leveraging analytics in gaming - tiny mogul games

17
Analy&cs in Gaming Rajdeep Gumaste Product Manager TMG

Upload: inmobi

Post on 29-Jul-2015

467 views

Category:

Technology


1 download

TRANSCRIPT

Page 1: Leveraging Analytics In Gaming - Tiny Mogul Games

Analy&cs  in  Gaming  Rajdeep  Gumaste  

Product  Manager  -­‐  TMG  

Page 2: Leveraging Analytics In Gaming - Tiny Mogul Games

What  is  Analy;cs?  •  Wiki  says    –  “The  process  of  discovery  and  communica;on  of  paBerns  in  data”  –  We  agree  

•  Metrics  to  measure  different  quan;;es    •  Some  metrics  considered  KPIs  for  benchmarking  •  Metrics  captured  directly  by  third  party  services  (like  GA)  and  by  tracking  in-­‐game  ac;vity  of  users  on  servers  

Page 3: Leveraging Analytics In Gaming - Tiny Mogul Games

GeKng  Started  –  What  happens  if  you  do  not  ‘Analy;cs’  ?    

Page 4: Leveraging Analytics In Gaming - Tiny Mogul Games

                           Who  decides  what  goes  in  ?  

No  ..really  ..  Why  Do  We  need  it?  

•  To  be  aware  of  what  works  and  what  doesn’t  •  To  target  users  in  a  smarter  way  –  for  promo;ons,  

acquisi;ons  etc.  •  To  roll  out  features  that  maBer  

•  Anybody  involved  in  the  project  can  contribute  to  the  process  of  seKng  up  an  in-­‐game  analy;cs  system  

 •  The  design  and  product  teams  work  in  tandem  as  they  define  the  

iden0ty  of  the  game  and  need  a  set  of  specific  metrics  to  validate  the  same  

Page 5: Leveraging Analytics In Gaming - Tiny Mogul Games

How  should  it  behave  (or  ideally  should  behave)?  

Be  Accurate  

Be  Quick  

Page 6: Leveraging Analytics In Gaming - Tiny Mogul Games

How  does  the  system  work?  

Product  Goals  

Translate  into  data  

requirements  

Incorporate  into  the  system  

Analyze  results  

Page 7: Leveraging Analytics In Gaming - Tiny Mogul Games

Pillars  of  Life  (  in  this  scenario)  

•  Acquisi&on  –  New  installs    •  Reten&on  –  %  people  installing  on  day  X  ,ac&ve  on  your  app  today    •  Engagement  –  Time  based  %  of  returning  users  (weekly  or  Monthly)    •  Mone&za&on  –  Show  me  the  money  !  

Page 8: Leveraging Analytics In Gaming - Tiny Mogul Games

A  Case  Study  

Page 9: Leveraging Analytics In Gaming - Tiny Mogul Games

Some  thumb  rules  

•  Always  have  a  hypothesis  (or  hypotheses)  for  a  problem  –  Don’t  fret  about  geKng  this  (them)  wrong.  Its  what  we  are  going  to  

prove/not  prove  using  the  analysis    

•  Make  sure  you  use  the  right  amount  of  data  for  your  analysis  –  Too  Much  :  Needlessly  cumbersome  and  maybe  inconclusive  –  Too  liBle  :  Inconclusive    

•  Explore  all  possible  reasons  for  a  par;cular  finding  in  the  data    •  There  may  not  always  be  a  problem  to  solve.  It  can  be  used  to  

do  usual  tasks  in  a  beBer  way.  

Page 10: Leveraging Analytics In Gaming - Tiny Mogul Games

Situa;on  1  

111  129   120   125  

157  136   130   122  

105  

148  

121  141  

154   154  134  

122   126   127  150  

0  20  40  60  80  100  120  140  160  180  

New  installs  

New  installs  

•  In  talks  with  vendors  for  acquisi;on  drive    •  Very  important  to  acquire  the  right  kind  of  users  if  you  want  to  make  the  game  

have  stable  numbers  ajer  the  acquisi;on  has  happened  

Page 11: Leveraging Analytics In Gaming - Tiny Mogul Games

Hypothesis  1  :  Acquisi;on  :  A  discovery  driven  approach    I  think  a  geographical  skew  exists  in  my  user  base.  I  need  to  target  my  acquisi;ons  in  a  smarter  way  

Approaches  :    •  Look  at  Geographical  skew  in,  Ac;ve  users,  New  users,  Engagement  •  Depending  on  whether  we  want  the  maximum  people  to  download,  or  s;ck  around  

ajer  downloading  ,we  can  choose  the  target  loca;on  

•  Further  Analyses  :    •  Deep  dive  into  the  data  to  see  if  a  carrier  skew  exists  

 

City   DAU   WAU   Installs  Weekly  Eng.  

