For personalized marketing, a new challenge of how to effectively algorithm the A/B testing to maximize user response is urgently to be overcome. In this paper, we present a new approach, the RL-LLM-AB test framework, for using reinforcement learning strategy optimization combined with LLM to automate and personalize A/B tests. The RL-LLM-AB test is built upon the pre-trained instruction-tuned language model. It first generates A/B versions of candidate content variants using a Prompt-Conditioned Generator, and then dynamically embeds and fuses the user portrait and the context of the current query with the multi-modal perception module to constitute the current interaction state. The content version is then selected in real-time through the policy optimization module with an Actor-Critic structure, and long-term revenue is estimated according to real-time feedback (such as click-through rate and conversion rate). Furthermore, a Memory-Augmented Reward Estimator is embedded into the framework to capture long-term user preference drift, which helps to generalize policy across multiple users and content contexts. Numerical results demonstrate the superiority of our proposed RL-LLM-ABTest over existing A/B testing methods, including classical A/B testing, Contextual Bandits, and benchmark reinforcement learning approaches on real-world marketing data.

Impact of Increased Digital Noise: Consumers today receive more digital marketing messages than they did a decade ago, many of them irrelevant. The cumulative effect of this "digital noise" makes it harder for retailers to engage and motivate prospective and existing customers. Personalized messages grounded in real-time, aggregated consumer insights can help retailers stand out on the right channels at the right time. Benefits of AI-Generated, Personalized Marketing: Increased brand engagement and increased conversion are the top two improvements that global marketers see as a result of implementing greater personalization, according to a study by Acquia in late 2021. Importance of First-Party Data: According to a study of US-based executives conducted by Coresight Research and Persado in November 2021, driving online sales (55.6% of respondents) and providing the right information to the right person at the right time (51.4%) are the top two benefits of using first-party data for marketing purposes.

What is personalized marketing, how does it boost your conversion rate, and how can machine learning help you tailor content to a wider audience?

Mark is a senior Salesforce consultant and developer at Cetrix Cloud Services. He headed the IT team at Cetrix for six years, with administration and development of Salesforce as its main responsibility. He also has 18 years of IT experience in software engineering and system integration behind him. He enjoys helping startups and non-profit organizations select and deploy the right technology for their specific needs.