Perceived consumer value drives value-based pricing, but academic research finds no mature digital model to convert that value into algorithmic prices. Studies document growing AI adoption in pricing yet reveal fragmented evidence, methodological limits and unaddressed ethical and governance risks.
Pricing Strategy in Digital Marketing: A Systematic Review of the Value-Based Approach
A PRISMA-based systematic review finds that perceived customer value is central to value-based pricing but there is no accepted integrative digital model linking measured/perceived value to algorithmic pricing, and the literature displays fragmented evidence, methodological weaknesses, and insufficient attention to ethics.
This study systematically examines the implementation of value-based pricing (VBP) in digital marketing and identifies persisting theoretical and empirical gaps. Utilizing a Systematic Literature Review (SLR) guided by PRISMA, 30 selected scholarly articles from the Scopus database (2020–2025) were analyzed based on specific inclusion criteria. The findings are categorized into five main themes: the centrality of perceived value, integration of technology and AI, organizational capabilities, differences in implementation between B2B and B2C contexts, and sector-specific characteristics. The review also reveals methodological and contextual limitations, as well as the absence of an integrative model that digitally aligns value and price. This study extends the theoretical framework of value-based pricing and offers strategic guidance for designing adaptive digital pricing systems rooted in consumer perception. Future research recommendations focus on the integration of ethics, fairness, and predictive technology into pricing algorithms.
Summary
Main Finding
The paper's main finding is that value-based pricing (VBP) in digital marketing centers on perceived customer value but remains underdeveloped in practice due to fragmented theory and empirical evidence. While technology and AI are increasingly integrated into pricing, there is no accepted integrative digital model that aligns measured/perceived value with algorithmic pricing. The literature shows promise for adaptive, consumer-perception–rooted digital pricing systems but highlights methodological, contextual, and ethical gaps that must be addressed.
Key Points
- Five thematic clusters in the literature:
- Perceived value as the core determinant of VBP decisions.
- Growing but uneven integration of technology and AI into pricing processes.
- Organizational capabilities (data, analytics, governance, cross‑functional alignment) are critical enablers.
- Distinct implementation challenges and dynamics in B2B vs B2C settings.
- Sector-specific characteristics (regulation, competition, product tangibility) shape VBP feasibility and design.
- Persistent gaps:
- Lack of an integrative digital model that maps perceived value to algorithmic prices.
- Methodological limitations across studies (limited samples, short time windows, inconsistent measures).
- Insufficient attention to ethics, fairness, and consumer welfare in algorithmic pricing research.
- Practical contribution:
- Extends VBP theory and offers strategic guidance for designing adaptive digital pricing systems anchored in consumer perception.
- Future research directions highlighted by the authors:
- Incorporate ethics, fairness, and transparency into pricing algorithms.
- Leverage predictive technologies to estimate and operationalize perceived value in real time.
Data & Methods
- Methodological approach: Systematic Literature Review (SLR) following PRISMA guidelines.
- Data source and scope: 30 scholarly articles retrieved from Scopus, published between 2020 and 2025, selected using pre‑specified inclusion criteria.
- Analysis: The selected studies were coded and synthesized into thematic categories (the five themes above), and methodological/contextual limitations and theoretical gaps were identified.
- Limitations noted by the review authors: restricted sample size and database coverage (Scopus-only), recent timeframe (emergent literature), and heterogeneity in study designs and measures across the included papers.
Implications for AI Economics
- Model development
- Need for integrative models that jointly estimate perceived consumer value and optimize algorithmic pricing — combining behavioral demand models with ML/AI forecasting and decision algorithms.
- Incorporate heterogeneity (individual-level preferences, context, channel) and dynamics (learning, reference-price effects) into pricing models.
- Algorithm design and evaluation
- Embed fairness, transparency, and welfare constraints into pricing algorithms; evaluate algorithms using not only profit metrics but also consumer surplus, distributional outcomes, and regulatory compliance.
