Data Driven Product Management - ProductTank Boston Feb '14

  • 356 views
Uploaded on

Practical Ideas and Tools PMs Can And Should Use to Make Decisions …

Practical Ideas and Tools PMs Can And Should Use to Make Decisions

Talk given at Boston ProductTank Meetup. http://www.meetup.com/ProductTank-Boston/events/165579612/

More in: Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
356
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
13
Comments
0
Likes
1

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Data-Driven Product Management Practical Ideas and Tools PMs Can And Should Use to Make Decisions 1
  • 2. About Me     Who here lives in Arlington? (Vote Dunn!) MIT mechanical engineer (but I never used it) 7 startups in 15 years Career path from support to implementation to QA to PM <date> – Confidential 2
  • 3. 3
  • 4. Most PMs Aren’t Visionaries  Ideas come from customers, colleagues, and prospects  Steve Jobs isn’t walking into this product meeting  PMs probe, interpret, and synthesize 4
  • 5. Ideas Are Not the Scarce Resource  Ideas come in sizes: markets, features, bug fixes, and optimizations  They have different motivations  Increased sales  Higher retention  Lower cost of goods  Unlimited resources, you could do it all – but we don’t have that  Someone has to decide what is next  This is why PMs get paid the big bucks 5
  • 6. Optimize for Enterprise Value  The PM’s job is to prioritize  What’s the North Star for your company?  Stars are directional – you can’t make a map to get to star  How do you know if you are pointed in the right direct?  How do you know if you are making progress?  How do you compare apples to oranges?  And compare that to bacon? 6
  • 7. “That is a knowable fact.”  What the advocate says       “No one uses that feature” “Everyone wants this!” “That breaks all the time” “You're not fixing enough bugs” “This problem happens to everyone!” “I’ve heard this request a million times”  What the data says  15% of users click that every week  We’ve had 3 customers ask for this feature  5% of support calls are associated with a bug 7
  • 8. Know Which Facts Are Knowable  Carefully separate opinion from fact, known from unknown  Huge, immediate reduction in complexity of the decision  Develop a third and fourth category 1. 2. 3. 4. We really don’t know Knowable fact We can know if we do . . . Before we decide, we really should know  A good PM uses all 4 categories to make a decision  This talk is more about 3 and 4 8
  • 9. Know Your Data. Wallow In It. 9
  • 10. Your Application Database Knows  Your customers using your app are telling you how they use it.  You need to get the data reproducibly  You need data, not reports  Know what you need to change  Know if your changes actually worked or not 10
  • 11. Measure It From The Start  Your application database can’t tell you everything  Make an early change that adds data and measurement  Pipeline speed  Funnel shape  Daily activity
  • 12. Measure the Good and the Bad  You have to know what the problems are  You have to know when they get worse
  • 13. Make a Dashboard of It
  • 14. When Do You Have to Decide?  Most of the time, the answer is “later”  Don’t decide until you have to  This is where the art meets the science  Know your downsides and worst-case scenarios, and mitigate them  Watch, and monitor  Agile (“agile”) really shines here  You will have the development bandwidth when you need it  Unfortunately frustrating for many customers and colleagues 14
  • 15. Time to Invest! 15
  • 16. Keep Investing! 16
  • 17. How Do You Decide?  Most decisions aren’t reduced to a time series  Comparing apples, oranges, and bacon  Your company needs all three  Collect all the data you can  Read what the customer said (or potential customer). Talk to them directly.  Talk to the people who interacted with them (support, consultant, sales rep, account manager)  Look at the usage  Look at the market and the competition 17
  • 18. Find a way to order the data      Whiteboards and stickies What themes can you find What time ordering can you find What pre-requisites can you find Which ideas are both cheap and enable discovery 18
  • 19. Build a framework  Whiteboards and stickies – and Excel  Just make one up         10 points for data loss 1 point for annoying 1 point per customer affected 3 points per big customer You are the most qualified person to do it See what maps to your intuition, what doesn’t Know the limitations of what you built Iterate 19
  • 20. Customer Pain (in thousands) <date> – Confidential 20
  • 21. My Tools  SQL  You need access to the data, not reports  NoSQL has query tools, too  Text editor  UltraEdit. Python, Perl work too  Turn dross into data  Excel  You do know how to make a pivot table, right? Find the lumpy parts.  Can you do vlookups in your sleep? Integrate your data sources  Tableau  Whiteboards and stickies  TheBrain mind-mapping software 21
  • 22. <date> – Confidential 23