Application of AI Agent Technology in Fashion Industry Marketing

KOS (Key Opinion Sales) generally refers to frontline service personnel such as brand guides, clerks, and sales staff, who also possess professional sales capabilities and a wealth of knowledge in niche industries and brands. A good KOS can provide consumers with a more professional and high-quality shopping experience, enhancing the brand’s credibility and influence. Additionally, they can become a new source of marketing growth for brands due to their strong ability to promote niche products, creating greater GMV conversions.

Therefore, KOS marketing has two core advantages: “the professionalism and credibility of persona content” and “the free flow of asset properties”.

It is well known that the decreasing amount of money in the market has become an undeniable fact. Various industries face growth pressures due to a cold macroeconomic environment, with overcapacity, insufficient consumption, and declining profits becoming the norm. Even the luxury goods industry, which has strong anti-cyclical capabilities, has shown signs of decline. From 2019 to 2023, Chinese consumers experienced two periods of negative growth in global luxury spending and growth rates, and the consumption capacity in the mainland market has significantly shrunk—consumption proportions have dropped by 20%. The Chinese market faces considerable pressure, primarily due to the gradual recovery of outbound tourism diverting some purchasing power, and economic uncertainty leading to weak domestic demand.

In recent years, after the so-called traffic frenzy stage, more and more brands are no longer obsessed with mass marketing strategies, but are beginning to think about how to establish deeper relationships with users to realize long-term brand value. Content, as an effective carrier connecting users, has gained more attention from brands, making content marketing a top priority for brand growth.

However, according to my observations and research, most brands in the content space reach users primarily through the “official account + paid KOL (Key Opinion Leader)/KOC (Key Opinion Consumer)” model. The former has limited traffic and influence, while the latter’s cost-effectiveness and effectiveness are increasingly being questioned. Many brands invest a significant amount of money in KOL/KOC, but the actual conversion effect is difficult to assess. Not only is data such as followers and exposure easily inflated, but the ROI (Return on Investment) is extremely low, making it hard to cultivate user loyalty. KOL/KOC can promote any category or even competing products, and their followers find it hard to become brand fans, meaning that the money spent is merely a cost item on the financial statement.

Additionally, there is a current trend in consumer shopping decisions towards “anti-exquisite” and “more savvy” behavior, meaning consumers are fatigued by meticulously packaged content and are more willing to accept “imperfect, authentic” brand content. The previously mentioned “beautiful blogger behind a messy uncle” reflects the increasing caution people have towards “real people not existing on the internet.” After seeing countless identical polished photos, they are more easily moved by authentic photos that show skin texture, even if they have flaws, simply because they feel more real. At the same time, consumers have also become “more savvy,” immune to flashy marketing rhetoric, and place great importance on the professionalism of content, seeking practical information.

In fact, paid KOL/KOC marketing, as driven by opinion leaders, still has significant external leverage and is beneficial for generating brand and product word-of-mouth from the perspective of influencers and users. However, a complete content marketing strategy should be the “official account + 3K (KOC + KOL + KOS)” model. Companies should leverage their existing offline store guides (KOS) to drive content matrix coverage, search placement, word-of-mouth promotion, and store customer acquisition. KOS represents free traffic and brand assets, yet few brands do this well.

Among those responsible for companies engaged in KOS marketing, many have mentioned that KOS marketing is a brand asset that I can continuously control, needing to be balanced with the “traffic rental” model of KOL/KOC marketing. Moreover, in today’s store traffic environment, relying on Walk In (customers coming on their own) will not bring in business; it is necessary for guides to establish online marketing acquisition capabilities and integrate with offline O2O.

To clearly showcase the current mainstream practices of KOS marketing, I have created the following diagram:

Centralized Model: The headquarters produces finished content for the guide side, with the content team at headquarters creating a large amount of comprehensive and non-template-based content, which significantly increases operational pressure.

