AI-Powered Personalisation in Charity Marketing: Transforming Donor Engagement and Lifetime Value Through Data-Driven Campaigns

1. INTRODUCTION

The charity sector of the United Kingdom comprises of 168,000 registered charities (Government of UK, 2024) that compete for limited funding resources and attention of donors (Hibabox, 2023). Conventional charity marketing in the United Kingdom usually depends on mass marketing campaigns and generic messaging, which become a failure to imitate and target donors having varying behaviours (CAF, 2024). This absence of personalisation of marketing campaigns through data-driven approaches leads to fatigue among donors, a decline in engagement rates, and reduced retention of donors. (Cherniavska et al., 2023) depict that charities in the UK lose almost 50% of new donors in the first year. At one end, the UK firms operating in the finance and retail sectors progressively use AI-driven personalisation to enhance customer experience, retention, and loyalty; the charity sector, on the other hand, substantially lags behind in the implementation of these technologies (Williams, 2025).

As a prominent solution to this problem, AI-based personalisation allows for hyper-targeted, data-driven engagement that can strengthen donor relationships and optimise donor lifetime value (DLV) (Onufriyenko, 2025). The report of (Zoe Amar Digital, 2024) indicates that average rates of donations can be increased by 27% (in terms of frequency of giving) and 32% (in terms of value of a single donation) when the charity has adopted AI-powered personalisation techniques instead of generic campaigns. The major UK charities, such as Cancer Research UK and British Heart Foundation, are placing bets on predictive analytics and machine learning algorithms to divide the donor databases, automate personalised messaging, and identify the churn (Singh et al., 2022).

In addition, the main challenge facing the non-profit industry of UK is not necessarily technological adoption but the strategic value of using AI-powered personalisation to support donor engagement and DLV without compromising trust (Sellen & Mönks, 2024). As a result, the non-profit sector of the UK fails to meet the regulatory requirements or breaks the structural integrity of organisations (Khastgir & Shalini, 2024). Personalisation has not been fully utilised by many UK charities. They are based on antiquated mail merges and CRM segmentation or introduced by exploiting AI tools without a thorough analysis of their effects on donor retention and loyalty, satisfaction, and long-term value (Evans, 2025). This fragmentary strategy has led to an unsustainable donor experience, low retention rates (which were never higher than 45% among one-time donors as reported in the Blackbaud Benchmark (Report, 2024). It led to an added problem of developing a disconnect amid sophisticated digital campaigns and genuine, relationship-based donor engagement, the basis on which charities are formed (NCVO, 2021).

In addition, while present studies (Gooyabadi et al., 2023; and Jasper et al., 2024) signified AI personalisation as major driver of donor engagement and DLV, they usually neglected the mediating role of data-driven campaigns which holds tendency to translate personalisation in marketing campaigns to quantifiable donor retention and engagement. Deprived of a detailed empirical analysis of this mediating mechanism the comprehension of ways that AI-enabled charity marketing initiatives impact donor relationships and life-time value in the long term, cannot be understood. Therefore, this study addresses this gap by superficially focussing on analysis of mediating effect of data-driven campaigns on the relationship between AI-powered personalisation, donor engagement and lifetime value.

Nevertheless, the question of whether the existing literature on AI personalisation in the charity sector reflects the characteristics of the specific interactions between AI and nonprofits remains unaddressed in existing research (Gooyabadi et al., 2023; and Jasper et al., 2024), which explains the necessity to conduct the study on the topic to explore the impacts of ethical, trust-related, and donor-based approaches on long-term involvement and lifetime value in charities in the United Kingdom. This study, thus, by introducing Relationship Marketing Theory and Customer Lifetime Value Theory contributes to further literature by offering an expression of how AI-powered personalisation, when mediated through data-driven campaigns, can promote trust, long-term relationships between donors, and long-term donation behaviour. Contextualising the concept of AI personalisation in these ethical and relational dimensions, this research provides a new perspective on technology-based fundraising and proves that it is possible to create digital strategies that would increase donor interaction and lifetime value without affecting trust or mission integrity.

