The continued growth of e-commerce and social media, combined with growing data volumes related to digital activity, has made it possible – and even an expectation - for brands to provide personal experiences to their customers. Effective digital marketing provides richer insights from customer interactions, allowing organizations to create better content, develop deeper prospect relationships, and ultimately achieve greater ROI from advertising. Personalized marketing utilizes modern tools, like machine learning and AI, then operationalizes insights from those tools. Common barriers to success when using data science technology include difficulty collaborating among team members, managing experiments and other modeling artifacts, scalability, and using predictions in applications.
In this webinar, the BlueGranite team will introduce important business and technology concepts in personalized marketing. Using Azure Machine Learning and a public Microsoft GitHub repository for recommendation engines, we’ll walk through an example of providing product recommendations and managing the data science lifecycle in a cloud service. We’ll address common challenges to data scientists and digital marketers including how to train, test, optimize, and deploy recommender models.
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