Decentralized Model: Headquarters sets demands for the guides, who create original content based on those demands, which adds significant pressure on guide personnel. The quality of guides in content creation varies greatly, making content generation difficult and reducing their willingness to participate.

Furthermore, data collection, organization, and analysis are also challenging. Some clients have already implemented data management through content matrix management tools, but the abundant supply of content and the continuous improvement of content quality still pose significant operational and training challenges.

AI Agent, or artificial intelligence agent, can independently undertake tasks, covering planning, implementation, memory, and tool usage. The human role shifts to assisting AI in setting clear business objectives, ensuring sufficient data and computing resources, and supervising and optimizing AI performance. In integrated marketing, AI Agent has the following key applications:

1. Personalized Recommendations

AI Agent can analyze consumers’ shopping history, browsing behavior, and preferences to help brands formulate intelligent marketing strategies. Utilizing AIGC marketing assistants, it creates a personal shopping assistant with cognitive abilities, providing consumers with personalized product recommendations, thereby enhancing user satisfaction, increasing user engagement, improving sales conversion rates, and reducing marketing costs.

2. Intelligent Customer Service and Support

AI Agent provides 24-hour online service, instantly responding to customer inquiries and automatically resolving order issues based on predefined solutions. Some advanced AI Agents can recognize changes in customer emotions and adjust response strategies accordingly, intelligently guiding customers to the most suitable service channels or human customer service based on the type and urgency of customer inquiries.

3. Content Generation and Optimization

AI Agent can generate appealing product descriptions and create marketing content that aligns with user interests and needs, integrating unified marketing plans through various forms of generated content such as text, images, audio, and video. Based on user feedback and interaction data across different social media platforms, it continuously optimizes content strategies to enhance the appeal and dissemination of content.

4. Inventory Management and Demand Forecasting

AI Agent can analyze historical sales data, market trends, and seasonal factors, combined with promotional activities, to predict product demand for the upcoming period. It monitors inventory levels and demand fluctuations in real-time, helping brands better plan production and procurement schedules, reducing the risk of inventory backlog or stockouts. For companies with multi-channel sales, AI Agent can achieve centralized inventory management and optimized allocation, ensuring that inventory levels across various sales channels match actual demand.

5. Price Optimization

AI Agent can analyze market dynamics, competitor pricing, and user behavior to predict supply and demand levels based on changes in external factors, providing dynamic pricing recommendations to maximize profits. It also monitors competitors’ price changes in real-time and adjusts its pricing based on market reactions, helping companies maintain a price advantage in fierce market competition.

6. User Behavior Analysis

AI Agent can analyze users’ online click streams, search queries, purchase histories, and social media activities to identify consumer needs and preferences, gaining insights into user behavior patterns and predicting future actions, helping companies optimize website layout and navigation to improve conversion rates.

7. After-Sales Service and Feedback Analysis

AI Agent can automatically collect and analyze user feedback, conduct sentiment analysis on customer feedback, quantify the ratio of positive and negative evaluations, and predict satisfaction trends through time series analysis. It can proactively identify potential issues and automatically generate periodic reports displaying key indicators such as customer satisfaction and complaint rates for management decision-making, aimed at improving products and services.

8. Multi-Channel Marketing Integration

AI Agent can assist brands in automating and personalizing marketing activities across multiple channels (such as email, social media, mobile applications, etc.), achieving cross-platform, multi-channel, and multi-type business scenario integration, including text and voice AI chatbots, covering web and client sides, ensuring consistent and effective marketing information without increasing operational burdens, thus enhancing user engagement and brand influence.

9. Advertising Placement Optimization

AI Agent can analyze customer data and identify different market segments based on demographics, behaviors, and preferences, intelligently selecting the timing, location, and format of ad placements according to the characteristics and behaviors of the target audience. By analyzing ad effectiveness in real-time, it helps companies optimize advertising strategies, allocate ad budgets efficiently, and improve ad click-through and conversion rates.

