Everyone Should Understand How AI-Driven Trust Transforms Modern Marketing
In the digital age of information explosion, consumers' purchasing journey is facing unprecedented complexity. A new survey shows that 71% of consumers in the Spanish market are satisfied with important purchasing decisions, but at the same time 70% will postpone their decisions due to too many choices - this contradiction reveals the core challenge of modern marketing. Consumers are overwhelmed by endless options and information, leading to "choice fatigue" becoming the norm. More than half of Spanish consumers even said that they "cannot bear the pressure of making wrong purchasing decisions." The cost of this lack of trust is extremely high: consumers who lack confidence are 18 times less likely to recommend a brand to friends and family, and their purchase abandonment rates increase significantly. The research, conducted in collaboration between Google ads and Ipsos and covering 18 markets and more than 18,000 respondents, found a key insight: when consumers have sufficient knowledge and information, their purchasing confidence increases by 4.9 times. This shows a clear path for marketers: with the help of artificial intelligence, providing highly relevant and personalized messages through tools such as Google ads can effectively rebuild consumer trust and convert it into actual business performance.
I. Current status and challenges of consumer trust
1. The decision-making dilemma of modern consumers: information overload and choice fatigue
Today's consumers are faced with unprecedented decision complexity. In a digital shopping environment, a single product may have dozens of models, hundreds of reviews, and countless comparison websites. This kind of information overload directly leads to the phenomenon of "choice paralysis". The research shows that although only 12% of consumers say they don't like shopping itself, as many as 74% of Spanish consumers will actively look for ways to simplify the purchasing process, reflecting the fundamental contradiction in the current consumer experience. What is even more alarming is that when faced with too many choices or information, 70% of consumers will delay or completely avoid making a decision. This decision-making delay not only prolongs the sales cycle, but is also likely to lead to permanent loss of potential customers. From a psychological perspective, 55% of consumers admit that they will continue to buy suboptimal products simply because "it's more convenient", even if they know there are better alternatives on the market. This "convenience trap" seriously hinders the opportunities for new brands to enter the market.
2. Spanish market data analysis: the contradiction between satisfaction and decision delay
The Spanish market provides a representative example of the trust paradox. On the surface, 71% of consumers are satisfied with important purchasing decisions, indicating that the overall market is healthy; however, a deeper analysis reveals that the same proportion of consumers (70%) will postpone decision-making due to too much information. This contradiction stems from the cognitive load in the decision-making process - consumers may ultimately be satisfied with the outcome of their choice, but the psychological cost of achieving the satisfactory outcome is too high. The data further showed that 76% of Spanish consumers consult multiple sources to verify the authenticity of information, and 78% said they "feel safer when they feel they have done sufficient research." These behavioral patterns highlight the dual importance of discoverability and trustworthiness, and explain why platforms like Google ads, which 84% of Spanish consumers believe can help them find "difficult to describe" things, play a key role in building purchasing confidence.
3. The business cost of lack of trust: brand recommendation rate and purchase abandonment rate
The impact of trust deficit on business performance is both immediate and far-reaching. Data shows that consumers who are confident when purchasing are 18 times more likely to recommend a brand to friends and family than those who are less confident, and are 6 times more likely to purchase again "without hesitation." On the contrary, consumers who lack confidence may not only give up purchasing, but also form a negative brand impression. From the perspective of the funnel model, the lack of trust leads to a double blow of decreased conversion rate and reduced customer lifetime value. It is worth noting that 66% of Spanish consumers will actively seek out brands that "understand their needs". This desire to be understood provides companies with an opportunity to establish emotional connections. For marketers, rebuilding trust is not only a moral imperative, but also a strategic imperative to improve key performance indicators such as ROAS, customer acquisition cost and retention rate.
II. Core Mechanisms of AI-Driven Trust
1. Knowledge construction: Category understanding improves purchasing confidence
Artificial intelligence is revolutionizing the way consumers acquire product knowledge. Research shows that consumers who have a deep understanding of a product category are 4.9 times more confident in making a purchase than those with lower levels of knowledge. Traditional marketing methods have difficulty achieving this level of knowledge transfer efficiency, and AI-driven tools such as "AI Overview" can analyze complex product information in an intuitive way. Taking car purchases as an example, consumers no longer need to manually compare dozens of parameters. AI can instantly generate a comparison of the advantages and disadvantages of each model, and even provide personalized suggestions based on the specific needs of consumers (such as "family car, emphasis on safety, budget of 30,000 euros"). This kind of intelligent knowledge construction greatly reduces consumers' learning costs, enabling them to reach a level of judgment close to that of experts in a short period of time, thereby generating purchasing confidence. The key for brands is to ensure that product information is structured and integrated into AI training data so that the brand can become a trusted source of information when consumers seek knowledge.