Delhi   500   3500   300   14%  

Pune   200   2000   200   10%  

Mumbai   150   2500   180   6%  

Bangalore   550   1000   50   55%  

Chennai   400   1500   50   27%  

1800   10500       17%  

Page 12: Leveraging Analytics In Gaming - Tiny Mogul Games

Problem  2  :  Reten;on  is  dipping  very  low  from  D3  to  D4  resul;ng  in  very  low  D7  –  mul;ple  reasons  –  content,  Game  features,  player  aspira;on  

45  

40  

35  

22  20  

18  16  

0  

5  

10  

15  

20  

25  

30  

35  

40  

45  

50  

D1   D2   D3   D4   D5   D6   D7  

Dx  Reten;on  

•  The  D1  ,  D7  numbers  are  respectable  on  their  own  

•  This  is  not  a  problem  that  will  break  the  game  

•  Do  we  s;ll  want  to  go  ahead  and  run  stats?  –  Of  Course!  

Page 13: Leveraging Analytics In Gaming - Tiny Mogul Games

 Hypothesis  :  My  users  are  exhaus;ng  the  content  very  fast  

Findings/insights  :    •  Ini;al  hypothesis  was  wrong  –  ques;ons  unanswered  •  Further  Analyses  :    

•  CIR  also  low  for  certain  topics  –  ques;on  difficulty  problem  •  #  of  people  playing  each  topic  –  Is  nature  of  the  topic  a  problem?  

Topic  Name  Total  

ques;ons  Ques;ons  answered   CIR  

Ques;ons  unanswered  

Topic  1   200   78   1   122  

topic  2   200   165   0.8   35  

topic  3   200   50   0.5   150  

topic  4   200   44   0.3   156  

topic  5   200   150   1   50  

Page 14: Leveraging Analytics In Gaming - Tiny Mogul Games

Problem  3  :  Low  Weekly  Engagement  :  %  of  weekly  ac;ve  users  playing  today  (DAU/WAU)  

20  

25  28  

15  

26  

21   20  

0  

5  

10  

15  

20  

25  

30  

06/02/15   06/01/15   5/31/2015   5/30/2015   5/29/2015   5/28/2015   5/27/2015  

Weekly  Engagement  

Weekly  Engagement  

•  Avg.  Weekly  engagement  is  around  20%,  meaning  only  1/5  of  the  users  in  the  last  week  have  returned  to  play  the  game  

•  Problem  :  In  the  presence  of  content  and  adequate  player  aspira;on,  the  features  in  the  game  are  the  likely  cause    

Page 15: Leveraging Analytics In Gaming - Tiny Mogul Games

 Hypothesis  :  Users  are  only  interested  in  playing  with  friends  as  opponents  

Findings:  •  The  finding  seems  to  be  inline  with  the  data  extracted.  Hypothesis  confirmed  

•  Dis;nct  difference  in  the  behavior  of  users  playing  challenges  with  friends  and  random  players  

•  Further  Analyses  :    •  Look  into  why  people  are  sending  such  few  challenges  to  friends  •  Check  if  a  loca;on  skew  exists  for  players  matchmaking  

Random  Topic  Name  

Challenges  sent  

Challenges  completed  

Comple;on  Rate  

Topic  1   250   78   31%  

topic  2   125   65   52%  

topic  3   450   150   33%  

topic  4   500   44   9%  

topic  5   650   150   23%  

Friends  Topic  Name  

Challenges  sent  

Challenges  completed  

Comple;on  Rate  

Topic  1   35   34   97%  

topic  2   60   50   83%  

topic  3   55   50   91%  

topic  4   20   16   80%  

topic  5   80   65   81%  

Page 16: Leveraging Analytics In Gaming - Tiny Mogul Games

Is  that  all?  –  Not  by  a  long  shot  

Page 17: Leveraging Analytics In Gaming - Tiny Mogul Games

Ques;ons?