{"product_id":"artificial-intelligence-for-crm-keeping-customers-informed","title":"Artificial Intelligence for CRM: Keeping Customers Informed","description":"\u003cp\u003eIn this review of Artificial Intelligence for Customer Relationship Management: Keeping Customers Informed, the author explores how AI techniques can transform everyday customer interactions. The book is aimed at researchers and practitioners who need practical approaches for making CRM systems act like a customer, and the single biggest reason to read it is its focus on using \u003cstrong\u003enatural language processing\u003c\/strong\u003e and small-data learning to improve real-world customer outcomes such as refunds, account recovery, and test result delivery.\u003c\/p\u003e\u003ch2\u003eKey Features\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAI-driven CRM focus:\u003c\/strong\u003e Provides concrete frameworks for applying AI to customer-facing tasks so systems can predict and respond to customer needs.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNLP applications:\u003c\/strong\u003e Shows how natural language processing can be used to read between the lines of customer communication and infer intent.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSmall-data learning:\u003c\/strong\u003e Emphasizes methods that work with limited customer data, which is practical for many enterprises with sparse labeled records.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExplainability:\u003c\/strong\u003e Discusses why explainable reasoning is important in CRM decisions, helping practitioners justify automated actions to stakeholders.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOperational scenarios:\u003c\/strong\u003e Uses examples like refunds for canceled flights, unfreezing bank accounts, and delivering health test results to illustrate real benefits.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eWho It's For\u003c\/h2\u003e\u003cp\u003eThis monograph is best for CRM researchers, data scientists, and product managers who want to integrate advanced reasoning and \u003cstrong\u003emachine learning\u003c\/strong\u003e into customer workflows rather than only analyzing historical data. It suits readers who appreciate a research-based, implementation-minded treatment of AI in service contexts.\u003c\/p\u003e\u003cp\u003ePractitioners seeking a quick, high-level marketing primer should look elsewhere; the book expects some familiarity with AI concepts and focuses on modeling and reasoning rather than introductory overviews.\u003c\/p\u003e\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eClear attention to real-world customer scenarios makes the techniques easy to relate to operational problems.\u003c\/li\u003e\n\u003cli\u003eFocus on small-data learning provides practical methods for organizations without large labeled datasets.\u003c\/li\u003e\n\u003cli\u003eEmphasis on explainability supports safer, more defensible CRM automation.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003eThe treatment is research-oriented and may be dense for readers without prior AI or ML background.\u003c\/li\u003e\u003c\/ul\u003e\u003ch2\u003eSpecifications\u003c\/h2\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eArtificial Intelligence for Customer Relationship Management: Keeping Customers Informed\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAuthor\u003c\/td\u003e\n\u003ctd\u003eBoris Galitsky\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSeries\u003c\/td\u003e\n\u003ctd\u003eHumanComputer Interaction Series\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePrimary topics\u003c\/td\u003e\n\u003ctd\u003eCRM, NLP, ML, explainability\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUse cases highlighted\u003c\/td\u003e\n\u003ctd\u003eRefunds, account unfreezing, health test results\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eApproach\u003c\/td\u003e\n\u003ctd\u003eSimulation, reasoning, learning from small data\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\u003ch2\u003eOur Verdict\u003c\/h2\u003e\u003cp\u003eFor professionals who want to make CRM systems that understand and predict customer actions, this monograph offers practical, research-backed techniques and valuable examples. It represents good value for data scientists and product teams committed to adding \u003cstrong\u003eintelligent reasoning\u003c\/strong\u003e to customer workflows; those needing a beginner primer should supplement it with an introductory text.\u003c\/p\u003e\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003eDoes this book cover practical examples?\u003c\/strong\u003e\u003cbr\u003eYes, it uses scenarios such as refunds, unfreezing accounts, and test result delivery to ground the techniques.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eIs prior AI knowledge required?\u003c\/strong\u003e\u003cbr\u003eSome familiarity with ML and NLP is helpful because the book is research-oriented and focuses on implementation methods.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWill it help with small datasets?\u003c\/strong\u003e\u003cbr\u003eYes, a core theme is learning and reasoning from small data tailored to CRM settings.\u003c\/p\u003e","brand":"Boris Galitsky","offers":[{"title":"Default Title","offer_id":48618077815003,"sku":"3030521699","price":171.91,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0724\/1043\/1707\/files\/61Sa1YqUcVL._SL1254.jpg?v=1778498306","url":"https:\/\/gearmusthave.com\/products\/artificial-intelligence-for-crm-keeping-customers-informed","provider":"GearMustHave","version":"1.0","type":"link"}