OA Donation Data from April 2016 to Oct 2020

The aim of this analysis is to identify and visualise donation data of alumni. Click the top left “Donations Data” button to navigate through the document.

• This document will run through the data analysis done using the donation data of alumni from April 1st 2016 to October 2020.

• This might help to identify variables that lead to greater donation by certain groups of individuals.

Donations Data

Problem Statement

1. To increase targetted aiming of donations, we need to analyse the factors that affect donation amounts. Which of these variables affect the donations?

1. Number of donations (x2 for gender means there are are 2 genders, male and female. Same for the remaining variables)
2. Median Amount of donations

• 1.1 Month x12
• 1.2 Gender x2
• 1.3 UG vs PG Donation Comparison x2
• 1.4.1 UG by Degree x6
• 1.4.2 UG by Graduation Class
• 1.5.1 PG by Degree x38
• 1.5.2 PG by Graduation Class
• 1.6 Job Title
• 1.7 Company

2. Once a an individual decides to donate, what are the causes they donate towards and what method of donation do they use?

1. Causes

• Gift Name x80
• Fund Description x56
• Gift Purpose x10

2. Payment type

• Fund Type x2
• Gift Type (Cash, Recurring, Pledge) x3
• Gift Payment type (Credit Card, cheque etc) x7

The focus will be on problem statement qn 1 in this analysis while for question 2 we will use the visuals from the first sharing.

Import

Now we import our dataset. 4 excel files. SMOO, IBC, Demographics and Master.

## Warning: Missing column names filled in: 'X21' [21], 'X22' [22], 'X23' [23],
## 'X24' [24], 'X25' [25], 'X26' [26], 'X27' [27], 'X28' [28], 'X29' [29],
## 'X30' [30], 'X31' [31], 'X32' [32], 'X33' [33], 'X34' [34], 'X35' [35],
## 'X36' [36], 'X37' [37], 'X38' [38], 'X39' [39], 'X40' [40], 'X41' [41],
## 'X42' [42], 'X43' [43], 'X44' [44], 'X45' [45], 'X46' [46], 'X47' [47],
## 'X48' [48], 'X49' [49], 'X50' [50], 'X51' [51], 'X52' [52], 'X53' [53],
## 'X54' [54], 'X55' [55], 'X56' [56], 'X57' [57], 'X58' [58], 'X59' [59],
## 'X60' [60], 'X61' [61], 'X62' [62], 'X63' [63], 'X64' [64], 'X65' [65],
## 'X66' [66], 'X67' [67], 'X68' [68], 'X69' [69], 'X70' [70], 'X71' [71],
## 'X72' [72], 'X73' [73], 'X74' [74], 'X75' [75], 'X76' [76], 'X77' [77],
## 'X78' [78], 'X79' [79], 'X80' [80], 'X81' [81], 'X82' [82], 'X83' [83],
## 'X84' [84], 'X85' [85], 'X86' [86], 'X87' [87], 'X88' [88], 'X89' [89],
## 'X90' [90], 'X91' [91], 'X92' [92], 'X93' [93], 'X94' [94], 'X95' [95],
## 'X96' [96], 'X97' [97], 'X98' [98], 'X99' [99], 'X100' [100], 'X101' [101],
## 'X102' [102], 'X103' [103], 'X104' [104], 'X105' [105], 'X106' [106],
## 'X107' [107], 'X108' [108], 'X109' [109], 'X110' [110], 'X111' [111],
## 'X112' [112], 'X113' [113], 'X114' [114], 'X115' [115], 'X116' [116],
## 'X117' [117], 'X118' [118], 'X119' [119], 'X120' [120], 'X121' [121],
## 'X122' [122], 'X123' [123], 'X124' [124], 'X125' [125], 'X126' [126],
## 'X127' [127], 'X128' [128], 'X129' [129], 'X130' [130], 'X131' [131],
## 'X132' [132], 'X133' [133], 'X134' [134], 'X135' [135], 'X136' [136],
## 'X137' [137], 'X138' [138], 'X139' [139], 'X140' [140], 'X141' [141],
## 'X142' [142], 'X143' [143], 'X144' [144], 'X145' [145], 'X146' [146],
## 'X147' [147], 'X148' [148], 'X149' [149], 'X150' [150], 'X151' [151],
## 'X152' [152], 'X153' [153], 'X154' [154], 'X155' [155], 'X156' [156],
## 'X157' [157], 'X158' [158], 'X159' [159], 'X160' [160], 'X161' [161],
## 'X162' [162], 'X163' [163], 'X164' [164], 'X165' [165], 'X166' [166],
## 'X167' [167], 'X168' [168], 'X169' [169], 'X170' [170], 'X171' [171],
## 'X172' [172], 'X173' [173], 'X174' [174], 'X175' [175], 'X176' [176],
## 'X177' [177], 'X178' [178], 'X179' [179], 'X180' [180], 'X181' [181],
## 'X182' [182], 'X183' [183], 'X184' [184], 'X185' [185], 'X186' [186],
## 'X187' [187], 'X188' [188], 'X189' [189], 'X190' [190], 'X191' [191],
## 'X192' [192], 'X193' [193], 'X194' [194], 'X195' [195], 'X196' [196],
## 'X197' [197], 'X198' [198], 'X199' [199], 'X200' [200], 'X201' [201],
## 'X202' [202], 'X203' [203], 'X204' [204], 'X205' [205], 'X206' [206],
## 'X207' [207], 'X208' [208], 'X209' [209], 'X210' [210], 'X211' [211],
## 'X212' [212], 'X213' [213], 'X214' [214], 'X215' [215], 'X216' [216],
## 'X217' [217], 'X218' [218], 'X219' [219], 'X220' [220], 'X221' [221],
## 'X222' [222], 'X223' [223], 'X224' [224], 'X225' [225], 'X226' [226],
## 'X227' [227], 'X228' [228], 'X229' [229], 'X230' [230], 'X231' [231],
## 'X232' [232], 'X233' [233], 'X234' [234], 'X235' [235], 'X236' [236],
## 'X237' [237], 'X238' [238], 'X239' [239], 'X240' [240], 'X241' [241],
## 'X242' [242], 'X243' [243], 'X244' [244], 'X245' [245], 'X246' [246],
## 'X247' [247], 'X248' [248], 'X249' [249], 'X250' [250], 'X251' [251]

