Abstract
Creator marketing has emerged as a dominant force within contemporary digital marketing strategy, yet brands continue to struggle with accurately measuring the value generated by influencer partnerships. This dissertation synthesises existing scholarly literature to examine how brands can effectively measure creator marketing value when traditional attribution models prove inadequate. The analysis reveals that creator marketing generates both quantifiable performance impacts, including direct sales and conversions, and qualitative brand equity gains such as trust, loyalty, and community engagement. Research demonstrates that single-touch attribution models systematically undervalue creator contributions by failing to capture the complex, multi-touchpoint nature of modern consumer journeys. This literature synthesis identifies four key value dimensions requiring measurement: short-term performance, long-term brand equity, community co-creation, and creator quality attributes. The findings advocate for hybrid measurement frameworks combining incrementality-based performance attribution with brand and community equity metrics, whilst acknowledging the inherent epistemic ambiguity within sponsored content valuation. The dissertation concludes by recommending that brands move beyond simplistic last-click attribution and vanity metrics towards sophisticated, multi-metric measurement approaches that reflect the true complexity of creator-generated value.
Introduction
The creator economy has fundamentally transformed how brands communicate with consumers, generating an estimated global market value exceeding £20 billion annually. Creator marketing, also termed influencer marketing, involves partnerships between brands and content creators who leverage their authentic relationships with engaged audiences to promote products, services, or brand narratives. Unlike traditional advertising, creator marketing operates through trusted intermediaries whose personal credibility and community relationships serve as conduits for brand messaging.
Despite substantial investment in creator partnerships, brands face persistent challenges in measuring the value these collaborations generate. Traditional digital marketing attribution models, designed primarily for direct-response advertising, struggle to capture the multifaceted value that creators provide. This measurement difficulty stems from several interconnected factors: the fragmented nature of consumer journeys across multiple platforms and devices; the blend of immediate conversion effects and longer-term brand-building outcomes; and the co-created nature of value involving brands, creators, and consumer communities.
The academic significance of this topic lies in its intersection of several critical marketing research streams, including attribution modelling, brand equity measurement, and value co-creation theory. Practically, the inability to accurately measure creator marketing value leads to suboptimal resource allocation, strained brand-creator relationships, and potential underinvestment in high-value partnerships. As Kannan, Reinartz and Verhoef (2016) note, attribution modelling represents one of the most pressing challenges facing contemporary marketing practice.
This dissertation addresses the fundamental question: how can brands effectively measure creator marketing value when attribution is inherently messy? By synthesising current scholarly understanding, this work provides both theoretical contributions to marketing measurement literature and practical guidance for practitioners navigating the complexities of creator partnership evaluation.
Aim and objectives
Aim
This dissertation aims to critically examine and synthesise current academic understanding of how brands can effectively measure the value generated through creator marketing partnerships when traditional attribution models prove inadequate.
Objectives
To achieve this aim, the following objectives guide the research:
1. To identify and categorise the multiple dimensions of value that creator marketing generates for brands, encompassing both performance-based and brand equity outcomes.
2. To critically evaluate the limitations of traditional attribution models when applied to creator marketing contexts.
3. To examine emerging attribution methodologies and measurement frameworks that address the complexity of creator-generated value.
4. To synthesise scholarly recommendations into a coherent framework for multi-metric creator marketing measurement.
5. To identify areas requiring further research and provide evidence-based recommendations for marketing practitioners.
Methodology
This dissertation employs a systematic literature synthesis methodology to examine creator marketing measurement and attribution. Literature synthesis represents an established approach within marketing scholarship, enabling the integration of diverse empirical findings and theoretical perspectives into coherent analytical frameworks (Snyder, 2019).
The research draws primarily upon peer-reviewed journal articles from recognised marketing, information systems, and business publications. Source selection prioritised empirical studies and conceptual frameworks published in journals indexed in established academic databases, including those appearing in the Journal of Marketing, Journal of the Academy of Marketing Science, International Journal of Research in Marketing, and Journal of Interactive Advertising. Additional sources from specialised publications addressing digital marketing, information management, and consumer behaviour supplemented the core literature.
The synthesis process involved iterative reading and thematic categorisation of identified sources. Initial categorisation organised literature according to three primary themes: value creation mechanisms in creator marketing; attribution model taxonomies and limitations; and measurement frameworks and metrics. Subsequent analysis identified cross-cutting themes including co-creation dynamics, epistemic uncertainty, and the integration of performance and brand metrics.
