Modeling Marketing Strategies for Small and Medium-Sized Enterprises in the Food Industry Using Reinforcement Learning and Natural Language Processing

Introduction

Rapid digital transformation has made artificial intelligence a strategic tool for redesigning marketing models, especially in SMEs that operate under resource scarcity and strong competitive pressure. In marketing, AI can support customer segmentation, prediction, personalization, pricing, and campaign optimization when it is embedded in managerial decision-making rather than treated as a purely technical add-on (Davenport et al., 2020; Huang & Rust, 2021; Magableh et al., 2024). This issue is particularly important in food-industry SMEs, where consumer preferences, seasonality, price sensitivity, and supply-chain complexity require adaptive and data-driven marketing decisions.

Prior research shows that AI and digital marketing can improve customer engagement, targeting, and firm performance, yet SMEs often face weaker data infrastructure, limited digital maturity, and insufficient technical expertise (Kshetri et al., 2024; OECD, 2021; Sharabati et al., 2024). Data-driven marketing can improve the speed and precision of managerial decisions, but its effectiveness depends on the quality, availability, and interpretability of data (Varian, 2014). Therefore, models developed for large corporations cannot be transferred mechanically to SMEs without considering local market conditions and organizational limitations.

The main gap addressed in this study is the lack of an integrated and locally adapted model that combines qualitative market understanding with advanced AI algorithms for food-industry SMEs. The study focuses on two complementary algorithmic functions: DQN for dynamic marketing optimization and DistilBERT for semantic analysis of customer-related textual data. DQN is theoretically appropriate because marketing decisions can be framed as sequential actions evaluated through reward feedback (Mnih et al., 2015; Sutton & Barto, 2018). DistilBERT is appropriate because customer comments, complaints, and managerial narratives are text-based data that can be analyzed using transformer-based language models (Devlin et al., 2019; Sanh et al., 2019; Vaswani et al., 2017).

The central research question is: how can AI algorithms be used to design an effective model for improving marketing strategies in food-industry SMEs? The study further examines the factors that influence AI adoption, the optimization of marketing strategies through DQN, the analysis of customer behavior through DistilBERT, and the comparative performance of the two algorithms.

Materials and Methods

Research Design

This study used an applied-developmental mixed-methods design. The qualitative phase explored expert views regarding AI adoption in food-industry SME marketing, while the quantitative phase validated the extracted themes and supported algorithmic modeling. The mixed-methods logic followed established research design principles for integrating qualitative exploration with quantitative validation (Creswell & Creswell, 2023). The qualitative validation approach was informed by procedures for credibility, dependability, and interpretive trustworthiness in qualitative inquiry (Creswell & Poth, 2018).

The study was cross-sectional. Data were collected through semi-structured interviews, structured questionnaires, and literature review. Thematic network analysis was used to extract basic, organizing, and global themes from the interviews (Attride-Stirling, 2001). The questionnaire sample size was determined using Cochran's formula for an unknown population (Cochran, 1977). Internal consistency was assessed using Cronbach's alpha, with values above 0.70 interpreted as acceptable according to common psychometric guidance (Nunnally & Bernstein, 1994). Descriptive and reliability analyses followed standard multivariate data-analysis principles (Hair et al., 2019).

Qualitative and Quantitative Phases

The qualitative population consisted of managers, marketing specialists, and owners of SMEs operating in the Iranian food industry who had experience in evaluating or applying AI technologies in marketing activities. Purposive sampling was used. Twelve expert interviews were conducted, with theoretical saturation reached after the first ten interviews and confirmed through two additional interviews. Interviews lasted approximately 45-75 minutes and were audio-recorded with informed consent.

The quantitative instrument was developed from the organizing themes extracted in the qualitative phase. Items were measured on a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). Because the exact size of the population was unknown, the Cochran formula for an infinite population was used, resulting in 384 respondents. Reliability was assessed using Cronbach's alpha, and normality was examined using the Kolmogorov-Smirnov test.

AI Modeling

Two AI models were evaluated. DQN was used to optimize marketing strategies by formulating market conditions as states, marketing actions as actions, and sales growth, customer engagement, and cost control as reward components. Experience replay and target-network mechanisms were used to improve training stability, consistent with deep reinforcement learning and later refinements addressing overestimation bias (Mnih et al., 2015; van Hasselt et al., 2016). DistilBERT was used for natural language processing of customer opinions and managerial narratives. Textual data were tokenized and transformed into semantic embeddings, allowing the model to detect customer preferences and behavioral patterns with lower computational cost than full BERT-based models (Devlin et al., 2019; Goodfellow et al., 2016; Sanh et al., 2019; Vaswani et al., 2017).

