International Journal of Current Research and Review
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IJCRR - Vol 06 Issue 22, November, 2014

Pages: 01-10

Date of Publication: 21-Nov-2014


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APPLICATION OF DATA MINING TECHNIQUES TO PROBLEMS IN FUND RAISING

Author: Adrian Udenze

Category: Technology

Abstract:Data mining in fund raising applications have been shown to significantly increase funds raised by charity organisations. This research investigates the accuracy of statistical classification techniques when applied to various prediction problems in fund raising. The results show that increased accuracy of predictions can be achieved by using actions taken by fund raisers as attributes as well as donor profiles. Where classification techniques fail, data mining results are shown to be useful for formulating and solving optimisation problems which are solved to provide the best course of actions for maximum return on investment.

Keywords: Data mining, Fund raising

Full Text:

INTRODUCTION The total amount of money given in charity for the year 2012 in the United Kingdom was £9.2 billion according to the BBC [1]. With such a vast potential for attracting funds, charity organisations are increasingly making use of state of the art techniques in data mining to identify potential donors, increase donation amounts and maximise return on investments. In particular, fund raisers would like to be able to identify donors from a list and also, correctly predict how much a donor is likely to give. If fund raisers can correctly predict how much an individual will donate then they can ask for the optimal amount of money from each donor and also ensure that the right amount of resources are expended in acquiring those funds. In [2], the author uses donor profiles consisting of a number of donor related attributes to construct Decision Trees [3] and Neural Networks [4] for predicting donors and non donors. The author observes mixed results with better results for predicting non donors than for donors. The work presented here builds on that research by providing a more in-depth investigation into the suitability of statistical classification techniques common in data mining for fund raising applications. In particular, the contributions of this work are as follows 1.) statistical classification techniques are shown to be suitable for predicting with reasonable certainty whether or not an individual will make a charitable donation depending on the profile attributes used. 2.) statistical classification techniques are shown however to have limitations on what can be predicted including which charity an individual will donate to and how much an individual will give. 3.) It is shown that whereas in [2] the author uses only donor attributes, actions taken by fund raisers have a correlation to amounts donated and can be used to improve accuracy of predictions. As far as the author is aware this is the first time the actions of the fund raiser are being considered as an attribute for classifying donors. 4.) Given that the aim of the data mining exercise within a charity organisation is to maximise funds by asking for the optimal amounts from donors and using minimal resources in so doing, in the absence of reliable prediction from conventional data mining classification techniques, the author shows that return on investment can still be improved on by formulating and solving an optimisation problem the result of which is a policy for carrying out actions that maximise return on investment. LITERATURE REVIEW Data mining has been shown to help organisations increase return on investment for given fundraising campaigns. In [5] the author shows results of a number of fundraising campaigns with data mining and without. The results show an average of 50% increase in amount raised per dollar spent for campaigns with statistical analysis. In [6], the authors state that data mining can improve existing models by finding additional important variables, identifying interaction terms and detect ing nonlinear relationships. Data mining is also useful in making sure that a fund raising officer asks for the right amount of money from a given prospect [7]. In [2] the author attempts to predict prospect donor amounts based on data collected on previous campaigns using decision trees and neural networks. The author concludes that neural networks give a slightly more accurate prediction however he observes that increased accuracy of donors may be obtained by collecting more data. Decision trees [3] are a common approach to discovering logical patterns within data sets. A tree is built using Binary Recursive Partitioning based on an iterative process of splitting a set of pre-classified test data into homogeneous segments and then splitting each segment or branch of the tree into more segments. The end result is a model of the test data that classifies each observation which can then be used for the classification of other records. Neural networks have been used extensively for data mining in various fields of science ranging from medical diagnostics, to online real time financial systems [4]. Their ability to model relationships in data make them useful for classifying tasks as well as predicting future events. Supervised learning usually takes the form of adjusting weights in a neuron such that the difference between network outputs when compared to desired outputs are minimised at which stage the network is fully trained and can be used for predicting or classifying tasks. In unsupervised learning, weights are adjusted to match world events in real time. Operations research provides a decision making mechanism for problems of limited resources and have been used extensively in industry for optimising return on investment [8]. Numerous optimisation problems including scheduling tasks, optimal route finding and budgeting have been solved using operations research techniques. The work in this paper builds on the work in [2] and [6]. Results of classification of