The Economic Dynamics of Desktop and Mobile Customer Analytics in Advancing Digital Branding Strategies: Insights from the Agri-Food Industry

Abstract

In the agri-food industry, strategic digital branding and digital marketing are essential for maintaining competitiveness. This study examines the economic dynamics and impact of desktop and mobile customer analytics on digital branding strategies within the sector. Through a comprehensive literature review, this research utilizes empirical evidence to validate hypotheses regarding the influence of desktop and mobile analytics metrics on key digital branding metrics and value creation. This study explores various branding indicators by utilizing descriptive statistics, correlation analyses, regression models, and fuzzy cognitive mapping (FCM). The findings reveal significant correlations between desktop and mobile analytics and digital branding outcomes, underscoring the critical role of digital analytics and Decision Support Systems (DSSs) in shaping modern branding strategies in the agri-food industry. This study highlights the economic implications of desktop and mobile customer analytics on digital branding, providing insights to enhance market performance and foster sustainable growth in the agri-food sector.

1. Introduction

In today’s digitally driven agri-food industry, strategic digital branding has emerged as a crucial determinant for firms striving to gain a competitive edge [1]. With the rapid expansion of online platforms and evolving consumer preferences, the efficacy of digital branding strategies increasingly hinges on integrating digital technologies. Among these technologies, desktop and mobile customer analytics are invaluable tools, offering deep insights into consumer behavior, preferences, and engagement patterns [2,3].

The agri-food sector functions at an intersection of consumer goods and agricultural production, requiring the development of robust digital strategies for interacting with end users and business clients alike. Unpredictable supply chains, regulatory compliance, and the demand for sustainable practices constitute a few of the specific challenges that this industry faces [4]. Agri-food businesses need to utilize customer analytics on desktop and mobile platforms in order to handle these difficulties. Agri-food companies can enhance their branding strategy, increase customer engagement, and eventually increase revenue by monitoring consumer behavior through digital channels [5]. However, while the existing literature acknowledges the potential significance of customer analytics, a notable gap exists in understanding how agri-food firms can effectively leverage these tools to enhance their brands from an economic standpoint. A burgeoning body of academic research has explored the application of customer analytics within the agri-food sector, shedding light on various facets of consumer behavior, market dynamics, and strategic implications for firms. For instance, Vlachopoulou et al. [6] conducted a comprehensive study on agri-food business models, highlighting the pivotal role of customer analytics in segmenting markets and tailoring marketing strategies. Similarly, Caiazza and Bigliardi [7] underscored the importance of leveraging digital channels and customer data to optimize marketing campaigns and bolster brand visibility. The research by Aghazadeh et al. [1] provides valuable insights into the implications of branding advantage on export performance within the agri-food sector. Additionally, studies like Anitha and Patil [8] and Seyedan and Mafakheri [9] delve into the impact of customer analytics on supply chain optimization, demonstrating how data-driven insights can improve demand forecasting. While both studies specifically address supply chain optimization, they indirectly reinforce the broader theme of leveraging data analytics to drive economic value for agri-food businesses. Similarly, while the paper by Kamble et al. [10] primarily focuses on supply chain management and sustainability, it indirectly addresses the economic perspective of customer analytics on branding by emphasizing the importance of data-driven decision-making, technology integration, and supply chain optimization for enhancing operational efficiency and fostering competitive advantage within the agri-food industry.

Different marketing tools function independently in today’s digital environment, which frequently results in fragmented consumer insights and ineffective resource allocation [7]. The existing literature focuses on either desktop or mobile analytics in isolation, ignoring the integrated effect on both platforms [6,11]. This disconnect underscores the need to integrate these technologies so as to obtain a holistic view of consumer behavior [5]. By leveraging both mobile and desktop analytics, agri-food companies can synchronize customer touchpoints across platforms, gaining a deeper understanding of user preferences and behaviors. Enhancing data coherence and enabling focused marketing tactics that effectively connect with customers on multiple devices are of paramount importance for the employment of this integrated approach [12]. For instance, the DIVA project aims to promote innovative digitech value chains in the agri-food, forestry, and environment sectors. By leveraging technologies like big data, cloud computing, robotics, artificial intelligence, and IoT, DIVA supports the development of new industrial value chains. Agri-food companies participating in DIVA can orchestrate customer touchpoints across platforms, enhancing data coherence and enabling targeted marketing strategies [6].

Motivated by these gaps and insights, this study aims to investigate the economic implications of customer analytics on digital branding within the agri-food sector, contributing to a deeper understanding of their value proposition. This study seeks to address the following research questions:

• What role do customer analytics play in optimizing digital branding efforts in the agri-food sector?
• What are the economic benefits of integrating customer analytics with digital branding strategies in the agri-food sector?
• How can digital branding and customer analytics contribute to sustainable practices in the agri-food sector?

By addressing these research questions, this study aims to provide a comprehensive understanding of the interplay between digital branding, customer analytics, and economic outcomes in the agri-food sector.

Building on the foundational work of Hossain et al. [13], which underscores the significance of data-driven marketing analytics in advancing value creation through customer analytics, this study closes the loop by investigating the ways in which different economic outcomes are impacted by the combined application of desktop and mobile consumer data. It concentrates on important brand KPIs that are impacted by this integrated strategy. As agri-food companies embrace omnichannel strategies more frequently to maximize their digital presence, it is essential that they embrace this integrated strategy [14]. Through an economic perspective rooted in the performance of agri-food enterprises, it becomes evident that metrics like branded traffic, webpage authority score, and various sources of website traffic play pivotal roles. These metrics are not just measures of business success; they are indicators of the effectiveness of branding strategies [15]. By investing in the optimization of these brand metrics, agri-food firms can enhance their performance, thereby fostering economic growth within the industry.

The primary objectives of this study are to enrich theoretical understanding by integrating insights from the desktop and mobile customer analytics literature and to offer actionable guidance for agri-food firms seeking to maximize the economic impact of their digital branding strategies. To achieve these objectives, this introduction outlines the structure of the study, commencing with a comprehensive review of the existing literature on digital branding, digital customer analytics, and their economic implications. Subsequently, the research methodology employed to investigate the impact of both desktop and mobile customer analytics on business branding optimization from an economic perspective is delineated. Following this, the findings of the current analysis are presented and engaged in a discussion of their theoretical and practical implications, with a specific focus on the economic ramifications for agri-food firms. Finally, a summary of key insights and proposed avenues for future research that could further deepen our understanding of the economic dynamics of brand promotion in the agri-food industry is provided.

2. Literature Review

2.1. Customer Analytics in Digital Branding

The agri-food sector faces increasingly complex challenges and opportunities due to the expanding global population and evolving dietary preferences. To address the projected doubling of food demand by 2050, agri-food companies are embracing various digital technologies to enhance production efficiency and mitigate environmental impact [5]. Across the spectrum, stakeholders in the agri-food industry adeptly navigate dynamic market landscapes to meet consumer demand while prioritizing sustainability and profitability [16].

Amidst this backdrop, customer analytics emerges as a cornerstone in modern marketing and business strategy. By delivering actionable knowledge for efficient AFSC management, big data utilization in Agriculture and Food Supply Chains (AFSCs) benefits researchers, practitioners, and policymakers by offering real-time analytical insights for proactive, data-driven decision-making [17]. Simultaneously, it enhances food firms’ understanding of consumer preferences, facilitates product development, and enhances overall business efficiency and working conditions [18]. Leveraging data-driven insights enables agri-food enterprises to tailor their offerings, services, and promotional efforts to effectively target and engage their customer base [19]. Big data has wide-ranging applications across various sectors, particularly in major service industries, where it refines the understanding of customer behavior and needs, resulting in tailored pricing, enhanced operational efficiency, and reduced overheads [20].