The following research objectives have been drawn for the current research;

To investigate the cause-and-effect relationship between AI-powered personalisation in charity marketing and donor engagement in the case of the UK non-profit sector.

To analyse the impact of AI-powered personalisation in charity marketing on donor lifetime value (DLV) in the context of the UK non-profit sector.

To investigate the mediating effect of data-driven campaigns on the relation of AI-powered personalisation in charity marketing with donor engagement and DLV.

Hence, the study is anticipated to make major contributions such as, the current study enhances academic knowledge on AI applications in non-profit marketing which is mainly remained explored in the context of commercial business enterprises and non-profitable area remains unexplored. The study provides evidence-based insights on ways that UK charities can amalgamate AI-enabled data analytics to formulate personalised donor journeys, thus, enhancing donor acquisition, retention as well as re-engagement. Lastly, in terms of policy and sector level contribution, the findings provision the development of frameworks and guidelines for ethical utilisation of digital and data-driven marketing campaigns in the UK charity sector which will guarantee compliance with UK GDPR.

2. LITERATURE REVIEW

2.1. Theoretical Framework

The theoretical framework of the current study is based on two relevant theories, which are comprised of Relationship Marketing Theory and Customer Lifetime Value (CLV) Theory (Firmansyah et al., 2024; Kumar, 2024). The Relationship Marketing Theory places massive emphasis on building partnering relationships through longer-term, trust-based relationships as the key basis of loyalty and maximisation of stakeholder value (Yau et al., 2021). This paradigm is of major concern to charities in the UK because sustainability is achieved when the charity cultivates a continuing donor relationship as opposed to a single transaction (Mariani et al., 2022). In contrast to the transactional strategies, where short-term benefits are of the essence, relationship marketing emphasises on personalised engagement, credibility, and mutual trust that should be maintained between the stakeholders (Pfajfar et al., 2022). In the context of this study, this perspective plays a crucial role in sustaining donor commitment among charities, which keeps the donations recurrent and creates more emotional impetus. Eventually, it leads to both donor engagement in the long-term, repeated donations and life-time value creation. Within the AI-based personalisation, this theory provides the reasonableness to UK charity organisations to consider prioritising the desire to create a significant, data-driven connection with the donors to develop a sense of long-term attachment, not just related to the current donations (Rather et al., 2021).

Besides, CLV Theory offers a quantitative perspective to consider the monetary value of individual donors with time, allowing charities operating in the UK to determine the effectiveness of the personalisation approach that employs AI in boosting donor lifetime value as one of the most significant performance indicators of non-profit sustainability (Solow et al., 2023). Incorporating these theories, the research critically reviews not only the effects of increased engagement with the help of AI-powered personalisation but also whether AI tools can help develop sustainable donor relationships and create lifetime value.

2.2. Literature Review and Hypotheses Development

2.2.1. AI-Powered Personalisation and Donor Engagement

Donor engagement through AI-powered and digital technologies has received massive attention in the literature related to charity marketing through data-driven campaigns. In this perspective, a recent study has been carried out by (Cheng & Wang, 2025) in the context of the United States’ charity sector. Adopting a primary quantitative research design, the data were gathered using a structured survey questionnaire from a sample of 591 chatbot users of charity marketing. Applying inferential statistics such as regression analysis, the author unveiled that AI-powered personalisation, leading to targeting functional and emotional values, derives donors’ trust, which, according to (Jasper et al., 2024; and Sellen & Mönks, 2024), leads to increased engagement with charity organisations and eventually sustained donation behaviour.

