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Product	
  Analy.cs	
  
	
  
Tlabs	
  –	
  March’	
  15	
  
	
  
By	
  @mayankdhingra	
  	
  
(The	
  Growth	
  Guy	
  –	
  Paytm)	
  
What	
  is	
  Product	
  Analy.cs	
  
Product	
  analy.cs	
  are	
  the	
  computa.onal	
  
analyses	
  of	
  data	
  about	
  a	
  product.	
  	
  
	
  
•  Passive	
  data	
  (not	
  explicitly	
  under	
  your	
  control)	
  
•  Ac.ve	
  data	
  	
  (generated	
  from	
  your	
  product	
  
that	
  you	
  have	
  explicitly	
  decided	
  to	
  capture)	
  
Why	
  Product	
  Analy.cs?	
  
	
  
	
  
	
   	
  That	
  which	
  is	
  measured	
  improves	
  	
  
	
   	
   	
   	
   	
   	
   	
   	
   	
   	
   	
  –	
  Pearson’s	
  Law	
  	
  
About	
  Me	
  
Previously:	
  
Currently:	
  
Build	
  -­‐	
  >	
  Measure	
  -­‐>	
  Learn	
  
Startup	
  Pyramid	
  
Analy.cs	
  Concepts	
  
1.  Segmenta.on	
  –	
  Grouping	
  on	
  a	
  characteris.c	
  
(Event	
  &	
  Customer)	
  	
  
	
  
2.	
  Funnels	
  –	
  Connec.ng	
  mul.ple	
  events	
  together	
  to	
  
analyze	
  the	
  en.re	
  path	
  
	
  
3.	
  Cohorts	
  -­‐	
  Groups	
  of	
  customers	
  that	
  have	
  a	
  
common	
  characteris.c	
  within	
  a	
  specified	
  .me	
  
period	
  
(Engagement	
  &	
  Reten.on)	
  
Analy.cs	
  Implementa.on	
  
1.  Define	
  Product	
  Vision	
  –	
  	
  
What	
  is	
  the	
  problem	
  the	
  product	
  is	
  solving	
  for	
  the	
  user?	
  
2.  Define	
  KPIs	
  that	
  meet	
  the	
  Product	
  Vision	
  –	
  	
  
KPIs	
  are	
  derived	
  from	
  the	
  product	
  vision	
  and	
  tell	
  you	
  how	
  well	
  your	
  
product	
  is	
  mee.ng	
  the	
  vision.	
  	
  
3.  Define	
  the	
  metrics	
  that	
  allow	
  you	
  to	
  hit	
  your	
  
KPIs	
  -­‐	
  Metrics	
  are	
  what	
  you	
  can	
  manipulate	
  to	
  hit	
  the	
  targets	
  set	
  by	
  
KPIs	
  
4.  Define	
  the	
  funnels	
  (via	
  user	
  journeys)	
  that	
  
affects	
  your	
  metrics	
  -­‐	
  Important	
  are	
  the	
  ones	
  that	
  
change	
  the	
  metrics	
  in	
  some	
  manner	
  
Metrics	
  
•  Good	
  Metrics	
  are	
  Compara.ve	
  &	
  Understandable	
  
•  Qualita.ve	
  vs	
  Quan.ta.ve	
  Metrics	
  
•  Vanity	
  vs	
  Ac.onable	
  Metrics	
  
•  Exploratory	
  vs	
  Repor.ng	
  Metrics	
  
•  Leading	
  vs	
  Lagging	
  Metrics	
  
•  Correlated	
  vs	
  Causal	
  Metrics	
  
Pirate	
  Metrics	
  -­‐	
  AARRR	
  
Aha!	
  Moments	
  
1.  Twiaer	
  -­‐	
  	
  Following	
  30	
  people	
  
2.  Facebook	
  –	
  7	
  Friends	
  in	
  10	
  days	
  
3.  Zynga	
  –	
  Day	
  1	
  Reten.on	
  
4.  Dropbox	
  –	
  First	
  File	
  Upload	
  
	
  
