Seminar #50 - Salesforce Data, Clean, Visualize, Analyze, & Dashboard

You can access Salesforce data directly inside Exploratory with the latest release (v6.6). In this seminar, Kan will demonstrate how you can access the data and introduce some of the data visualization and analysis methods to maximize the value of your Salesforce data.

- Access and Query Data
- Transform and Visualize Data
- Create Dashboard
- Forecast Leads with Prophet
- Predict for Customer Win
- Use Parameters to Interact

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UI Tool: Exploratory(https://exploratory.io/)
Exploratory Online Seminar: https://exploratory.io/online-seminar

Transcript

1. EXPLORATORY
   Online Seminar #50
   X
   Get, Clean, Visualize, and Analyze Salesforce Data with Exploratory
   

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2. Kan Nishida
   CEO/co-founder
   Exploratory
   Summary
   In Spring 2016, launched Exploratory, Inc. to democratize
   Data Science.
   Prior to Exploratory, Kan was a director of product
   development at Oracle leading teams to build various Data
   Science products in areas including Machine Learning, BI,
   Data Visualization, Mobile Analytics, Big Data, etc.
   While at Oracle, Kan also provided training and consulting
   services to help organizations transform with data.
   @KanAugust
   Speaker
   

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3. 3
   Questions Communication
   Data Access
   Data Wrangling
   Visualization
   Analytics
   (Statistics / Machine
   Learning)
   Data Science Workflow
   

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4. 4
   Questions Communication
   (Dashboard, Note, Slides)
   Data Access
   Data Wrangling
   Visualization
   Analytics
   (Statistics / Machine
   Learning)
   ExploratoryɹModern & Simple UI
   

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5. EXPLORATORY
   Online Seminar #50
   X
   Get, Clean, Visualize, and Analyze Salesforce Data with Exploratory
   

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6. Agenda
   • Get Data
   • Clean and Transform Data
   • Visualize Data
   • Create, Publish, Share, and Schedule Dashboard
   • Parameterize Dashboard
   • Forecast Leads
   • Predict Win
   6
   

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7. Agenda
   • Get Data
   • Clean and Transform Data
   • Visualize Data
   • Create, Publish, Share, and Schedule Dashboard
   • Parameterize Dashboard
   • Forecast Leads
   • Predict Win
   7
   

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8. Supported Salesforce Editions
   8
   • Professional
   • Enterprise
   • Unlimited
   • Performance
   • Developer
   

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9. UI SQL
   2 Modes
   

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10. UI SQL
    2 Modes
    

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13. 13
    Select a table you want to import data for.
    

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14. 14
    Select columns.
    

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15. Use Filter to limit the data.
    15
    

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16. Once the data is imported, it automatically generates the Summary view.
    16
    

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17. UI SQL
    2 Modes
    

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18. SQL (SOQL)
    

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19. You can write SQL (SOQL) Queries to get data from Salesforce.
    

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20. Agenda
    • Get Data
    • Clean and Transform Data
    • Visualize Data
    • Create, Publish, Share, and Schedule Dashboard
    • Parameterize Dashboard
    • Forecast Leads
    • Predict Win
    20
    

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21. Clean and Transform data from the column header menu.
    

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22. You can update any of the existing data wrangling steps.
    22
    

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23. Click ‘Re-import’ button to get the latest data from Salesforce.
    23
    

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24. All the data wrangling steps will be automatically applied.
    24
    

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25. Agenda
    • Get Data
    • Clean and Transform Data
    • Visualize Data
    • Create, Publish, Share, and Schedule Dashboard
    • Parameterize Dashboard
    • Forecast Leads
    • Predict Win
    25
    

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26. Click the chart icon to quickly create a chart.
    26
    

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27. 27
    It will create a new chart under the Chart view.
    

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28. 28
    You can customize the chart by updating the column assignments and the chart
    configuration.
    

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29. But, here is one thing…
    

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30. 30
    The stage names are sorted alphabetically…
    

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31. It’s better to sort them following the natural order of the stages.
    31
    

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32. Prospecting
    Conversion
    Hearing
    32
    Proposal
    This order is confusing…
    Stages for Opportunity
    

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33. Prospecting Conversion
    Hearing
    33
    Proposal
    This is intuitive!
    Stages for Opportunity
    

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34. 1 3 4
    34
    Factor - Ordered Categories
    2
    Prospecting Conversion
    Hearing Proposal
    

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35. Stage
    Hearing
    Conversion
    Proposal
    Prospecting
    Character (Categorical)
    35
    Stage Level
    Prospecting 1
    Hearing 2
    Proposal 3
    Conversion 4
    Factor (Ordinal)
    

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36. View Slide

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38. A new data wrangling step is added to the Step pane.
    38
    

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39. 39
    The values are sorted following the level setting for the Factor data type columns.
    