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_logical(),
##   `Constituent ID` = col_character(),
##   `Specific Record` = col_double(),
##   ID2 = col_character(),
##   `Key Indicator` = col_character(),
##   `Fund Type` = col_character(),
##   `Gift Date` = col_character(),
##   `Gift Name` = col_character(),
##   `Gift Amount` = col_character(),
##   `Gift Type` = col_character(),
##   `Fund Description` = col_character(),
##   `Appeal ID` = col_character(),
##   `Gift Specific Attributes NetCommunity Page Description` = col_character(),
##   `Gift Specific A_butes Donor Acknowledgement Name Description` = col_character(),
##   `Gift Reference Date` = col_character(),
##   `Gift Reference Number` = col_character(),
##   `Gift Payment Type` = col_character()
## )
## ℹ Use `spec()` for the full column specifications.

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   `Specific Record` = col_double(),
##   `Constituent ID` = col_character(),
##   `Key Indicator` = col_character(),
##   `Gift ID` = col_character(),
##   `Gift Date` = col_character(),
##   `Gift Name` = col_character(),
##   `Gift Payment Type` = col_character(),
##   `Gift Amount` = col_character(),
##   `Gift Type` = col_character(),
##   `Gift Specific Attributes NetCommunity Page Description` = col_character()
## )

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   `Constituent ID` = col_character(),
##   Gender = col_character(),
##   `Primary Business Position` = col_character(),
##   `Primary Business Organisation Name` = col_character(),
##   `Education School Type` = col_character(),
##   `Education Class of` = col_character(),
##   `Education Date Graduated` = col_character(),
##   `Education Degree` = col_character()
## )