2. Information discoverability: thorough research brings a sense of security
78% of Spanish consumers say they feel safer when they feel they have done the "necessary research" – highlighting the core value of discoverability. AI-enhanced tools like Google Lens and Circle to Search are redefining the very nature of research. Consumers can now get precise information by taking photos of products, circling specific features, or even using vague descriptions (such as "the blender with the blue button"). This rise in visual search (business-related queries account for 25% of Lens searches) has created entirely new consumer touchpoints. For example, a cooking enthusiast used Google Lens to take a photo of a blender at a friend's home and was able to not only immediately identify the brand and model, but also view cross-platform price comparisons and professional reviews. This seamless experience directly converted curiosity into purchase intent. For marketers, optimizing visual assets (such as high-quality product images and videos) and ensuring consistency of product information across multiple channels have become key strategies for improving discoverability.
3. Dynamic personalization: understanding of needs and communication expectations
Modern consumers expect not just static personalization based on demographics, but intelligent interactions that adapt to their dynamic buying stage. 74% of Spanish consumers want brands to show they understand their needs, and 61% expect this understanding to be reflected in brand communications. AI makes this highly contextualized communication possible — for example, a consumer in the early stages of research might receive educational content, while another user who has added items to their cart but not yet checked out might receive a limited-time discount. Volvo Cars' case demonstrates the power of this approach: by analyzing over a million relevant search queries (such as "electric SUV") with AI and dynamically adjusting bidding strategies based on the user intent stage (information gathering vs. test drive booking), the company was able to increase sales leads by 47% while doubling its search advertising ROI. This "value-based bidding" (VBB) strategy exemplifies how AI can translate understanding of consumer intent into precise marketing actions.
III. AI Trust Solution for Google ads
1. Search technology innovation: Lens visual search and Circle to Search
Visual search technology in Google ads is reshaping the consumer product discovery journey. Google ads Lens and the innovative Circle to Search feature eliminate the limitations of traditional keyword searches, allowing users to initiate searches using images, screenshots, and even photos of physical objects. This technology is particularly important for the retail industry - a quarter of searches using Lens have commercial intent and the conversion rate is significantly higher than text searches. For example, if a consumer sees a Pandora bracelet worn by a friend, he or she can just take a photo with Lens to immediately get product information, inventory at nearby stores, and price comparisons. This seamless discovery experience greatly reduces friction in the purchasing process. The key for advertisers is to ensure that product images are well-stocked and tagged, and to consider uploading 360-degree product views in Google ads Merchant Center to maximize exposure through visual search. Data shows that brands that integrate these features have seen an average increase of 30% in brand searches and direct traffic, proving that visual search has become an integral part of the consumer journey.
2. Demand generation tools: Breaking the "convenience trap"
Google ads demand generation tools offer a breakthrough solution to the "convenience trap" caused by consumer inertia (55% of consumers will continue to buy suboptimal products because of convenience). These AI-driven tools can identify consumers who are in "persuadable moments" — for example, someone who has long purchased the same brand of laundry detergent but recently searched for "a detergent that removes stains better." By analyzing hundreds of behavioral signals, AI can transform implicit needs into explicit purchase intent and present consumers with new options they may not have known existed. Tira Beauty's case study demonstrates the effectiveness of this approach: through Gemini-generated product descriptions, the brand increased organic clicks on its facial cleanser products by 50% and grew online sales by 31%. This kind of demand generation not only expands the total market size, but also creates a fair competition environment for innovative brands and breaks down the market barriers that existing brands have established through consumer inertia.
3. Generative AI Application: Gemini Optimizes Product Descriptions
Generative AI has demonstrated amazing results in product information optimization. Google ads's Gemini model analyzes existing product listings (titles, descriptions, images, etc.), automatically extracts key attributes and generates more attractive and search-relevant content. This technology solves two core pain points: for retailers with a large number of SKUs (such as Tira Beauty, which has thousands of beauty products), manually optimizing each product description is almost an impossible task; and for small and medium-sized enterprises, there is often a lack of professional copywriting resources. Actual tests show that the product list optimized by Gemini not only increases the click-through rate by 50%, but also enhances the subsequent conversion quality due to the improved content relevance. This AI-enhanced merchandising strategy is particularly suitable for the peak retail season, when consumers are overwhelmed by a massive amount of promotional information, and clear, complete and highly relevant product descriptions can effectively build purchasing confidence. It is worth noting that this application should not completely replace human review, but should be regarded as a "first draft generator" that marketers can inject brand voice and strategic focus.