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40. Open the chart and move the Pin to the latest step.
    40
    

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41. Currently, the stages are ordered from the bottom to the top.
    41
    

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42. You can change the direction of the order.
    42
    

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43. Agenda
    • Get Data
    • Clean and Transform Data
    • Visualize Data
    • Create, Publish, Share, and Schedule Dashboard
    • Parameterize Dashboard
    • Forecast Leads
    • Predict Win
    43
    

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44. 44
    Here’s a typical Dashboard in Exploratory.
    

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45. 45
    Numbers
    Analytics - Forecasting Analytics - Cohort Analysis
    Map Chart
    You can bring in various types of numbers and charts.
    

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46. 46
    You can bring in various types of data.
    

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47. 47
    And…
    

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48. Since you can create charts with different steps of Data Wrangling…
    

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49. 49
    You can bring in charts that are based on different steps.
    

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50. 50
    Create a new Dashboard.
    

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51. 51
    Add an existing chart.
    

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53. 53
    Adjust the height of each row section by drag-and-drop.
    

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54. 54
    Adjust the width of each column section by drag-and-drop.
    

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55. 55
    Click ‘Run’ button to preview the output.
    

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56. 56
    By clicking the ‘Re-Import Data’ button, you can query the latest data from Salesforce
    and apply all the data wrangling steps that are required to generate the charts.
    

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57. Publish the Dashboard to:
    1. Share
    2. Interact
    3. Schedule
    57
    

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58. Publish the Dashboard either in Private or Public mode.
    

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59. 59
    Once it’s published the Dashboard will have its own unique URL.
    

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60. 60
    Open the Dashboard in a web browser.
    

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61. 61
    You can invite specific people to share your Dashboard securely.
    

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62. 62
    Type their email addresses and write an inviting message.
    

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63. 63
    They will receive an email like the below.
    

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64. 64
    If they don’t have Exploratory accounts yet they can sign up for free ‘Viewer’
    accounts.
    

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65. Schedule your Dashboard so that the data inside the Dashboard is kept up-to-date
    automatically.
    65
    

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66. You can subscribe a notification email every time the scheduling job is run.
    66
    

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67. 67
    Other invited members can also subscribe the notification emails.
    

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68. You will receive notification emails with thumbnail images.
    68
    is updated.
    is updated.
    

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69. 69
    Dashboard
    Dashboard
    Exploratory Desktop
    Exploratory Server
    Publish
    OAuth
    OAuth
    How the scheduling works.
    

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70. 70
    Take a look at the past seminar for more details on the lifecycle of Dashboard!
    

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71. Select ‘How To (Tutorials)’.
    71
    

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72. Click on ‘Dashboard’.
    72
    

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73. Click on ‘Dashboard’.
    73
    

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74. Agenda
    • Get Data
    • Clean and Transform Data
    • Visualize Data
    • Create, Publish, Share, and Schedule Dashboard
    • Parameterize Dashboard
    • Forecast Leads
    • Predict Win
    74
    

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75. 75
    Me
    Team Mates
    I want data for
    Europe.
    I want data for
    Africa.
    I want data for
    Asia.
    Exploratory Server
    Everybody has a different interest.
    Publish
    

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76. 76
    By parameterizing the Dashboard, you can let them choose which part of the
    data they want to see.
    

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77. • Data Source Filter
    • Step Filter
    • Chart Filter
    3 Type of Filters
    

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78. You can create Filters in the data import dialog so that you can limit the
    data you query from Salesforce.
    78
    

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79. 79
    You can parameterize this Filter!
    

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80. 80
    You can create Filters on top of the imported data as data wrangling steps.
    

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81. 81
    You can parameterize the Step Filter as well!
    

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82. You can create Filters inside the Chart. This Chart Filter works only for the
    particular chart.
    82
    

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83. 83
    And, you can parameterize the Chart Filter as well!
    

