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Analyze this… Analytics Strategies for iOS and Rails

Analyze this… Analytics Strategies for iOS and Rails

You've just build a killer app with a killer website. Downloads are skyrocketing and you are getting lots of good feedback from users. Then someone asks you for data. Where are people using the app? How many times have people used a certain feature? Can you make predictions about user interactions based on past usage? In this session, let's take a look at what goes into mining mobile data and analyzing the insights. We'll discuss different approaches to gathering, storing, and querying data, which metrics and KPIs to focus on, and how to interpret and use information in order to determine the real drivers behind user behavior.

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Lance Gleason

April 13, 2013
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Transcript

  1. Twitter @lgleasain Github lgleasain www.lancegleason.com www.polyglotprogrammincinc.com lgleason@polyglotprogramminginc.com 1 Sunday, April

    14, 13
  2. Introductions 2 Sunday, April 14, 13

  3. 3 Sunday, April 14, 13

  4. 4 Sunday, April 14, 13

  5. 5 Sunday, April 14, 13

  6. http://www.polyglotprogramminginc.com/purr- programming-2-0/ 6 Sunday, April 14, 13

  7. Data Science 7 Sunday, April 14, 13

  8. 8 Sunday, April 14, 13

  9. 8 Sunday, April 14, 13

  10. 8 Sunday, April 14, 13

  11. 9 Sunday, April 14, 13

  12. 10 Sunday, April 14, 13

  13. Analytics 11 Sunday, April 14, 13

  14. 12 Sunday, April 14, 13

  15. 13 Sunday, April 14, 13

  16. http://www.torlaune.de/euro-2012/spieler-relationen/ http://www.nytimes.com/interactive/2012/05/17/business/ dealbook/how-the-facebook-offering-compares.html?_r=0 http://www.nytimes.com/interactive/2012/08/24/us/drought- crops.html 14 Sunday, April 14, 13

  17. ? 15 Sunday, April 14, 13

  18. 16 Sunday, April 14, 13

  19. Which Customers Bought the Most? 17 Sunday, April 14, 13

  20. Which Will Buy the Most? 18 Sunday, April 14, 13

  21. http://www.kaggle.com/c/titanic-gettingStarted 19 Sunday, April 14, 13

  22. 20 Sunday, April 14, 13

  23. Coupon App 21 Sunday, April 14, 13

  24. 22 Sunday, April 14, 13

  25. •Users can search for deals via proximity to location 22

    Sunday, April 14, 13
  26. •Users can search for deals via proximity to location •Customers

    can save coupons 22 Sunday, April 14, 13
  27. •Users can search for deals via proximity to location •Customers

    can save coupons •When a coupon is used it is no longer available for use 22 Sunday, April 14, 13
  28. •Users can search for deals via proximity to location •Customers

    can save coupons •When a coupon is used it is no longer available for use •Customers can search via categories 22 Sunday, April 14, 13
  29. •Users can search for deals via proximity to location •Customers

    can save coupons •When a coupon is used it is no longer available for use •Customers can search via categories •Advertisers pay for data and preferential ad pacement 22 Sunday, April 14, 13
  30. How many sales are being generated by this app? 23

    Sunday, April 14, 13
  31. Am I getting more active users? 24 Sunday, April 14,

    13
  32. How many users are saving coupons? 25 Sunday, April 14,

    13
  33. How many users are saving coupons? •Which stores have the

    most saves. 25 Sunday, April 14, 13
  34. How many users are saving coupons? •Which stores have the

    most saves. •Do certain categories get more saves? 25 Sunday, April 14, 13
  35. How many are using coupons? 26 Sunday, April 14, 13

  36. How many are using coupons? •Which stores have the most

    uses? 26 Sunday, April 14, 13
  37. How many are using coupons? •Which stores have the most

    uses? •Do certain categories get used more? 26 Sunday, April 14, 13
  38. How many are using coupons? •Which stores have the most

    uses? •Do certain categories get used more? •Which physical store is the user in? 26 Sunday, April 14, 13
  39. Which stores are they at? 27 Sunday, April 14, 13

  40. Hidden Insights 28 Sunday, April 14, 13

  41. 29 Sunday, April 14, 13

  42. 30 Sunday, April 14, 13

  43. Appstore Data 31 Sunday, April 14, 13

  44. 32 Sunday, April 14, 13

  45. 32 Sunday, April 14, 13

  46. 32 Sunday, April 14, 13

  47. 32 Sunday, April 14, 13

  48. 32 Sunday, April 14, 13

  49. 33 Sunday, April 14, 13

  50. 34 Sunday, April 14, 13

  51. 35 Sunday, April 14, 13

  52. 36 Sunday, April 14, 13

  53. 37 Sunday, April 14, 13

  54. Logging (Papertrail/ Loggly) 37 Sunday, April 14, 13

  55. Logging (Papertrail/ Loggly) Amazon S3 37 Sunday, April 14, 13

  56. {"measure":"instance","instance": "stores","store_id": 64696,"company_id": 210,"store_name":"bebe", "controller":"api/v1/ stores","action":"index"} 38 Sunday, April 14,

