Recorded August 2019
You will find the slides used in this webinar HERE.
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 introduces important business and technology concepts in personalized marketing. Using Azure Machine Learning and a public Microsoft GitHub repository for recommendation engines, we walk through an example of providing product recommendations and managing the data science lifecycle in a cloud service. We also address common challenges to data scientists and digital marketers including how to train, test, optimize, and deploy recommender models.
Check out BlueGranite's Retail and CPG Industry page for more retail and consumer goods business analytics solutions.
Andy Lathrop, Principal Consultant
Andy is passionate about helping customers employ modern AI technology to solve tough problems and make their business better. Drawing on a diverse background including military service, non-profit work, teaching, and over 17 years in enterprise analytics, Andy loves working on projects that require leadership, teamwork, and technical skills. He has expertise in AI solutions and business analytics using Azure Cognitive Services and Machine Learning, R, Python, Monte Carlo simulation, discrete-event simulation, Power BI, and Spotfire. He holds a B.S. degree in operations research and M.S. degree in predictive analytics.