Customer Lifetime Value (CLV) is an important metric that measures the total value a customer will bring to a business over their lifetime. The Beta Geometric Negative Binomial Distribution (BGNBD) and Gamma Gamma Distribution are two models that can be used to calculate CLV, taking into account both the frequency and value of customer transactions. This article explains the BGNBD and Gamma Gamma Distribution models, and how they can be used to calculate CLV for NFT (Non-Fungible Token) transaction data in a blockchain setting. By estimating the parameters of these models using historical transaction data, businesses can gain insights into the lifetime value of their customers and make data-driven decisions about marketing and customer retention strategies.

eBay partners with external vendors, which allows customers to freely select a vendor to complete their eBay experiences. However, vendor outages can hinder customer experiences. Consequently, eBay can disable a problematic vendor to prevent customer loss. Disabling the vendor too late risks losing customers willing to switch to other vendors, while disabling it too early risks losing those unwilling to switch. In this paper, we propose a data-driven solution to answer whether eBay should disable a problematic vendor and when to disable it. Our solution involves forecasting customer behavior. First, we use a multiplicative seasonality model to represent behavior if all vendors are fully functioning. Next, we use a Monte Carlo simulation to represent behavior if the problematic vendor remains enabled. Finally, we use a linear model to represent behavior if the vendor is disabled. By comparing these forecasts, we determine the optimal time for eBay to disable the problematic vendor.

We propose a method that can generate customer trajectories and purchasing behaviors in retail stores simultaneously using Transformer-based deep learning structure. Utilizing customer trajectory data, layout diagrams, and retail scanner data obtained from a retail store, we trained a GPT-2 architecture from scratch to generate indoor trajectories and purchase actions. Additionally, we explored the effectiveness of fine-tuning the pre-trained model with data from another store. Results demonstrate that our method reproduces in-store trajectories and purchase behaviors more accurately than LSTM and SVM models, with fine-tuning significantly reducing the required training data.

By incorporating state-space graph in recent years, driven by rapid technological advancements, embeddings into the LSTM model, we further enrich the evolving customer expectations, and increased model's understanding of the relationships and dependencies competition. As customers demand more personalized and among various features within the dataset, which may convenient services, financial institutions are under pressure lead to improved performance. This combination of LSTM to develop a deeper understanding of their clients' needs and models and state graph embeddings offers a more scalable preferences. This has led to a growing interest in leveraging and efficient solution in predicting customer goals and actions, data-driven approaches to gain insights into customer behavior while maintaining a high level of accuracy and robustness and predict future actions.

This study presents insights gathered from surveys and discussions with specialists in three domains, aiming to find essential elements for a universal explanation framework that could be applied to these and other similar use cases. The insights are incorporated into a software tool that utilizes GP algorithms, known for their interpretability. The applications analyzed include a medical scenario (involving predictive ML), a retail use case (involving prescriptive ML), and an energy use case (also involving predictive ML). We interviewed professionals from each sector, transcribing their conversations for further analysis. Additionally, experts and non-experts in these fields filled out questionnaires designed to probe various dimensions of explanatory methods. The findings indicate a universal preference for sacrificing a degree of accuracy in favor of greater explainability. Additionally, we highlight the significance of feature importance and counterfactual explanations as critical components of such a framework. Our questionnaires are publicly available to facilitate the dissemination of knowledge in the field of XAI.

The majority of existing recommender systems rely on user ratings, which are limited by the lack of user collaboration and the sparsity problem. To address these issues, this study proposes a behavior-based recommender system that leverages customers' natural behaviors, such as browsing and clicking, on e-commerce platforms. The proposed recommendation system involves clustering active customers, determining neighborhoods, collecting similar users, calculating product reputation based on similar users, and recommending high-reputation products. To overcome the complexity of customer behaviors and traditional clustering methods, an unsupervised clustering approach based on product categories is developed to enhance the recommendation methodology. This study makes notable contributions in several aspects. Firstly, a groundbreaking behavior-based recommendation methodology is developed, incorporating customer behavior to generate accurate and tailored recommendations leading to improved customer satisfaction and engagement. Secondly, an original unsupervised clustering method, focusing on product categories, enables more precise clustering and facilitates accurate recommendations. Finally, an approach to determine neighborhoods for active customers within clusters is established, ensuring grouping of customers with similar behavioral patterns to enhance recommendation accuracy and relevance. The proposed recommendation methodology and clustering method contribute to improved recommendation performance, offering valuable insights for researchers and practitioners in the field of e-commerce recommendation systems. Additionally, the proposed method outperforms benchmark methods in experiments conducted using a behavior dataset from the well-known e-commerce site Alibaba.

