1. Introduction

Traditionally focused primarily on product or service innovation (and secondarily on process innovation), the concept of innovation has significantly broadened its boundaries in the digital economy [1]. The rapid rise of the digital economy, with the rapid development of information technology, especially technologies such as the Internet, big data, artificial intelligence, and cloud computing, the world is accelerating its transition into a digital economy era that is driven by data and centered on platform operations. As consumers' paths for obtaining information in the digital environment have become more fragmented, interactive and personalized, the traditional linear transformation sequence of the AIDA model has been disrupted. Users may first "be persuaded" on social media, then jump to the e-commerce platform to check reviews, and finally place an order in the live streaming room. This non-linear and dynamic consumption path has become the norm. On the Reconfiguration of Brand Marketing Logic, Digital technology has endowed brands with the ability to connect with consumers in both directions, and it has also made users more sensitive to marketing content and more selective in their choices. Therefore, the traditional "wide coverage, high frequency" advertising logic gives way to "precise recommendation, real-time interaction" marketing strategies. And how to optimize the user journey in the digital environment and precisely layout marketing touchpoints has become the key to enterprise competition. So, the objective is to systematically deconstruct the reshaping mechanism of the digital economy on each core link of the marketing path (reach, awareness, interaction, conversion, retention, and co-creation), distill a universal framework of change, and evaluate the challenges and response strategies. In the traditional Market Insights and Consumer Research. Understanding consumers' behaviors and preferences is mostly achieved through market research questionnaires, focus groups, industry reports, etc. However, such methods have low timeliness, limited sample size and high costs. However, for the digital economy through big data analysis, scholars can collect and analyze massive user behavior data (such as search, browsing, purchasing, social interaction, location, etc.) in real time, revealing deep-seated, dynamic consumer demands, preferences, pain points and trends. And can utilize AI to predict future demands, popular trends and consumer behavior patterns. In terms of product development and innovation, the traditional approach involves enterprises conducting research and development, which has a long cycle and leads to delayed market feedback. However, in the field of digital economy, feedback from users, creative ideas, or allowing users to participate in the design of products can be collected through certain platforms. At the same time, based on real-time market data and user feedback, the functions and design of the products can be adjusted. In terms of price setting, under the traditional model, the price is determined relatively statically. However, in the digital economy, it can be dynamically adjusted based on supply and demand relationships, user profiles (payment willingness), and inventory quantities. This study adopts a three-stage framework of "theoretical construction - empirical verification - strategy derivation". Although digital marketing systems offer significant advantages, some Small and Medium-Sized Enterprises (SMEs) still grapple with insufficient technological adoption, talent shortages, and limited capital investment during their digital transformation. Furthermore, certain enterprises remain overly reliant on traditional marketing models, failing to adapt promptly to digital trends. Consequently, they gradually lose ground in market competition [2].

2. Theoretical foundation and literature review

Digital market expansion has created an overwhelming array of choices for consumers, thereby heightening rather than reducing intermediaries' critical role in navigating this complexity [3]. Digital economy refers to the new way of doing business by using new technologies such as the Internet, big data and artificial intelligence. It is composed of "digital technology", "data-driven" and "intelligent applications". Data-driven exchange and value creation-enabled by the digital economy-represent the authentic innovation behind the sharing economy [4]. Digital technology refers to tools such as computers, mobile phones, software, etc. Specifically, it includes things like mini-program shopping malls and live-streaming for product promotion. This enables businesses to sell goods 24 hours a day, and customers can place orders at any time. Data-driven means using customer information to make decisions. For instance, some shopping apps offer "You Might Also Like" recommendations, and food delivery platforms suggest restaurants based on the user's location. This approach enables precise marketing and reduces the waste of advertising. The last one is intelligent applications, which involve robots performing tasks automatically, such as automated responses by customer service robots and system predictions of popular products. This can reduce labor costs and increase decision-making speed. Compared with traditional methods, in the digital economy, the ways and methods of selling goods have become faster and more diverse. Understanding customers can also be done by relying on feedback from big data rather than relying on experience. The digital economy also causes the evolutionary history of the sales path. In the past, it was always the enterprises that led the customers around, through advertising, then to the store for sales promotion, and finally to make the purchase. Nowadays, it has completely reversed. Instead, it is the customers who are leading the enterprises. Customers make purchases on various consumption platforms, and it is the enterprises that handle those orders.

3. The reshaping mechanism of marketing paths by the digital economy

The transformation of marketing paradigms has shifted from supply-driven to demand-driven. Under the traditional model, it is a linear supply chain (Push model), from research and development-production-channels-promotion-consumers. The typical feature is that it relies on prediction, estimating demand based on historical data (with an error rate > 30%), resource waste, 70% of marketing budgets being wasted on non-target audiences. Delayed response, market feedback cycle ≥ 90 days. Under the new data-driven demand chain (pull model), as shown in figure 1.