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   `Constituent ID` = col_character(),
##   `Gift Date` = col_character(),
##   `Gift Date FY` = col_character(),
##   `Gift Payment Type` = col_character(),
##   `Gift Name` = col_character(),
##   `Fund Type` = col_character(),
##   `Fund ID` = col_character(),
##   `Fund Description` = col_character(),
##   `Fund Split Amount` = col_character(),
##   `Gift Purpose` = col_character(),
##   `Gift Type` = col_character(),
##   `Appeal ID` = col_character(),
##   `Campaign ID` = col_character()
## )

Tidy & Transform

smoo data

## Rows: 250
## Columns: 14
## $ `Constituent ID`    <chr> "01094526", "2020-1000", "01004439", "2020-972", …
## $ `Specific Record`   <dbl> 20700, 191716, 5759, 191652, 77676, 143054, 6063,…
## $ `Key Indicator`     <chr> "Individual", "Individual", "Individual", "Indivi…
## $ `Fund Type`         <chr> "Term", "Term", "Term", "Term", "Term", "Term", "…
## $ `Gift Date`         <chr> "2020-10-03", "2020-10-01", "2020-10-01", "2020-0…
## $ `Gift Name`         <chr> "SMU SMOO Challenge", "SMU SMOO Challenge", "SMU …
## $ `Gift Amount`       <dbl> 50, 300, 200, 100, 250, 120, 100, 100, 20, 30, 12…
## $ `Gift Type`         <chr> "Cash", "Cash", "Cash", "Cash", "Cash", "Cash", "…
## $ `Fund Description`  <chr> "SMU Bursary Fund", "SMU Bursary Fund", "SMU Burs…
## $ `Appeal ID`         <chr> "SMOOCh2020", "SMOOCh2020", "SMOOCh2020", "SMOOCh…
## $ `Gift Payment Type` <chr> "Credit Card", "Credit Card", "Credit Card", "Cre…
## $ year                <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2…
## $ month               <dbl> 10, 10, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9…
## $ date                <int> 3, 1, 1, 29, 26, 24, 24, 24, 24, 24, 24, 24, 24, …

IBC data

## Rows: 125
## Columns: 12
## $ `Specific Record`   <dbl> 42812, 2986, 24741, 12146, 15588, 25123, 21938, 2…
## $ `Constituent ID`    <chr> "01072914", "01002243", "01054393", "01006551", "…
## $ `Key Indicator`     <chr> "Individual", "Individual", "Individual", "Indivi…
## $ `Gift ID`           <chr> "2019-1858", "2019-1853", "2019-1884", "2019-1867…
## $ `Gift Date`         <chr> "2019-08-14", "2019-08-14", "2019-08-15", "2019-0…
## $ `Gift Name`         <chr> "SMU Alumni Community Fund", "SMU Alumni Communit…
## $ `Gift Payment Type` <chr> "Credit Card", "Credit Card", "Cash", "Credit Car…
## $ `Gift Amount`       <dbl> 5000, 5000, 5000, 5000, 5000, 2000, 5000, 1000, 5…
## $ `Gift Type`         <chr> "Cash", "Cash", "Pledge", "Cash", "Cash", "Cash",…
## $ year                <dbl> 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2…
## $ month               <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9…
## $ date                <int> 14, 14, 15, 15, 15, 15, 17, 18, 21, 27, 27, 29, 3…

Demographic data

## Rows: 779
## Columns: 8
## $ `Constituent ID`                     <chr> "01002036", "01002049", "0100208…
## $ Gender                               <chr> "Male", "Female", "Female", "Mal…
## $ `Primary Business Position`          <chr> "Senior Manager, Marketing - SEA…
## $ `Primary Business Organisation Name` <chr> "Canpotex International Pte Ltd"…
## $ `Education School Type`              <chr> "Primary", "Primary", "Primary",…
## $ `Education Class of`                 <chr> "2008", "2005", "2004", "2009", …
## $ `Education Date Graduated`           <chr> "4/6/2008", "30/5/2005", "30/5/2…
## $ `Education Degree`                   <chr> "BSc (ISM)", "BBM", "BBM", "BBM"…