In order to place this research within the larger framework of sustainable development, it is critical to recognize the substantial contribution that the agri-food sector provides to sustainability. Food goods made ethically and sustainably are in greater demand from consumers [21]. Customer analytics, through monitoring and marketing data like carbon footprints, water usage, and fair-trade certifications, may help agri-food companies in showcasing their sustainability initiatives. Incorporating big data analytics into digital branding strategies contributes to the achievement of sustainability goals in addition to improving economic performance [22]. Customer analytics optimize marketing strategies, ensuring efficient resource use and reducing waste, which contributes to more sustainable business practices [14]. For instance, targeting the right audience with precise marketing campaigns can significantly reduce resource wastage. By understanding consumer preferences and behaviors, agri-food companies can promote sustainable consumption patterns, such as identifying and promoting products that are produced sustainably or have a lower environmental impact [23]. This economic stability supports investment in sustainable practices and technologies, creating a positive feedback loop that benefits both the environment and society. Efficient digital branding strategies can further reduce environmental impact by optimizing supply chains and minimizing excess inventory, thus lowering carbon footprints [24]. Therefore, the application of customer analytics in the agri-food industry aligns with the principles of sustainable development, making it economically beneficial while also being environmentally and socially responsible.

Moncey and Baskaran [25] emphasize the pivotal role of digital marketing analytics in customer engagement and brand awareness, highlighting its direct impact on customer loyalty. However, despite the growing influence of customer analytics data in digital marketing, there remains a notable gap in the literature concerning the selection and utilization of digital marketing analytics tools aligned with overarching strategic objectives [26]. To this end, addressing this gap is essential for agri-food companies seeking to maximize the potential of digital marketing analytics in driving consumer relationship-building and decision-making processes.

Furthermore, the emergence of innovative approaches such as “Custolytics”, coined by Yerpude and Singhal [27], underscores the evolving landscape of customer analytics. Custolytics, a fusion of “customer” and “analytics”, offers a novel framework for forecasting demand and predicting sales across various parameters, thereby facilitating the development of effective customer engagement strategies. This research delves into IoT-driven customer analytics, particularly in emerging markets, highlighting the potential for transformative insights to drive business growth and market competitiveness.

In addition to customer engagement strategies, the focus on digital branding strategies within the agri-food industry represents a critical avenue for exploration. For instance, recent studies have utilized data mining techniques to predict the performance metrics of posts on brands’ Facebook pages, revealing the type of content as the most influential feature, thereby offering valuable insights for managerial decision-making [28]. Recognizing the significance of specific brand metrics is imperative for optimizing digital branding efforts and augmenting market visibility [29]. Through the strategic utilization of these metrics, agri-food companies can refine their digital branding strategies to align with the evolving needs and preferences of consumers in an increasingly digitalized marketplace. Thus, the convergence of customer analytics and digital branding represents a fertile ground for research and innovation within the agri-food sector, promising profound implications for market differentiation, consumer engagement, and long-term business success.

2.2. Economic Implications of Customer Analytics in the Agri-Food Sector

The integration of customer analytics within the agri-food sector yields profound economic ramifications, crucial in shaping market dynamics and fostering sustainable growth [30]. Through sophisticated data analytics techniques, enterprises acquire a nuanced understanding of consumer behavior and preferences, guiding strategic decisions with far-reaching economic implications [31]. This strategic comprehension extends to the assessment and enhancement of customer analytics, influencing decisions on resource allocation and market positioning [32].

Delving into the economic perspective of customer analytics, they exert influence across various facets of business operations, significantly impacting overall company performance [33]. By scrutinizing consumer data, firms tailor their marketing strategies to target specific consumer segments more effectively [34], culminating in heightened brand recognition [35], augmented customer engagement [36], and ultimately, elevated sales volumes and revenue streams [37]. Additionally, customer analytics empower enterprises to discern emerging market trends and consumer preferences [9], enabling the development and introduction of novel products that better align with market demand [38], thereby bolstering market competitiveness and profitability [39].

Moreover, customer analytics contribute to enhancing consumer satisfaction and loyalty within the agri-food realm. Through the adept understanding of consumer preferences and addressing their needs, firms elevate the overall consumer experience, leading to heightened levels of satisfaction and allegiance [40,41,42]. This, in turn, engenders repeat patronage, positive word-of-mouth endorsements, and augmented customer lifetime value, fostering enduring economic sustainability and profitability [43].

In a broader context, the economic implications of customer analytics extend beyond immediate gains to long-term value creation. By continuously analyzing consumer data and adapting strategies to evolving market dynamics, agri-food companies cultivate enduring relationships with customers, engender brand loyalty, and stimulate sustained revenue growth [44]. This focus on long-term value creation not only fortifies firms but also contributes to the overall economic development and sustainability of the agri-food sector [45]. It underscores the importance of brand metrics in gauging the effectiveness of long-term customer engagement and revenue generation strategies.

Overall, the economic implications of customer analytics within the agri-food sector are profound, directly shaping brand metrics and playing a pivotal role in driving market success and competitiveness. As agri-food enterprises continue to harness the power of customer analytics, they stand poised to unlock new opportunities for growth and innovation in an increasingly dynamic and competitive market landscape.

2.3. Navigating Consumer Insights: Customer Analytics in Agri-Food

Recent advancements in digital technologies and data analytics have expanded the applications of customer analytics in the agri-food sector. For example, Industry 4.0 (I4.0) technologies influence innovation in agri-food firms by enabling process innovations to meet customer demands and enhance product quality [46]. Similarly, advancements in technology, particularly big data, enhance collaboration strategies, forecasting, and optimization, thereby boosting enterprise competitiveness in the agro-industrial sector [47]. Additionally, there is an emphasis on the need for firms to prioritize long-term, shared value creation and integrate digital technologies to mitigate environmental impact and yield economic, environmental, and social benefits [5].

Caiazza and Bigliardi [7] conducted a compelling study that identified four pivotal pillars of web marketing in the agri-food sector, including behavioral tracking and predictive analytics. They underscore how agri-food companies are harnessing software tools to monitor user behavior online, with the goal of predicting consumer preferences and delivering personalized experiences. This strategic approach enables companies to gain valuable insights into consumer needs and behaviors, empowering them to adapt their branding strategies accordingly. By tailoring their approach to match consumer preferences, companies not only attract new customers but also cultivate loyalty among existing ones. Ultimately, this results in heightened customer satisfaction and sustained engagement with the brand over the long term.

The importance of integrating scientific knowledge of human behavior with interdisciplinary methods to attain Consumer Intimacy (CI) cannot be overstated. This involves utilizing behavioral analytics, artificial intelligence, and digital technologies to craft evidence-based interventions that promote health, food, and nutrition [48]. To this end, the agri-food sector is increasingly leveraging customer analytics to gain deep insights into consumer behavior, preferences, and trends. By tracking and analyzing consumer interactions across digital platforms, agri-food companies gain valuable insights into consumer behavior and preferences [49]. This data-driven approach enables personalized consumer experiences, refined branding strategies, and informed product development initiatives [50]. Through targeted marketing campaigns and tailored product offerings, companies can enhance customer satisfaction, foster loyalty, and drive business growth. Leveraging customer analytics allows agri-food companies to stay competitive and relevant in an increasingly digitalized marketplace, ensuring sustained success and market leadership.

To address challenges specific to the agri-food industry, customer analytics insights are essential [51,52]. Agri-food companies, for instance, can use mobile analytics to monitor the success of their mobile-based loyalty programs, which are crucial for retaining customers who choose organic and sustainable goods. However, desktop analytics are crucial for comprehending B2B clients’ purchasing habits, such as wholesalers and retailers. Combining these metrics makes it easier to develop a unified brand strategy that appeals to both corporate clients and individual consumers. Furthermore, driven by various reasons including weather, pests, and disease outbreaks, the agri-food business frequently deals with unanticipated supply chains [53]. Customer analytics can help organizations manage inventories and predict demand by analyzing purchasing behavior and seasonality trends. Furthermore, in the agri-food industry, building and maintaining client trust is critical [54]. Comprehensive analytics can be utilized to increase transparency in product sourcing, manufacturing operations, and supply chain practices. This can help agri-food companies develop stronger relationships with their customers. Desktop and mobile analytics, for example, can be used to monitor the effectiveness of transparency initiatives such as the interactive farm-to-table maps on the company’s website.

Since user behaviors and engagement patterns on mobile and desktop platforms differ, it is important to distinguish between them in the context of digital marketing [12,55]. Due to the separation of these data, agri-food companies can now more precisely personalize their marketing campaigns by speed- and simplicity-optimizing mobile content and enhancing both mobile and desktop experiences with advanced e-commerce capabilities and rich information. By leveraging the benefits of both platforms, businesses can create a cohesive and effective online presence that maximizes user engagement and conversion rates across a variety of consumer touchpoints.