IV. Cross-scenario AI trust practice cases
1. Volvo Cars: AI Keyword Expansion Strategy
Faced with fierce competition in the electric vehicle market, Volvo Cars implemented a groundbreaking AI keyword strategy in 22 markets in Europe, the Middle East and Africa. Traditional methods rely on manually maintained keyword lists, which have difficulty keeping up with the rapid evolution of consumer search behavior. Volvo's solution is to use broad matching and AI-driven keyword expansion - starting from core keywords such as "electric SUV", the algorithm automatically identifies more than one million related queries to capture long-tail demand. This approach not only expands coverage, but also more accurately targets consumers at different decision-making stages. Combined with a Value-Based Bidding (VBB) strategy (dynamically adjusting bids based on expected conversion value), Volvo doubled its search advertising ROI and increased sales leads by 47% in a three-month A/B test. This case proves that AI keyword strategy can simultaneously solve the conflicting needs of scalability and accuracy, and is particularly suitable for brands operating in multiple markets.
2. Tira Beauty: Sales growth from AI-generated content
Indian beauty brand Tira Beauty faced the challenge of optimizing thousands of product listings on large e-commerce platforms. Manually updating the title, description, and attributes of each SKU is not only time-consuming, but also difficult to ensure consistency and search relevance. The company turned to Google ads generative AI tool, Gemini, which automatically analyzes product images and existing copy, extracts key features, and generates optimized content. The results were impressive: organic clicks on facial cleansers increased by 50%, driving a 31% increase in overall online sales and a 47% increase in advertising return on investment (ROAS). The key to this success is that AI solves the "content bottleneck" - allowing small and medium-sized enterprises to have merchandising capabilities comparable to those of large retailers. Tira Beauty has plans to expand this model to all product categories and explore more generative AI applications, such as automated advertising creative generation and personalized recommendations.
3. Pandora Jewelry: Omni-channel AI advertising collaboration
Danish jewellery brand Pandora's holiday season campaign in Australia showcased how AI can integrate the online and offline consumer journey. Faced with fierce competition during the retail peak season, Pandora adopted a Performance Max campaign with a "store target" strategy, using local inventory ads to show real-time product availability at nearby stores. This seamless omni-channel experience allows consumers to research online and pick up their products offline, alleviating concerns about holiday delivery time pressures. At the same time, AI dynamically optimizes the content presented in ads—emphasizing limited-time discounts for high-intent users and providing product education information for consumers in the early research stages. Results showed that this dynamic, intent-based presentation increased Pandora's overall return on ad spend (ROAS) by 21% year over year, demonstrating the value of AI in orchestrating complex consumer journeys. This case study is particularly worth learning for multi-channel retailers, as it shows how to turn the advantages of physical stores into differentiating factors in digital marketing.
V. Future development trend of trust technology
1. Multimodal AI Agent: Chief Simplification Officer of the Consumer Journey
The next 18 months will see the rise of "multimodal AI agents" – systems that can process text, voice, image and video inputs and act as "chief simplification officers" for the consumer journey. Unlike current single-function AI tools, these agents will have cross-session memory and context awareness capabilities. Taking the early experiments of Kingfisher Group (which owns home brands such as B&Q) as an example, its AI agent can understand bathroom photos uploaded by consumers and combine them with voice inquiries ("How do I solve the mold problem in this corner?") to not only recommend products but also provide installation tutorials and prevention advice. This end-to-end solution capability will significantly reduce consumers' research burden, and it is expected that by 2025, brands that adopt such agents will be 15-20% ahead of their competitors in customer satisfaction indicators. The implication for marketers is that future content strategies need to consider multimodal presentation to ensure that product information can be interpreted and delivered by AI agents in a flexible way.
2. Shoppable Videos: Seamless Conversion Experience
The influence of video content on purchasing decisions continues to grow, and Shoppable Videos converts this influence directly into sales. This technology allows viewers to purchase products directly by clicking on them while watching a video, without leaving the playback environment.Google ads solution is to seamlessly integrate Merchant Feed with video and expand reach through Demand Gen campaigns. Early testing shows that shoppable videos have a conversion rate 3-5 times higher than traditional video ads, and are particularly suitable for visually driven categories such as fashion, beauty and home. By 2025, as the scale of "social commerce" exceeds $1 trillion, this technology will become standard for brands. The key to success is to create shopping touchpoints that are native to the context of the video — for example, labeling the products used directly in a beauty tutorial, or highlighting the purchase of furniture in a home renovation video.