The methodological approach acknowledges inherent limitations. Literature synthesis cannot generate primary empirical evidence and remains dependent upon the quality and scope of existing research. Additionally, the rapidly evolving nature of creator marketing means that some scholarly findings may not fully reflect current platform dynamics or technological capabilities. Despite these limitations, synthesis methodology offers particular value for emerging research domains where consolidation of fragmented knowledge advances both theoretical understanding and practical application.
Literature review
The nature of creator-generated value
Creator marketing generates value through multiple mechanisms operating across different temporal and relational dimensions. Understanding these value creation pathways provides essential foundation for measurement framework development.
Research demonstrates that internet celebrity endorsements significantly increase e-commerce sales through both direct content effects and brand-fan interactions, with additional spillover effects emerging from fan community dynamics (Geng et al., 2020). This finding highlights that creator value extends beyond simple product promotion to encompass community mobilisation and network effects. Lou and Yuan (2019) established that influencer content increases consumer trust in branded posts, which subsequently boosts brand awareness and purchase intention, demonstrating the sequential relationship between trust-building and commercial outcomes.
Beyond transactional effects, creator marketing contributes to broader brand equity development. Social media marketing activities, including creator partnerships, build brand trust, loyalty, and value co-creation intentions among consumers (Sohaib and Han, 2023). This aligns with established brand equity theory whilst highlighting the distinctive mechanisms through which creator content operates compared to brand-owned media.
Contemporary scholarship increasingly recognises that creator marketing value emerges through co-creation processes involving brands, creators, and consumer communities. Merz, Zarantonello and Grappi (2017) developed measures for customer co-creation value, demonstrating that customers’ content creation, advocacy, and development behaviours measurably enhance perceived brand value. France et al. (2020) extended this work by exploring the interplay between customer perceived brand value and co-creation behaviour dimensions. The triadic nature of value co-creation in creator marketing contexts has received specific attention from Buhalis and Volchek (2021) and Jafari et al. (2025), both emphasising that value cannot be attributed solely to any single actor within the system.
Creator-specific value determinants
Recent research has developed increasingly sophisticated models of creator-specific value determinants. Libai et al. (2025) provide comprehensive analysis of influencer marketing value chains, demonstrating that creator value depends upon credibility, authenticity, and engagement characteristics. Significantly, this research indicates that authenticity often favours micro-creators over those with pure reach advantages, challenging simplistic follower-count metrics.
Dzreke and Dzreke (2025) propose an “influencer equity equation” analysing how authenticity, credibility, and engagement interact to affect brand equity outcomes. Their framework suggests that creator selection and ongoing partnership evaluation require attention to these qualitative dimensions rather than exclusively quantitative reach metrics.
Arsel, Zanette and Da Rocha Melo (2024) offer a distinctive perspective by conceptualising sponsored content as an “epistemic market object” where value remains partly unknowable. Their analysis reveals that platformisation of brand-creator partnerships creates power and information asymmetries between market actors, complicating straightforward value measurement. This epistemological framing suggests that some degree of measurement uncertainty may be inherent rather than resolvable through improved methodology alone.
Attribution model limitations
Traditional attribution models face fundamental limitations when applied to creator marketing contexts. The dominant heuristic approaches, including last-click, first-click, and linear attribution, were designed primarily for direct-response digital advertising operating within contained digital ecosystems. These models systematically misattribute value when consumer journeys span multiple touchpoints, platforms, and temporal periods.
Kannan, Reinartz and Verhoef (2016) establish that the path to purchase in contemporary marketing environments involves complex, non-linear journeys that heuristic attribution models fail to capture accurately. Their seminal work identifies attribution modelling as requiring fundamental reconceptualisation to address multi-channel, multi-device consumer behaviour.
Kufile et al. (2023) specifically address cross-platform marketing performance evaluation, demonstrating that heuristic models under-credit earlier social and creator touchpoints whilst over-attributing value to proximate conversion-driving activities. This systematic bias leads to misallocation of marketing spend away from awareness-building and consideration-stage activities towards conversion-proximate tactics.
Buhalis and Volchek (2021) provide comprehensive taxonomy of marketing attribution approaches, positioning creator marketing within broader big data analytics frameworks. Their analysis confirms that single-touch attribution models fail to capture the contribution of touchpoints operating at different stages of the consumer journey, particularly those involving social proof and trust-building mechanisms characteristic of creator content.
Emerging attribution methodologies
Scholarly literature identifies several advanced attribution methodologies offering improved accuracy for creator marketing contexts. Data-driven attribution approaches employ statistical models to estimate each channel’s contribution based on observed conversion patterns rather than predetermined heuristic rules.