Categories	
  of	
  Aha!	
  Moments	
  
1.  Network	
  Density	
  
2.  Content	
  Added	
  
3.  Visit/Usage	
  Frequency	
  
Aha!	
  Moments	
  
Iden.fy	
  which	
  ac.ons	
  separate	
  retained	
  
customers	
  from	
  lost	
  ones	
  
Recommended	
  Links	
  
1.  How	
  to	
  build	
  products	
  users	
  love	
  (Video)	
  
2.  Facebook’s	
  Aha	
  moment,	
  simpler	
  than	
  you	
  think	
  
3.  Super	
  Normal	
  
4.  A	
  product	
  managers	
  job	
  
5.  The	
  only	
  metric	
  that	
  maaers	
  –	
  Josh	
  Elman	
  	
  
6.  Inspired:	
  How	
  to	
  create	
  products	
  Customers	
  
love	
  (Book)	
  
7.  Reten.on	
  is	
  king	
  –	
  Andrew	
  Chen	
  

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Talk on Product Analytics at T labs, Noida

  • 1. Product  Analy.cs     Tlabs  –  March’  15     By  @mayankdhingra     (The  Growth  Guy  –  Paytm)  
  • 2. What  is  Product  Analy.cs   Product  analy.cs  are  the  computa.onal   analyses  of  data  about  a  product.       •  Passive  data  (not  explicitly  under  your  control)   •  Ac.ve  data    (generated  from  your  product   that  you  have  explicitly  decided  to  capture)  
  • 3. Why  Product  Analy.cs?          That  which  is  measured  improves                          –  Pearson’s  Law    
  • 4. About  Me   Previously:   Currently:  
  • 5. Build  -­‐  >  Measure  -­‐>  Learn  
  • 7. Analy.cs  Concepts   1.  Segmenta.on  –  Grouping  on  a  characteris.c   (Event  &  Customer)       2.  Funnels  –  Connec.ng  mul.ple  events  together  to   analyze  the  en.re  path     3.  Cohorts  -­‐  Groups  of  customers  that  have  a   common  characteris.c  within  a  specified  .me   period   (Engagement  &  Reten.on)  
  • 8. Analy.cs  Implementa.on   1.  Define  Product  Vision  –     What  is  the  problem  the  product  is  solving  for  the  user?   2.  Define  KPIs  that  meet  the  Product  Vision  –     KPIs  are  derived  from  the  product  vision  and  tell  you  how  well  your   product  is  mee.ng  the  vision.     3.  Define  the  metrics  that  allow  you  to  hit  your   KPIs  -­‐  Metrics  are  what  you  can  manipulate  to  hit  the  targets  set  by   KPIs   4.  Define  the  funnels  (via  user  journeys)  that   affects  your  metrics  -­‐  Important  are  the  ones  that   change  the  metrics  in  some  manner  
  • 9. Metrics   •  Good  Metrics  are  Compara.ve  &  Understandable   •  Qualita.ve  vs  Quan.ta.ve  Metrics   •  Vanity  vs  Ac.onable  Metrics   •  Exploratory  vs  Repor.ng  Metrics   •  Leading  vs  Lagging  Metrics   •  Correlated  vs  Causal  Metrics  
  • 11. Aha!  Moments   1.  Twiaer  -­‐    Following  30  people   2.  Facebook  –  7  Friends  in  10  days   3.  Zynga  –  Day  1  Reten.on   4.  Dropbox  –  First  File  Upload     Categories  of  Aha!  Moments   1.  Network  Density   2.  Content  Added   3.  Visit/Usage  Frequency  
  • 12. Aha!  Moments   Iden.fy  which  ac.ons  separate  retained   customers  from  lost  ones  
  • 13. Recommended  Links   1.  How  to  build  products  users  love  (Video)   2.  Facebook’s  Aha  moment,  simpler  than  you  think   3.  Super  Normal   4.  A  product  managers  job   5.  The  only  metric  that  maaers  –  Josh  Elman     6.  Inspired:  How  to  create  products  Customers   love  (Book)   7.  Reten.on  is  king  –  Andrew  Chen