It forms a complete demand chain, starting from consumer insights to product iterations, to precise touchpoint reach, then dynamic value delivery, and finally returning to consumer insights.

The three major changes in the consumer purchasing process. Marketing communications now target individuals not just based on purchasing behavior, but also their online activities, including search engine usage and social media engagement—moving beyond broad audience strategies [5]. From "highway" to "ant nest", before 2010, the common consumption path for people was from TV advertisements to supermarket shelves and finally to payment and departure. Up to now, consumers will first watch reviews on YouTube, then compare prices on different platforms, ask for opinions from people around them, and then place an order. After receiving the goods, they will go to the website for inspection. If they are not satisfied afterwards, they may comment on X or IG. Figure 2 shows the battle maps of three enterprises, namely the three key battlefields, and their operation procedures.

Customer decisions have shifted from being "rational" to "emotional". Currently, 78% of orders are placed due to emotional impulses. Businesses can take advantage of this phenomenon to evoke different emotions in consumers. For instance, if consumers come across videos of people with acne-ridden and pockmarked faces, they might feel anxious. In such cases, businesses can push advertisements for facial masks and skin care products to stimulate their consumption. Full-item discounts can be categorized as either merchant-issued promotional coupons or system-automated discounts, both configurable with tiered incentive structures. As a staple of e-commerce promotions, these high-value coupons offer compelling visual appeal that drives conversion rates and increases average order value [6]. The next item is the reformation of the enterprise marketing capability system, and the data decision-making ability, from "an experienced gambler" to "a battlefield commander". The traditional problem is that the decisions made by the marketing department are based on the boss's intuition, combined with last year's data, and the imitation of competing brands. Among them, the boss's own judgment accounts for the majority. As shown in Figure 3, it shows the upgrade plan in this situation.

Content production capacity from "self-talk" to "emotional engine" Neuronal formula for content. In terms of operation template, first, hijack the amygdala for the first three seconds to create some surprises or amplify some pain points. Then, activate the prefrontal cortex at the eighth second, and weaken the commercial elements. Organizational change capability, breaking the "departmental prison", in traditional situations, it was the marketing department placing advertisements to bring in traffic - the e-commerce department complaining that "all are low-price junk customers"- the supply chain was unable to replenish goods quickly, this was a traditional deadlock, but now the special forces model can be used [7]. Figure 4 shows how the Special Forces mode work.

The last one is the innovation of value creation and delivery models, shifting from selling products to selling relationships: Privately-owned assets. In the traditional scenario, the cost of acquiring new customers is $100, the cost of losing customers is $1, and the lifetime value of customers is $120. Figure 5 shows how does the innovative model operates.

From selling ownership to selling usage rights. The subscription revolution, the industry truth is that consumers' interest in "permanent ownership" has declined, and 62% of young people prefer "constant renewal and replacement". The three-tier subscription system can be employed, like, Basic layer: Intelligent replenishment, Advancement: Innovation Pass, Top-level layer: Performance as a service. Then is from Enterprise Creation to User Co-creation, Democratization of Value, the figure 6 shows the operation framework [8]. Geely leverages these insights to refine its R&D process and enhance vehicle design, identifying customer pain points and underserved needs observed during real-world vehicle operation [5].

4. Case analysis

Verifies through the data triangle, respectively for enterprises, users and industries. The enterprise side can verify through executive interviews and digital transformation documents. The user side can use social public opinion and purchase trajectories. Finally, the industry side can validate through policy reports and competitor dynamics.

Platform empowerment (Pusbo) - as a clothing company, there are several obvious problems, such as a very low inventory turnover rate of only 2.1 times per year, far below the industry average, and a high rate of loss of young customers at 68%. After reconfiguring through digital marketing channels, they achieved improvements at the perception level by scraping the word cloud of the search platform for "down jackets", discovering a significant increase in the demand for lightweight quilting. At the response level, they launched a lightweight quilting series within 28 days and then targeted distributed traffic on video platforms to "women afraid of heaviness", and through virtual store am trial wear and an additional 30-minute express delivery, the proportion of young customers who achieved sales exceeding 100 million in the first month increased from 12% to 37%. The inventory turnover rate rose to 4.8 times per year. Through empirical research, it was found that for enterprises with a digital marketing budget less than 15% of their revenue, the risk of bankruptcy within three years is as high as 300%. At the same time, when the proportion of the digital marketing budget is greater than or equal to 50%, the Return on Assets (ROA) begins to increase non-linearly [9]. From data insights to action implementation within less than three hours, the market share increases rapidly by 2.4 times. The last one is the Key Opinion Consumer (KOC) leverage effect, that is, the content of a real user is equivalent to the advertising effect of 2000 yuan. Social networks provide instant access to targeted audiences for product promotion, eliminating historical challenges in audience acquisition. Their true value lies in pre-segmented user bases that inherently solve targeting complexities [4]. Of course, there are also some risk warnings. For those enterprise customers whose algorithm dependence is greater than 70%, the risk of customer satisfaction will increase to 230%. When the interaction frequency of private domain users is less than once per week, the churn rate will reach as high as 15% per month.

