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Data-driven Product Management

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My keynote at Product Camp Hyderabad, Feb 2, 2019. In this talk I discussed the topic of how being data-driven could make product management change from "just in case" to "just in time"

Publicado en: Software

Data-driven Product Management

  1. 1. Data-Driven Product Management Tathagat Varma Country Manager, NerdWallet
  2. 2. Disclaimer These are my personal views and don’t represent my employer Nerdwallet in any manner.
  3. 3. Our Mission Provide clarity for all of life’s financial decisions. Our Vision A world where everyone makes financial decisions with confidence.
  4. 4. PM’s #1 Job?
  5. 5. By…
  6. 6. But…
  7. 7. So, just do it!
  8. 8. Product bloats…
  9. 9. UX goes downhill…
  10. 10. Features remain unused…
  11. 11. Investment is wasted…
  12. 12. Though users are ok :)
  13. 13. Almost forgot…
  14. 14. Meet “HIPPO” “Highest Paid Person’s Opinion”
  15. 15. HIPPOs have an…
  16. 16. They’re always right!
  17. 17. And data is so wrong! Pic:
  18. 18. So, who are they? “I can’t say it any better, HiPPO’s rule the world, they overrule your data, they impose their opinions on you and your company customers, they think they know best (sometimes they do), their mere presence in a meeting prevents ideas from coming up.”
  19. 19. And they’re real!
  20. 20. So, what’s the solution? “…depersonalize decision making, simply don’t make it about you or what you think. Go outside, get context from other places. Include external or internal benchmarks in your analysis. Get competitive data (we are at x% of zz metric and our competition is at x+9% of zz metric).” Be incessantly focussed on your company customers and dragging their voice to the table (for example via experimentation and testing or via open ended survey questions). Very few people, HiPPO’s included, can argue with a customer’s voice, the customer after all is the queen / king!
  21. 21. Today’s context… • Time: Quarter? Month? Week? Day? Hours?… • Money: Never enough! • Competition: Faster and closer than you think! • Customers: Merciless, demanding and no loyalty! • Product: Always evolving, only features not enough anymore…
  22. 22. Data-driven PM?
  23. 23. Overarching “Process” • Vision: Set clear quantitative goals that represent long-term vision (~1-3y), or “To Be”. This serves as the true north for the product team. • Strategy: Identify KPIs that help establish “As Is” baseline and track the trajectory towards goals using “actionable metrics” (~1-3m). Pivot when strategy won’t lead to the vision. • Execution: Cull out metrics that help track and optimize operations (daily, weekly, hourly?). Perform “GPS” style course-correction to ensure performance is in range. Might also track “vanity metrics”. • Innovation: Establish “safe to fail” experiments inside sprints (or shorter!) to test out new ideas and build data to support rolling-in a new feature (or deprecating old ones!)
  24. 24. Goals (become “x” by “y”) • Marketshare: E.g. Increase from 23% to 31% in 2019 • Revenue: E.g. Achieve topline of $750B by 2022 • Profitability: E.g. Break-even by 2025 • Operations: E.g. Improve unit economics by 30% by 2020 • Customer: E.g. Become the most-preferred brand by 2023 • Product: E.g. Be #1 source for xyz • …
  25. 25. KPIs (x1, x2,…) & Metrics (where is x right now?) • Marketshare: Activation, Installed Base, Penetration, … • Revenue: Free-to-Paid Conversion, Retention, CTR, CPM, ARR/ MRR, … • Profitability: CAC, ARPU, LTV, … • Operations: Acquisition, Onboarding, Traffic Sources, UVs, … • Customer: DAU/MAU, CSAT, NPS, Referral, TS, PV, Bounce rate, … • Product: Feature parity, Feature usage, Quality, Product Rating, … • Marketing: Organic vs Paid Traffic, …
  26. 26. Goals, KPIs & Metrics
  27. 27. Scientific Method
  28. 28. Recap • Old-school MRD / PRD is too inefficient, too slow, too costly • Relying on HIPPO could lead to unpredictable, unactionable and unrepeatable results. • Set clear hard number long-term goals, and cull out KPIs and interim milestones to inform PMs what we are after! Link incremental investment! • The goal of a release, or even a sprint is to move the needle, and not just to checkbox on the backlog. • Requirements emerge as “interesting hypotheses” based on experimental, feedback, anecdotal and intuitive data but must be rigorously validated before roll-out. • Execute using a closed-loop system modeled around the scientific method. Monitor product metrics, track customer KPIs and accomplish business goals!
  29. 29. References • • #4fb355d940cf • • • • • • • product-5b85352b3500 • • and-people-than-technology/ • • • • • • •