- Prioritize interpretable models or post-hoc explanations to enable audits and stakeholder trust.
- Methods and empirical strategy
- Use field experiments, A/B tests, instrumental variables, and natural experiments to establish causal links between price, perceived value, and outcomes.
- Simulations and multi-agent models (including RL agents) can explore market-level consequences of widespread algorithmic VBP.
- Organizational and market considerations
- AI economics research should account for firm capabilities (data infrastructure, ML ops, governance) as constraints on real-world adoption and as sources of market asymmetries.
- Distinguish B2B and B2C settings: B2B pricing often requires account-level models, negotiation dynamics, and longer time horizons; B2C often supports personalization and real-time dynamic pricing.
- Sector-specific regulation and competitive structure alter incentives and permissible algorithm features—research should model regulatory constraints explicitly.
- Data and measurement
- Develop validated measures/proxies for perceived value that are compatible with automated, real-time estimation (e.g., inferred willingness-to-pay from behavioral traces, stated preference augmentation).
- Address data privacy, sampling bias, and the limits of observational data when estimating value and optimizing prices.
- Policy and welfare
- Study market-level effects of algorithmic VBP on consumer welfare, market power, price dispersion, and entry/competition dynamics.
- Inform policy debates on algorithmic collusion, price discrimination, and consumer protection with empirical evidence and calibrated models.
Actionable research agenda items for AI economists: - Build and validate hybrid behavioral–machine learning models that predict perceived value at scale and integrate them into pricing optimization frameworks. - Design controlled field experiments that test fairness constraints and welfare outcomes of adaptive pricing algorithms. - Model macro/market-level effects of broad adoption of AI-driven VBP using agent-based simulations and dynamic equilibrium analysis. - Investigate organizational frictions (data quality, governance) through case studies and empirical work to assess real-world feasibility.
If you want, I can draft a short research proposal or an experiment design that operationalizes one of the future directions (e.g., testing fairness constraints in a personalized pricing algorithm).
Assessment
Paper Typereview_meta
Evidence Strengthn/a — This is a systematic literature review synthesizing existing studies rather than producing new causal estimates; it aggregates heterogeneous descriptive and empirical work without a primary causal identification strategy.
Methods Rigormedium — The review follows PRISMA and uses explicit inclusion criteria and structured coding, which strengthens transparency and reproducibility; however, it analyzes only 30 articles from a single database (Scopus), covers a short recent timeframe (2020–2025), and the included studies are heterogeneous, limiting comprehensiveness and increasing selection/publication-bias risk.
Sample30 scholarly articles on value-based and algorithmic pricing in digital marketing, retrieved from Scopus and published between 2020 and 2025, which were coded and synthesized into thematic clusters and methodological/gap analyses.
