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Analytics & Testing for PM
Zakka Fauzan
- prepared exclusively for PDC’17 -
Brief Information
´ All presented here is related to (quantitative) data.
´ However, analytics can be either qualitative or quantitative data (or both).
Analytics
´ Why, as PM, do we need to do analysis?
´ Because analytics is one of MANY skills needed by a PM.
´ Here I will just explain two experiments to be analyze:
´ Results of feature developed
´ Results of feature enhanced
Principle of Doing Analytics
´ Always test it, more about this later.
´ MVP! You can measure better with MVP.
´ Beware of MVP, it should be Minimum and Viable both.
Principle of Doing Analytics
Easy way to understand
MVP, what it should be.
Principle of Doing Analytics
´ Put the tracker everywhere so you have better analytics.
´ When it’s written everywhere, it does mean everywhere.
´ “Easy” case is when seeing the whole funnel, not acquisition vs activation only
(e.g., sessions vs transactions), but also sessions for each page.
´ Difficult case, when seeing a form, not only see “how many visiting the form” vs
“how many finished filling the form”, but also see the drop/exit rate for each field.
Principle of Doing Analytics
´ Be skeptical
´ Be aware of correlation trap, a.k.a. cum hoc ergo propter hoc.
´ When we find any correlation, make sure that it’s causation (or not).
´ Case in point, Bukalapak in 2014, #pelapak vs #trx.
´ When having a causation, there are 3 possible cases.
Principle of Doing Analytics
´ Remember of the pirate metrics of a startup
´ AARRR – Acquisition, Activation, Retention, Revenue, Referral
´ There are two approaches on this:
´ If you know exactly where you want to fix a thing, get focused on it
´ If you are not sure or don’t know, check every part of the metrics, fix the one having lots
of low-hanging-fruit enhancement.
´ At one time (define it by yourself, be it week, 2 weeks, sprint, month, or whatever)
just get focused on one or two out of those five metrics.
´ Too much focuses = no focus at all.
Tools for Analytics
´ Google Analytics
´ Google Tag Manager
´ Google Search Console (GWT) especially if your products involve search
engine a lot
´ Mixpanel
´ Some analytics tool for qualitative data can as well be used if you’re
executing qualitative analytics
Testing
´ Same questions for analytics skill applies here as well, PM does need testing
skill as one of a lot of skills he needs to master.
´ There are other advantages:
´ Being more independent from data people.
´ Getting respected. One of the easiest ways to gain respect from people around
is by speaking their language. Hence, understanding the (general) language of
data is important.
Types of Testing
´ Historical Testing
´ On-off Testing
´ Quite similar to historical testing
´ AB testing / Multivariate testing
´ Multivariate testing is just AB testing with more variants (more than two)
´ When not to use AB testing?
Some tips for doing A/B Testing
´ Make sure that your A/B testing is 100%
random.
´ A/B testing should be done at least
one week, because each day is
unique.
´ Wait for enough confidence level
(usually 90/95/99%) before deciding.
´ If it’s possible, always run the test AFTER
the winner is decided.
´ Measure MORE than one metric.
Some extra notes regarding A/B Testing
´ To be able to do A/B testing properly, make sure these four property
supports:
´ Resource – time, money, etc
´ Tool - next slide
´ Focus – because sometimes doing A/B testing will make tasks executed a bit
slower
´ People – it’s not enough to have data-mindset on only data, product or even
engineering people, it needs to be spread widely on every part of the company,
even to the C-levels.
Best Resources for AB Testing (in my
opinion)
Tools for Testing
´ Visual Web Optimizer
´ Optimizely
´ Mixpanel
´ Growthhackers.com
´ Build it yourself!
Thank you

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"Analytics & Testing for Product Manager" by Zakka Fauzan (Bukalapak)

  • 1. Analytics & Testing for PM Zakka Fauzan - prepared exclusively for PDC’17 -
  • 2. Brief Information ´ All presented here is related to (quantitative) data. ´ However, analytics can be either qualitative or quantitative data (or both).
  • 3. Analytics ´ Why, as PM, do we need to do analysis? ´ Because analytics is one of MANY skills needed by a PM. ´ Here I will just explain two experiments to be analyze: ´ Results of feature developed ´ Results of feature enhanced
  • 4. Principle of Doing Analytics ´ Always test it, more about this later. ´ MVP! You can measure better with MVP. ´ Beware of MVP, it should be Minimum and Viable both.
  • 5. Principle of Doing Analytics Easy way to understand MVP, what it should be.
  • 6. Principle of Doing Analytics ´ Put the tracker everywhere so you have better analytics. ´ When it’s written everywhere, it does mean everywhere. ´ “Easy” case is when seeing the whole funnel, not acquisition vs activation only (e.g., sessions vs transactions), but also sessions for each page. ´ Difficult case, when seeing a form, not only see “how many visiting the form” vs “how many finished filling the form”, but also see the drop/exit rate for each field.
  • 7. Principle of Doing Analytics ´ Be skeptical ´ Be aware of correlation trap, a.k.a. cum hoc ergo propter hoc. ´ When we find any correlation, make sure that it’s causation (or not). ´ Case in point, Bukalapak in 2014, #pelapak vs #trx. ´ When having a causation, there are 3 possible cases.
  • 8. Principle of Doing Analytics ´ Remember of the pirate metrics of a startup ´ AARRR – Acquisition, Activation, Retention, Revenue, Referral ´ There are two approaches on this: ´ If you know exactly where you want to fix a thing, get focused on it ´ If you are not sure or don’t know, check every part of the metrics, fix the one having lots of low-hanging-fruit enhancement. ´ At one time (define it by yourself, be it week, 2 weeks, sprint, month, or whatever) just get focused on one or two out of those five metrics. ´ Too much focuses = no focus at all.
  • 9. Tools for Analytics ´ Google Analytics ´ Google Tag Manager ´ Google Search Console (GWT) especially if your products involve search engine a lot ´ Mixpanel ´ Some analytics tool for qualitative data can as well be used if you’re executing qualitative analytics
  • 10. Testing ´ Same questions for analytics skill applies here as well, PM does need testing skill as one of a lot of skills he needs to master. ´ There are other advantages: ´ Being more independent from data people. ´ Getting respected. One of the easiest ways to gain respect from people around is by speaking their language. Hence, understanding the (general) language of data is important.
  • 11. Types of Testing ´ Historical Testing ´ On-off Testing ´ Quite similar to historical testing ´ AB testing / Multivariate testing ´ Multivariate testing is just AB testing with more variants (more than two) ´ When not to use AB testing?
  • 12. Some tips for doing A/B Testing ´ Make sure that your A/B testing is 100% random. ´ A/B testing should be done at least one week, because each day is unique. ´ Wait for enough confidence level (usually 90/95/99%) before deciding. ´ If it’s possible, always run the test AFTER the winner is decided. ´ Measure MORE than one metric.
  • 13. Some extra notes regarding A/B Testing ´ To be able to do A/B testing properly, make sure these four property supports: ´ Resource – time, money, etc ´ Tool - next slide ´ Focus – because sometimes doing A/B testing will make tasks executed a bit slower ´ People – it’s not enough to have data-mindset on only data, product or even engineering people, it needs to be spread widely on every part of the company, even to the C-levels.
  • 14. Best Resources for AB Testing (in my opinion)
  • 15. Tools for Testing ´ Visual Web Optimizer ´ Optimizely ´ Mixpanel ´ Growthhackers.com ´ Build it yourself!