Although the empirical evidence presented by (Cheng & Wang, 2025) on the loyalty of donors caused by AI-powered personalisation promoting donor engagement and trust, the emphasis on chatbots does not take into consideration the larger ecosystem of AI tools that affect donor behaviour. This is compounded by the fact that (Cheng & Wang, 2025) utilise a U.S.-centred sample, which holds limited transferability since the UK context. Furthermore, placing emphasis on emotional targeting, (Jasper et al., 2024) consider one of the valid aspects of donor engagement, which is emotional targeting; the researchers did not build a connection between emotional targeting attained through AI-powered personalised marketing and long-term donor value, thus leaving an apparent theoretical gap between emotional appeals and Donor Lifetime Value (DLV). Likewise, (Sellen & Mönks, 2024) focus on transparency adheres with Relationship Marketing Theory, but their use of qualitative methods leads to provide insufficient empirical evidence on how AI-powered personalisation leads to donor engagement and DLV. Hence, it can be said that these studies hold strengths in the identification of the key engagement drivers but do not incorporate the ideation of DLV and Relationship Marketing Theory, which can be critical in explaining the role of AI-personalisation in fostering trust, extending to the maintenance of long-term donor relationships and DLV in the UK charity context.

Moreover, a similar study was carried out by (Sammer et al., 2024) to analyse the effects of the AI-FEED web-based platform on user and donor engagement to address challenges related to food crisis and nutrition insecurity in the US. Collecting data through semi-structured interviews from various food charity service users and donors, convincing results were drawn that the food charity module is helpful for charities to develop educational content and envisage client needs by means of AI-driven tools. The study effectively depicted ways that AI-FEED eases customised content and anticipates needs of clients. Though it depends on qualitative methods restricts generalisation of the results across broader charity sector contexts. On the other hand, the narrow emphasis on charities in the food sector and lack of longitudinal dataset limit comprehension of donor engagement outcomes over the long-term period. These methodological limitations make the findings context-specific and less applicable to wider charity organisation contexts.

Although researchers, including (Cheng & Wang, 2025; Jasper et al., 2024; and Sellen & Monks, 2024), revealed that such factors as emotional targeting, trust, and transparency are the most important drivers of donor engagement, they are rather descriptive and context-related. The focus of (Cheng & Wang, 2025) on chatbots in the U.S. limits generalisation beyond the charity sector in the U.S. and in the wider AI tools framework. In similar vein, (Sammer et al., 2024) emphasise the customisation of the content based on AI, but they are limited to the food charity sector and have qualitative data, which does not allow making long-term conclusions. A conceptual mechanism can be based on the Relationship Marketing Theory (RMT) and the Customer Lifetime Value (CLV) Theory by assuming that RMT describes how AI personalisation enhances trust and commitment to relationship, resulting in long-term engagement, whereas CLV Theory views engagement as the source of long-term donor value. Collectively, the two frameworks explain how AI-enabled personalisation can be helpful to systematically improve donor interactions, develop persistent relational loyalty, and eventually increase the lifetime value, which bridges the empirical evidence gaps in the context of the UK charity. These arguments lead to the formulation of the first hypothesis, H1, of the study.

H1: There is a statistically significant impact of AI-powered personalisation in charity marketing on donor engagement in the UK non-profit sector.

2.2.2. AI-Powered Personalisation and Donor Lifetime Value

According to (Alsolbi et al., 2023), the personalisation strategies in charity marketing allow for personalising and enriching the relationships of the donors, generating relevant, individualised experiences which, according to Relationship Marketing Theory, must be critical in expanding donor lifetime value (DLV). (Day et al., 2025) observe that donor-focused messages are very important in enhancing retention levels, which is a significant factor causing DLV in charities. Likewise, (Lv & Huang, 2024) revealed that customised interaction yields an increase in the number of repeat donations, which directly improves DLV. Nonetheless, (Shukla & Tripathi, 2025) have found out that impersonal mass appeals are dangerous to donor attrition and they, therefore, restrict lifetime donations. (Gooyabadi et al., 2023) pointed out that predictive personalisation increases lifetime value, at least in the commercial setting, which means that it has potential in the charity setting, but there is little empirical evidence on this. The perspectives create an urgent need to seek to better understand whether the use of an AI to personalise an approach to DLV is realistically adding value to the process. In contrast, (Khastgir & Shalini, 2024) argued that excessive reliance on data to implement data-driven AI-powered personalisation can lead to the spread of misinformation, echo chambers, and the corrosion of trust amid donors and charity organisations. This is because, excessive reliance on data-analytics leads to risk of cybersecurity risks, leak of confidential information and data breaches which tends to compromise trust of donors on charity organisations.

