Speaker "Jim Porzak" Details Back



Structuring Data for Self-Serve Customer Insights


All parts of modern customer centric organizations need to understand their customers in order to serve them better. While it is impossible for business intelligence teams to anticipate all questions the business may ask, we can create a self-serve customer insights data mart with customer focused data elements that can be queried both on a production and ad hoc basis.

Data Scientists will have preprocessed and validated data ready for use in their modeling, segmentation, and recommendation efforts thus saving time and agro in addition to eliminating data munging ambiguities.

Business Analysts can use friendly front ends – either a SQL browser or by any number visual of data exploration tools such as Tableau – to quickly pull customer insights and, as useful, integrate into dashboards.

The trick is make the data 1) analyst ready, 2) complete at different levels of abstraction, and 3) model customer decision points. You will learn the details of those three design criteria and see concrete examples of how the theory is applied to both subscription and product businesses.


Bio: Jim is a semi-retired data scientist specializing in data-driven customer insights. Since last fall he has been primarily engaged by ( a LinkedIn company) working with the marketing, product, and content teams. Past experience includes,, Responsys, LA Times, 24 Hour Fitness and Sun Microsystems, to name a few. Jim specializes in using customer behavioral and demographic data to predict purchase and/or churn propensity, build cluster based customer segments, and do routine marketing analytics. Jim is very active in the open-source community – particularly in R-the open-source software environment for statistical computing and graphics. He is a frequent speaker at conferences in the US & Europe. See his blog “Data Science for Customer Insights”, for past talks, reflections ad current views.