Despite the significant strides in applying customer analytics within the agri-food industry, persistent challenges remain, particularly in digital branding, which directly influence economic outcomes. Further research is necessary to explore the untapped potential of customer analytics in addressing these challenges and uncovering innovative opportunities for growth and innovation in the agri-food sector, especially in optimizing digital branding strategies to enhance economic performance. Jin et al. [56] advocate for the seamless integration of advanced big data analytics and artificial intelligence (AI) technology into digital marketing, promoting the evolution and optimization of sustainable digital marketing practices. By harnessing customer data and employing advanced analytics techniques, agri-food companies can refine their digital branding strategies to resonate effectively with evolving consumer preferences in an increasingly digitalized marketplace, thereby stimulating economic growth. This prompts the formulation of the following research question:

RQ1. 

How does the utilization of customer analytics influence digital branding strategies and their economic implications within the agri-food sector?

According to Tabianan et al. [57], customer segmentation based on similar behavioral factors plays a crucial role in maintaining long-term customer relationships, enhancing business profitability, and optimizing the visibility of e-offers to attract potential customers. Meanwhile, Chaudhary et al. [58] employ big data technology to analyze social media data, predict consumer behavior, and refine outcomes through data preprocessing techniques, utilizing machine learning models for accurate predictions. Additionally, Hossain et al. [14] delve into the emergence and implications of Customer-Analytics-driven Value Creation Capability (CAVCC) within the context of data-driven marketing analytics, highlighting its direct and indirect impacts on sustained competitive advantage. Theoretical frameworks such as those pertaining to customer relationships, predictive customer behavior analysis, and user engagement serve as fundamental pillars for understanding consumer behavior and preferences across various digital channels, offering insights that extend beyond traditional brand metrics to encompass wider economic implications.

Following the settlement of RQ1, the authors proceed into the development of relevant research hypotheses that form the base of this paper’s research methodology. Recent research by Drivas et al. [59] demonstrates that high levels of branded traffic often correlate with increased conversion rates and subsequent consumer purchasing behavior. Therefore, the effective deployment of customer analytics could empower agri-food companies to optimize their branding endeavors, consequently leading to a surge in branded traffic directed to their digital platforms [60]. This heightened brand visibility and engagement contribute to enhanced market performance and profitability, as consumers are more inclined to become paying customers when they hold a favorable perception of the brand [61]. Consequently, agri-food firms stand to reap economic benefits such as heightened conversion rates and increased revenue generation by harnessing customer analytics to bolster branded traffic. This leads to the formulation of Hypothesis 1.

H1. 

The volume of branded traffic to agri-food firms’ websites is influenced by key customer analytics metrics, including time on site, pages per visit, and bounce rate, on both desktop and mobile platforms.

Hypothesis 1 suggests that both desktop and mobile customer analytics have a significant relationship with agri-food firms’ branded traffic. This hypothesis is founded on the premise that customer analytics, derived from both desktop and mobile platforms, play a crucial role in influencing the level of branded traffic directed to agri-food firm websites. Customer analytics metrics such as time on site, pages per visit, and bounce rate are direct indicators of user engagement and experience [62]. Separating these metrics by desktop and mobile platforms is essential because user behavior significantly differs between these media types [63]. For instance, mobile users may have shorter sessions but more frequent visits, whereas desktop users might spend more time per session, engaging in more detailed content consumption. Understanding these differences helps agri-food companies optimize their digital strategies for each platform, enhancing overall branded traffic. If the hypothesis is supported, it suggests that agri-food companies can leverage insights from desktop and mobile customer analytics to enhance their digital branding strategies effectively. By optimizing these analytics, firms may experience an increase in branded traffic to their websites, which can translate into heightened brand visibility and consumer engagement. Increased branded traffic often correlates with improved market performance and profitability, as consumers are more likely to convert into paying customers when they have positive interactions with a brand [64].

Moreover, customer analytics contribute to the augmentation of webpage authority scores, serving as indicators of a brand’s credibility and influence in the online domain [49]. Studies conducted by Król and Zdonek [65] and Roumeliotis et al. [66] highlight the economic significance of maintaining a strong webpage authority score, emphasizing its role in establishing consumer trust and confidence in the brand. Elevated webpage authority scores typically correlate with heightened consumer trust levels, fostering more favorable perceptions and subsequent purchasing decisions [65]. As agri-food firms invest in enhancing their webpage authority scores through customer-analytics-driven strategies, they can realize economic advantages such as enhanced brand reputation and augmented consumer confidence, culminating in amplified sales and revenue. This leads to the formulation of Hypothesis 2.

H2. 

The authority score of agri-food firms’ websites is influenced by customer engagement metrics such as average session duration, pages per visit, and returning visitor rate on both desktop and mobile platforms.

Hypothesis 2 proposes a significant association between both desktop and mobile customer analytics and agri-food firms’ webpage authority scores. This hypothesis is rooted in the understanding that customer analytics, whether derived from desktop or mobile platforms, play a crucial role in influencing the perceived credibility and influence of agri-food firm websites, as reflected by their webpage authority scores. These metrics are measured separately for desktop and mobile because user interaction patterns vary between these devices [63]. Mobile analytics can show quick, on-the-go engagements, whereas desktop analytics can provide insights into deeper, more comprehensive interactions. This distinction is vital for agri-food companies to tailor their content and engagement strategies effectively for each platform. Should this hypothesis be supported, it suggests that by optimizing these analytical tools, firms may witness enhancements in their webpage authority scores, signifying increased credibility and influence in the digital domain. Such improvements in the webpage authority score can contribute to reinforcing the perceived trustworthiness of agri-food firm websites among consumers, potentially leading to heightened engagement and conversions.

Moreover, organic and paid traffic sources contribute significantly to brand promotion by expanding the brand’s reach to targeted audiences. Organic traffic, stemming from unpaid search engine results, reflects the brand’s visibility and relevance in search engine rankings [67]. Similarly, paid traffic generated through advertising campaigns amplifies brand exposure and influences consumer perceptions and purchase intentions [68]. Rajan et al. [69] highlights the distinction between organic search, which emphasizes optimizing content for search engine visibility through keyword optimization, and paid search, which involves paying for advertisements to appear in search results. In today’s digital landscape, companies must prioritize Search Engine Optimization (SEO) to ensure their websites are both user-friendly and prominently featured in search engine results, thus bolstering visibility and attracting a larger customer base [70]. Strategic investments in diverse traffic generation strategies, informed by insights from customer analytics, have the potential to yield sustained long-term economic benefits in terms of brand exposure and sales conversions [71]. This underpins the formulation of Hypothesis 3.

H3. 

Organic and paid website traffic to agri-food firms’ websites is influenced by customer analytics metrics such as pages per visit and average session duration on both desktop and mobile platforms.

Hypothesis 3 posits a significant relationship between desktop and mobile customer analytics and agri-food firms’ organic and paid website traffic. This hypothesis is grounded in the understanding that customer analytics, derived from both desktop and mobile platforms, provide valuable insights into consumer behavior and preferences, which can inform digital marketing strategies aimed at driving organic and paid traffic to agri-food firm websites. This analysis suggests that investments in customer analytics capabilities can yield tangible economic benefits by optimizing traffic generation efforts, enhancing brand visibility, and ultimately driving sales conversions. These metrics differ between desktop and mobile due to varying user behavior [63]. For example, mobile users might respond more to localized and immediate search results, while desktop users might engage more with detailed product information and reviews. By analyzing these metrics separately for each platform, agri-food companies can optimize their SEO and advertising strategies to maximize traffic and conversions. Therefore, if Hypothesis 3 is supported, it would indicate the importance of integrating customer analytics into digital marketing strategies to maximize the economic impact of organic and paid website traffic generation within the agri-food industry.