In spite of emphasising the role of individual relationships in the growth of DLV, (Alsolbi et al., 2023) do not discuss how this can be applied to the real-life scenario of donation behaviour among various stakeholders, such as donors. The implication is that such approaches are only effective for donors who are already engaged. (Day et al., 2025) implicated donor-centred messages in retention but do not assess decreasing returns that can present the issue of message fatigue when considered aggravated over time. Although (Lv & Huang, 2024) conclude that customised interaction enhances repeat donations, they do not state whether it is more cost-effective, which indicates that customised methods would be more effective with charities that have large donor bases than small ones.

Though (Alsolbi et al., 2023) emphasise personalisation as the factor promoting enriched donor relationships, the research is only conceptual and does not focus on behavioural validation, which restricts its practical application. According to (Day et al., 2025), AI-powered personalisation campaigns help to attain donor retention, which fits in the short-term orientation of trust under the light of RMT, but fail to make conclusive arguments and evidence on the sustainability of this relationship in the long run. Likewise, (Lv & Huang, 2024) demonstrate that customised marketing campaigns also increase the donations made by repeat donors; however, the lack of attention to cost-effective data-driven campaigns as a mediating factor limits their application in the context of the charities subjected to scarce financial resources, an essential component in DLV optimisation. Moreover, (Gooyabadi et al., 2023) developed these insights of predictive personalisation in the context of commercial business enterprises and neglect the charity sector. Together, these studies support the idea that AI personalisation can empower donor engagement and DLV, but the fragmentary nature, theoretical boundedness, and situational constriction of these studies raise a fundamental concern of whether AI-powered personalisation campaigns can actually lead to a higher level of donor lifetime value in the charity sector.

Despite the fact that the previous studies provide evidence of the significance of individualised engagement in the motivational process of the donor lifetime value (DLV), the existing literature has several significant limitations. The concept of personalised relationships as developed by (Alsolbi et al., 2023) lacks empirical validation, which reduces its practical applicability. In contrast, (Day et al., 2025) emphasise the use of donor-centred messages to retain donors but fail to address such aspects as the fatigue of the messages that can weaken donor engagement in the long term. (Lv & Huang, 2024) show that tailored interactions have higher repeat donations but do not consider cost-efficiency, which is a crucial aspect of resource-limited charities. On the other hand, (Gooyabadi et al., 2023) discuss predictive personalisation in business, ignoring the need to offer charity and the concern of trust that is especially relevant to charitable activities. Based on the Relationship Marketing Theory, the AI personalisation enhances trust and commitment of relationships, whereas the CLV Theory considers the long-term engagement as the driver of the long-term donor value suggested by (Ali & Shabn, 2024) as well. Combined, these views demonstrate a serious gap, such as, though AI-based personalisation has its potential, empirical results on its impact on improving the effectiveness of the DLV among the UK charities with ethical, trust, and cost aspects are still limited. These arguments lead to the formulation of the second hypothesis, H2 of the current study:

H2: There is a statistically significant impact of AI-powered personalisation in charity marketing on donor lifetime value in the UK non-profit sector.

2.2.3. Mediating Effect of Data-Driven Technologies on AI-Powered Personalisation, Donor Engagement and Lifetime Value

Formulation of strategy based on evidence using data-driven technologies has now become a prominent approach to enhance donor engagement and create lifetime value. In this similar vein, the study carried out by (Onuche, 2025) examined ways that adopting data-driven approaches enabled with AI technologies impacts engagement strategies, resource allocation, transparency, and boosts trust within stakeholders. Applying secondary data collection methods and analysing it, the research unveiled that data-driven technologies such as predictive analytics enhance operational efficiency of charity organisations in terms of errors in donor’s fund allocation by 30%. This signifies that the advanced technologies of data governance enhance transparency in charity organisations, which eventually boosts the trust of the donors and thus results in increased donor engagement and lifetime value.

