We are looking for a candidate for an industrial Ph.D. position who will work in the realm of “Donor Lifetime Modeling”. The industrial Ph.D. position is a collaboration between ZOI/The Agency Scandinavia (Jacob Møllemose) and the Department of Marketing at the Copenhagen Business School (Edlira Shehu).
The objective of the industrial Ph.D. project is to develop a donor lifetime value (DLV) model, which will be able to predict (1) the likelihood of donation, (2) churn risk, and (3) future donor value. The model will be tested in the field and will be implemented by the industrial partner, The Agency Scandinavia as a SaaS platform.
The project’s success is measured by (1) the development of the DLV model, (2) the improved performance of the collaborating NPOs, and (3) successful Ph.D. completion.
The candidate should have an outstanding master’s degree and a strong focus on quantitative topics, such as quantitative marketing or econometrics. We expect a very good knowledge of statistical and empirical research methods, including the ability to work with large data sets using appropriate software and programming skills in R or Python.
The candidate should be able to visit Copenhagen for Ph.D. courses as required.
To see if you are eligible to apply as a Ph.D. candidate with Copenhagen Business School please visit https://www.cbs.dk/en/research/departments-and-centres/department-of-marketing/vacancies
Background Description of the Ph.D. project
The final objective of the current industrial Ph.D. project is to develop a donor lifetime value (DLV) model, which will be able to predict (1) the likelihood of donation, (2) the value of the donation, given that a donor donates, and (3) future donor value. The model will be implemented by the industrial partner, ZOI/The Agency Scandinavia.
Nonprofit organizations (NPO) are facing the challenge to improve the effectiveness of their donor activities and keep the donors connected to their organizations. To this end, many NPOs collect donor data aiming to implement analytical approaches. However, NPOs often lack the resources, time, and know-how of econometric or machine learning approaches to use this data for better managerial decision-making. In addition, existing analytical models usually are developed for the commercial context. However, donation behavior builds on different motivational schemes than customer purchases, so that there is a need for theoretical knowledge of donor motivation and dedicated modeling of a DLV model – the LTV model for donors. In addition, the LTV of a donor should consider not only the revenue from their donations, but also non-monetary contributions, such as voluntary work, fundraising events, or referrals to other donors.
ZOI/The Agency Scandinavia is a consulting company in the fundraising sector. The purpose of this project is to develop and implement a DLV and Churn prediction software on the Ph.D. model. The model builds the basis for software, which will deliver automated DLV scores, including predicting the churn of donors. NPOs can deliver data, e.g., monthly, and receive predicted donor values. Thus, NPOs will have access to marketing analytics and machine learning out-of-the-box. The project makes three central contributions. Academically, it will contribute to the very limited empirical research on DLV. It is managerially relevant since NPOs can increase their donor activity effectiveness. The project has also significant societal value since due to the higher marketing effectiveness, NPOs will have more funds available to pursue activities according to their core societal purposes.
NPOs are facing increasing competition due to reductions in governmental giving (OECD 2016; 2019), and a decrease of the average donor-charity relationship duration (Sargeant 1998; 2001; Shang et al. 2019). Consequently, NPOs are seeking to increase the effectiveness of marketing activities, and direct these activities towards their most valuable donors. NPOs have recognized the relevance of donor management, and have started using donor management systems.
The fundraising sector has seen a shift in the dominant paradigm away from one-time donations to building successful donor relationships (Sargeant 2001). In a transaction-based approach, the fundraising strategy is typically driven by returns from each campaign. Consequently, NPOS following a transaction-based approach aim to achieve the highest possible return on investment for each campaign. A relationship approach, by contrast, aims to build a long-term relationship instead of maximizing short-term donations. At the heart of the relationship, the approach is the concept of donor lifetime value (DLV). When NPOS can predict a donor’s LV, they can tailor their marketing activities to meet the individual’s needs and still ensure an adequate lifetime ROI.
The stream of research on donor management is still emerging, despite the high societal relevance and managerial need for insights. A wide stream of research has analyzed factors that influence donation behavior, including individual differences (Reed et al. 2007), marketing response (Aravindakshan et al. 2015), and situational factors (Liu and Aaker 2008). Other studies analyze donor motives (Small et al. 2007; Kogut and Ritov 2005; Erlandsson et al. 2015). Donors may give due to cognitive factors, e.g., the impact of their donation (Grant et al. 2007; Cryder et al. 2013; Duncan 2004), the organizational impact of an NPO (Fajardo et al. 2018), or the efficient use of their donation (Gneezy et al. 2014).
Another more recent stream of research analyzes aspects of donor relationship management. However, most of the studies analyze how to improve donor activities using a transaction-based approach. These studies analyze how NPOs should frame their donor communication to improve donor response. Typically, NPOs use thank you letters, supported by survey research demonstrating that thank you letters can increase re-donations (Bennett 2006; Merchant et al. 2010). However, given the additional information NPOs have for existing donors, re-donation appeals can be more personalized. Thus, some studies consider how personalizing donation appeals by incorporating elements from previous donation history affects re-donation behavior (e.g., De Bruyn and Prokopec 2013; Thomas et al. 2015). Kessler and Milkman (2018) move away from donation amount and show that simply incorporating the date of the last donation generates more donations among existing donors. These studies typically measure donor retention by short-term metrics, e.g., re-donation likelihood after the mailing. Thus, little is known on the long-term effects on donor relationships to charities.
NPOs need insights on how to implement effective marketing activities in the long run, i.e., from a relationship-based approach. To this end, NPOs can use DLV models. To date, though, research on DLV is very scarce and mostly conceptual, with few exceptions (e.g., Sargeant 1998; Jamal and Zhang 2009; Schweidel and Knox 2013). Existing studies build on customer lifetime values (CLV; Kumar 2018; Kummar et al. 2010a, 2010b). CLV has been analyzed from various perspectives, such as customer acquisition (Lewis 2006), customer retention (Reinartz et al. 2005), customer loyalty (Reinartz and Kumar 2002), customer satisfaction (Anderson and Mittal 2000) among others. These studies attempt to shape customer management based on future profitability. Similar to for-profit firms, NPOs need to build relationships with their donors to be able to raise donations. However, the donation setting differs substantially from a commercial context. Donors do not purchase products or services but donate their time, money, or blood to support a cause. This implies that many of the positive donor outcomes cannot be directly measured by monetary values. The few existing empirical studies do not consider non-monetary elements (such as donor referrals or voluntary work. Thus, existing research on DLV misses empirical approaches that go beyond short-term profitability, and consider non-monetary elements To summarize, there is a need for empirical research on DLV, which develops dedicated analytical models fitting the characteristics of the charitable sector. This industrial Ph.D. aims to address this gap.
Please apply soon for this unique opportunity. Contact Jacob Møllemose now for details about the application process.