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This episode of The Analytics Edge is brought to you by NetSpring and showcases John Humphrey, former Head of Data Platform Product and Chief Data Officer at Intuit MailChimp. John joins us to discuss data strategies for Customer Analytics, from continuing investments in Customer 360 to the emergence of warehouse-native data platforms.
He delves into the technology gaps that forced a detour from our earlier vision of Customer Analytics. As products became more digital, the data platforms at the time were unable to handle all the event data being produced – leading to the emergence of specialized first-generation product analytics platforms, data silos, and fragmented analytics platforms for product analytics.
John introduces 4 levels of Customer Analytics maturity to help data leaders rationalize earlier investments, and how the cloud data warehouse is enabling warehouse-native strategies and applications to break down those earlier silos, and finally now, deliver on the promise of C360 and Customer Analytics.
Bio:John Humphrey is the former Head of Data Platform Product and Chief Data Officer at Intuit MailChimp, with over 2 decades of experience and expertise in data science and data engineering. He was the first data analyst at Goodreads (later acquired by Amazon), helped take LegalZoom public, and has had multiple stints in data leadership roles at Meta and Intuit along the way. John earned a masters in Systems Engineering from the University of Virginia and holds a bachelors in Management Science from Virginia Tech.
Key Quote:
“TBD”- John Humphrey
Episode Timestamps(Segment 1)
(1:16) Earlier roles in product analytics
(3:30) Shortcomings of 1st-gen product analytics
(9:08) Shortcomings of BI for product analytics
(11:26) Funnels in BI vs. product analytics
(13:04) Customer 360 and the data warehouse today
(15:23) Data streams for complete C360
(17:42) Customer 360 versus a CDP
(20:48) Aligning C360 & CDP strategies with the warehouse
(Segment 2)
(23:53) Benefits of cloud data warehouses
(26:11) Analytics tools for all types of customer data.
(29:38) Maturity model for Customer Analytics
(Segment 3)
(34:02) AI-powered Customer Analytics
(Segment 4)
(37:50) Takeaways
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