Furthermore, the analysis of traffic sources, including direct, organic, and paid traffic, enables companies to refine their digital marketing strategies for maximum economic impact [12]. Research by Ponzoa and Erdmann [72] and Widiastuti [73] underscores the importance of direct traffic in driving brand visibility and fostering consumer engagement. According to Kakalejčík et al. [74], direct traffic emerges as the most effective marketing channel for companies, highlighting the significant impact of strong branding on driving sales, particularly among repeat customers who are already familiar with the company’s domain name or have created bookmarks in their web browsers. By identifying the most effective traffic sources through customer analytics, firms can allocate their marketing budget more judiciously, ensuring a superior return on investment. Recent studies have observed a strong correlation between website visits from various channels and purchases originating from direct traffic sources [12], laying the groundwork for hypothesis 4.

H4. 

Direct traffic to agri-food firms’ websites is influenced by customer analytics metrics such as session frequency and bounce rate on both desktop and mobile platforms.

H4 proposes that both desktop and mobile customer analytics play a significant role in shaping agri-food firms’ direct traffic sources. This hypothesis stems from the understanding that customer analytics derived from desktop and mobile platforms are instrumental in influencing the volume and characteristics of direct traffic directed towards agri-food firm websites. Differentiating these metrics by desktop and mobile is crucial because user interaction patterns differ significantly [63]. Desktop users might prefer more detailed and comprehensive content, leading to longer sessions and more frequent returns, while mobile users might engage in shorter, more frequent sessions. Understanding these differences allows agri-food companies to enhance their direct traffic by tailoring their digital presence to the preferences of their users on each platform. Confirming H4 would validate the notion that agri-food firms can benefit from strategically utilizing customer analytics to optimize their direct traffic sources, thereby strengthening their online presence and driving economic outcomes through enhanced sales performance.

3. Research Methodology

The main goal of this study is to explore the link between mobile and desktop customer analytics in the promotion of agri-food companies’ digital brand names, using a three-phase approach. The research methodology involves collecting data from leading agri-food companies using advanced customer analytics tools. By focusing on key metrics such as branded traffic, authority scores, and customer engagement across mobile and desktop platforms, this study aims to uncover actionable insights specific to the agri-food sector. This approach ensures that the findings are directly applicable and beneficial to agri-food companies seeking to enhance their digital brand presence. The methodology involves distinct stages: big data collection, statistical analysis, and dynamic modeling.

3.1. Data Sources

The data collection phase focused on obtaining web analytics data, as described in

Table 1. Description of customer analytics [76].

Analytics/Metrics	Description
Branded Traffic	This metric counts the number of visitors arriving at a website through search queries that include the brand’s name or variations of it.
Authority Score	This metric evaluates the overall quality and influence of a website based on factors such as backlinks, organic search performance, and other authority indicators.
Organic Traffic	This metric measures the number of visitors who arrive at a website through unpaid search engine results.
Paid Traffic	This metric counts the number of visitors who come to a website through paid advertisements, such as pay-per-click (PPC) campaigns.
Direct Sources	This metric tracks the number of visitors who land on a website by directly typing the URL into their browser or through bookmarks, bypassing search engines and other referral sources.
Desktop Bounce Rate	This metric indicates the percentage of visitors using desktop devices who leave a website after viewing only one page.
Desktop Pages per Visit	This metric measures the average number of pages viewed by visitors using desktop devices during a single visit to a website.
Desktop Time on Site	This metric tracks the average duration of time that visitors using desktop devices spend on a website during a single visit.
Desktop New Visitors	This metric counts the number of first-time visitors using desktop devices who access a website.
Mobile Bounce Rate	This metric indicates the percentage of visitors using mobile devices who leave a website after viewing only one page.
Mobile Pages per Visit	This metric measures the average number of pages viewed by visitors using mobile devices during a single visit to a website.
Mobile Time on Site	This metric tracks the average duration of time that visitors using mobile devices spend on a website during a single visit.
Mobile New Visitors	This metric counts the number of first-time visitors using mobile devices who access a website.

Source: Authors’ elaboration.

below, from the corporate websites of five prominent agri-food firms: Nestle SA (HQ: Vevey, Switzerland), Mondelez International Inc. (HQ: Chicago, IL, USA), The Kraft Heinz Co. (HQ: Chicago, IL, USA and Pittsburgh, PA, USA), Danone SA (HQ: Paris, France), and The Hershey Co (HQ: Hershey, PA, USA). These firms were selected based on their market capitalization in 2023, as identified by Globaldata [75]. Data were gathered using Semrush’s Decision Support System (DSS) platform [76] over a period of 180 days, from 1 August 2023 to 31 March 2024.

3.2. Methodology

In the first stage, this study focuses on gathering website analytics data from selected organizations within the agri-food sector. This data, sourced from five major firms using the Semrush [76] software, will serve as the foundation for subsequent statistical analysis.

In the statistical analysis stage, the collected data undergo thorough examination using descriptive statistics, correlation analysis, and linear regression models. The objective of the developed simple linear regression (SLR) models is to elucidate the statistical significance of the relationships among the study variables. These analyses aim to explore how mobile and desktop customer analytics metrics impact key digital branding metrics such as branded traffic, authority score, organic and paid traffic, and direct sources traffic. This phase not only tests research hypotheses but also provides empirical insights into effective digital marketing strategies employed by agri-food firms.

The final stage involves employing Fuzzy Cognitive Maps (FCMs) to model and simulate complex relationships between variables identified in the statistical analyses. To do so, the online DSS platform of MentalModeler [77] is utilized. FCMs are chosen for their better capability to represent and reason over uncertain and dynamic systems, making them particularly suitable for modeling digital marketing dynamics [78]. By incorporating time dynamics and facilitating scenario simulations, FCMs enhance strategic decision-making and provide a deeper understanding of the interactions between consumer behavior metrics and economic outcomes within the digital marketing landscape [79,80].

FCMs offer several advantages in the context of this study. They provide a visual representation of causal relationships among variables, allowing for intuitive understanding and analysis of complex systems. Moreover, FCMs accommodate uncertainties and dynamic changes in consumer behavior and market conditions, ensuring robustness in strategic planning and optimization of digital marketing efforts [81]. This study employs a scenario-based approach to assess how mobile and desktop customer analytics impact digital brand promotion within the agri-food sector. This method allows the exploration of diverse hypothetical situations mirroring real-world conditions and strategic choices made by agri-food firms. Each scenario in the results section explores unique metric combinations, revealing their specific implications for digital branding. Tailored to simulate various strategic decisions and market conditions relevant to agri-food companies, these scenarios provide actionable insights into optimizing the integration of mobile and desktop customer analytics. Detailed discussions in the results section connect theoretical frameworks with practical applications in digital marketing and brand management.

4. Results

4.1. Statistical Analysis

Following the delineation of this study’s sample and methodology, the authors proceed with the requisite statistical examination to derive the pertinent coefficients from the interrelations among the variables. Primarily,

Table 2. Descriptive statistics.

	Mean	Min	Max	Std. Deviation	Skewness	Kurtosis
Branded Traffic	56.23	44.00	73.00	9.54	0.582	−0.929
Authority Score	53.18	52.80	54.00	0.50	1.111	−0.608
Organic Traffic	364,217.41	345,548.00	423,170.00	20,663.04	1.962	1.947
Paid Traffic	92.83	0.00	648.00	206.21	1.943	1.908
Direct Sources	323,284.57	263,604.00	411,527.00	53,683.07	0.636	−0.663
Desktop Bounce Rate	2.47	2.33	2.67	0.12	0.473	−0.348
Desktop Pages per Visit	15.56	13.32	16.81	1.22	−1.128	0.720
Desktop Time on Site	2592.14	2096.00	3110.00	381.08	0.348	−1.192
Desktop New Visitors	141,826.14	121,348.00	150,794.00	10,054.30	−1.719	1.866
Mobile Bounce Rate	2.94	2.42	3.41	0.335	−0.142	−0.632
Mobile Pages per Visit	6.41	5.47	7.91	0.94	0.963	−0.817
Mobile Time on Site	1120.86	160.00	2052.00	740.65	0.130	−1.653
Mobile New Visitors	143,785.29	107,043.00	201,049.00	36,522.08	0.666	−1.025

Source: Authors’ elaboration.

showcases the essential descriptive statistics pertaining to both the independent and dependent variables. Subsequently,

Table 3. Correlation analysis.

