This paper introduces a recurrent neural network approach for predicting user lifetime value in Software as a Service (SaaS) applications. The approach accounts for three connected time dimensions. These dimensions are the user cohort (the date the user joined), user age-in-system (the time since the user joined the service) and the calendar date the user is an age-in-system (i.e., contemporaneous information).The recurrent neural networks use a multi-cell architecture, where each cell resembles a long short-term memory neural network. The approach is applied to predicting both acquisition (new users) and rolling (existing user) lifetime values for a variety of time horizons. It is found to significantly improve median absolute percent error versus light gradient boost models and Buy Until You Die models.

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.

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.

Understanding customer lifetime value is key to nurturing long-term customer relationships, however, estimating it is far from straightforward. In the retail banking industry, commonly used approaches rely on simple heuristics and do not take advantage of the high predictive ability of modern machine learning techniques. We present a general framework for modelling customer lifetime value which may be applied to industries with long-lasting contractual and product-centric customer relationships, of which retail banking is an example. This framework is novel in facilitating CLV predictions over arbitrary time horizons and product-based propensity models. We also detail an implementation of this model which is currently in production at a large UK lender. In testing, we estimate an 43% improvement in out-of-time CLV prediction error relative to a popular baseline approach. Propensity models derived from our CLV model have been used to support customer contact marketing campaigns. In testing, we saw that the top 10% of customers ranked by their propensity to take up investment products were 3.2 times more likely to take up an investment product in the next year than a customer chosen at random.

Explicit reasonable utilization of artificial intelligence incorporates current web search tools, individual collaborator programs that figure out communication in language, self-driving vehicles, and suggestion motors, for example, those utilized by Spotify and Netflix. There are four levels or sorts of Artificial intelligence -- two of which we have accomplished, and two which stay hypothetical at this stage. The "theory of mind" phrasing comes from brain science, and this situation alludes to a simulated intelligence understanding that people have contemplations and feelings which then, at that point, thus, influence the simulated intelligence's way of behaving. ML is a lot of moving and terms these days. Machine Learning (ML) is a sub-section of Artificial intelligence.

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.

Fremont, CA: Conversational AI is an umbrella phrase that refers to numerous approaches to allowing computers to converse with humans. This technology extends from simple natural language processing (NLP) models to more powerful machine learning (ML) models capable of interpreting various inputs and carrying on more intricate conversations. Chatbots, which employ NLP to read user inputs and carry on a conversation, is one of the most frequent uses of conversational AI. Examples of such uses are virtual assistants, customer service chatbots, and voice assistants. Well-informed consumers expect to connect via mobile apps, the web, interactive voice response (IVR), chat, or messaging channels. In addition, they want a consistent and engaging experience that is quick, simple, and personalized.

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].

Knowing how much value each particular customer adds to your business is crucial, not least because it helps define the customer acquisition and retention budgets. Customer Lifetime Value models are a useful tool for gaining such insights. There seems to be, however, a lot of confusion about them, and apparently, everyone is using them wrong. How can we predict lifetime value and customer churn from transaction data only? What does BG/NBD stand for?

Using artificial intelligence and automation to digitize and upgrade banking processes is the new imperative for banks and credit unions. They have come to realize that becoming truly digital institutions requires much more than offering a mobile banking app, or turning paper files into digital files. Digital transformation requires a much more comprehensive approach. One of the most challenging areas is in lending, the bread and butter of banking. Where loan originations used to take place in a face-to-face meeting between a banker and client -- taking upwards of two to three hours (depending on the type of loan) -- they are now taking place online and on mobile phones.