10. Market Trend Analysis

AI Agent can integrate and analyze vast amounts of data from various sources, including social media trends, consumer behavior, and sales data, using machine learning and deep learning technologies to establish predictive models for future market trends, such as fashion trends and changes in consumer demand. These predictions help companies make more informed decisions, strategically position themselves in the market, and develop more effective market entry and expansion strategies.

Currently, some products and tools available in the market are more of a “shallow application”, mainly empowering the guide side with some AIGC tools, such as text generation, image generation, and some video editing, content templates, and other small tools. However, these AIGC tools present challenges even for professional marketers or content creators, let alone for guides who primarily have offline capabilities. Moreover, many brands’ guides are older, and their acceptance of these new tools can be problematic, which may backfire and increase the burden on guides. Additionally, AIGC content is often rejected by content platforms, which limit the flow of content that shows AI traces because both content platforms and consumers prefer authentic, high-quality content.

However, it is encouraging to see some KOS marketing solutions in the market that have effectively integrated AI capabilities into the overall workflow of KOS marketing, enhancing efficiency and addressing certain pain points. I will mention a few of these products’ AI applications at important nodes in the business flow.

AI Content Insights: AI searches and filters high-quality content libraries on platforms, automatically tagging content, including images and text, extracting and gaining insights into key content directions and elements for subsequent content planning and creation, ensuring a baseline for content quality. By continuously publishing a large number of KOS samples, these insights can be data-driven and corrected, allowing for model optimization.

AI Content Templates: Asking guides to create original content can indeed be challenging for them. However, if most of the content is already completed, guides only need to replicate a few similar images based on reference images, completing uploads that form personalized notes. This template-based approach greatly reduces the burden on guides while addressing repetition issues.

AI Content Creation: Through offline shooting by guides and professional ground teams, a large amount of high-quality material is accumulated, forming a usable material library. In content production, based on content topics, AI identifies the most suitable materials to combine into different content products for publication. This process does not involve direct AIGC generation, but rather a combination of “human shooting + AI recognition”, leveraging AI’s strong capabilities in multi-modal recognition while avoiding the embarrassment of heavy AIGC traces that could lead to platform restrictions.

AI Content Verification: After guides re-create content, a review is needed to avoid content risks. AI can identify prohibited words on platforms and determine compliance with re-creation standards, including checking whether celebrity IPs have expired permissions, thus replacing many quality control tasks that would typically require human intervention.

AI Inquiry Recognition and Response: Users may frequently inquire about the content published by guides, asking about products, discounts, styles, materials, and other professional questions. These inquiries are valuable leads for potential customers, and AI can accurately recognize these inquiries from interested customers. On one hand, AI retrieves responses from a corpus for automatic replies, acting as an automated customer service; on the other hand, it can notify the respective guide personnel to manually follow up on responses, guiding potential customer leads into stores.

AI Data Analysis: With a large amount of data published by guides, AI can automatically collect and organize this data, generating reports across various dimensions (content level, employee level, etc.) according to the requirements of operational staff, enabling multi-dimensional analysis and review of the operational process. This continuous feedback loop enhances content quality and operational management, allowing the model to remember the elements of high-quality content, which can be incorporated into future searches for quality reference content or generating quality content templates, further improving content quality.

In an era where traffic dividends have peaked, KOS marketing has become a rapidly growing marketing trend. More and more brands are investing in it, and with the continuous development of AI and the increasing convenience of business implementation, it is believed that KOS marketing will experience explosive growth. Companies that utilize AI-KOS capabilities will have a greater chance of reaping the marketing benefits brought by KOS assets.

The continuous development of AI indeed provides many individuals and startups with opportunities for innovation and disruption. Only companies and teams that master the true usage of AI and can solve real problems, even startups, will reap significant rewards. As Kevin Kelly said, “AI will not replace you, but those who learn to use AI will!”.