Themesadoption org_design governance
GeneralizabilityScopus-only search limits coverage of relevant journals, conference proceedings, and working papers (possible publication and database bias), Small sample size (30 papers) from an emergent literature reduces representativeness, Short recent timeframe (2020–2025) may omit foundational older work and longer-term evidence, Heterogeneity across study designs, measures, sectors, and geographies limits ability to generalize findings, Findings may not transfer equally across B2B vs B2C contexts, regulated vs unregulated sectors, or firms with differing data/analytics capabilities
Claims (15)
| Claim | Direction | Confidence | Outcome | Details |
|---|---|---|---|---|
| Perceived customer value is the core determinant of value-based pricing (VBP) decisions in digital marketing. Firm Revenue | positive | high | Pricing decisions / price levels (determination by perceived customer value) |
n=30
qualitative (perceived customer value is core determinant of VBP decisions)
0.04
|
| Value-based pricing remains underdeveloped in practice because theory and empirical evidence are fragmented and sparse. Adoption Rate | negative | medium | Adoption/maturity of VBP practices (practical development) |
n=30
qualitative (VBP underdeveloped in practice due to fragmented evidence)
0.02
|
| There is no accepted integrative digital model that maps measured or perceived value to algorithmic pricing. Other | null_result | medium | Existence of integrative digital VBP model (mapping perceived value to algorithmic prices) |
n=30
absence (no accepted integrative digital VBP model found)
0.02
|
| Technology and AI are increasingly integrated into pricing processes, but this integration is uneven across contexts and the literature. Adoption Rate | mixed | medium | Extent/presence of AI/technology integration in pricing processes |
n=30
qualitative (technology/AI integration is increasing but uneven)
0.02
|
| Organizational capabilities (data, analytics, governance, cross-functional alignment) are critical enablers of successful digital VBP. Organizational Efficiency | positive | medium-high | Adoption/success of digital VBP linked to organizational capability levels |
n=30
qualitative (organizational capabilities are critical enablers)
0.0
|
| Implementation challenges and pricing dynamics differ between B2B and B2C settings. Adoption Rate | mixed | medium | Implementation feasibility and dynamics in B2B vs B2C (e.g., personalization feasibility, negotiation dynamics) |
n=30
qualitative (B2B vs B2C implementation differences exist)
0.02
|
| Sector-specific characteristics (regulation, competition intensity, product tangibility) shape the feasibility and design of VBP systems. Adoption Rate | mixed | medium | Feasibility/design attributes of VBP across sectors |
n=30
qualitative (sectoral characteristics shape VBP feasibility/design)
0.02
|
| Existing empirical studies on digital VBP exhibit methodological limitations, including small/limited samples, short time windows, and inconsistent measures. Other | negative | high | Methodological rigor and validity of existing digital VBP studies |
n=30
qualitative (existing empirical studies exhibit methodological limitations)
0.04
|
| There has been insufficient attention in the literature to ethics, fairness, and consumer welfare in algorithmic pricing. Ai Safety And Ethics | negative | high | Coverage of ethics/fairness/consumer welfare topics in digital pricing literature |
n=30
qualitative (insufficient attention to ethics/fairness/consumer welfare in literature)
0.04
|
| The paper extends VBP theory and provides strategic guidance for designing adaptive digital pricing systems anchored in consumer perception. Other | positive | medium | Theoretical extension and actionable guidance (qualitative contribution to VBP theory/practice) |
n=30
qualitative contribution (theory extension and guidance offered)
0.02
|
| Future research should incorporate ethics, fairness, and transparency into pricing algorithms and leverage predictive technologies to estimate and operationalize perceived value in real time. Other | positive | speculative | Research agenda uptake: inclusion of ethics/transparency and real-time perceived-value estimation in future studies |
n=30
recommendation (future research should include ethics/transparency and real-time perceived-value estimation)
0.0
|
| The review followed PRISMA guidelines and included 30 scholarly articles retrieved from Scopus, published between 2020 and 2025, selected using pre-specified inclusion criteria. Other | null_result | high | Scope of literature reviewed (database, timeframe, sample size) |
n=30
0.04
|
| Limitations of the review include restricted sample size, Scopus-only coverage, emergent-literature timeframe, and heterogeneity in study designs and measures, which constrain generalizability. Other | negative | high | Generalisability and completeness of the review's conclusions |
n=30
qualitative limitations (restricted sample, Scopus-only, emergent timeframe, heterogeneity)
0.04
|
| AI economics research should build hybrid behavioral–machine learning models that predict perceived value at scale and integrate them into pricing optimization frameworks. Decision Quality | positive | speculative | Future modeling approaches (hybrid behavioral–ML integration into pricing optimization) |
0.0
|
| To establish causal links between price, perceived value, and outcomes, researchers should use field experiments, A/B tests, instrumental variables, and natural experiments. Research Productivity | positive | speculative | Causal identification quality in future VBP research (use of experimental/quasi-experimental methods) |
0.0
|