Bayesian network models enable probabilistic attribution that accounts for touchpoint sequencing and interaction effects. BenMrad and Hnich (2024) demonstrate intelligent attribution modelling using artificial intelligence architectures that improve digital marketing performance measurement accuracy. Khedekar (2025) specifically addresses influencer marketing ROI measurement through an AI-driven framework bridging performance and brand equity metrics within FMCG contexts.
Incrementality testing represents a complementary approach that employs experimental or quasi-experimental designs to isolate true lift from correlation. Rather than modelling attribution across observed touchpoints, incrementality approaches compare outcomes between exposed and unexposed consumer groups to estimate causal effects. This methodology proves particularly valuable for creator marketing where confounding factors, including selection effects from creator audience characteristics, complicate observational attribution (Hanssens and Pauwels, 2016).
Integrating brand metrics into attribution
A consistent theme across recent scholarship concerns the necessity of integrating brand and customer equity metrics into attribution frameworks. Traditional performance attribution focuses exclusively on conversion-related outcomes, systematically undervaluing marketing activities that build brand equity, trust, and loyalty.
Hanssens and Pauwels (2016) argue compellingly for demonstrating marketing value through metrics spanning immediate sales effects and longer-term brand equity outcomes. Their framework positions brand metrics not as alternatives to performance measurement but as essential complements enabling comprehensive ROI assessment.
Libai et al. (2025) extend this argument specifically to influencer marketing, identifying that exclusive focus on conversion metrics produces biased ROI views that undervalue creator contributions to brand building. Similarly, Dzreke and Dzreke (2025) demonstrate that influencer marketing’s effect on brand equity requires dedicated measurement alongside performance attribution.
Peres et al. (2024) call for expanded scholarly research into creator economy dynamics, highlighting the need for measurement frameworks that capture community effects, advocacy behaviours, and ongoing value creation beyond discrete campaign periods.
Value dimensions and measurement metrics
Synthesising across the literature, four primary value dimensions emerge requiring distinct measurement approaches within creator marketing contexts.
Short-term performance encompasses incremental sales, conversions, assisted conversions, and click-through rates. These metrics respond to data-driven attribution and incrementality testing approaches, enabling quantification of immediate commercial impact (Buhalis and Volchek, 2021; Geng et al., 2020; Kannan, Reinartz and Verhoef, 2016).
Long-term brand equity includes brand awareness, preference, trust, and loyalty scores. These outcomes require survey-based measurement, brand tracking studies, and longitudinal analysis to capture effects operating beyond immediate campaign windows (Lou and Yuan, 2019; Sohaib and Han, 2023; Merz, Zarantonello and Grappi, 2017).
Community and co-creation effects involve user-generated content volume, advocacy behaviours, and creator-fan interaction intensity. Measurement approaches include social listening, content analysis, and network analytics capturing community dynamics (France et al., 2020; Geng et al., 2020; Jafari et al., 2025).
Creator quality attributes encompass credibility, authenticity, audience fit, and engagement depth. Assessment requires both quantitative engagement metrics and qualitative evaluation of creator-audience relationships (Libai et al., 2025; Lou and Yuan, 2019; Arsel, Zanette and Da Rocha Melo, 2024).
Discussion
Addressing the attribution challenge
The literature synthesis reveals that creator marketing measurement challenges stem from fundamental misalignment between traditional attribution model assumptions and creator marketing reality. Last-click and other heuristic attribution approaches assume discrete, traceable touchpoints leading linearly toward conversion. Creator marketing, however, operates through trust-building, community engagement, and brand equity development that influence consumer behaviour through mechanisms poorly captured by click-stream data.
The evidence strongly supports moving beyond last-click attribution toward data-driven approaches employing machine learning, Bayesian networks, and incrementality testing. These methodologies offer improved accuracy by modelling touchpoint interactions, accounting for temporal effects, and separating correlation from causation. However, the literature also cautions against technological solutionism; even sophisticated attribution models cannot fully capture value dimensions operating outside digital tracking systems.
The conceptualisation of sponsored content as an epistemic market object (Arsel, Zanette and Da Rocha Melo, 2024) provides important theoretical grounding for accepting inherent measurement uncertainty. Rather than assuming perfect measurability as an achievable goal, brands should acknowledge that some creator value remains irreducibly ambiguous. This epistemological humility suggests that measurement frameworks should incorporate systematic learning processes, negotiated standards between brand and creator partners, and tolerance for imprecision alongside quantitative metrics.
