BIG DATA CONFERENCE
Mario A Vinasco
Director of Analytics
Credit Sesame, US
Mario Vinasco has over 15 years of progressive experience in data driven analytics with emphasis in machine learning and data science programming creatively applied to eCommerce, advertising, customer acquisition/retention and marketing investment. Mario specializes in developing and applying leading edge business analytics to complex business problems using big data and predictive modeling platforms.
Mario holds a Masters in engineering economics from Stanford University and currently works as Director of Analytics and Data Science at Credit Sesame a disruptive FinTech company in the San Francisco Bay Area, responsible for customer management, retention and prediction.
Until recently, Mario worked for Uber Technologies applying data science to marketing investment optimization, advanced segmentation of customers by propensity to act, churn, open email and the set up sophisticated experiments to test and validate hypothesis.
At Facebook in the marketing analytics group he was responsible for improving the effectiveness of Facebook’s own consumer-facing campaigns. Key projects included ad-effectiveness measurement of Facebook’s brand marketing activities, and product campaigns for key product priorities using advanced experimentation techniques.
Prior roles included VP of business intelligence in digital textbook startup, people analytics manager at Google and eCommerce Sr manager at Symantec.
The Intuition Behind Machine Learning In Marketing
Since 2013, important breakthroughs and advances in technology have made it possible to run sophisticated predictive models capable of classifying images, text, sound and are now pervasive in many applications such as self-driving cars, chatbots, translation, among other fields.
Marketing has not been an exception to use these new technologies collectively known as Machine Learning (ML); a sub-field of Artificial Intelligence (AI).
This talk presents the key insights that make AI/ML useful for marketing and demystifies the core technology and illustrates case studies where my team applied the technology.
We use AI in creative ways to:
– Improve the signal on A|B experiments and have better reads and insights
– Advanced segmentation of customers by a propensity to act, churn, open an email
– Cross-sell predictions
– Models of resurrection and reactivation
– Natural Language to provide insights on content
– Loyalty programs
In this talk, he will discuss how predictive models are used across these areas