prospects using decision trees and neural networks are presented using donor profiles as done in [2]. Furthermore, this work shows that increased accuracy of prediction can be achieved using a new set of attributes namely, actions taken by fund raisers. The authors also show that by using the results of the classification exercise to formulate and solve an optimisation problem, return on investment can be maximised. METHODOLOGY The data set used for experiments was acquired from an international human rights organisation and consisted of details of past marketing campaigns. Records included profiles of donors as well as non donors. The data set consisted of 10,000 entries initially. The data was cleaned and records with incomplete or inaccurate data were deleted. A sample of the records used is shown in Table 1 below. The aim of this research was to model the data set such that the model could be used to predict as accurately as possible the following: 1. Charity donor and non donor. To predict from the data set which individual was likely to make a charitable contribution two classification models were used. First decision trees were constructed using the Tree package in R available from the cran.r project website at the time of writing [9] and then neural networks using the nnet package also available from the cran.r project website, for different numbers of variables. 70% of 1000 records of donors and 1000 records of non donors were used for training, the remaining 30% was set aside for testing. The resulting models from training were then used on the test data set for predictions; results are presented in the results section. 2. Donate to a given charity. Neural networks were constructed with 70% of 500 records each for four charities and 30% set aside for testing. The number of records used for training was limited by the number of records available. Test results are presented in the results section. 3. How much will a donor contribute to a given charity. The donor amount field was discretized and placed in 7 bins which were used as classes namely {0, 1-1000, 1000-5000, 5000-10000, 10000-50000, 50000-100000, >100000}. Thus, the aim was to predict in which one of the 7 bins or classes a prospect belonged. To ensure there was no bias in the results, equal numbers of records for each class was used in the data set. After selecting, transforming and cleaning the data the number of entries was reduced to 7000 i.e. 1000 for each class. Finally the data set was split into two, 70% for training and 30% for testing, results are presented in the results section. Next, actions taken by fundraisers were used as variables in addition to the donor variables. For a given campaign, several actions are available to the fund raisers e.g. telephone calling, emailing, organising events, carrying out an advertising campaign on radio or television. In addition, several of these actions may be undertaken e.g. send an email and then telephone. Results of the enhanced models are presented in the results section. 4. Finally, the results of the data mining exercise were used to formulate an optimisation problem which was then solved as a Linear Programming problem using the R package linear programming solver, available from the cran.r project website at the time of printing [9]. E.g. for a given marketing campaign, the fundraisers have a set of four actions {Telephone, Email, Organise an event, Email and Telephone, Telephone and Organise and Event}. Each action has an associated return on investment R and actions A have constraints C placed on them. The optimisation problem can then be formulated as Maximise R1 A1 + R2 A2 + R3 A3 + ... RnAn (1) Such that A1 + A2 + A3 ... 0, X2 > 0, x3 > 0, x4 > 0 Solving using linear programming, the result is obtained as 0x1 102.27x2 0x3 1.79x4. Objective = £2106.82 Translated as: Make 102.27 phone calls and organise 1.79 events and the campaign should realise £2106.82! This solution provides the best course of action to raise the most money. Note however that the campaign team cannot organise 1.79 events nor can they make 102.27 phone calls i.e. either 102 phone calls or 103 phone calls. To overcome this the solver is forced to return only integer values which provides a new result of 0x1 0x2 0x3 2x4 Objective = £1800. Translated as: Organise 2 events which will raise £1800! Results observed from test data using data mining as well as solving a formulated optimisation problem results in an increase of between 15% and 18% on return on investment compared to data mining alone. CONCLUSIONS Data mining for fund raising experiments using two classification techniques, decision trees and neural networks have shown to be effective in identifying potential donors but less so in identifying which charity an donor is likely to donate to and how much. The addition of actions taken by fund raisers to data mining variables increased accuracy of predictions compared to variables consisting of only donor profiles. The link between actions taken by fund raisers and donor amounts was explored further by formulating and solving a constrained decision prob- lem the result of which is a course of action that yields maximum return on investment. Future work will be on investigating modelling techniques for sparsely correlated data and using the models to formulate and solve constrained decision making problems in fund raising. ACKNOWLEDGEMENTS The author acknowledges the immense help received from the scholars whose articles are cited and included in references of this manuscript. The author is also grateful to the authors / editors / publishers of all those articles, journals and books where the literature for this article has been reviewed and discussed. The author also acknowledges the contributions of Amnesty International UK.

 

References:

1. BBC News. (2012) ‘Charity donations down 20%, UK Giving Report 2012, ‘www.bbc.co.uk/news/uk-20304267’ Accessed Nov 2014.

2. Heiat, N. (2011) ‘Data Mining Applications in Fund Raising’, in World Journal of Social Sciences, Vol. 1. No. 4. 14- 22.