Toward hybrid measurement frameworks
The synthesis supports development of hybrid measurement frameworks integrating performance attribution with brand equity metrics. Such frameworks address the systematic undervaluation of creator contributions that occurs when measurement focuses exclusively on conversion outcomes.
Practical implementation requires brands to establish measurement protocols across all four identified value dimensions. Short-term performance metrics should employ incrementality-based attribution where feasible, using holdout testing or matched market experiments to estimate true lift. Long-term brand equity requires ongoing brand tracking incorporating awareness, consideration, and preference measures alongside trust and loyalty indicators.
Community and co-creation metrics demand investment in social listening capabilities and content analysis protocols. The triadic co-creation model emphasises that value emerges through interactions among brands, creators, and consumers, suggesting that measurement should capture community dynamics rather than focusing exclusively on brand-to-consumer effects.
Creator quality assessment requires moving beyond follower counts and surface engagement metrics toward evaluation of authenticity, credibility, and audience fit. The consistent finding that micro-creators often outperform larger influencers on authenticity dimensions suggests that quality assessment should incorporate qualitative evaluation alongside quantitative reach metrics.
Implications for practice
For marketing practitioners, the synthesis offers several actionable implications. First, organisations should audit current measurement approaches to identify reliance on heuristic attribution models that undervalue creator contributions. Second, investment in data infrastructure supporting data-driven attribution and incrementality testing enables more accurate performance measurement.
Third, brand tracking and equity measurement should be explicitly integrated into creator marketing evaluation rather than treated as separate activities. Fourth, creator selection and partnership evaluation should incorporate systematic assessment of authenticity, credibility, and audience fit alongside reach metrics.
Fifth, organisations should develop tolerance for measurement ambiguity whilst maintaining rigorous evaluation processes. The epistemic uncertainty inherent in creator marketing does not justify abandoning measurement discipline; rather, it suggests that measurement should combine multiple imperfect indicators rather than seeking single definitive metrics.
Theoretical contributions
This synthesis advances theoretical understanding by integrating previously fragmented literature streams. The connection between co-creation theory and attribution modelling offers particular promise for framework development. Traditional attribution implicitly assumes value flows from brand through intermediary to consumer; co-creation perspectives reconceptualise value as emerging through triadic interactions, suggesting attribution models should account for consumer-generated value and community effects.
The epistemological framing of sponsored content as partially unknowable challenges assumptions underlying much attribution research. If some value dimensions remain irreducibly ambiguous, then measurement framework development should incorporate explicit uncertainty quantification and acknowledge limitations alongside metric specifications.
Conclusions
This dissertation has examined how brands can effectively measure creator marketing value when traditional attribution models prove inadequate. Through systematic literature synthesis, the research achieved its stated objectives whilst identifying important areas for continued investigation.
The first objective, identifying value dimensions in creator marketing, was addressed through analysis revealing four primary dimensions: short-term performance, long-term brand equity, community co-creation, and creator quality attributes. Each dimension requires distinct measurement approaches and metrics.
The second objective, evaluating attribution model limitations, was achieved through examination of scholarly critiques demonstrating that heuristic models systematically undervalue earlier touchpoints and misallocate marketing spend. Creator marketing proves particularly susceptible to these limitations given its trust-building and community engagement mechanisms.
The third objective, examining emerging methodologies, was addressed through analysis of data-driven attribution approaches, incrementality testing, and integrated brand-performance frameworks. These methodologies offer improved accuracy whilst acknowledging inherent measurement complexity.
The fourth objective, synthesising recommendations into coherent framework, resulted in the hybrid measurement approach combining incrementality-based performance attribution with brand and community equity metrics alongside systematic creator quality evaluation.
The fifth objective, identifying future research needs, emerges from gaps apparent throughout the synthesis. Longitudinal studies examining creator marketing effects over extended timeframes remain relatively scarce. Methodological development for measuring community co-creation effects requires additional attention. Cross-cultural comparison of creator marketing dynamics and measurement approaches offers promising research opportunities.
The central conclusion affirms that brands best measure creator marketing value by combining incrementality-based performance attribution with brand and community equity metrics, whilst systematically evaluating creator authenticity and fit rather than relying on simplistic last-click or vanity metrics. This hybrid approach acknowledges both the measurable and ambiguous dimensions of creator-generated value, enabling more accurate evaluation and improved resource allocation within an inherently complex marketing channel.
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