Master data

## Rows: 1,264
## Columns: 13
## $ `Constituent ID`    <chr> "01094526", "01032965", "01004439", "01359586", "…
## $ `Gift Date`         <chr> "2020-10-03", "2020-10-04", "2020-10-01", "2020-0…
## $ `Gift Date FY`      <chr> "FY2020 YTD", "FY2020 YTD", "FY2020 YTD", "FY2020…
## $ `Gift Payment Type` <chr> "Credit Card", "Credit Card", "Credit Card", "Oth…
## $ `Gift Name`         <chr> "SMU SMOO Challenge", "Ian Taylor Memorial Fund",…
## $ `Fund Type`         <chr> "Term", "Endowment", "Term", "Term", "Term", "End…
## $ `Fund Description`  <chr> "SMU Bursary Fund", "Ian Taylor Memorial Fund", "…
## $ `Gift Purpose`      <chr> "Bursary", "Financial assistance", "Bursary", "Bu…
## $ `Gift Type`         <chr> "Cash", "Cash", "Cash", "Cash", "Cash", "Pledge",…
## $ `Gift Amount`       <dbl> 50, 8000, 200, 120, 100, 8000, 50, 50, 100, 200, …
## $ year                <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2…
## $ month               <dbl> 10, 10, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9…
## $ date                <int> 3, 4, 1, 24, 24, 29, 24, 24, 24, 24, 24, 24, 26, …

Join the 4 individual datatables into one main dataframe

## Rows: 2,288
## Columns: 25
## $ `Constituent ID`                     <chr> "01002036", "01002036", "0100204…
## $ `Gift Date`                          <chr> "2016-05-24", NA, "2017-09-13", …
## $ `Gift Name`                          <chr> "SMU Steven Miller Scholarship",…
## $ `Gift Amount`                        <dbl> 1000, NA, 50, NA, 200, 200, 5000…
## $ `Gift Type`                          <chr> "Cash", NA, "Cash", NA, "Cash", …
## $ `Gift Payment Type`                  <chr> "Credit Card", NA, "Credit Card"…
## $ year                                 <dbl> 2016, NA, 2017, NA, 2020, 2020, …
## $ month                                <dbl> 5, NA, 9, NA, 9, 9, 11, 11, 9, 9…
## $ date                                 <int> 24, NA, 13, NA, 24, 24, 9, 9, 1,…
## $ fy                                   <int> 2017, NA, 2018, NA, 2021, 2021, …
## $ `Fund Type`                          <chr> "Endowment", NA, "Term", NA, "Te…
## $ `Fund Description`                   <chr> "SMU Steven Miller Scholarship",…
## $ `Specific Record`                    <dbl> NA, NA, NA, NA, 5655, NA, NA, 11…
## $ `Key Indicator`                      <chr> NA, NA, NA, NA, "Individual", NA…
## $ `Appeal ID`                          <chr> NA, NA, NA, NA, "SMOOCh2020", NA…
## $ `Gift ID`                            <chr> NA, NA, NA, NA, NA, NA, NA, "201…
## $ Gender                               <chr> NA, "Male", NA, "Female", "Femal…
## $ `Primary Business Position`          <chr> NA, "Senior Manager, Marketing -…
## $ `Primary Business Organisation Name` <chr> NA, "Canpotex International Pte …
## $ `Education School Type`              <chr> NA, "Primary", NA, "Primary", "P…
## $ `Education Class of`                 <chr> NA, "2008", NA, "2005", "2004", …
## $ `Education Date Graduated`           <chr> NA, "4/6/2008", NA, "30/5/2005",…
## $ `Education Degree`                   <chr> NA, "BSc (ISM)", NA, "BBM", "BBM…
## $ `Gift Date FY`                       <chr> "FY2016", NA, "FY2017", NA, NA, …
## $ `Gift Purpose`                       <chr> "Scholarship", NA, "Financial as…