3. Greenberg D, Pardo B., Hariharan K. And Gerber E. (2013) ‘Crowd funding support tools: predicting success and failure’, in Proceedings of Chi EA 13 Extended Abstracts on Human Donors Factors in Computing Systems, pages 1815- 1820, ACM New York.

4. Jain N. and Srivastava V. (2013) ‘Data Mining techniques: A survey paper’, in International Journal of Engineering and Technology’ Vol2. Issue 11.

5. Wylie, P. (2008) ‘Baseball, Fundraising, and the 80/20 Rule: Studies in Data Mining’ Washington, DC : CASE (Council for Advancement and Support of Education).

6. Devale, A. B. and Kulkarni, R. V. (2012) ‘Applications of data mining techniques in life insurance’, in International Journal of Data Mining and Knowledge Management Process (IJDKP) Vol.2, No.4: 31-40.

7. Birkholz, J. (2008) ‘Fundraising Analytics: Using Data to Guide Strategy’ 1st edition, John Wiley and Sons, Inc.

8. Xing Y. Li L. and Bi. Z. (2013) ‘Operations research in service industries: A comprehensive review’, in Systems Research and Behavioural Science, Special Issue: Systems Science in Industrial Sectors, Vol 30, Issue 3, pages 300 – 353.

9. Cran.r project (2014), ‘Linear Programming package’ in www.cran.r-project.org/web/packages, Accessed Nov 2014.


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One article from every issue is selected for the ‘Best Article Award’. Authors of selected ‘Best Article’ are rewarded with a certificate. IJCRR Editorial Board members select one ‘Best Article’ from the published issue based on originality, novelty, social usefulness of the work. The corresponding author of selected ‘Best Article Award’ is communicated and information of award is displayed on IJCRR’s website. Drop a mail to editor@ijcrr.com for more details.

