Interview: Mario Vinasco, Facebook on Advancing Marketing Analytics through Rigorous Experimentation
We discuss marketing analytics at Facebook, multi-channel performance assessment, success factors, lessons from Look Back feature, advice, and more.
He has over 18 years of progressive experience in data driven analytics with emphasis in database programming and predictive models 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 platforms, including Hadoop, columnar and traditional relational models.
Mario holds a Masters in engineering economics from Stanford University.
Here is my interview with him:
Anmol Rajpurohit: Q1. What are the main responsibilities of the Marketing Analytics team at Facebook? What benchmarks does the team use to measure its success?
We measure our success with a combination of product metrics and brand survey questions that track user satisfaction, trust and favorability.
AR: Q2. What are the most common tools and technologies used by the Marketing Analytics team to achieve its goals? How has the experience been to move from relational databases to Hive?
AR: Q3. Today, most consumers are having a multi-channel experience. Moreover, the lines between various channels are increasingly becoming blurry. How is this trend impacting Marketing Analytics' ability to judiciously attribute the performance across channels?
It is still part science and part art, and that is when a good analyst brings value to the table.
AR: Q4. What factors (both business and technical) play a key role in determining the success of Predictive Analytics projects?
AR: Q5. Even in the Big Data era, one of the consistent issues is incomplete and/or inaccurate data. How do you deal with this challenge?
AR: Q6. What were the key lessons for the Marketing Analytics team from the launch, monitoring and analysis of Lookback video feature on Facebook platform?
Lookback in particular, motivated friends to like and comment substantially more on this video than in any other piece of content.
We also learned a great deal about the technical difficulties of monitoring and measuring these products and we moved to network experimentation techniques for SayThanks.