Modeling data construction and validation procedure. To address reproducibility, the structured DQN input matrix was constructed from the 25 organizing themes measured in the questionnaire. Each case represented one respondent-level marketing scenario, with normalized theme scores used as state features. The action set consisted of alternative marketing-policy options derived from the qualitative phase, including personalization, targeted promotion, customer-segment prioritization, channel selection, and cost-control actions. The reward function combined three components: expected sales improvement, customer-engagement potential, and marketing-cost reduction. Because no longitudinal sales log or deployed campaign data were available, reward values were computed from expert-coded scenario scores and should be interpreted as an internally validated decision-support simulation.

For DQN, the dataset was divided into training, validation, and test subsets using a 70/15/15 split. Model stability was checked through repeated random splits. The model used experience replay, a target network, epsilon-greedy exploration, mini-batch training, and mean squared error as the optimization loss. The state representation, action alternatives, and reward components were kept identical across validation runs to avoid post-hoc adjustment.

For DistilBERT, the textual dataset consisted of anonymized interview excerpts, managerial narratives, and customer-oriented marketing statements coded during the qualitative phase. Texts were cleaned, tokenized with the DistilBERT tokenizer, truncated or padded to a fixed sequence length, and divided into training, validation, and test sets using the same 70/15/15 logic. The model was fine-tuned for classification of marketing-relevant themes. Accuracy, precision, recall, F1-score, and cross-entropy loss were calculated on the held-out test subset.

Results

The qualitative analysis produced 25 organizing themes grouped into broader domains. These domains and themes are summarized in Table 2.

Table 3 reports the selected descriptive findings. Respondents generally agreed that the identified factors are important for AI adoption in food-industry marketing. Mean scores ranged from 3.45 to 4.25.

The Kolmogorov-Smirnov test confirmed that all 25 variables followed a normal distribution, with significance values ranging from 0.08 to 0.19. Therefore, the data were suitable for parametric analyses. Reliability analysis using Cronbach's alpha showed acceptable to excellent internal consistency, with coefficients ranging from 0.78 to 0.88. Selected reliability results are shown in Table 4.

Table 5 compares the performance of DQN and DistilBERT. DQN achieved the strongest overall performance for marketing-strategy optimization, while DistilBERT performed strongly in textual customer analysis.

Table 6 shows the top five factors by model-based importance. Both models identified senior management technology adoption and marketing-strategy personalization as the most important factors.

The error-distribution results further indicated that DQN had fewer large errors than DistilBERT, with only one case above the highest error threshold compared with three cases for DistilBERT. DistilBERT produced more low-error cases in textual prediction, but DQN demonstrated greater stability across the full error distribution. Therefore, DQN was identified as the more appropriate model for optimizing marketing strategies in resource-constrained SMEs that require rapid and flexible decision-making, whereas DistilBERT was more suitable for SMEs with rich textual customer data and a need for customer-feedback interpretation (see Table 7).

Discussion

The findings indicate that AI can support marketing-strategy development in food-industry SMEs, but its value depends on managerial commitment, data quality, organizational readiness, and financial feasibility. Senior management technology adoption emerged as the strongest factor in both the descriptive results and the model-based rankings. This finding is consistent with the AI-marketing literature, which emphasizes that AI creates business value when it is embedded in strategic decision-making rather than treated as a purely technical tool (Davenport et al., 2020; Huang & Rust, 2021; Magableh et al., 2024). In SMEs, where decision authority is often concentrated in senior managers, leadership commitment becomes a necessary condition for successful implementation.

The superior performance of DQN suggests that reinforcement learning is particularly suitable for marketing environments requiring sequential and adaptive decision-making. DQN learns optimal actions through reward feedback and is designed for dynamic environments in which present decisions affect future outcomes (Mnih et al., 2015; Sutton & Barto, 2018). The inclusion of target networks and experience replay is also consistent with evidence that stabilizing Q-learning improves performance and reduces instability in deep reinforcement learning (van Hasselt et al., 2016). In this study, DQN achieved higher overall accuracy than DistilBERT and produced stronger results for dynamic marketing optimization, implying that SMEs may benefit from AI systems that continuously adjust marketing actions based on sales, customer engagement, and cost-related outcomes.