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Best Article Award

A Study by Amr Y. Zakaria et al. entitled "Single Nucleotide Polymorphisms of ATP-Binding Cassette Gene(ABCC3 rs4793665) affect High Dose Methotrexate-Induced Nephrotoxicity in Children with Osteosarcoma" is awarded Best Article for Vol 13 issue 19
A Study by Kholis Ernawati et al. entitled "The Utilization of Mobile-Based Information Technology in the Management of Dengue Fever in the Community Year 2019-2020: Systematic Review" is awarded Best Article for Vol 13 issue 18
A Study by Bhat Asifa et al. entitled "Efficacy of Modified Carbapenem Inactivation Method for Carbapenemase Detection and Comparative Evaluation with Polymerase Chain Reaction for the Identification of Carbapenemase Producing Klebsiella pneumonia Isolates" is awarded Best Article for Vol 13 issue 17
A Study by Gupta R. et al. entitled "A Clinical Study of Paediatric Tracheostomy: Our Experience in a Tertiary Care Hospital in North India" is awarded Best Article for Vol 13 issue 16
A Study by Chandran Anand et al. entitled "A Prospective Study on Assessment of Quality of Life of Patients Receiving Sorafenib for Hepatocellular Carcinoma" is awarded Best article for Vol 13 issue 15
A Study by Rosa PS et al. entitled "Emotional State Due to the Covid – 19 Pandemic in People Residing in a Vulnerable Area in North Lima" is awarded Best Article for Vol 13 issue 14
A Study by Suvarna Sunder J et al. entitled "Endodontic Revascularization of Necrotic Permanent Anterior Tooth with Platelet Rich Fibrin, Platelet Rich Plasma, and Blood Clot - A Comparative Study" is awarded Best Article for Vol 13 issue 13
A Study by Mona Isam Eldin Osman et al. entitled "Psychological Impact and Risk Factors of Sexual Abuse on Sudanese Children in Khartoum State" is awarded Best Article for Vol 13 issue 12
A Study by Khaw Ming Sheng & Sathiapriya Ramiah entitled "Web Based Suicide Prevention Application for Patients Suffering from Depression" is awarded Best Article for Vol 13 issue 11
A Study by Purushottam S. G. et al. entitled "Development of Fenofibrate Solid Dispersions for the Plausible Aqueous Solubility Augmentation of this BCS Class-II Drug" is awarded Best article for Vol 13 issue 10
A Study by Kumar S. et al. entitled "A Study on Clinical Spectrum, Laboratory Profile, Complications and Outcome of Pediatric Scrub Typhus Patients Admitted to an Intensive Care Unit from a Tertiary Care Hospital from Eastern India" is awarded Best Article for Vol 13 issue 09
A Study by Mardhiah Kamaruddin et al. entitled "The Pattern of Creatinine Clearance in Gestational and Chronic Hypertension Women from the Third Trimester to 12 Weeks Postpartum" is awarded Best Article for Vol 13 issue 08
A Study by Sarmila G. B. et al. entitled "Study to Compare the Efficacy of Orally Administered Melatonin and Clonidine for Attenuation of Hemodynamic Response During Laryngoscopy and Endotracheal Intubation in Gastrointestinal Surgeries" is awarded Best Article for Vol 13 issue 07
A Study by M. Muthu Uma Maheswari et al. entitled "A Study on C-reactive Protein and Liver Function Tests in Laboratory RT-PCR Positive Covid-19 Patients in a Tertiary Care Centre – A Retrospective Study" is awarded Best Article of Vol 13 issue 06 Special issue Modern approaches for diagnosis of COVID-19 and current status of awareness
A Study by Gainneos PD et al. entitled "A Comparative Evaluation of the Levels of Salivary IgA in HIV Affected Children and the Children of the General Population within the Age Group of 9 – 12 Years – A Cross-Sectional Study" is awarded Best Article of Vol 13 issue 05 Special issue on Recent Advances in Dentistry for better Oral Health
A Study by Alkhansa Mahmoud et al. entitled "mRNA Expression of Somatostatin Receptors (1-5) in MCF7 and MDA-MB231 Breast Cancer Cells" is awarded Best Article of Vol 13 issue 06
A Study by Chen YY and Ghazali SRB entitled "Lifetime Trauma, posttraumatic stress disorder Symptoms and Early Adolescence Risk Factors for Poor Physical Health Outcome Among Malaysian Adolescents" is awarded Best Article of Vol 13 issue 04 Special issue on Current Updates in Plant Biology to Medicine to Healthcare Awareness in Malaysia
A Study by Kumari PM et al. entitled "Study to Evaluate the Adverse Drug Reactions in a Tertiary Care Teaching Hospital in Tamilnadu - A Cross-Sectional Study" is awarded Best Article for Vol 13 issue 05
A Study by Anu et al. entitled "Effectiveness of Cytological Scoring Systems for Evaluation of Breast Lesion Cytology with its Histopathological Correlation" is awarded Best Article of Vol 13 issue 04
A Study by Sharipov R. Kh. et al. entitled "Interaction of Correction of Lipid Peroxidation Disorders with Oxibral" is awarded Best Article of Vol 13 issue 03
A Study by Tarek Elwakil et al. entitled "Led Light Photobiomodulation Effect on Wound Healing Combined with Phenytoin in Mice Model" is awarded Best Article of Vol 13 issue 02
A Study by Mohita Ray et al. entitled "Accuracy of Intra-Operative Frozen Section Consultation of Gastrointestinal Biopsy Samples in Correlation with the Final Histopathological Diagnosis" is awarded Best Article for Vol 13 issue 01
A Study by Badritdinova MN et al. entitled "Peculiarities of a Pain in Patients with Ischemic Heart Disease in the Presence of Individual Combines of the Metabolic Syndrome" is awarded Best Article for Vol 12 issue 24
A Study by Sindhu Priya E S et al. entitled "Neuroprotective activity of Pyrazolone Derivatives Against Paraquat-induced Oxidative Stress and Locomotor Impairment in Drosophila melanogaster" is awarded Best Article for Vol 12 issue 23