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84. 84
    Click the Parameter button.
    

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85. 85
    All the parameters that are required to generate the chart data will automatically show
    up in the pane.
    

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86. For the Dashboard, all the parameters that are required to generate data for all the
    charts inside the Dashboard are automatically added to the Parameter pane.
    86
    

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87. For the published Dashboard, you can open the Parameter pane and turn
    on the interactive mode to start using the parameters.
    87
    

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88. A bit more details on how the Parameter works.
    

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89. 89
    1. Data Source
    2. Create Calculation
    3. Filter
    Created data wrangling steps and charts.
    Chart 2
    Chart 1
    

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90. 90
    Closed Date
    1. Data Source
    2. Create Calculation
    3. Filter
    Created Filters.
    Probability
    Type
    Chart 2
    Chart 1
    

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91. 91
    Closed Date
    1. Data Source
    2. Create Calculation
    3. Filter
    Created Dashboard with Chart 2.
    Dashboard 1 Probability
    Type
    Chart 2
    Chart 1
    

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92. 92
    Closed Date
    1. Data Source
    2. Create Calculation
    3. Filter
    Dashboard 1 Probability
    When you open the Parameter pane you will see‘Closed Date’ and
    ‘Probability’ parameters automatically.
    

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93. 93
    Closed Date
    1. Data Source
    2. Create Calculation
    3. Filter
    Now, we added Chart 1 to the Dashboard.
    Dashboard 1
    Probability
    Type
    

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94. Dashboard 1
    Type
    94
    Closed Date
    1. Data Source
    2. Create Calculation
    3. Filter
    When you open the Parameter pane, the ‘Stage’ parameter is automatically
    added.
    Probability
    

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95. Dashboard 1
    Type
    95
    Closed Date
    1. Data Source
    2. Create Calculation
    3. Filter
    Probability
    If we change the Probability parameter only the Chart 2 gets updated.
    

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96. Dashboard 1
    Type
    96
    Closed Date
    1. Data Source
    2. Create Calculation
    3. Filter
    Probability
    If we change the Stage parameter only the Chart 1 gets updated.
    

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97. Dashboard 1
    Type
    97
    Closed Date
    1. Data Source
    2. Create Calculation
    Probability
    If we change the Closed Date parameter all the
    steps and the charts will be updated.
    3. Filter
    

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98. You can also parameterize the SQL queries.
    

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103. Agenda
     • Get Data
     • Clean and Transform Data
     • Visualize Data
     • Create, Publish, Share, and Schedule Dashboard
     • Parameterize Dashboard
     • Forecast Leads
     • Predict Win
     103
     

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104. 104
     Can we forecast how the number of leads
     will look like in the next 12 months?
     

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105. 105
     Want to forecast the number of leads in the next 12 months.
     Actual Forecasted
     

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106. • Time interval between data has to be same throughout the data
     • Day with NA is not allowed
     • Seasonality with multiple periods (Week and Year) is hard to handle
     • Parameter tuning is hard and requires a forecasting expert level
     knowledge.
     Problems with Traditional Time Series Model
     

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107. • A ‘curve fitting’ algorithm to build time series
     forcasting models.
     • Designed for ease of use without expert
     knowledge on time series forecasting or
     statistics.
     • Built by Data Scientists (Sean J. Taylor & co.) at
     Facebook and open sourced. (https://
     facebook.github.io/prophet)
     Prophet
     Sean J. Taylor
     @seanjtaylor
     

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108. Build a model by finding a best smooth line which can be represented as
     sum of the following components.
     • Overall growth trend
     • Seasonality - Yearly, Weekly, Daily, etc.
     • Holiday effects - X’mas, New Year, July 4th, etc.
     • External Predictors
     Prophet - Additive Model
     

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109. 109
     Lead Data
     

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113. Agenda
     • Get Data
     • Clean and Transform Data
     • Visualize Data
     • Create, Publish, Share, and Schedule Dashboard
     • Parameterize Dashboard
     • Forecast Leads
     • Predict Win
     113
     

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114. 114
     Can we predict which opportunities we can win?
     