## Joining, by = c("Constituent ID", "Gift Date", "Gift Name", "Gift Payment Type", "Gift Amount", "Gift Type", "year", "month", "date", "fy")

## Joining, by = c("Constituent ID", "Specific Record", "Key Indicator", "Fund Type", "Gift Date", "Gift Name", "Gift Amount", "Gift Type", "Fund Description", "Gift Payment Type", "year", "month", "date", "fy")

## Joining, by = "Constituent ID"

Final Data Set

glimpse(donations)

## Rows: 1,533
## Columns: 21
## $ `Constituent ID`                     <chr> "01002036", "01002049", "0100208…
## $ Gender                               <chr> "Male", "Female", "Female", "Fem…
## $ `Primary Business Position`          <chr> "Senior Manager, Marketing - SEA…
## $ `Primary Business Organisation Name` <chr> "Canpotex International Pte Ltd"…
## $ `Education School Type`              <chr> "Primary", "Primary", "Primary",…
## $ `Education Class of`                 <int> 2008, 2005, 2004, 2004, 2009, 20…
## $ `Education Date Graduated`           <date> 2008-06-04, 2005-05-30, 2005-05…
## $ `Education Degree`                   <chr> "BSc (IS)", "BBM", "BBM", "BBM",…
## $ `Fund Type`                          <chr> "Endowment", "Term", "Term", "Te…
## $ `Gift Date`                          <date> 2016-05-24, 2017-09-13, 2020-09…
## $ `Gift Name`                          <chr> "SMU Steven Miller Scholarship",…
## $ `Gift Amount`                        <dbl> 1000, 50, 200, 200, 5000, 5000, …
## $ `Gift Type`                          <chr> "Cash", "Cash", "Cash", "Cash", …
## $ `Fund Description`                   <chr> "SMU Steven Miller Scholarship",…
## $ `Gift Payment Type`                  <chr> "Credit Card", "Credit Card", "N…
## $ year                                 <dbl> 2016, 2017, 2020, 2020, 2019, 20…
## $ month                                <chr> "May", "Sep", "Sep", "Sep", "Nov…
## $ date                                 <int> 24, 13, 24, 24, 9, 1, 8, 15, 15,…
## $ fy                                   <int> 2017, 2018, 2021, 2021, 2020, 20…
## $ `Gift Purpose`                       <chr> "Scholarship", "Financial assist…
## $ grad_year                            <dbl> 2008, 2005, 2005, 2005, 2009, 20…

Model

1.1 Month

1.2 Gender

## `summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.

1.3 UG vs PG Donation Comparison

## `summarise()` has grouped output by 'Education School Type'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'Education School Type'. You can override using the `.groups` argument.

