Better Living Through Analytics - Strategies for Data Decisions

Product School
5 Sep 2017
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
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Better Living Through Analytics - Strategies for Data Decisions

Notes de l'éditeur

  1. When you checked in tonight, you got an email inviting you to join our slack community In that community, we have 12k product people who have come through different companies like google, facebook, uber Sharing information about events, job offers from our partner companies, and valuable online content Please check your email and join - it’s free
  2. Opening remarks: Good evening Hope you’re doing well Name, rank, serial number Thanks for inviting me Evangelizing for the ZipRecruiter analytics model - the industry is figuring itself out Questions at the end, please Who is in the audience? Analysts? Product? Marketing or Finance?
  3. Go over the analytics at ziprecruiter - it’s a good model Talk about how to avoid friction, which can show up even when everyone is acting in good faith
  4. Context is important
  5. Background is more engineering than stats Experience at a small company was good to learn about poorly-defined problems, experience at JPMC was good to learn about scale ZipRecruiter is a little of both
  6. Fast growing, founded in 2010 Major player in the employment/recruiting space Raised millions of dollars in funding Great place to work!
  7. The experience needs to be good for both employers and jobseekers, but finding good matches is hard, as tinder users know Analytics support is also needed to determine strategy for marketing and product We can improve operations by automating fraud detection
  8. Gatekeepers of statistical decisions making Work with everyone Know what is and is not feasible
  9. Organizations do analytics differently Sometimes analysts are “embedded” Sometimes they are independent advisors which are separated
  10. Early and mid-stage companies are still often figuring this out! The structure can change too
  11. Up next: How can analytics and product work together? Avoid friction? Data scientist, not a tech talk, one equation
  12. How can analytics help? What does a successful partnership look like?
  13. Analysis can reveal what the best product decisions are from multiple alternatives Automating analysis can lead to efficiency gains and product improvements
  14. It’s not a consulting gig, it’s a partnership! Both teams should trust and support each other This is related to incentives: For product, the key is a successful, on-time, on-budget shipment of something that people like. A project manager can afford to be wrong and iterate quickly. For analytics, the key is applying the right techniques to reliably get correct answers. An analyst would rather be slow but cautiously correct For the partnership to be successful, both sides need to respect the goals of the other!
  15. If we have a roadmap for the project, we can figure out who needs to be involved at each step Not having a clear division of responsibility leads to confusion and stepping on toes
  16. Analysts care about all of these, product only needs to care about the left side of the line.
  17. If we have a roadmap for the project, we can figure out who needs to be involved at each step
  18. This is a parable about partnership This demonstrates, I think, the success of our model
  19. The conversion rate at N responses is not exactly 30.4532324234234%
  20. Remember: Analysts care about all of these, product only needs to care about the left side of the line. Product needs to help us understand: What data we can use What the specific problem is What the metric they want to move is If the proposed model (curve) would be useful
  21. Tribal knowledge of the organization There are no known best practices - but I think these are pretty good Each of these has two sides - what product can do, and what analytics can do
  22. Story: Case study about - avoid presenting estimates as iron-clad facts Your users need to understand the best case and the worst case in your forecasts
  23. A/B tests are part of the industrial practice In my experience, they are worth the investment ZipRecruiter - system for launching, tracking, analyzing A/B tests has paid off a lot
  24. New analysts often tend to give detailed technical reports - that goes in the appendix No one cares about your damned P-values
  25. This goes hand-in-hand with the last one - it’s about supporting decision making, not doing statistics for its own sake
  26. Story: Scammer detection - initial improvement leading to bigger wins - easier to manage than a full overhaul involving so many people