DistilBERT also showed strong performance, particularly for customer-text analysis. This result is coherent with the development of transformer architectures and BERT-based language representation models (Devlin et al., 2019; Vaswani et al., 2017). DistilBERT reduces computational requirements while preserving much of BERT's language-understanding capacity, making it relevant for SMEs with limited infrastructure (Sanh et al., 2019). Its practical value lies in analyzing customer reviews, complaints, comments, and social-media feedback, while DQN is better suited to policy optimization.

Personalization was another central finding. Marketing-strategy personalization received one of the highest mean scores and was ranked as a major factor in both AI models. This is consistent with studies showing that digital and AI-enabled marketing can improve engagement through segmentation, targeted communication, and individualized value propositions (Kshetri et al., 2024; Sharabati et al., 2024). However, personalization depends on reliable data. The importance assigned to existing data quality confirms that incomplete or fragmented customer data can limit AI performance, a point also consistent with the broader literature on data-driven decision-making (Varian, 2014).

The results further show that financial constraints remain a major barrier. AI implementation costs and R&D investment received lower mean scores than managerial adoption and personalization capability. This suggests that even when SMEs recognize the strategic value of AI, they may struggle to implement it due to limited budgets, lack of specialists, and insufficient infrastructure. This interpretation aligns with research on the digital transformation of SMEs, which stresses that adoption depends on absorptive capacity, organizational readiness, and access to affordable digital infrastructure (OECD, 2021). Therefore, gradual implementation is recommended, beginning with low-cost cloud tools, pilot campaigns, and narrowly defined use cases such as customer-feedback analysis or seasonal promotion optimization.

Finally, the ethical and legal dimensions should not be treated as secondary. Since AI marketing depends on customer data, privacy, transparency, and customer trust directly influence adoption. AI-driven personalization may increase engagement, but opaque or excessive data use may undermine trust. SMEs should therefore combine technical implementation with clear data-governance rules, transparent communication with customers, and human oversight. Overall, the study supports a dual-model approach: DQN for dynamic optimization of marketing strategies and DistilBERT for semantic analysis of customer feedback.

Conclusion

This study proposed and evaluated a context-specific AI-based model for improving marketing strategies in food-industry SMEs. The findings showed that senior management technology adoption, marketing-strategy personalization, awareness of AI's competitive advantages, compatibility with existing marketing processes, and innovative organizational culture are central factors in AI adoption. DQN outperformed DistilBERT in dynamic marketing optimization, whereas DistilBERT showed strong performance in analyzing textual customer data. The proposed framework suggests that SMEs can improve marketing effectiveness by combining reinforcement learning for adaptive decision-making with NLP tools for customer insight extraction. The study focused on food-industry SMEs in Iran and used cross-sectional questionnaire and interview data. Therefore, generalization to other industries or national markets should be made cautiously. Future research should test the model using longitudinal data, real sales records, customer-platform behavioral data, and experimental marketing campaigns. Further studies may also compare DQN and DistilBERT with alternative models such as gradient boosting, large language models, or hybrid reinforcement-learning and NLP architectures. However, the framework should be regarded as a validated analytical prototype rather than a fully deployed commercial system. Its practical effectiveness should be tested with longitudinal sales records, live customer-platform data, and controlled marketing experiments before strong managerial claims are made.

Declarations

Ethical Considerations

The study was conducted in accordance with ethical principles for research involving human participants. All interviewees and survey respondents were informed about the purpose of the study, participation was voluntary, and informed consent was obtained before data collection. Participants were assured that their responses would remain confidential and would be reported only in aggregate form. No personally identifiable information was reported in the manuscript.

Author Contributions

Jafar Taherzadeh: conceptualization, data collection, formal analysis, software/modeling, and writing-original draft. Hasan Vahedi: methodology, validation, supervision, and writing-review and editing. Seyed Hossein Hosseini: data interpretation, investigation, and writing-review and editing. Mehdi Sanei: resources, project administration, and writing-review and editing. All authors reviewed and approved the final manuscript.

Funding

The authors received no specific funding for this research.

Conflict of Interest

The authors declare that they have no conflict of interest.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request, subject to ethical and confidentiality restrictions.

Artificial Intelligence Statement

Generative artificial intelligence tools were used only for language editing, English refinement, and editorial structuring. All scientific content, methodological decisions, data analysis, interpretation of findings, and final approval remain the responsibility of the authors.