1.4.1 UG by Degree 1.4.2 UG by Graduation Class

## Rows: 1,272
## Columns: 21
## $ `Constituent ID`                     <chr> "01002036", "01002049", "0100208…
## $ Gender                               <chr> "Male", "Female", "Female", "Fem…
## $ `Primary Business Position`          <chr> "Senior Manager, Marketing - SEA…
## $ `Primary Business Organisation Name` <chr> "Canpotex International Pte Ltd"…
## $ `Education School Type`              <chr> "Primary", "Primary", "Primary",…
## $ `Education Class of`                 <int> 2008, 2005, 2004, 2004, 2009, 20…
## $ `Education Date Graduated`           <date> 2008-06-04, 2005-05-30, 2005-05…
## $ `Education Degree`                   <chr> "BSc (IS)", "BBM", "BBM", "BBM",…
## $ `Fund Type`                          <chr> "Endowment", "Term", "Term", "Te…
## $ `Gift Date`                          <date> 2016-05-24, 2017-09-13, 2020-09…
## $ `Gift Name`                          <chr> "SMU Steven Miller Scholarship",…
## $ `Gift Amount`                        <dbl> 1000, 50, 200, 200, 5000, 5000, …
## $ `Gift Type`                          <chr> "Cash", "Cash", "Cash", "Cash", …
## $ `Fund Description`                   <chr> "SMU Steven Miller Scholarship",…
## $ `Gift Payment Type`                  <chr> "Credit Card", "Credit Card", "N…
## $ year                                 <dbl> 2016, 2017, 2020, 2020, 2019, 20…
## $ month                                <chr> "May", "Sep", "Sep", "Sep", "Nov…
## $ date                                 <int> 24, 13, 24, 24, 9, 1, 8, 15, 15,…
## $ fy                                   <int> 2017, 2018, 2021, 2021, 2020, 20…
## $ `Gift Purpose`                       <chr> "Scholarship", "Financial assist…
## $ grad_year                            <dbl> 2008, 2005, 2005, 2005, 2009, 20…

## `summarise()` has grouped output by 'Education Class of'. You can override using the `.groups` argument.

1.5.1 PG by Degree 1.5.2 PG by Graduation Class

## Rows: 1,262
## Columns: 21
## $ `Constituent ID`                     <chr> "01002036", "01002049", "0100208…
## $ Gender                               <chr> "Male", "Female", "Female", "Fem…
## $ `Primary Business Position`          <chr> "Senior Manager, Marketing - SEA…
## $ `Primary Business Organisation Name` <chr> "Canpotex International Pte Ltd"…
## $ `Education School Type`              <chr> "Primary", "Primary", "Primary",…
## $ `Education Class of`                 <int> 2008, 2005, 2004, 2004, 2009, 20…
## $ `Education Date Graduated`           <date> 2008-06-04, 2005-05-30, 2005-05…
## $ `Education Degree`                   <chr> "BSc (IS)", "BBM", "BBM", "BBM",…
## $ `Fund Type`                          <chr> "Endowment", "Term", "Term", "Te…
## $ `Gift Date`                          <date> 2016-05-24, 2017-09-13, 2020-09…
## $ `Gift Name`                          <chr> "SMU Steven Miller Scholarship",…
## $ `Gift Amount`                        <dbl> 1000, 50, 200, 200, 5000, 5000, …
## $ `Gift Type`                          <chr> "Cash", "Cash", "Cash", "Cash", …
## $ `Fund Description`                   <chr> "SMU Steven Miller Scholarship",…
## $ `Gift Payment Type`                  <chr> "Credit Card", "Credit Card", "N…
## $ year                                 <dbl> 2016, 2017, 2020, 2020, 2019, 20…
## $ month                                <chr> "May", "Sep", "Sep", "Sep", "Nov…
## $ date                                 <int> 24, 13, 24, 24, 9, 1, 8, 15, 15,…
## $ fy                                   <int> 2017, 2018, 2021, 2021, 2020, 20…
## $ `Gift Purpose`                       <chr> "Scholarship", "Financial assist…
## $ grad_year                            <dbl> 2008, 2005, 2005, 2005, 2009, 20…

## `summarise()` has grouped output by 'Education Class of'. You can override using the `.groups` argument.

1.6 Job Title

1.7 Company

Visualize: Donation Variables

1.1 Month

## Warning: Ignoring unknown parameters: binwidth, bins, pad

Interpretation: July to September have the highest number of donations coming in. This could likely be due to LYM campaign over the years bringing in donors.

1.2 Gender

Interpretation: There are more Male donors than Female donors. There was a drop in donations in 2018 followed by a high rise in 2019

Interpretation: Despite there being more Male donors, the MEDIAN amount donated is generally the same for both genders besides in Financial Year 2020.