    13
  57. 39 Sunday, April 14, 13

  58. Amazon Elastic Map Reduce 39 Sunday, April 14, 13

  59. Amazon Elastic Map Reduce 39 Sunday, April 14, 13

  60. DynamoDB 40 Sunday, April 14, 13

  61. CREATE EXTERNAL TABLE events_1 ( id bigint, received_at string, generated_at

    string, source_id bigint, source_name string, source_ip string, facility string, severity string, program string, message string ) PARTITIONED BY ( dt string ) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' STORED AS TEXTFILE LOCATION 's3://mybucket/papertrail/logs/production'; 41 Sunday, April 14, 13
  62. ALTER TABLE events_1 RECOVER PARTITIONS; 42 Sunday, April 14, 13

  63. CREATE EXTERNAL TABLE promotions_1 (id string, received_at string, source_id string,

    source_ip string, source_name string,measure string, instance string, promotion_id string, company_id string, controller string, action string) stored by 'org.apache.hadoop.hive.dynamodb.DynamoDBStorageHan dler' TBLPROPERTIES ("dynamodb.table.name" = "sh_promotions_latest", "dynamodb.column.mapping" = "id:id,received_at:received_at,source_id:source_id,source_i p:source_ip,source_name:source_name,measure:measure,i nstance:instance,promotion_id:promotion_id,company_id:c ompany_id,controller:controller,action:action"); 43 Sunday, April 14, 13
  64. alter table promotions_1 recover partitions; 44 Sunday, April 14, 13

  65. insert overwrite table promotions_1 select id, received_at, source_id, source_ip, source_name,

    get_json_object(message, '$.measure') as measure, get_json_object(message, '$.instance') as instance, get_json_object(message, '$.promotion_id') as promotion_id, get_json_object(message, '$.company_id') as company_id, get_json_object(message, '$.controller') as controller, get_json_object(message, '$.action') as action from events_1 where message like '%"promotion"%' ; 45 Sunday, April 14, 13
  66. 46 Sunday, April 14, 13

  67. 47 Sunday, April 14, 13

  68. d3js.org 48 Sunday, April 14, 13

  69. 49 Sunday, April 14, 13

  70. 49 Sunday, April 14, 13

  71. 49 Sunday, April 14, 13

  72. False Positives 50 Sunday, April 14, 13

  73. 51 Sunday, April 14, 13

  74. Nearly ALL sick people have eaten PEAS (obviously then, the

    effects are cumulative). 52 Sunday, April 14, 13
  75. An estimated 99.9% of all people who die from cancer

    or heart attacks have eaten PEAS. 53 Sunday, April 14, 13
  76. Another 99.9% of people involved in auto accidents ate PEAS

    within 60-days before the accident. 54 Sunday, April 14, 13
  77. Among people born in 1839 who later dined on PEAS,

    there has been a 100% mortality rate 55 Sunday, April 14, 13
  78. Peas Will Kill You 56 Sunday, April 14, 13

  79. We had 4000 app downloads this month. We are doing

    great.... 57 Sunday, April 14, 13
  80. 58 Sunday, April 14, 13

  81. Most people use the app once and then uninstall it.

    59 Sunday, April 14, 13
  82. 60 Sunday, April 14, 13

  83. My shopping app just saw a spike in weekly usage

    after I made UI changes. 61 Sunday, April 14, 13
  84. That UI change led to more users! 62 Sunday, April

    14, 13
  85. 63 Sunday, April 14, 13

  86. The change went live during the last week of November.

    64 Sunday, April 14, 13
  87. 65 Sunday, April 14, 13

  88. Be Wary of N of 1 Experiments 66 Sunday, April

    14, 13
  89. Segmentation 67 Sunday, April 14, 13

  90. Sparse Data 68 Sunday, April 14, 13

  91. To Get Statistically Meaningful Results you will need thousands of

    data points 69 Sunday, April 14, 13
  92. The Results Need to Pass the Smell Test 70 Sunday,

    April 14, 13
  93. 71 Sunday, April 14, 13

  94. Collect Lots of Data Early 72 Sunday, April 14, 13

  95. Go For Low Hanging Fruit First 73 Sunday, April 14,

    13
  96. Try to Gather Data Rich Data Points (whenever possible) 74

    Sunday, April 14, 13
  97. 75 Sunday, April 14, 13

  98. Twitter @lgleasain Github lgleasain www.lancegleason.com www.polyglotprogrammincinc.com lgleason@polyglotprogramminginc.com 76 Sunday, April

    14, 13