Predictive Customer Lifetime value modeling: Improving customer engagement and business performance

CookUnity, a meal subscription service, has witnessed substantial annual revenue growth over the past three years. However, this growth has primarily been driven by the acquisition of new users to expand the customer base, rather than an evident increase in customers' spending levels. If it weren't for the raised subscription prices, the company's customer lifetime value (CLV) would have remained the same as it was three years ago. Consequently, the company's leadership recognizes the need to adopt a holistic approach to unlock an enhancement in CLV. The objective of this thesis is to develop a comprehensive understanding of CLV, its implications, and how companies leverage it to inform strategic decisions. Throughout the course of this study, our central focus is to deliver a fully functional and efficient machine learning solution to CookUnity. This solution will possess exceptional predictive capabilities, enabling accurate forecasting of each customer's future CLV. By equipping CookUnity with this powerful tool, our aim is to empower the company to strategically leverage CLV for sustained growth. To achieve this objective, we analyze various methodologies and approaches to CLV analysis, evaluating their applicability and effectiveness within the context of CookUnity. We thoroughly explore available data sources that can serve as predictors of CLV, ensuring the incorporation of the most relevant and meaningful variables in our model. Additionally, we assess different research methodologies to identify the top-performing approach and examine its implications for implementation at CookUnity. By implementing data-driven strategies based on our predictive CLV model, CookUnity will be able to optimize order levels and maximize the lifetime value of its customer base. The outcome of this thesis will be a robust ML solution with remarkable prediction accuracy and practical usability within the company. Furthermore, the insights gained from our research will contribute to a broader understanding of CLV in the subscription-based business context, stimulating further exploration and advancement in this field of study

A Hybrid Tabu Search and Genetic Algorithm Imputation Approach for Incomplete Data

The common problem for data collection is happening missing value during the data collection and processing process that the quality of the data testing is decreased. A computational based technique for dealing with missing values, namely Genetic Algorithm Imputation (GAI). The usage was used to estimate the dataset's missing values. GAI generates the optimal set of missing values with the acquisition of information as a function of fitness to measure individual solutions' performance. GAI conducts continuous searching until the missing criteria value is found according to best fitness. So, it is trapped in optimal conditions temporarily. The improvement of GAI with tabu search is known as TS-GAI, that strength is two metaheuristic techniques modified at the mutase stage to distract the local optima's search.  In applying missing values, this technique works better when many possible values are used instead of the mixed attribute having missing values. Because the new generation chromosome values generate many opportunities to make up for the missing values. The experimental results show that the TS-GAI shows better performance on 30% MV with a fitness value of 0.212. It converges at 159 iterations. Generally, TS-GAI is a faster iteration than simple GAI and it has a lower RMSE level than other imputation techniques

Customer churn prediction in the banking industry

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe objective of this project is to create a predictive model that will decrease customer churn in a Portuguese bank. That is, we intend to identify customers who could be considering closing their checking accounts. For the bank to be able to take the necessary corrective measures, the model also aims to determine the characteristics of the customers that decided to leave. This model will make use of customer data that the organization already has to hand. Data pre-processing with data cleansing, transformation, and reduction was the initial stage of the analysis. The dataset is imbalanced, meaning that we have a small number of positive outcomes or churners; thus, under-sampling and other approaches were employed to address this issue. The predictive models used are logistic regression, support vector machine, decision trees and artificial neural networks, and for each, parameter tuning was also conducted. In conclusion, regarding the customer churn prediction, the recommended model is a support vector machine with a precision of 0.84 and an AUROC of 0.905. These findings will contribute to the customer lifetime value, helping the bank better understand their customers' behavior and allow them to draw strategies accordingly with the information obtained

A comparative study of tree-based models for churn prediction : a case study in the telecommunication sector

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMIn the recent years the topic of customer churn gains an increasing importance, which is the phenomena of the customers abandoning the company to another in the future. Customer churn plays an important role especially in the more saturated industries like telecommunication industry. Since the existing customers are very valuable and the acquisition cost of new customers is very high nowadays. The companies want to know which of their customers and when are they going to churn to another provider, so that measures can be taken to retain the customers who are at risk of churning. Such measures could be in the form of incentives to the churners, but the downside is the wrong classification of a churners will cost the company a lot, especially when incentives are given to some non-churner customers. The common challenge to predict customer churn will be how to pre-process the data and which algorithm to choose, especially when the dataset is heterogeneous which is very common for telecommunication companies’ datasets. The presented thesis aims at predicting customer churn for telecommunication sector using different decision tree algorithms and its ensemble models