1.3 UG vs PG Donation Comparison

Interpretation: The number of UG student donors are greater which is expected due to their larger numbers

1.4.1 UG by Degree

Interpretation: The number of donors from School of Law is surprisingly greater than the rest of the schools. It could show that Law students are more willing to giving back. However looking at the median, Business and Economics students donate the most.

UG: Donations Boxplot for less than $500 donations

From the above we see that Business and Economics students donate the greatest using the median amount donated. Now we will narrow the scope to look at donations below $500 to see if this holds in smaller donation values as well. Boxplot: The donation amount has been limited to $500 to get a clearer picture of smaller donations. The boxplot represents the median, lower quartile and upper quartile of donations. The purple lines represent the mean (with a lower and upper range).

Interpretation: Looking at the smaller range of donations, we see that at the smaller end, Economics and Information Systems donors give slightly more.

1.4.2 UG by Graduation Class

Interpretation: Cohorts that graduted 5 years ago are most willing to donate. Additionall those that gradated 2 to 8 years ago in general are active in donation as well. However Alumni who graduated more than 10 years ago(before 2010), see a fall in number of donations.

As for the median donations, we see a rise in amount donated the longer it has been since it graduated

1.5.1 PG by Degree

Due to the high number of degrees available for PG, it was harder to showcase. Below we reduce the number to top 10 for the number of donations as well as median

This shows the PG degrees with highest number of donations are Masters of Wealth Management donors. As for the median donations, the MSc(Econs) and MTSC lead the way

1.5.2 PG by Graduation Class

This shows the donation trend of PG students from the year they graduated. There are a greater number of donations from recent graduates. As for the median amount, it was relatively high for those PG in the early years 2004-2007, but this could be an anomaly due to low numbers. Otherwise, the median amount stays more or less the same

1.6 Job Title

Job Title: Number of Donations

Interpretation: Word Cloud for number of donations by Job title

Job title: Median Donation Amount

Interpretation: Word Cloud for median donation amount by Job title

Job Title: Highest Median Donations ****Interpretation: Top 10 positions with highest median donations. Mostly from Finance side**

1.7 Company

Company: No. of donations

Interpretation: Word Cloud for number of donations by Company Company: Median Donation Amount - Due to the extremely large donation amounts, the medians are unable to be loaded in a wordcloud visualisation.

Visualize: Payment Variables

Question 2: The various types of payments, reasons for donations, etc. Essentially payment related that we went through during the first sharing.

2.1 Most used method of payment

Most used method of payment

2.2 Donations by Payment Type and Fund Type

Donations by Payment Type and Fund Type

2.3 Donations by Gift Type and Fund Type

Donations by Gift Type and Fund Type

2.4 Donations by Gift Purpose and Fund Type

Donations by Gift Purpose and Fund Type

2.5 By Financial Year and Gift Type

By Financial Year and Gift Type

2.6 By Financial Year and Gift Purpose

By Financial Year and Gift Purpose

2.7 By Financial Year and Gift Payment Type

By Financial Year and Gift Payment Type

2.8 By Financial Year and Fund Type

By Financial Year and Fund Type

Interpretation and implications of the Results

From these visualisations, we found certain charecteristics in each variable. The months of July-Sept are when most number of donations come in. Number of donations are higher in males than females while the median is almost similar. For PG, they donate a greater amount on median that UG alumni. The various degrees in UG and PG that donate higher allows us to seek out a more direct approach when required as well.

Limitations and Future Directions

Note: Due to the information pulled from RE, I wrangled, tidied and cleaned the data the best I could. There were a few items that do need to be noted 1. For the demographics list, for individuals with more than one degree (UG/PG), only their earliest or primary degree was used. 2. For SMOO Challenge, bigger sums without complete data eg “David” were not included as only their names and donations amounts were provided and nothing else.

**Future Direction: With this initial analysis, theres potential to gain more definite results in terms of the significance of each of these variables and how important each variable is relative to the other. With more data coming in, the patterns that emerge will begin to be more clear.

References

Excel sheets pulled from RE system from April 2016 to Oct 2020