Shameek Sinha – IE Business School, IE University Vijay Mahajan and Frenkel ter Hofstede –

1. Duration Dependence of Donation Behavior: Explaining Heterogeneity in Donation Incidence and Amount through Community Characteristics Shameek Sinha – IE Business School, IE University Vijay Mahajan and Frenkel ter Hofstede – McCombs School of Business, University of Texas at Austin

2. Non-Profit Charities (NPC): Why do we Marketers even Care? • Total charitable giving of $290.89 billion which is around 2% of GDP ** • 73% of total fundraising are individual donors ** • 3.8% growth in charitable giving and 2.7% growth in individual contributions ** • 1,280,739 NPCs out of which 65% raise more than $10 million or more * • Significant majority of NPCs (89%) use direct response methods for solicitation and 45% of those increased their direct mail fundraising * • However, 41% of NPC’s fail to meet their fundraising goals * * Source: Guidestar Survey for Direct mail Nonprofit Fundraising (2012) ** Source: Giving USA (2011)

3. Background on Empirical Context and Data • Non-profit organization uses direct mail to solicit contributions from past donors (Source: DMEF). • Contributions and solicitations in Texas: Weekly data for 13767 donors. • Time span covering a period of approximately 15 years (unbalanced – average ~ 521 weeks). • Contributions and solicitations by date, amount on each incidence and costs of each solicitation. • History of solicitations and contributions – censored data. • Community characteristics: (Sources: uselectionatlas.org, FBI Crime Statistics, ARDA, TEA) • ZIPCODE-level • Counties-level

4. Donor Heterogeneity: How Communities Differ? El Paso - 79912 Houston - 77024 Mission - 78572

5. 78572 : 79912 : 77024 – A Visual Comparison Mission - 78572 El Paso - 79912 Houston - 77024

6. 78572 : 79912 : 77024 – A Numerical Comparison

7. Community Characteristics: What Matters? • ZIPCODE-level: • Socio-Demographics: race; household-size; household-type; age; education level; income level; wealth-rating; home-value; home-ownership. • Credit-Financials: age of tradelines; balance of tradelines; tradelines with satisfactory ratings; tradelines with derogatory ratings; no. of tradelines delinquent. • County-level: • Political Beliefs: % of republican votes. • Religious Beliefs: % of Mainline Christians; % of Evangelical Christians; % of Catholic Christians; % of Other Christians. • Community Security: % of violent crimes. • Educational Quality: no. of public schools; school rating.

8. Targeting Potential Donors Using Donor Profiles within Communities Amount of contributions Inter-contribution duration

9. Communities – Why they matter? (ZIPCODE-level)

10. Communities – Why they matter? (County-level)

11. Literature: Donor Characteristics Influencing Donation Behavior? • Demographics (Lee and Chang, 2007) e.g. age, gender, education, race, income, marital status, religion, family size etc. • Psychographics (Bussell and Forbes, 2002) e.g. self-esteem, empathy, guilt, social-justice, familiarity with causes, awareness, responsibility, generosity etc. • Past experience with charities ( Schlegelmilch, Love and Diamantopoulos, 1996) e.g. previous experience, no. of times approached etc. • Community Effects (Corcoran et al., 1990; Schultz, 1984; Datcher, 1982, DeMarzo et al., 2005) e.g. demographic composition, financial composition etc.

12. Duration Dependence of Contribution and Solicitation Behavior Duration between two contributions (Budgetary Implications) Contribution1 Contribution2 Solicitation1 Solicitation2 Solicitation3 Solicitation4 Solicitation5 Duration between solicitation and contribution (Wait/ Gather Information)

13. Donation Response Framework Periods 1,2,…, (t-1) Period t • Modeling Incidence • and Amount • Donation Response: • Interval-Censored • Proportional Hazard • With Complimentary • Log-log Link • Donation amount: • Censored log-Normal • Distribution • Donor heterogeneity • Hierarchical • Specification Seasonality Solicitation/ No Solicitation for Cause Decision to Contribute for Cause Durations Amount of Contribution for Cause Contribution/ No Contribution for Cause ZIPCODE and County -Level Community Characteristics

14. Relevant Literature

15. Donation Incidence Model • Donors: i = 1, 2… n • Time Periods: t = 1, 2… T • Model: where if donor i makes a contribution in period t = 0 otherwise. and : contribution amount of a donor i at time t. • Likelihood of contribution incidence for donor i – • Proportional hazard function for donor i :

16. Donation Incidence Model • Survival Function: • Discrete analog of hazard specification: • Re-arranging: • Baseline Hazard: • Hazard function:

17. Donation Incidence Model with : Duration from last contribution at time t : Duration from last solicitation at time t : Seasonality dummy (months November – January) Heterogeneity specification – where : demographic and financial variables : vector of parameters for the donor level covariates : variance-covariance matrix

18. Donation Amount Model • Censored Log-Normal distribution of contribution amount – • Specification for mean – • Heterogeneity specification – : vector of parameters for the donor level covariates : variance-covariance matrix

19. Bayesian MCMC Estimation • Priors on donor-specific parameters – • Priors on population-level parameters – • : Non-conjugate Incidence Model Random-walk Metropolis Hastings • : Conjugate Amount Model Gibbs Sampler • 45000 draws; 22500 burn-in samples; thinning parameter:15

20. Donation Incidence and Amount Model Results

21. Duration Dependence of Donation Incidence

22. Duration Dependence of Donation Amount

23. Community Effects on Donation Incidence

24. Community Effects on Donation Incidence

25. Community Effects on Donation Amount

26. Community Effects on Donation Amount

27. Incidence and Amount Model Predictions • Three sets of predictions: (for approximately 20% of the total donor-time observations) • In-sample for existing donors within the observation period (individual level parameters) . • Out-of-sample for existing donors outside the observation period (individual level parameters). • Out-of-sample for new donors outside the observation period (population level parameters). • Incidence model predictions: Dynamic method for incidence and duration (approximately 67% accuracy based on hit rate). • Amount model predictions: Conditional on incidence, static method (approximately 79 % accuracy based on hit rate).

28. Predictions – Representative Donors El Paso (79912) Houston (77024) Mission (78572)

29. Lessons Learned about Donation Behavior • Durations from past gifts and past appeals have impact on current gift incidence and gift amount. • Evidence of both linear and non-linear effects more pronounced for donation incidence, not so much for donation amount. • Significant seasonal patterns evident in donation incidence, absent for donation amount. • Community characteristics impact incidence – race, age, income level, wealth rating, balance of tradelines, number of delinquent tradelines, political affiliation, crime rate, public education system. • Community interactions also matter for amount – household size, household with families, home value, home ownership, balance of tradelines, tradelines with satisfactory ratings, number of delinquent tradelines, wealth rating, political affiliations, public education system , religious beliefs (Catholics, Evangelicals and Other Christians). • In-sample predictions support targeting existing donors efficiently; out-of-sample predictions provides a compelling methodology for targeting existing and potential donors with donor portfolios.

30. Questions and Comments THANK YOU