Reinforcement Learning for Dynamic Customer Journey Optimization in Salesforce Marketing Cloud Article Swipe
In today’s digital world, delivering personalized customer experiences is paramount for businesses that are aiming to foster engagement and drive conversions. However, many organizations grapple with challenges such as data inconsistencies and outdated technologies. A report by Contentful highlights that 57% of senior marketing executives struggle with data inconsistencies when personalizing customer experiences, and only 24% of firms effectively invest in omnichannel personalization due to departmental silos and outdated technology1. Reinforcement Learning (RL), a subset of machine learning, offers a promising solution to these challenges by enabling systems to learn optimal strategies through trial and error interactions with the environment. In the context of marketing, RL can dynamically adapt customer journeys in real-time, optimizing for long-term customer value rather than short-term metrics . Salesforce Marketing Cloud serves as a solid platform for implementing RL-driven strategies, offering tools like Journey Builder and Audience Studio that facilitate the orchestration of personalized customer experiences across multiple channels . This whitepaper aims to explore the integration of Reinforcement Learning into Salesforce Marketing Cloud for dynamic customer journey optimization. It will explore the challenges of current personalization methods, elucidate the principles of RL, and provide guidance on implementing RL strategies within the Salesforce ecosystem to enhance customer engagement and business outcomes.
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Metadata
- Type
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- Language
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- Landing Page
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- https://doi.org/10.35629/3795-10023340
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All OpenAlex metadata
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https://openalex.org/W4412361321Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.35629/3795-10023340Digital Object Identifier
- Title
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Reinforcement Learning for Dynamic Customer Journey Optimization in Salesforce Marketing CloudWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-02-01Full publication date if available
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Manish GuptaList of authors in order
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https://doi.org/10.35629/3795-10023340Publisher landing page
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https://doi.org/10.35629/3795-10023340Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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Reinforcement learning, Reinforcement, Cloud computing, Computer science, Business, Marketing, Engineering, Artificial intelligence, Operating system, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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