A Study by Habiba Suhail et al. entitled "Effect of Majoon Murmakki in Dysmenorrhoea (Usre Tams): A Standard Controlled Clinical Study" is awarded Best Article for Vol 12 issue 22
A Study by Ghaffar UB et al. entitled "Correlation between Height and Foot Length in Saudi Population in Majmaah, Saudi Arabia" is awarded Best Article for Vol 12 issue 21
A Study by situs slot and Siti Sarah Binti Maidin entitled "Sleep Well: Mobile Application to Address Sleeping Problems" is awarded Best Article for Vol 12 issue 20
A Study by Avijit Singh et al. situs slot "Comparison of Post Operative Clinical Outcomes Between “Made in India” TTK Chitra Mechanical Heart Valve Versus St Jude Mechanical Heart Valve in Valve Replacement Surgery" is awarded Best Article for Vol 12 issue 19
A Study by Sonali Banerjee and Mary Mathews N. entitled "Exploring Quality of Life and Perceived Experiences Among Couples Undergoing Fertility Treatment in Western India: A Mixed Methodology" is awarded Best Article for Vol 12 issue 18
A Study by Jabbar Desai et al. entitled "Prevalence of Obstructive Airway Disease in Patients with Ischemic Heart Disease and Hypertension" is awarded Best Article for Vol 12 issue 17
A Study by Juna Byun et al. entitled "Study on Difference in Coronavirus-19 Related Anxiety between Face-to-face and Non-face-to-face Classes among University Students in South Korea" is awarded Best Article for Vol 12 issue 16
A Study by Sudha Ramachandra & Vinay Chavan entitled "Enhanced-Hybrid-Age Layered Population Structure (E-Hybrid-ALPS): A Genetic Algorithm with Adaptive Crossover for Molecular Docking Studies of Drug Discovery Process" is awarded Best article for Vol 12 issue 15
A Study by Varsha M. Shindhe et al. entitled "A Study on Effect of Smokeless Tobacco on Pulmonary Function Tests in Class IV Workers of USM-KLE (Universiti Sains Malaysia-Karnataka Lingayat Education Society) International Medical Programme, Belagavi" is awarded Best article of Vol 12 issue 14, July 2020
A study by Amruta Choudhary et al. entitled "Family Planning Knowledge, Attitude and Practice Among Women of Reproductive Age from Rural Area of Central India" is awarded Best Article for special issue "Modern Therapeutics Applications"
A study by Raunak Das entitled "Study of Cardiovascular Dysfunctions in Interstitial Lung Diseas epatients by Correlating the Levels of Serum NT PRO BNP and Microalbuminuria (Biomarkers of Cardiovascular Dysfunction) with Echocardiographic, Bronchoscopic and HighResolution Computed Tomography Findings of These ILD Patients" is awarded Best Article of Vol 12 issue 13 
A Study by Kannamani Ramasamy et al. entitled "COVID-19 Situation at Chennai City – Forecasting for the Better Pandemic Management" is awarded best article for  Vol 12 issue 12
A Study by Muhammet Lutfi SELCUK and Fatma situs slot entitled "Distinction of Gray and White Matter for Some Histological Staining Methods in New Zealand Rabbit's Brain" is awarded best article for  Vol 12 issue 11
A Study by Anamul Haq et al. entitled "Etiology of Abnormal Uterine Bleeding in Adolescents – Emphasis Upon Polycystic Ovarian Syndrome" is awarded best article for  Vol 12 issue 10
A Study by situs slot et al entitled "Estimation of Reference Interval of Serum Progesterone During Three Trimesters of Normal Pregnancy in a Tertiary Care Hospital of Kolkata" is awarded best article for  Vol 12 issue 09
A Study by Ilona Gracie De Souza & Pavan Kumar G. entitled "Effect of Releasing Myofascial Chain in Patients with Patellofemoral Pain Syndrome - A Randomized Clinical Trial" is awarded best article for  Vol 12 issue 08
A Study by Virendra Atam et. al. entitled "Clinical Profile and Short - Term Mortality Predictors in Acute Stroke with Emphasis on Stress Hyperglycemia and THRIVE Score : An Observational Study" is awarded best article for  Vol 12 issue 07
A Study by K. Krupashree et. al. entitled "Protective Effects of Picrorhizakurroa Against Fumonisin B1 Induced Hepatotoxicity in Mice" is awarded best article for issue Vol 10 issue 20
A study by Mithun K.P. et al "Larvicidal Activity of Crude Solanum Nigrum Leaf and Berries Extract Against Dengue Vector-Aedesaegypti" is awarded Best Article for Vol 10 issue 14 of IJCRR
A study by Asha Menon "Women in Child Care and Early Education: Truly Nontraditional Work" is awarded Best Article for Vol 10 issue 13
A study by Deep J. M. "Prevalence of Molar-Incisor Hypomineralization in 7-13 Years Old Children of Biratnagar, Nepal: A Cross Sectional Study" is awarded Best Article for Vol 10 issue 11 of IJCRR
A review by Chitra et al to analyse relation between Obesity and Type 2 diabetes is awarded 'Best Article' for Vol 10 issue 10 by IJCRR. 
A study by Karanpreet et al "Pregnancy Induced Hypertension: A Study on Its Multisystem Involvement" is given Best Paper Award for Vol 10 issue 09
Late to bed everyday? You may die early, get depression situs slot
Egg a day tied to lower risk of heart disease situs slot
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A Study by Ese Anibor et al. "Evaluation of Temporomandibular Joint Disorders Among Delta State University Students in Abraka, Nigeria" from Vol 13 issue 16 received Emerging Researcher Award


A Study by Alkhansa Mahmoud et al. entitled "mRNA Expression of Somatostatin Receptors (1-5) in MCF7 and MDA-MB231 Breast Cancer Cells" from Vol 13 issue 06 received Emerging Researcher Award


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