project presentation christine carl nina nowak aldo córdova tiziana scarnà november 12th, 2015

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Team work Only online communication; main channels: GitHub, Skype, Slack, Google Drive Daily interactions (scrum meetings and additional meetings on demand) For each milestone, we defined specific subtasks for each individual Team membersMain tasksSoftware stack Christine Donor segmentation, demographics, predictive modeling, geographical and correspondence analysis R (ggplot2, caret, cluster, leaflet, ggmap, dplyr) Nina Analysis of temporal (dis)engagement patterns, donor value, gender-related feature extraction Python (pandas, matplotlib, scipy.stats, seaborn) Aldo Behavioural/temporal and demographic analysis of regular donors, demographics Python (pandas, matplotlib, statsmodels, scipy.stats, etc.) Tiziana Event-related feature analysis: monetary value of events, donor engagement R (ggplot2, plyr)

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Project presentation Christine Carl Nina Nowak Aldo Crdova Tiziana Scarn November 12th, 2015 Donor relationship management at Winston's Wish Established in 1992 First/leading childhood bereavement charity in the UK Supports children after the death of a parent or sibling Headquarters: Cheltenham, England Over children supported, raised 2.457million funds in 2014/15 Revenue depends on donations from individuals, organizations as well as fundraising activities 1. What characterizes existing donors? 2. How can we develop existing donors in order to maximize their lifetime value? 3. How can we attract new donors? 4. Recommendations for data strategy Team work Only online communication; main channels: GitHub, Skype, Slack, Google Drive Daily interactions (scrum meetings and additional meetings on demand) For each milestone, we defined specific subtasks for each individual Team membersMain tasksSoftware stack Christine Donor segmentation, demographics, predictive modeling, geographical and correspondence analysis R (ggplot2, caret, cluster, leaflet, ggmap, dplyr) Nina Analysis of temporal (dis)engagement patterns, donor value, gender-related feature extraction Python (pandas, matplotlib, scipy.stats, seaborn) Aldo Behavioural/temporal and demographic analysis of regular donors, demographics Python (pandas, matplotlib, statsmodels, scipy.stats, etc.) Tiziana Event-related feature analysis: monetary value of events, donor engagement R (ggplot2, plyr) Donor characterization Donors and their donation behaviour Demographics Gender Age Marital Status Education Income Religion... Other characteristics Donation frequency Awareness of charity (via events, campaigns, direct correspondance) What are the characteristics of the most valuable donor? info in database mostly complete info in database incomplete (particularly before 2009) some info available in database little or no info available in database Other demographic features from ONS (Office of National Statistics) 2011, matched to donors based on postal code Income per household per week Religion Employment Social grade 1: London Donor subgroups differ not only in donor behavior but also demographic features and interaction patterns with WW. Tailor interaction to cluster subgroups and identify opportunities for individualized actions 3: Mses 10: loyalists clustering: distance metric: gower, hierarchical clustering: ward2D Global view of the donor landscape The majority of donors live where WW offers services or performs charity events London and Leeds are areas with relatively many donors compared to beneficiaries Where do donors come from? individual donors beneficiaries low high individuals density Database overview More female than male donors donations of men higher on average 1-time donors majority Regular donors rare, but give more, on average than the other groups Most regular donors give monthly Quarter-yearly and quasiregular** donors give most, on average ** quasi-regular: loyal donors who give regularly over many years without contract and without regular time pattern Regular donors who started as N-time donors give more than those who started as regular Age and marital status Literature suggests that age and marital status are important donor characteristics Very small fraction of donors has age and marital status information Most donors are married and between 36 and 45 year old No statistically significant differences in donor behaviour WW should collect more information about age and marital status Distribution of total Donations Normalized by number of donors per group Distributions differ between genders, donor types, regular types and demographic features Differences in donor value area between distributions (if statistically different) Distribution differences Test differences between distributions of total donations of donor subgroups statistically: There are differences between 1-time donors and all others (expected) Gender N-time and regular donors Low and high Unemployment, Income, SocialAB and SocialDE Donors regular from the beginning and N- >Regular donors Kruskal-Wallis H test (3+ groups) Wilcoxon-Mann-Whitney test (2 groups) (non-parametric) What is the average value of a donor? Regular donors give more money in total than N-time donors One-time donors give smallest