Bank Customer and Relationship Improvement using Machine Learning

2021

Visual information investigation includes utilizing present-day P.C. illustrations and show instruments to investigate information. The utilization of such visual research techniques has gotten progressively across the board all through the sociologies. This proposed framework centres around client information in business banks and expects to utilize visual portrayals and connection strategies to give another vision of client relationship the board or customer relationship management (CRM).Features of the discoveries incorporate that consumer loyalty has a remarkable positive impact on client dedication in the financial assistance industry, particularly with the salary improving, the connection between them turns out to be increasingly robust. At last, recommendations are advanced on the most proficient method to enhance consumer loyalty and increment client dedication. They are continually vigilant for utilizing them to improve their business advantages, for example, right item advancement to right sort of clients, typical usage of self-administration channels, diminished visits to branches for money exchanges, etc. Select Bank's analysis of bank customer awareness about who to bank financing, credit to give something constraints. In addition, it can make the guarantor to show signs of improvement in their potential and existing customers to understand. As customers analyze is significantly essential for such activities, analysis of a client to create a clear framework for banks. The central aspect of this study is to use the used dataset (labelled) and to create a new label as the target for classification, which reduces the clustering execution time and gets the best accuracy results. The data set ('default of credit card clients) is obtained from the archive of UCI (University of California, Irvine) M.L. Repository.

A Banking Platform to Leverage Data Driven Marketing with Machine Learning

Entropy, 2022

Payment data is one of the most valuable assets that retail banks can leverage as the major competitive advantage with respect to new entrants such as Fintech companies or giant internet companies. In marketing, the value behind data relates to the power of encoding customer preferences: the better you know your customer, the better your marketing strategy. In this paper, we present a B2B2C lead generation application based on payment transaction data within the online banking system. In this approach, the bank is an intermediary between its private customers and merchants. The bank uses its competence in Machine Learning driven marketing to build a lead generation application that helps merchants run data driven campaigns through the banking channels to reach retail customers. The bank’s retail customers trade the utility hidden in its payment transaction data for special offers and discounts offered by merchants. During the entire process banks protects the privacy of the retail c...