5. Discussion: challenges, strategies and trends

Core challenges and systemic risks, the first data governance, in different parts of the world, its regulation is very fragmented. For the EU, its core regulation is GDP2, and the penalty is 4% of its global revenue. But in California, USA, its regulation becomes CPA. The penalty is $7,500 for each violation. In China, its core regulation is the Personal Information Protection Law, and the penalty is 5% of its annual revenue. However, in Brazil, its regulation is LGDP, and the penalty benchmark is 2% of its revenue (with the maximum being $50 million). Xiaozhu supplements conventional two-way ratings with advanced trust mechanisms: biometric door locks block unauthorized entry, while integration with Ant Financials’ private Zhima Credit allows hosts to screen guests’ creditworthiness. This approach tackles the inherent uncertainty in peer-to-peer home-sharing markets [5]. The second aspect is algorithm accountability: the black box dilemma. A study by Massachusetts Institute of Technology (MIT) found that 67% of consumers do not trust Artificial Intelligence (AI) that lacks transparency. In such cases, some mitigation tools can be employed, such as explainable AI, third-party audits, and human supervision mechanisms. The response strategies and solutions can be initially achieved by establishing a three-layer protection system. The first layer is the technical layer for privacy engineering. "As soon as GDPR took effect, more than 1,000 U.S. websites restricted access to users from Europe"[3].

The second layer is data trust, which can refer to the model in the UK. The beneficiaries are enterprise users, the trustees are third-party certification institutions, the supervisors are law firms, and the third layer is the cost optimization plan. For small and medium-sized enterprises, SAS-compliant tools can be adopted [10]. Next, algorithm governance transparency and the balancing mechanism can utilize AI interpretable AI. The basic approach is LIME local explanation, which can be applied to the reasons for credit approval denials. For an advanced approach, SHAP value attribution can be used in the analysis of product recommendation weights. The highest level is similar to counterfactual explanation, which can be used in the detection of bias in talent selection. The standardized process of algorithmic auditing can also be used.

Then comes organizational transformation, the digital transformation special forces. The first one is the innovation of the organizational structure, as shown in figure 7.

Then comes the reconfiguration of the incentive mechanism, shifting from the traditional departmental Key Performance Indicator (KPI) assessment to campaign-based bonus systems (such as bonus pools), from seniority-based promotions to digital medal systems (blockchain verification). The fourth aspect is the response to emerging risks, with generative AI control principles such as copyright filtering capable of being integrated into adobe authentication, deep-fake defense like NVIDIA watermarking technology, and ethical review committees releasing the "Generative Content Ethical White Paper" every quarter. The last part is about future trends. The first one is the AI agent revolution, from a tool to a decision-making subject. The second one is the new battlefield of virtual and real coexistence in spatial computing marketing. The third one is the value reconstruction of web3.0, from transactions to co-governance.

6. Conclusion

This paper finds that the marketing path of digital economy reconstruction mainly revolves around three aspects. At the consumer end, the decision-making path becomes non-linear (the number of touchpoints increases from 2.3 to 7.5), with emotional-driven purchases accounting for 78%. At the end of the enterprise, data-driven decision-making has a response time of less than three hours, with AI handling 40% of routine decisions. In the industrial sector, platforms empower small businesses to go global, and the subscription economy contributes 35% of the revenue. Theoretical contributions have achieved two major breakthroughs in scope. In the core logical dimension, the traditional funnel model represents the enterprise's promotion, but in the perception response model, it represents a user demand radar. Regarding organizational form, in the traditional situation, departments are highly fragmented. However, in the new model, digital special teams have emerged. Regarding efficiency indicators, in the traditional situation, conversion rate is given more importance, but in the new model, the timeliness of demand satisfaction is emphasized. The second breakthrough is the "frog leap principle". The latecomer industries have achieved overtaking by leveraging platforms and crossing through data. Research limitations and future directions: The current limitations are that the case coverage is insufficient and does not include agriculture and mining. New agricultural digitalization cases can be added. Regarding the technical aspect, there is insufficient coverage of Web3.0 timing for tracking Ant Chain NFT sales practices. Smart contracts autonomously allocate transaction profits, enhance operational efficiency, lower processing fees, and establish a transparent, high-performance blockchain framework specifically designed for agricultural commerce. The future direction generation is the AI revolution research, AIGC coverage of creative productivity, exploration of brain-computer interface sales, exploration of Neuralink's transformation of consumer decision-making chains, and construction of a privacy protection framework resistant to quantum computing for the quantum-safe economy.