In todays competitive landscape, businesses grapple with customer retention. Churn prediction models, although beneficial, often lack accuracy due to the reliance on a single data source. The intricate nature of human behavior and high dimensional customer data further complicate these efforts. To address these concerns, this paper proposes a multimodal fusion learning model for identifying customer churn risk levels in financial service providers. Our multimodal approach integrates customer sentiments financial literacy (FL) level, and financial behavioral data, enabling more accurate and bias-free churn prediction models. The proposed FL model utilizes a SMOGN COREG supervised model to gauge customer FL levels from their financial data. The baseline churn model applies an ensemble artificial neural network and oversampling techniques to predict churn propensity in high-dimensional financial data. We also incorporate a speech emotion recognition model employing a pre-trained CNN-VGG16 to recognize customer emotions based on pitch, energy, and tone. To integrate these diverse features while retaining unique insights, we introduced late and hybrid fusion techniques that complementary boost coordinated multimodal co learning. Robust metrics were utilized to evaluate the proposed multimodal fusion model and hence the approach validity, including mean average precision and macro-averaged F1 score. Our novel approach demonstrates a marked improvement in churn prediction, achieving a test accuracy of 91.2%, a Mean Average Precision (MAP) score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid fusion learning technique compared with late fusion and baseline models. Furthermore, the analysis demonstrates a positive correlation between negative emotions, low FL scores, and high-risk customers.

Home delivery failures, traffic congestion, and relatively large handling times have a negative impact on the profitability of last-mile logistics. These external factors contribute to up to $28\%$ of the overall costs and $25\%$ of emissions for the home delivery supply chain. A potential solution, showing annual growth rates up to $36\%$, is the delivery to parcel lockers or parcel shops, denoted by out-of-home (OOH) delivery. In the academic literature, models of customer behavior with respect to OOH delivery were so far limited to deterministic settings, contrasting with the stochastic nature of actual customer choices. We model the sequential decision-making problem of which OOH location to offer against what incentive for each incoming customer, taking into account future customer arrivals and choices. We propose Dynamic Selection and Pricing of OOH (DSPO), an algorithmic pipeline that uses a novel spatial-temporal state encoding as input to a convolutional neural network. We demonstrate the performance of our method by benchmarking it against three state-of-the-art approaches. Our extensive numerical study, guided by real-world data, reveals that DSPO can save $20.8\%$ in costs compared to a situation without OOH locations, $8.1\%$ compared to a static selection and pricing policy, and $4.6\%$ compared to a state-of-the-art demand management benchmark. We provide comprehensive insights into the complex interplay between OOH delivery dynamics and customer behavior influenced by pricing strategies. The implications of our findings suggest practitioners to adopt dynamic selection and pricing policies as OOH delivery gains a larger market share.

Thank you for reading my latest article The Top 4 Examples Of How ChatGPT Can Be Used In Telecom. Here at LinkedIn and at Forbes I regularly write about management and technology trends. To read my future articles simply join my network here or click'Follow'. Also feel free to connect with me via Twitter, Facebook, Instagram, Slideshare or YouTube. The telecom industry has experienced a lot of change and challenges in recent years, and with that comes a need for more efficient and effective communication systems.

If you've been keeping up with the news lately, you've probably noticed that AI is everywhere. From the concept of self-driving cars to newcomers like voice generation, deepfake videos, and OpenAI (Midjourney and ChatGPT), AI is changing the way we live and work. But it's not all sunshine and rainbows – there are also concerns about the ethical implications of AI, particularly when it comes to fraud. The first question we must ask ourselves is: why is AI a dangerous fraud trend in banking? AI has the power to automate and streamline banking processes, which can be exploited by fraudsters.