	Branded Traffic	Authority Score	Organic Traffic	Paid Traffic	Direct Sources	Desktop Bounce Rate	Desktop Pages per Visit	Desktop Time on Site	Desktop New Visitors	Mobile Bounce Rate	Mobile Pages per Visit	Mobile Time on Site	Mobile New Visitors
Branded Traffic	1	0.143	0.196	−0.081	−0.540	0.648	−0.038	0.608	−0.583	−0.557	0.149	−0.410	−0.574
Authority Score	0.143	1	0.054	−0.133	0.671	−0.735 *	0.644	−0.162	−0.096	0.175	−0.146	0.399	0.937 **
Organic Traffic	0.196	0.054	1	−0.117	−0.132	−0.019	−0.252	−0.248	0.266	0.151	0.035	0.114	−0.079
Paid Traffic	−0.081	−0.133	−0.117	1	0.148	−0.511	0.449	−0.150	−0.196	−0.057	−0.271	0.512	0.688
Direct Sources	−0.540	0.671	−0.132	0.148	1	−0.654	0.596	0.121	−0.020	0.468	0.201	−0.101	0.698
Desktop Bounce Rate	0.648	−0.735 *	−0.019	−0.511	−0.654	1	−0.551	0.094	−0.372	−0.490	−0.449	−0.531	−0.574
Desktop Pages per Visit	−0.038	0.644	−0.252	0.449	0.596	−0.551	1	0.471	−0.490	−0.250	0.405	0.214	0.609
Desktop Time on Site	0.608	−0.162	−0.248	−0.150	0.121	0.094	0.471	1	−0.658	−0.140	0.497	−0.553	−0.249
Desktop New Visitors	−0.583	−0.096	0.266	−0.196	−0.020	−0.372	−0.490	−0.658	1	0.586	0.191	0.436	−0.173
Mobile Bounce Rate	−0.557	0.175	0.151	−0.057	0.468	−0.490	−0.250	−0.140	0.586	1	0.133	−0.193	0.056
Mobile Pages per Visit	0.149	−0.146	0.035	−0.271	0.201	−0.449	0.405	0.497	0.191	0.133	1	0.138	−0.287
Mobile Time on Site	−0.410	0.399	0.114	0.512	−0.101	−0.531	0.214	−0.553	0.436	−0.193	0.138	1	0.322
Mobile New Visitors	−0.574	0.937 **	−0.079	0.688	0.698	−0.574	0.609	−0.249	−0.173	0.056	−0.287	0.322	1

*, ** Indicate statistical significance at the 95% and 99% levels, respectively. Source: Authors elaboration.

delineates the correlations among the variables under investigation.

Table 3 presents the correlation analysis results between key mobile and desktop customer analytics metrics and their economic implications for digital brand promotion within the agri-food sector. The correlation coefficients indicate the strength and direction of relationships between variables, providing valuable insights into how these metrics influence various economic outcomes. Specifically, we examined correlations between metrics such as branded traffic, authority score, organic and paid traffic, direct sources traffic, and specific customer engagement metrics on both mobile and desktop platforms.

For instance, branded traffic shows moderate positive correlations with desktop time on site (r = 0.608) and mobile pages per visit (r = 0.149). This suggests that longer engagement times on both desktop and mobile platforms positively influence branded traffic, indicating heightened user interaction and brand awareness. This heightened interaction is crucial as it can lead to higher conversion rates and customer loyalty, ultimately boosting sales and revenue.

Similarly, the authority score exhibits notable correlations with several metrics, particularly with mobile new visitors (r = 0.937**), indicating a strong positive relationship. This implies that the increased engagement of new visitors on mobile platforms enhances brand authority, reflecting a robust online presence and consumer trust. A robust authority score not only indicates trustworthiness but also influences consumer decisions, potentially increasing sales volumes and market share within the competitive agri-food sector.

Moreover, organic traffic demonstrates positive correlations with desktop new visitors (r = 0.266) and mobile time on site (r = 0.114), implying that organic traffic plays a role in driving both desktop and mobile user engagement. By leveraging effective SEO strategies, agri-food companies can reduce customer acquisition costs associated with paid advertising while increasing organic traffic, which often leads to higher-quality leads and conversions.

Conversely, the positive correlation between paid traffic and mobile new visitors (r = 0.688) suggests that investing in targeted paid advertising campaigns can effectively attract new visitors on mobile platforms. However, the negative correlation with desktop bounce rate (r = −0.511) indicates that optimizing desktop user experience is crucial to reducing bounce rates and maximizing the effectiveness of paid advertising investments. This suggests that while higher bounce rates on desktop may increase reliance on paid traffic for visitor acquisition, mobile platforms benefit more from attracting new visitors organically.

Direct sources exhibit a strong negative correlation with desktop bounce rate (r = −0.654) and a positive correlation with authority score (r = 0.671). These relationships indicate that direct sources contribute to enhancing brand credibility and reducing bounce rates on desktop platforms, thereby improving overall user engagement and brand perception. By nurturing direct traffic channels through brand building and customer engagement initiatives, agri-food companies can improve user retention and increase customer lifetime value.

In conclusion, the correlation analysis in Table 3 underscores the importance of integrated marketing strategies that consider both mobile and desktop platforms. These insights are crucial for agri-food firms seeking to navigate the digital landscape effectively, capitalize on consumer behavior trends, and optimize their digital branding efforts in a competitive environment.

As seen in

Table 4. Impact of agri-food firms’ desktop and mobile customer analytics on their digital branding variables.

Variables	Standardized Coefficient	R2	F	p-Value	D-W Stat
Branded Traffic
Desktop Time on Site	−5.337	1.000	-	0.000 **	1.015
Desktop New Visitors	−6.741			0.000 **
Mobile Bounce Rate	2.474			0.000 **
Mobile Pages per Visit	2.985			0.000 **
Mobile Time on Site	0.448			0.000 **
Mobile New Visitors	−2.491			0.000 **
Authority Score
Desktop Time on Site	2.401	1.000	-	0.000 **	0.583
Desktop New Visitors	2.133			0.000 **
Mobile Bounce Rate	−0.563			0.000 **
Mobile Pages per Visit	−1.321			0.000 **
Mobile Time on Site	0.411			0.000 **
Mobile New Visitors	1.423			0.000 **
Organic Traffic
Desktop Time on Site	−12.768	1.000	-	0.000 **	0.333
Desktop New Visitors	−14.340			0.000 **
Mobile Bounce Rate	6.293			0.000 **
Mobile Pages per Visit	6.876			0.000 **
Mobile Time on Site	1.003			0.000 **
Mobile New Visitors	−4.432			0.000 **
Paid Traffic
Desktop Time on Site	−0.293	1.000	-	0.000 **	0.391
Desktop New Visitors	−2.020			0.000 **
Mobile Bounce Rate	1.479			0.000 **
Mobile Pages per Visit	−0.300			0.000 **
Mobile Time on Site	1.703			0.000 **
Mobile New Visitors	−0.451			0.000 **
Direct Sources Traffic
Desktop Time on Site	1.080	1.000	-	0.000 **	1.205
Desktop New Visitors	1.821			0.000 **
Mobile Bounce Rate	−0.729			0.000 **
Mobile Pages per Visit	0.018			0.000 **
Mobile Time on Site	−0.970			0.000 **
Mobile New Visitors	1.639			0.000 **

** Indicates statistical significance at the 99% level. Source: Authors’ elaboration.

, the branded traffic, authority score, organic and paid traffic, and direct sources traffic of agri-food firms in the sample were chosen as the dependent variables, while web analytics metrics such as desktop and mobile customer analytics (time on site, new visitors, pages per visit, and bounce rate) were selected as independent variables. In these SLR models, which examined branded traffic, authority score, organic and paid traffic, and direct sources traffic (as dependent variables) with desktop and mobile customer analytics (time on site, new visitors, pages per visit, and bounce rate) as independent variables, the model’s variables were found to be statistically significant, with p-values < a = 0.01 level of significance, and R² = 1.000.