amount of money Men give more than Women Low income, high unemployment, low social status regions correlated to lower donations Regular donors who were N-time donors first are more valuable Quarter-yearly and quasiregular donors seem to give more than yearly and monthly donors Recommendations based on donor characterization Individualize correspondence on subgroups of the database based on segmentation Target more men, they have a ~2x higher donor value than women Try to convert N-time donors into regular donors Offer contracts with quarter-yearly payments Consider indicators for good donor behavior like income, unemployment and social status Development of regular donors How do donors develop into regular donors? Many people sign monthly contract without having donated previously to WW Also, people who have donated more than once are likely to engage in monthly donations (with contracts) Once a person has donated twice (or more), it may be a good moment to offer them regular donation schemas. Chi-squared test (1 donation vs >1 donation): Chi2 = 148.3; p = 4e-34 Number of donations People who become monthly donors vs. those who only donate a few times Number of donations before the contract Frequency (individuals) Percentage How do donors develop into regular donors? (2)...Regarding people with 2 or more donations: those who are more prone to sign a contract often donated more money in the past than in their last donation If you observe that someone donated more (at least once in the past) than in his/her most recent donation, it may be a good moment to offer him/her a (monthly) direct debt contract. Chi-squared test (1 donation vs >1 donation): Chi2 = 14.1; p = 1.8e-4 Last payment is maximum Donated much and then less Percentage Gender differences among monthly donors Many women donate up to 15 GBP every month Men do not engage so often in monthly contracts, although on average each of them donates more than the average female You may invite women to donate between 5-15 GBP every month --reachable; high revenue Men normally donate a bit more (20-25 GBP) -- perhaps rethink a tailored strategy to address them Linear regression test: Beta = 4.8; p < 1e-3; adj-R2: Number of donors Amount (GBP) Amount donated every month (only people with direct debt contracts) Correspondence between WW and donors correlates with donor development With a median transition time of ~35 month some N- or 1-time donors turn into regulars. Within this duration before turning regular we compared correspondences for donors that developed into regular donors and those who did not Donors who turn regular receive more correspondence from WW Thank you, newletter and mailshort seem to particularly strengthen WW-donor relationship Concentrate on these communication types for non-regular donors in order increase engagement Thank you letters are most important for turning into a regular donor, followed by other sociodemographic characteristics Misclassified nonregulars may be very similar to regulars and are promising candidates for donor development. nonregularregular nonregular regular45143 thank you age any correspondence ratio religion ratio retired ratio social grade AB title Mrs Mr first trans type MER + (0.7) + (62) + (9.7) + (0.7) + (0.15) + (0.31) + (0.75) - (0.04) - (0.1) - (48) - (7.4) + (0.7) + (0.15) - (0.30) - (0.53) + (0.18) Can we predict development into regular donors? regularnonregulars prediction classification: stochastic gradient boosting (decision trees), 5-x 10-fold crossvalidation, 20% test holdout, ROC = 0.82 reference How can WW augment the proportion of regular donors? Concentrate fundraising efforts to donors that have previously donated twice or more Correspondence and in particular thank you messages are promising for strengthening the charity-donor relationship Other important factors that influence donor development are social demographics like age, social grade and retirement, but also the type of the first financial interaction with WW Attracting new donors What are first interactions of donors with WW? Many contacts that first interacted through events or merchandising, become donors. Only people that first interacted with WW through a cash donation, become regular donors later on. Almost all donors that come to WW through and event or merchandise will give only once. After the first donation, WW may keep in touch with them to potentially trigger further donations. How to engage new donors Indirect income of events is often very different to direct income Events with low direct or indirect income may still engage many new donors Most successful across all factors is the Sunrise Walk Glos New donor engagement and indirect income should be considered for evaluation of fundraising success and planning of future events. Ultimate Abseil # of new donors brought by event total donations of new donors brought by events Recommendations for attracting new donors Events should not only be organized with respect to the direct revenue they generate, but also with a clear vision about potential donor recruitment The interactive map suggests regions that may be most valuable for expanding fundraising activities in the future Evaluation of the approach Use data driven segmentation and predictive models to develop a characterization schema of the donors