Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications. However, the heavy sparsity of consumption events and the interference of data variance and noise obstruct LTV estimation. Many existing LTV prediction methods directly train a single-view LTV predictor on consumption samples, which may yield inaccurate and even biased knowledge extraction. In this paper, we propose a contrastive multi-view framework for LTV prediction, which is a plug-and-play solution compatible with various backbone models. It synthesizes multiple heterogeneous LTV regressors with complementary knowledge to improve model robustness and captures sample relatedness via contrastive learning to mitigate the dependency on data abundance. Concretely, we use a decomposed scheme that converts the LTV prediction problem into a combination of estimating consumption probability and payment amount. To alleviate the impact of noisy data on model learning, we propose a multi-view framework that jointly optimizes multiple types of regressors with diverse characteristics and advantages to encode and fuse comprehensive knowledge. To fully exploit the potential of limited training samples, we propose a hybrid contrastive learning method to help capture the relatedness between samples in both classification and regression tasks. We conduct extensive experiments on a real-world game LTV prediction dataset and the results validate the effectiveness of our method. We have deployed our solution online in Huawei's mobile game center and achieved 32.26% of total payment amount gains.

Tasks critical to enterprise profitability, such as customer churn prediction, fraudulent account detection or customer lifetime value estimation, are often tackled by models trained on features engineered from customer data in tabular format. Application-specific feature engineering adds development, operationalization and maintenance costs over time. Recent advances in representation learning present an opportunity to simplify and generalize feature engineering across applications. When applying these advancements to tabular data researchers deal with data heterogeneity, variations in customer engagement history or the sheer volume of enterprise datasets. In this paper, we propose a novel approach to encode tabular data containing customer transactions, purchase history and other interactions into a generic representation of a customer's association with the business. We then evaluate these embeddings as features to train multiple models spanning a variety of applications. CASPR, Customer Activity Sequence-based Prediction and Representation, applies Transformer architecture to encode activity sequences to improve model performance and avoid bespoke feature engineering across applications.

In the course of time, data science has proved its high value and efficiency. Data scientists find more and more new ways to implement big data solutions in daily life. Nowadays data is a fuel needed for a successful company. Telecommunication companies are not an exception. Due to these circumstances, they cannot afford not to use data science.

In the times when data is of extreme value, industries cannot afford to ignore it. The only right choice for them is to find new ways to use data for their benefit. These words have become a motto for the sales industry along with the others. As far as the feature of the repetitiveness may characterize sales industry, there are numerous dimensions where data science may be applied. All in all, data science brings growth, improvements, efficiency, and effectiveness in sales.

The relationship between machine learning (ML) and marketing has been strengthening in the past years, resulting in the emergence of a new set of strategies and tools that optimize the process. A modern marketer is left with no choice except to jump on bandwagon to stay in competition and maintain the required skill-set in the industry. One the other hand, we have all been wishing to have this revolutionary way of performing marketing tasks. Your customers are at the center of your business, and you can finally deliver on this fact by using ML to your advantage. With the ability to track and analyze data with the purpose of driving customer engagement, ML has numerous uses in marketing.

What is the impact of artificial intelligence in insurance? Well, there are a lot of use cases for artificial intelligence in everyday life, but what about AI in insurance? The effects of artificial intelligence in business heavily include insurance. Are you scared of AI jargon? We have already created a detailed AI glossary for the most commonly used artificial intelligence terms and explained the basics of artificial intelligence as well as the risks and benefits of artificial intelligence for organizations and others. So, it's time to explore the role of artificial intelligence in insurance sector. One of the most revolutionary advances has been the use of AI in insurance, which has been hailed as having significant economic and societal advantages that eventually boost risk pooling and improve risk reduction, mitigation, and prevention.

Customer lifetime value (CLV or LTV) refers broadly to the revenue that a company can attribute to one or more customer over the length of their relationship with the company [55]. The process of predicting the lifetime value consists in producing one or more monetary values that correspond to the sum of all the different types of revenues that a specific customer, or a specific cohort, will generate in the future. The purposes of this prediction are manifold: for example, having an early estimation of a customer's potential value allows more accurate budgeting for future investment; moreover, monitoring the remaining potential revenue from an established customer could permit preemptive actions in case of decreased engagement. Predicting customer lifetime value is a complex challenge and, to date, there is no single established practice. Furthermore, due to its wide potential impact in different business aspects, the problem is being researched in different communities using a plethora of different techniques, varying from parametric statistical models to deep learning [28, 70].

Welcome to the course on Data Science & Deep Learning for Business 20 Case Studies! This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade! "I'm only half way through this course, but i have to say WOW. It's so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it's broken down so simply. Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! "It is pretty different in format, from others.

Welcome to the course on Data Science & Deep Learning for Business 20 Case Studies! This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade! "I'm only half way through this course, but i have to say WOW. It's so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it's broken down so simply. Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! "It is pretty different in format, from others.

Identifying how much personalization to offer – and to whom – will separate winners from losers. Hyper-personalization is one of three areas we focus on as part of the digital consumption cross-industry theme. The other themes we examine are products and services to experiences and ownership to access. More than 70% of customers now expect more personalized experiences with the brands they interact with,¹ and digital technology is enabling companies to meet these expectations by delivering personalization to large numbers of customers at a low cost. Spectacular advances in artificial intelligence (AI) and software intelligence are enabling companies to take personalization to the next level, making products and services highly relevant to a very large number of customers at the same time.