Feature Selection with Integrated Gaussian Seahorse Optimization Data Mining for Cross-border Business Cooperation between the Malaysian Medical Industry and Tourism Industry

The cross-border collaboration between the medical industry and the tourism industry has gained significant attention as a promising avenue for economic growth and development. Data mining techniques are employed to extract valuable patterns and insights from large-scale datasets, shedding light on the opportunities and challenges associated with this collaborative effort. This study proposes an integrated approach that combines feature selection with Gaussian Seahorse Optimization Data Mining (GSH-DM) to identify the most relevant features and optimize the data mining process. The GSH-DM assembling comprehensive datasets encompassing information from both the Malaysian medical industry and tourism industry. The integrated GSH-DM model then applies the Gaussian Seahorse Optimization algorithm to optimize the data mining process, enhancing the accuracy and efficiency of pattern discovery. the GSH-DM model, this study aims to uncover hidden patterns, relationships, and predictive models that can guide decision-making and strategy development for cross-border business cooperation. The findings of this study contribute to a deeper understanding of the factors that influence cross-border business cooperation between the Malaysian medical industry and the tourism industry. The integrated GSH-DM approach showcases the potential of combining feature selection techniques with advanced optimization algorithms in data mining applications. The results of GSH-DM provide actionable insights for stakeholders, enabling them to make informed decisions and foster successful cross-border collaborations between the Malaysian medical industry and the tourism industry. The analysis of the results demonstrated that GSH-DM exhibits improved performance for feature selection and classification

Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce

Electronic commerce is revolutionizing the way we think about data modeling, by making it possible to integrate the processes of (costly) data acquisition and model induction. The opportunity for improving modeling through costly data acquisition presents itself for a diverse set of electronic commerce modeling tasks, from personalization to customer lifetime value modeling; we illustrate with the running example of choosing offers to display to web-site visitors, which captures important aspects in a familiar setting. Considering data acquisition costs explicitly can allow the building of predictive models at significantly lower costs, and a modeler may be able to improve performance via new sources of information that previously were too expensive to consider. However, existing techniques for integrating modeling and data acquisition cannot deal with the rich environment that electronic commerce presents. We discuss several possible data acquisition settings, the challenges involved in the integration with modeling, and various research areas that may supply parts of an ultimate solution. We also present and demonstrate briefly a unified framework within which one can integrate acquisitions of different types, with any cost structure and any predictive modeling objectiveNYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

The impact of different touchpoints on brand consideration

Marketers face the challenge of resource allocation across a range of touchpoints. Hence understanding their relative impact is important, but previous research tends to examine brand advertising, retailer touchpoints, word-of-mouth, and traditional earned touchpoints separately. This article presents an approach to understanding the relative impact of multiple touchpoints. It exemplifies this approach with six touchpoint types: brand advertising, retailer advertising, in-store communications, word-of-mouth, peer observation (seeing other customers), and traditional earned media such as editorial. Using the real-time experience tracking (RET) method by which respondents report on touchpoints by contemporaneous text message, the impact of touchpoints on change in brand consideration is studied in four consumer categories: electrical goods, technology products, mobile handsets, and soft drinks. Both touchpoint frequency and touchpoint positivity, the valence of the customer's affective response to the touchpoint, are modeled. While relative touchpoint effects vary somewhat by category, a pooled model suggests the positivity of in-store communication is in general more influential than that of other touchpoints including brand advertising. An almost entirely neglected touchpoint, peer observation, is consistently significant. Overall, findings evidence the relative impact of retailers, social effects and third party endorsement in addition to brand advertising. Touchpoint positivity adds explanatory power to the prediction of change in consideration as compared with touchpoint frequency alone. This suggests the importance of methods that track touchpoint perceptual response as well as frequency, to complement current analytic approaches such as media mix modeling based on media spend or exposure alone