A 1% increase in desktop time on site, new visitors, mobile bounce rate, pages per visit, time on site, and new visitors led to a −533.7%, −674.1%, 247.4%, 298.5%, 44.8%, and −249.1% change in agri-food firms’ branded traffic, respectively, while it variates the authority score by −240.1%, 213.3%, −56.3%, −132.1%, 41.1%, and 142.3%, respectively. Moreover, it can be discerned that for every 1% increase in desktop time on site, new visitors, mobile bounce rate, pages per visit, time on site, and new visitors, agri-food firms’ organic traffic variates by −1276.8%, −1434.0%, 629.3%, 687.6%, 100.3%, and −443.2% in agri-food organic traffic, respectively, while paid traffic by −29.3%, −202.0%, 147.9%, −30.0%, 170.3%, and −45.1%, respectively. Finally, the direct sources traffic variates by 108.0%, 182.1%, −72.9%, 1.8%, −97.0%, and 163.9%, respectively.

4.2. Fuzzy Cognitive Mapping Model

For the second level of analysis, the authors developed a fuzzy cognitive map (FCM) of the dynamic relationships between the elements of the system created by the dependent variables (branded traffic, authority score, organic traffic, paid traffic, and direct sources) and the most important independent variables investigated in our study, as indicated by the statistical analysis (Desktop Time on Site, Desktop New Visitors, Mobile Bounce Rate, Mobile Pages per Visit, Mobile Time on Site, Mobile New Visitors) (Figure 1). Web technologies are increasingly being widely adopted by businesses and organizations, which has significantly improved their ability to make strategic decisions [82]. By defining important system characteristics such as system variables, correlations (positive or negative) between variables, and the strength of these correlations, FCM, a parameterized version of idea mapping, helps create static models that capture knowledge [83]. A visual representation of the relationships between the variables is provided by arrows, which show cause-and-effect correlations. The intensity of these causal relationships is indicated by the different line widths. Furthermore, the arrows’ color indicates a positive or negative correlation between the variables. In this fast-paced and cutthroat environment, fuzzy cognitive mapping can provide insightful information about our study assumptions. FCM uses graphs and graph-based studies as well as analytical tools based on the structure of idea maps to look at how variables inside a model relate to one another.

To further study the FCM model, the authors developed six scenarios, simulating different states of resource allocation. Scenario 1 (Figure 2), scenario 2 (Figure 3), and scenario 3 (Figure 4) illustrate the dynamic of the dependent variables (branded traffic, authority score, organic traffic, paid traffic, and direct sources) when agri-food firms decrease their investments by 25%, 50%, and 75% on the independent variables (Desktop Time on Site, Desktop New Visitors, Mobile Bounce Rate, Mobile Pages per Visit, Mobile Time on Site, Mobile New Visitors). Scenario 4 (Figure 5), scenario 5 (Figure 6), and scenario 6 (Figure 7) illustrate the dynamic of the dependent variables (branded traffic, authority score, organic traffic, paid traffic, and direct sources) when agri-food firms increase their investments by 25%, 50%, and 75% on the independent variables (Desktop Time on Site, Desktop New Visitors, Mobile Bounce Rate, Mobile Pages per Visit, Mobile Time on Site, Mobile New Visitors).

In scenario 1, when the agri-food firms decrease their investments in digital marketing activities by 25%, the branded traffic increases by 38%, where all other variables decrease (authority score by 30%, organic traffic by 3%, paid traffic by 19%, and direct sources by 38%). The increased branded traffic by 38% suggests that despite reduced investments, certain aspects of digital marketing, such as website traffic, may still experience growth due to existing brand awareness. However, the notable decreases in authority score (by 30%), organic traffic (by 3%), paid traffic (by 19%), and direct sources (by 38%) indicate a negative impact on overall web performance metrics. This suggests that while reduced investments may initially lead to short-term gains in branded traffic, they can significantly undermine other crucial aspects of digital presence, such as credibility, organic reach, and user engagement.

In scenario 2, when the agri-food firms decrease their investments in digital marketing activities by 50%, the branded traffic increases by 47%, where all other variables decrease (authority score by 39%, organic traffic by 4%, paid traffic by 22%, and direct sources by 49%). Scenario 2 suggests that despite the significant reduction in investments, there is still a considerable impact on driving traffic to the website, possibly due to existing brand recognition. However, the substantial decreases in authority score (by 39%), organic traffic (by 4%), paid traffic (by 22%), and direct sources (by 49%) indicate a detrimental effect on various aspects of digital marketing performance. This underscores the importance of maintaining adequate investments in digital marketing efforts to sustain overall web performance metrics and ensure long-term success in the competitive agri-food industry.

In scenario 3, when the agri-food firms decrease their investments in digital marketing activities by 75%, the branded traffic increases by 55%, where all other variables decrease (authority score by 46%, organic traffic by 5%, paid traffic by 25%, and direct sources by 57%). Scenario 3 suggests that even with a significant reduction in investments, there is still a notable impact on driving traffic to the website, potentially due to existing brand recognition. However, the substantial decreases in authority score (by 46%), organic traffic (by 5%), paid traffic (by 25%), and direct sources (by 57%) indicate a significant negative effect on various aspects of digital marketing performance. This emphasizes how crucial it is to keep up sufficient spending on digital marketing initiatives to sustain overall site performance metrics and guarantee long-term success in the cutthroat agri-food sector landscape.

In Scenario 4, when the agri-food firms increase their investments in digital marketing activities by 25%, the branded traffic increases by 18%, where all other variables decrease (authority score by 15%, organic traffic by 2%, paid traffic by 12%, and direct sources by 16%). Despite the boost in branded traffic, the decline in other key metrics suggests that the overall impact of the increased investment may not be as substantial as anticipated. This underscores the complexity of optimizing digital marketing strategies, as adjustments in investment levels can have varying effects on different aspects of web performance.

In scenario 5, when the agri-food firms increase their investments in digital marketing activities by 50%, the branded traffic increases by 1%, where all other variables decrease (authority score by 8%, organic traffic by 1%, paid traffic by 8%, and direct sources by 7%). Despite the significant increase in investment, the minimal improvement in branded traffic suggests a diminishing return on investment, highlighting the need for a more nuanced approach to resource allocation in digital marketing endeavors.

In scenario 6, when the agri-food firms increase their investments in digital marketing activities by 75%, the branded traffic and direct sources increase by 3% and 1%, respectively, where all other variables decrease (authority score by 2%, organic traffic by 1%, and paid traffic by 5%). Despite the notable increase in investment, the marginal improvements in branded traffic and direct sources underscore the complexities of optimizing resource allocation in digital marketing strategies, emphasizing the need for a balanced and strategic approach to investment decisions.

Across the six scenarios exploring variations in investment levels in digital marketing activities, distinct patterns emerge in the outcomes for key variables within agri-food firms’ online presence. Decreasing investments by 25%, 50%, and 75% consistently results in augmented branded traffic, albeit with diminishing returns as the investment decreases further. However, this increase in branded traffic is accompanied by significant declines in authority score, organic traffic, paid traffic, and direct sources. Conversely, increasing investments by 25%, 50%, and 75% yields mixed results, with modest increases in branded traffic observed alongside declines in other variables, such as authority score, organic traffic, paid traffic, and direct sources. These outcomes underscore the intricate relationship between investment levels and performance metrics in digital marketing strategies, emphasizing the importance of strategic resource allocation to achieve optimal outcomes in agri-food firms’ online presence.

5. Discussion

Embarking on an exploratory journey into the realm of digital analytics within the agri-food industry, this study delves into the intricate relationship between desktop and mobile customer analytics and diverse brand promotion outcomes. Beginning with H1, the findings from the regression analysis (Table 4) unveil a significant relationship between desktop and mobile customer analytics metrics and agri-food firms’ branded traffic, thus reinforcing our first hypothesis (H1). Noteworthy variables such as desktop time on site, new visitors, bounce rate, and mobile engagement metrics emerge as influential factors driving branded traffic within the agri-food sector. The results underscore the pivotal roles of user engagement, site accessibility, and content relevance in attracting and retaining visitors, ultimately impacting branded traffic metrics and supporting the outcomes of previous studies [28,60]. For instance, a longer time spent on the desktop site or higher engagement metrics on mobile devices correlate positively with increased branded traffic, emphasizing the significance of providing a seamless and engaging user experience across all digital touchpoints. Increased branded traffic can lead to increased conversion rates and revenue generation, contributing to the economic growth of agri-food companies [84].

