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.

AI can be way off the mark in its answers to human queries. It can also be entertaining, such as in the recent headline-grabbing cases of DALL-E, which generates art based on user prompts, or ChatGPT, which similarly generates prose. It would be natural for contact center agents to view AI as a threat that could automate them right out of a paycheck. In live customer service, AI can handle many tasks. Those include agent assist, which plumbs knowledgebases for answers to customer questions via human prompts or automatically when listening to the conversation via speech recognition.

Call Center AI Market is projected to grow from USD 1.6 billion in 2022 to USD 4.1 billion by 2027; it is expected to grow at a CAGR of 21.3% % during 2022–2027 according to report published by MarketsandMarkets. AI can provide call center agents with robust historic data and insights about a customer, empowering agents to deliver cross-selling and up-selling opportunities. Organizations can also leverage AI-enabled chatbots and virtual agents to automate repetitive and manual processes, such as order placement, balance inquiries, general inquiries, technical assistance, and other customer services. Based on components, the market size of the solutions segment is expected to hold a larger market share in 2022, while the services segment is projected to grow at a higher CAGR during the forecast period. Call center AI solutions ensure the strengthening of customer relationships, resulting in increased first call resolution rate and improved customer experience. Call center AI solutions ensure the strengthening of customer relationships, resulting in increased first call resolution rate and improved customer experience.

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].

GPT-3 (Generative Pre-trained Transformer 3) is a language model released by OpenAI on June 11, 2020. Access was originally granted via a waiting list. The API was made generally available in November 2021. Since then, dozens of startups have launched AI-powered writing assistants. The most popular on the market at time of writing (July 2022) are Jasper (formerly Jarvis), Rytr and Copy.ai.

There is no better time to begin using artificial intelligence (AI) to optimize your marketing efforts. You can use AI to make marketing more effective. In fact, this could be one of the most disruptive technologies of our time. The key to becoming a marketing leader lies in the ability to identify what works best for your particular niche or industry and apply the latest in AI technology to optimize those efforts. AI can help you improve the effectiveness of your marketing efforts by helping you to automate, scale, and personalize them.

A chatbot is software that simulates human-like conversations with users via text messages on chat. Its key task is to help users by providing answers to their questions. If we dive deep, chatbots are pieces of conversational software powered by artificial intelligence that have the capability to engage in one-to-one chat with customers on their preferred chat platform such as Facebook Messenger, Whatsapp, Instagram, Telegram, Slack and many more conversational platforms. Chatbots, run by pre-programmed algorithms, natural language processing and/or machine learning and conversed in ways that mimicked human communication. Unlike other automated customer service solutions such as IVRS systems that were universally disliked for their robotic nature, Chatbots are seen to get closer to passing the Turing Test convincingly simulating a human conversational partner so well that it was difficult to sense one was chatting with a machine. British Al pioneer Alan Turing in 1950 proposed a test to determine whether machines could think. According to the Turing test, a computer could demonstrate intelligence if a human interviewer, conversing with an unseen human and an unseen computer, could not tell which was which. Although much work has been done in many of the subgroups that fall under the Al umbrella, critics believe that no computer can truly pass the Turing test.

Informatica, an enterprise cloud data management leader, announced the Intelligent Data Management Cloud (IDMC) for Financial Services, an end-to-end integrated data management cloud that enables the entire data lifecycle, including data discovery, ingestion, integration of data and applications, quality improvement, single views and business 360 applications, governance, privacy, and data sharing and democratization. IDMC for Financial Services leverages Informatica's cloud native solutions as an integrated platform to help financial services companies access and leverage Fit for Business Use data to support their top business priorities including: Improve Customer Experience: IDMC for Financial Services allows companies to access and deliver clean, trusted and valid data between the systems that support customer engagement and interaction across any channel, device or business unit. In addition, it enables companies to organize, relate and deliver a 360-degree view of the business for everyone from customer service, sales, and financial advisors to insurance agents to deliver exceptional customer service at their time of need. Grow the Business: IDMC for Financial Services helps marketing and sales organizations identify new cross-sell opportunities to expand wallet share with existing customers to help drive revenue streams and retain customer relationships. It enables users to obtain clean, valid and holistic data about each customer relationship, the accounts or policies they own, and how they are related to other customers, employees or business entities.

Artificial intelligence (AI) and cognitive computing can work together closely by connecting technology with the physical world. The concepts of AI and cognitive computing are deployed widely in various sectors. AI and cognitive computing can self-learn and adapt to new surroundings. However, there is a slight difference between these two technologies. AI creates devices that can act smarter than humans, whereas cognitive computing creates devices that adapt to the surroundings and communicate with humans naturally.

In today's highly connected digital world, understanding, managing and securing the identity of individuals and things is essential to safety and success of both businesses and their customers. Billions of people connect from anywhere, use a wide variety of devices and expect a seamless yet secure experience. The ForgeRock mission is to provide the most simple and comprehensive Identity and Access Management Platform to help our customers deepen their relationships with their consumers and improve the productivity and connectivity of their employees and partners. Our identity solution enables great digital experiences and is embedded with a rich set of security, privacy and consent features. We deliver our platform through both cloud services and on-premises software.