Introduction to Personalized Marketing

featuring Microsoft's Azure Machine Learning

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. 

 this webinar is designed for

  • Chief marketing managers and digital marketing managers interested in using machine learning as part of their analytics strategy (and don’t mind seeing a bit of code).
  • Data science team managers and data scientists interested in cloud ml tools for collaboration and managing projects.  We’ll use a recommender model, but the concepts apply across ml use cases.

 

 Webinar goals

  • Understand the top benefits of personalized marketing and common machine learning techniques to enable personalized consumer experiences. 
  • Review fundamentals of recommendation systems: their purpose, data requirements and common algorithms (focusing on Python-based environments)
  • Understand the benefits of using Microsoft Azure Machine Learning and related Microsoft assets for building better operational recommender systems.

 

PRESENTERS

Andy Lathrop, Principal Consultant 

andy-lathrop-v2Andy 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.

Jason Cantrell, Senior Consultant

JasonCantrell-1

Jason is a Senior Consultant at BlueGranite with a passion for data science. He has over 10 years of experience delivering Data and Analytics solutions for various employers. Jason also has over 6 years of experience delivering Data and Analytics solutions in a consulting role, which includes technical pre-sales, solution architecture design, development, and implementation.

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