Transitioning to H2, the results of the regression analysis (Table 4) unveil a significant association between desktop and mobile customer analytics metrics and agri-food firms’ authority score, thereby validating our second hypothesis (H2). Variables such as desktop time on site, new visitors, and mobile bounce rate emerge as influential factors contributing to the authority score within the agri-food domain. The implications of these findings underscore the critical role of user engagement metrics in shaping the authority and credibility of agri-food firms’ digital presence. Prolonged time spent on the desktop site, a higher influx of new visitors, and lower bounce rates on mobile platforms correlate positively with increased authority scores, emphasizing the importance of delivering compelling and relevant content across desktop and mobile channels to establish trust and credibility among users [64,65,66]. A higher authority score not only enhances brand credibility but also influences consumer trust and purchasing decisions, ultimately driving sales and revenue growth. Investments in website optimization and content quality to improve authority scores can yield long-term economic benefits for agri-food companies by establishing them as trusted leaders in the industry.

Progressing to H3, the results from the regression analyses (Table 4) offer insights into the relationship between desktop and mobile customer analytics metrics and organic and paid traffic within agri-food firms, supporting our third hypothesis (H3). The analysis unveils a significant association between desktop and mobile customer analytics metrics and organic traffic. Specifically, variables such as desktop time on site, new visitors, and mobile bounce rate exhibit notable coefficients, indicating their impact on organic traffic generation. These findings underscore the importance of user engagement metrics in driving organic traffic to agri-food websites, as suggested by previous studies [12]. Increased time spent on desktop sites and higher volumes of new visitors correlates positively with organic traffic, highlighting the significance of content quality and user experience in attracting organic visitors [29]. Increased organic and paid traffic directly impact sales and revenue generation by expanding the customer base and driving more conversions.

Similarly, the regression results demonstrate a significant relationship between desktop and mobile customer analytics metrics and paid traffic. Notably, variables such as desktop time on site, new visitors, and mobile bounce rate display substantial coefficients, indicating their influence on paid traffic generation. These findings suggest that user engagement metrics play a crucial role in driving paid traffic to agri-food websites. Higher engagement levels and lower bounce rates on mobile platforms may lead to increased click-through rates on paid advertisements, resulting in higher paid traffic volumes [12], leading to significant economic gains.

Lastly, for H4, the results from the regression analysis presented in Table 4 provide insights into the relationship between desktop and mobile customer analytics metrics and direct sources of traffic within agri-food firms, addressing our fourth hypothesis (H4). The analysis reveals a significant association between desktop and mobile customer analytics metrics and direct sources of traffic. Notably, variables such as desktop time on site, new visitors, mobile bounce rate, pages per visit, time on site, and new visitors exhibit substantial coefficients, indicating their impact on direct sources of traffic generation. These findings highlight the importance of user engagement metrics and site visitation patterns in driving direct sources of traffic to agri-food websites, as suggested by Ponzoa and Erdmann [73] and Widiastuti [74]. Increased time spent on desktop and mobile sites, higher volumes of new visitors, and lower bounce rates correlate positively with direct source traffic, suggesting that enhanced user engagement and site accessibility contributed to greater direct traffic from known sources. Investments in user-friendly website design and targeted advertising to optimize direct traffic acquisition can lead to increased customer retention and lifetime value, thereby contributing to the sustained economic success of agri-food companies [85].

In the discussion of the findings, it is essential to delve into the application of FCM and the development of the six scenarios to understand the economic implications of varying investment levels in digital marketing activities for agri-food firms. The six scenarios developed in this study provide a comprehensive analysis of how different levels of investment in digital marketing impact key performance metrics within agri-food firms. By employing FCM, the authors were able to model and simulate the complex interrelationships between variables, offering insights into the dynamic behavior of these metrics under different conditions.

Across the six scenarios exploring variations in investment levels, distinct patterns emerge in the outcomes for key variables within agri-food firms’ online presence. Decreasing investments by 25%, 50%, and 75% consistently results in augmented branded traffic, albeit with diminishing returns as the investment decreases further. This phenomenon can be attributed to the initial surge in visibility and engagement that lower investments trigger. However, this increase in branded traffic is accompanied by significant declines in authority score, organic traffic, paid traffic, and direct sources. These declines highlight the negative repercussions of under-investment in maintaining a robust online presence and the diminished capacity to attract high-quality traffic through organic and paid channels.

Conversely, increasing investments by 25%, 50%, and 75% yields mixed results, with modest increases in branded traffic observed alongside declines in other variables, such as authority score, organic traffic, paid traffic, and direct sources. These outcomes suggest that while higher investments can bolster branded traffic to some extent, they do not automatically translate into proportional gains across all metrics. The diminishing returns and occasional declines in other areas underscore the complexity of digital marketing dynamics. It indicates that simply increasing investment without strategic targeting and optimization may lead to inefficiencies and suboptimal outcomes.

To achieve optimal outcomes, agri-food firms should consider a balanced approach that integrates data-driven insights from customer analytics with targeted investment strategies. This may involve focusing on specific areas such as improving user engagement, content relevance, and site accessibility across both desktop and mobile platforms. Additionally, continuous monitoring and adaptive strategies based on real-time analytics can help firms fine-tune their marketing efforts and maximize returns on investment.

6. Conclusions

In the dynamic realm of digital marketing, grasping the intricate nexus between user engagement metrics, performance indicators, and economic outcomes is indispensable for agri-food firms striving to excel in a competitive market environment. Through a comprehensive analysis integrating regression modeling, FCM, and scenario simulations, this study elucidates the complex dynamics governing the convergence of digital marketing strategies and economic ramifications within the agri-food domain. Building upon established theoretical frameworks in digital marketing and consumer behavior, this research unveils the economic significance of leveraging customer analytics to enhance brand promotion strategies and drive sustainable growth [32].

6.1. Theoretical Implications

The findings of this study contribute significantly to digital marketing theory by empirically validating the importance of user engagement metrics in shaping various economic outcomes for agri-food firms. The regression analysis unveils significant associations between desktop and mobile customer analytics metrics and key economic indicators, providing empirical evidence to support existing theoretical frameworks [28,60]. By highlighting the pivotal role of user engagement, content relevance, and site accessibility in driving economic performance metrics, this study advances our understanding of the underlying mechanisms that govern digital marketing effectiveness.

Firstly, the concept of branding traffic is intricately linked to conversion rates, a fundamental metric in assessing the economic viability of marketing efforts [86]. By elucidating the relationship between desktop and mobile customer analytics and branded traffic, this study underscores the economic imperative of optimizing user engagement to drive higher conversion rates and, ultimately, revenue generation [34]. This study’s findings offer valuable insights into consumer behavior trends within the agri-food industry, emphasizing the need for tailored digital marketing strategies to maximize branded traffic across both platforms. Strategic recommendations aimed at optimizing digital marketing strategies include initiatives such as website performance enhancements, mobile responsiveness improvements, and content personalization efforts tailored to specific user segments, thus refining strategies to effectively drive branded traffic and enhance online visibility.

Secondly, the notion of webpage authority score extends beyond mere visibility to encompass trust and credibility, crucial factors influencing purchasing behavior in the digital marketplace [87]. Through the analysis of desktop and mobile customer analytics, this research illuminates how enhancing the page authority score can foster consumer trust and confidence, thereby translating into increased sales and market share for agri-food firms. Moreover, the findings highlight the interconnectedness of desktop and mobile customer analytics metrics in influencing the authority score of agri-food firms, enabling tailored digital marketing strategies to bolster authority and influence within the industry. Investments in website optimization, content development, and user experience enhancements are recommended to foster greater engagement and trust among target audiences, thus refining digital marketing strategies over time.

Furthermore, the empirical findings highlight the economic implications of organic and paid website traffic in driving brand visibility and customer acquisition [71]. By elucidating the relationship between customer analytics and website traffic sources, this study provides actionable insights for optimizing marketing investments and maximizing return on investment for agri-food companies. Recommendations include focusing on factors such as website engagement, content relevance, and user experience across desktop and mobile platforms to effectively attract and retain organic and paid visitors, thus maximizing traffic generation efforts and enhancing brand visibility. Additionally, ongoing monitoring and analysis of customer analytics metrics could provide valuable feedback for optimizing digital marketing initiatives and maximizing return on investment.