(Werke & Bogale, 2024) unveiled that data-driven segmentation substantially strengthens the identification of donors for charity organisations, which improves engagement precision. Also, (Faruq et al., 2024) revealed that AI-enabled governance of data enhances the pertinence of charity messages, which boosts the trust of donors. Similar arguments were proposed by (Hasan et al., 2025), unveiling that the predictive analytics mediate amid personalisation efforts and behaviour of donors during charity marketing, which amplifies the rates of donations. Similarly, (Santos, 2025) argued that the amalgamation of real-time data into data-driven AI-powered charity marketing campaigns leads to personalisation of donors’ touchpoints, such as developing an emotional connection with the noble cause, which impacts donor satisfaction and results in long-term engagement with the charity organisation and lifetime value.

Data-driven technologies have become a strategy to optimise the donor engagement and lifetime value. In this perspective, (Onuche, 2025) shows that operational efficiency is made to be enhanced by using AI-powered predictive analytics, which result in a 30% decline in errors during fund allocation, and how sound data governance leads to transparency and trust of donors. On the other hand, (Werke & Bogale, 2024) underline that donor targeting based on data enhances the accuracy of the engagement, whereas (Faruq et al., 2024) demonstrate that data managed by AI makes charity messages more relevant, which subsequent increases the level of trust. This is further elaborated by (Santos, 2025) who shows how real-time data helps to personalise the touchpoints of the donors, enhancing emotional appeal and long-term commitment. Combining Relationship Marketing Theory and CLV Theory, these works propose that AI-based, data-driven strategies are not only more effective at improving immediate interactions with donors but are also the base of long-term engagement and higher donor lifetime value, although empirical evidence in the context of the UK charity sector is rather scarce.

Also, (Onuche, 2025) provides a convincing source of insightful opinions about how AI can improve data strategies to increase donor confidence and efficiency in operations, however, the use of secondary data provides the research with limited causal validity and the context of a business. The point is that it might not equally applicable to all the charities in the UK. (Werke & Bogale, 2024) demonstrate that data-driven segmentation increases the precision of targeting donors, though they do not look at whether that contributes to the existence of long-term relations with donors. (Santos, 2025) associates real-time data incorporation and emotional donor association but there was no quantitative support which is why it is hard to determine the intensity of the engagement and life-time value creation. Such shortcomings demonstrate the necessity of research in specific contexts with empirical basis. Therefore, this literature finding leads to the formulation of the third hypothesis, H3, of the current study:

H3: There is a statistically significant mediating effect of data-driven technologies on the relationship of AI-powered personalisation with donor engagement and lifetime value.

2.3. Research Gaps

The review of existing literature on the AI-powered personalisation in charity marketing to impact donor engagement and lifetime value has encountered several gaps, which drive motivation to carry out this study. At first, there is a lack of empirical evidence on the relationship across these specified variables in the case of the UK non-profit sector, as recent studies carried out by (Cheng & Wang, 2025; and Sammer et al., 2024) in the case of USA, which limits generalisation of the findings on the UK non-profit or charity sector. On the other hand, a study carried out by (Onuche, 2025) lacks empirical evidence on the relationship amid how data-driven campaigns enabled with AI in the marketing practices of charity organisations drive donor engagement and life-time value. In addition, a theoretical gap persists in the integration of Relationship Marketing Theory and CLV Theory in designing AI-driven donor engagement strategies in the charity industry (Kumar, 2024). Although the two theories share the fact that they focus on the role of trust, personalisation, and long-term relational value, their use in the context of AI-enabled, data-mediated fundraising has received little research (Madanchian, 2024). This study aims to fill this gap by critically evaluating the role that AI-personalisation through data-driven campaigns plays in improving the lifetime value and donor engagement in non-profit organisations in the UK through the lens of Relationship Marketing Theory and CLV Theory.

Therefore, this study is an attempt to bridge this gap and make novel contributions to the existing conceptual framework by providing quantitative evidence on how AI-powered personalisation in charity marketing can derive donor engagement and lifetime and mediation effects of data-driven campaigns on the relationship of these variables in the case of the UK non-profit sector. Hence, the conceptual framework of the current study is shown in Fig. (1).