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115. 115
     Algorithm Model
     Build a Prediction Model.
     Conversion Age Time Country Industry
     TRUE 60 120 Japan Ad
     FALSE 45 55 US Medical
     FALSE 52 20 US Media
     TRUE 48 140 Japan Ad
     TRUE 53 80 UK Bank
     FALSE 35 30 Japan Media
     

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116. 116
     Predict
     Conversion Age Time Country Industry
     TRUE 25 120 Japan Ad
     FALSE 23 55 US Media
     FALSE 40 150 US Ad
     Conversion Age Time Country Industry
     ? 25 120 Japan Ad
     ? 23 55 US Media
     ? 40 150 US Ad
     Algorithm Model
     Conversion Age Time Country Industry
     TRUE 60 120 Japan Ad
     FALSE 45 55 US Medical
     FALSE 52 20 US Media
     TRUE 48 140 Japan Ad
     TRUE 53 80 UK Bank
     FALSE 35 30 Japan Media
     

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117. 117
     A model is a definition of a pattern the algorithm
     has captured in the data.
     Algorithm Model
     Conversion Age Time Country Industry
     TRUE 60 120 Japan Ad
     FALSE 45 55 US Medical
     FALSE 52 20 US Media
     TRUE 48 140 Japan Ad
     TRUE 53 80 UK Bank
     FALSE 35 30 Japan Media
     

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118. 118
     Not just for the prediction, we can also use it to learn a lot
     about the patterns in data.
     Insight
     • Which variables have stronger relationship with the target variable.
     • How are they related?
     • Are they significant?
     • What is the quality if we used this model to predict?
     Algorithm Model
     

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119. 119
     Opportunity Data
     

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120. 120
     The StageName column indicates whether we have won a given opportunity or not.
     

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121. 121
     What are the characteristics of these ‘Won’ opportunities?
     

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122. In order to use the Correlation Mode and build a prediction model, we need to
     convert the StageName variable to Logical (True / False) data type.
     122
     

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123. Create a calculation that returns TRUE if a given value is ‘Closed Won’.
     123
     

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125. 125
     11.7% of the existing opportunities are ‘Closed Win’.
     

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126. 126
     Click ‘Correlate’ button to open the Correlation mode.
     

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127. Select ‘Is_Won’ variable as the target variable.
     

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128. It shows the relationship between the Is_Win and all other variables with the
     charts along with metrics.
     

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129. Sort the columns based on the AUC values, which indicate the strength of
     the relationship.
     129
     

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130. The ‘CallAmount’ has a strong relationship with the ‘Is_Won’.
     130
     

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131. We want to build a prediction model, but which one?
     

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132. Numeric TRUE/FALSE TRUE/FALSE + Time
     Linear
     Regression
     Random Forest
     / XGBoost
     Statistical
     Learning
     Machine
     Learning
     Logistic
     Regression
     Cox
     Regression
     Survival Forest
     132
     Regression
     Model
     Classification
     Model
     Survival Model
     Data Type
     Statistical
     Learning
     Machine
     Learning
     Statistical
     Learning
     Machine
     Learning
     Random Forest
     / XGBoost
     

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133. Let’s try with Logistic Regression model.
     

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134. Select multiple variables and select one of the prediction model algorithms.
     134
     

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135. 135
     A logistic regression model is created to predict the probability of winning.
     

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136. Under the ‘Prediction’ tab, you can see how the probability of winning
     changes as the values in a given variable change.
     136
     

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137. 137
     Under the Importance tab, you can see which variables are more important to
     predict the probability of winning.
     

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138. 138
     Under the Summary tab, you can see the quality of the prediction model.
     

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139. 139
     Under the Data tab, you can see the predicted result for all the existing accounts.
     

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140. 140
     This prediction result is for the existing data for which we know the answer already.
     

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141. But, we want to predict the probability of winning for the
     future opportunities, not the past opportunities.
     

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142. You can predict for the future opportunities by using the
     model we have just created.
     

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143. 143
     Import a new opportunity data.
     

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145. 145
     Select the model we have just created under the Analytics view.
     

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146. 146
     A set of new columns is added as the prediction values.
     

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147. That’s it for today!
     

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148. Next Seminar
     

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149. EXPLORATORY
     Online Seminar #52
     7/7/2021 (Wed) 11AM PT
     Machine Learning -
     Variable Importance
     

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150. View Slide

151. Information
     Email
     kan@exploratory.io
     Website
     https://exploratory.io
     Twitter
     @ExploratoryData
     Seminar
     https://exploratory.io/online-seminar
     

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152. Q & A
     152
     

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153. EXPLORATORY
     153
     

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