specific to WW Analysis of charity-donor links on the individual donor level for temporal as well as interaction patterns We provided indicators that help WW to identify promising candidates for donor engagement and development A segmentation into donor groups will allow WW to individualize the donor relationship We suggested promising geographical locations for WW to expand based on demographic data Recommendations of data strategy will provide a better basis for future analysis Innovation Business value Future steps of analysis and data strategy More demographic data may allow better segmentation analysis of user groups. Integrated in a dashboard, such segmentation analysis would allow individually tailored donor-relationship management. Collecting demographic information for evaluating future donor value will in general help to evaluate donors on the individual donor level, among them donor age is a key feature Further analysis of donors whose family received support from WW will help to quantify their donor value as well as identify most promising interaction patterns. Further analysis of temporal patterns of WW-donor interaction for a precise recommendation on when and how to interact with donors Cluster description Cluster 1 - The Londoners: Mostly from London, this predominantly male cluster contains both ex-donors and new, currently active donors of very high household income, social status, and employment rates that donate intermediary to high amounts mostly in single payments. Most but not all of these people were regularly contacted by WW during their donor development. Contains a strong but small and mostly lapsed subcluster of regular donors that have also donated generously. Cluster 2 - The generous recents: A mostly male cluster donating rather large sums in mostly few donations, these people originate from mid-to-high income neighborhoods modestly enriched by retired people. This cluster of people started interacting with WW fairly recently and has been contacted by WW during their donor development. Cluster 3 - The advice seeking Mses: Female, mostly very young cluster of people from the South East that almost exclusively started with WW by buying merchandise. Having donated only small amounts in single payments, these people are of mixed social class and income. These people may have consulted the hotline and were advised to buy a guidebook from WW and donate once afterwards as service in return. Cluster 4 - The young relatives?: This cluster is somewhat enriched in younger folks that donate intermediary amounts mostly in single donations. Most of them live in regions of intermediary-to-high household incomes and they include a 50% subgroup of people with high social class. They were active for an intermediary amount of time and were contacted regularly by WW during their donor development. These cluster might include many relatives of beneficiaries because of the communication with WW and age structure, although they are mostly not marked as beneficiaries or beneficiary related in the database Cluster 5 - The generous lower social grades: This cluster contains relatively many people from the south west that are of lower social class and income and have a relatively high ratio of unemployment. Despite their lower means, they were generous with their donations, most of them in single installments but also containing a smaller subgroup of regular donors. Most of these people have stopped donating long ago, but about a third of them are currently active. Cluster 6 - The working middle class: Cluster of mostly females that give a slightly above average amount of total donations, includes regulars that are still active as well as many bygone donors. Only a subgroup of the regulars were frequently contacted by by WW. Mostly are within job, although some of the regulars are retired. Cluster 7 - The elderly recents: Cluster of new recent donors that are mostly one-time donors as of now and include many men. However, about 10% of these are already regular donors. This cluster contains comparatively many retired people and is of intermediary income and social class. These people have not yet received many messages from WW that could influence their donor development. Cluster 8 - The less generous bygones: Cluster of mostly female and often younger ex-donors that aren't active anymore and used to donate lower sums in only one donation despite the fact that many are of higher social class and income. Did not receive many communications from WW during their donor development. Cluster 9 - The generous bygones: Cluster of ex-donors that aren't active anymore but used to donate high sums in one or few payments, intermixed with regular ex-donors. More men than women. Includes relatively many beneficiaries. Of mid- to high social status and including working people as well as retired people of average household income from the south west. Cluster 10 - The loyalists: Cluster of older, married folks from the south west also containing beneficiaries who regularly donate smaller amounts, are active for a long time and are currently still donating. They have better-than-average household income and are often retired and comparatively high in social status. Also, these received many communications from WW during their donor development.