In addition, this study underscores the economic importance of direct traffic sources in facilitating brand-consumer relationships and driving repeat purchases [12]. By analyzing the impact of desktop and mobile customer analytics on direct traffic, this research elucidates how enhancing user engagement can lead to greater brand loyalty and long-term customer value, contributing to sustainable economic growth within the agri-food industry. Investments in user-friendly website design, targeted advertising, and referral partnerships are recommended to enhance direct traffic generation and strengthen brand relationships.

From an economic vantage point, these findings accentuate the substantial potential of desktop and mobile customer analytics to yield tangible benefits for agri-food firms, enhancing the efficacy and efficiency of brand promotion endeavors, as suggested by various scholars in the field [34]. By leveraging insights derived from desktop and mobile analytics to optimize their digital marketing strategies, agri-food companies can attain heightened levels of brand visibility, engagement, and ultimately, conversion rates. This, in turn, can translate into augmented sales figures, expanded market share, and heightened profitability, thereby fostering economic growth and bolstering competitiveness within the agri-food sector, as proposed by prior studies [70,86,87].

Moreover, the incorporation of FCM as a theoretical tool enriches our understanding of the dynamic nature of digital marketing strategies and their economic implications. FCM facilitates the visualization of causal relationships and the simulation of different scenarios, offering insights into how changes in digital marketing investments affect economic performance metrics within agri-food firms [83,84]. By adopting FCM as a theoretical framework, researchers can gain deeper insights into the complexities of digital marketing dynamics and develop more nuanced models to guide strategic decision-making in the agri-food industry.

Furthermore, this study underscores the importance of adopting a holistic approach to digital marketing optimization, considering both short-term gains and long-term economic sustainability. By emphasizing the interplay between user engagement metrics and economic outcomes, the findings contribute to the development of theoretical frameworks that prioritize economic objectives alongside traditional marketing metrics [60,65]. This holistic approach enables agri-food firms to optimize their digital marketing strategies in alignment with broader economic goals, thereby maximizing the overall impact of their marketing efforts.

In conclusion, the application of desktop and mobile customer analytics in the agri-food industry is not only economically beneficial but also aligns with the principles of sustainable development. By enhancing resource efficiency, promoting sustainable consumption, and supporting economic sustainability, the insights from this study contribute to the broader goals of sustainability. These findings underscore the importance of integrating digital analytics with sustainability initiatives, ensuring that the agri-food sector can thrive in an environmentally and socially responsible manner.

6.2. Practical Implications

Agri-food firms can leverage the insights from this study to inform their strategic decision-making processes and enhance their competitive advantage in the digital marketplace. By strategically investing in areas that drive both short-term traffic growth and long-term economic value, firms can optimize their online presence and achieve sustainable growth in a rapidly evolving industry [29,85].

One practical implication of this study is the importance of continuous performance monitoring and adaptive strategies based on real-time analytics. By leveraging analytics tools to track key economic indicators and adjust their strategies dynamically, agri-food firms can capitalize on emerging opportunities and mitigate risks in the digital landscape [12,66]. This proactive approach to digital marketing optimization enables firms to stay ahead of the competition and adapt to changing market dynamics effectively.

Furthermore, the scenario analysis provides valuable insights into the nuanced relationship between digital marketing investments and economic outcomes. By carefully evaluating the trade-offs between increasing or decreasing investments in digital marketing activities, agri-food firms can develop resilient strategies that align with their economic objectives and drive sustainable growth [70,73]. This strategic approach to resource allocation empowers firms to make informed decisions that maximize the economic impact of their digital marketing efforts while minimizing potential risks.

In conclusion, our study offers theoretical insights into the complex interplay between digital marketing strategies and economic implications within the agri-food industry. By integrating economic considerations into digital marketing frameworks and adopting a strategic approach to resource allocation, agri-food firms can unlock new opportunities for innovation, growth, and economic prosperity in the digital age.

Although this research focuses on the agri-food sector, it is valuable to consider its similarities with findings from other areas within the primary sector, such as forestry, fisheries, and mining. These industries, like agri-food, face challenges related to market dynamics, consumer preferences, and sustainability, such as the following:

• Forestry: Similar to agri-food, the forestry sector benefits from customer analytics to optimize supply chain operations and enhance sustainability practices. Research by Zhang et al. [87], demonstrates that big data analytics enhance forestry management by leveraging advanced computing capabilities and algorithms to improve the accuracy of simulations and optimizations in biomass supply chains.
• Fisheries: The use of digital branding and customer analytics in the fisheries sector has shown to enhance consumer engagement and traceability, akin to the agri-food sector. Studies by Gladju et al. [88] and Lim [89], highlight how data analytics improve consumer trust and product transparency, crucial for both industries. Probst [90] states that data analytics enhance fisheries management by leveraging advanced technologies such as blockchain, data mining, and artificial intelligence to improve transparency, efficiency, and sustainability throughout the seafood supply chain. These technologies enable the real-time organization, storage, and analysis of large volumes of data, enhancing decision-making across various stakeholders including producers, wholesalers, retailers, consumers, management authorities, and scientists.
• Mining: While more industrial, the mining sector’s adoption of data analytics for predictive maintenance and operational efficiency shares similarities with how agri-food companies use analytics to forecast demand and optimize production. Research by Bag et al. [91] and Barnewold & Lottermoser [92], indicates that these practices lead to significant cost savings and improved operational sustainability.

By examining these parallels, one can infer that the strategic use of customer analytics and digital branding offers broad applicability across various primary sector industries, supporting enhanced economic and sustainable outcomes.

6.3. Research Limitations

While this study provides valuable insights into the intersection of digital marketing strategies and economic implications within the agri-food industry, it is essential to acknowledge several limitations that may have impacted the comprehensiveness of the findings.

• Predominantly Quantitative Approach: This study primarily relied on quantitative analysis (regression modeling, FCM), potentially overlooking qualitative nuances and contextual factors influencing digital marketing effectiveness and economic outcomes.
• Potential Confounding Variables: Despite efforts to control for confounding variables, the complex nature of digital marketing and economic outcomes may have introduced unaccounted-for variables or biases.
• Focus on Specific Variables: This study focused primarily on the digital marketing strategies and economic outcomes of agri-food firms, potentially neglecting mediating or moderating variables (e.g., market competition, technological innovation, regulatory environments) that could influence these relationships.
• Scope of Industry: This study’s findings are specific to the agri-food industry, and extrapolation to other industries or sectors may require caution due to potential differences in market dynamics, consumer behavior, and regulatory environments.

Despite these limitations, this study significantly contributes to the existing literature by offering empirical evidence of the economic significance of leveraging customer analytics to enhance brand promotion strategies and drive sustainable growth within the agri-food industry. However, it is crucial to acknowledge these limitations to guide future research endeavors in addressing remaining gaps and advancing knowledge in this field.

6.4. Recommendations for Future Studies

Building on the findings of this study, future research should consider the following areas to further advance the understanding and application of customer analytics in digital branding within the agri-food and broader primary sectors:

• Adopt a Mixed-Methods Approach: Future research should consider integrating qualitative data collection methods such as interviews or focus groups alongside quantitative analysis. This approach can capture nuanced insights and contextual factors that may influence the effectiveness of digital marketing strategies and their economic outcomes in the agri-food industry.
• Utilize Advanced Statistical Techniques: To address the complex nature of digital marketing and economic outcomes, future studies could employ advanced statistical techniques beyond regression modeling and FCM. Techniques such as structural equation modeling, machine learning algorithms, or causal inference methods could provide deeper insights into the relationships and dynamics at play.
• Expand Scope to Include Mediating Variables: Future studies could investigate the role of mediating or moderating variables (e.g., market competition, technological innovation, regulatory environments) that could influence the relationship between digital marketing efforts and economic performance in the agri-food sector. This broader scope would provide a more comprehensive understanding of the mechanisms driving outcomes.
• Compare Across Different Industries: While focusing on the agri-food industry is valuable, future studies could compare findings across different industries or sectors. This comparative approach would elucidate whether findings are industry-specific or generalizable across various contexts.