3. Ethical AI Marketing: Transparency and Inclusive Design
As AI applications become more widespread, ethical considerations will shift from marginal issues to core competitiveness. Research shows that 85% of the world's population is concerned about the impact of climate change, and that inclusive advertising makes consumers six times more likely to make a purchase. The future of ethical AI marketing needs to take into account three dimensions: environmental sustainability (such as labeling the carbon footprint of products), AI transparency (clearly labeling generated content) and diversity and inclusion (avoiding algorithmic bias). Google ads "All-In" toolkit provides a practical framework for inclusive marketing, while the open source Meridian measurement system helps brands assess the social impact of their marketing campaigns. For example, a sports brand used AI to generate product models with diverse body shapes and stated, "This image is generated by AI and is intended to show the diversity of the real human body." This transparent approach has been highly recognized by consumers. By 2025, it is expected that 15-20% of brands will formally incorporate ethical indicators into their marketing KPI system, reflecting consumers' growing attention to responsible marketing.
VI. AI Trust Strategy for Retail Peak Season
1. New Customer Acquisition: Performance Max and High-Value Customer Identification
The retail peak season is a prime time to acquire new customers - data shows that 90% of consumers in Asia Pacific will try new brands during this period. Google ads Performance Max (PMax) campaigns combined with the New Customer Acquisition (NCA) objective can effectively target these high-potential converters. The case of Gossby, a Vietnamese printing retailer, is particularly convincing: through PMax's "High Value Customer Optimization" function, the brand found that 62% of conversions came from new customers, of which 30% were identified as high lifetime value customers, whose single transaction amount was 43% higher than that of existing customers. At the heart of this strategy are AI models that analyze hundreds of signals—such as browsing behavior, device usage, and cross-site activity—to predict customer value, rather than relying solely on demographics or past interactions. Practical advice for marketers includes launching NCA campaigns 4-6 weeks before the peak season to build model accuracy, and setting differentiated bidding strategies to allocate higher budgets to high-value customer groups.
2. Omnichannel touchpoint integration: online ordering and in-store pickup
During peak season, consumers typically switch between online and offline channels multiple times—60% of shoppers take more than six cross-channel actions before making a purchase decision. AI-driven omnichannel attribution is critical to understanding this complex journey. The case of Pandora jewelry demonstrates successful practices: by displaying real-time product availability at nearby stores through "local inventory ads" and providing "buy online, pick up in store" options, the brand not only satisfies consumers' desire for immediate ownership, but also solves the pain point of delivery delays during peak seasons. On a technical level, this requires integrating online advertising data with store POS systems and using store visitor data to optimize geo-targeting strategies. Results show that brands that adopt this strategy see an average increase in in-store traffic of 25-40% during peak season, while reducing online shopping cart abandonment rates by 15-20%.
VII. Topkee's Google ads Solution
Topkee provides one-stop online advertising services based on Google ads, focusing on improving clients' lead acquisition and sales conversion efficiency through data-driven strategies. The solution covers the entire marketing chain from early evaluation to later optimization, and is suitable for the needs of enterprises of different sizes. Its core services can be systematically divided into the following levels:
1. Data infrastructure and website evaluation
Topkee uses the latest website scoring tools to conduct a comprehensive diagnosis, generate a detailed report including SEO structure, content value and technical issues, and make specific optimization suggestions. This stage not only examines the search engine friendliness of the existing website, but also ensures that the information meets the needs of the target audience through page content detection, thereby increasing the conversion potential of natural traffic and advertising landing pages.
2. Ad management driven by smart tools
Through the self-developed TTO CDP, customers can centrally manage multi-account budget allocation, tag ID association and conversion event settings. This tool automatically synchronizes data across channels, supports dynamic bid adjustments based on conversion goals, and enhances tracking accuracy through the TM module.
Conclusion:
From the trust paradox in the Spanish market to Volvo Cars' 47% sales lead growth, data consistently proves that in an age of information overload, AI-driven relevance is the key to rebuilding consumer trust. Modern consumers no longer passively receive information, but actively seek intelligent interactions that understand their dynamic needs - 74% of consumers clearly express this expectation. Google ads AI solutions, from visual search to generative product descriptions, provide a practical path to turn this expectation into a competitive advantage. The economic value of trust cannot be ignored: confident consumers are 18 times more likely to recommend and 6 times more likely to repurchase. When the retail peak season arrives, these differences will directly translate into market share and profit margins. We invite you to rethink every touchpoint in the consumer journey and think about how AI can remove friction, provide knowledge, and build emotional connections.
