Shopping: Consumers waffle over using social, generative AI to shop
- InsightTrendsWorld
- May 1
- 10 min read
Why is the topic trending?
Consumer Hesitation Towards New Shopping Technologies: The article highlights a surprising finding that many consumers are not readily adopting social shopping and generative AI tools for their shopping needs.
Divergent Research Findings: The article contrasts the findings of a KPMG survey, which indicates consumer reluctance, with data from Capgemini suggesting greater acceptance of these technologies, creating a point of discussion.
Privacy and Trust Concerns: The KPMG survey identifies consumer discomfort with generative AI analyzing their personal online data, pointing to potential issues of privacy and trust in technology.
Implications for Retailers and Brands: The findings have significant implications for how retailers and brands invest in and implement these emerging technologies in their customer engagement strategies.
Understanding Consumer Preferences: The article explores the underlying reasons for consumer preferences in shopping, such as a preference for independent research over online ads, which influences their adoption of new tools.
Overview:
The article discusses the findings of a KPMG survey that reveals a significant level of consumer hesitation towards using social shopping and generative AI tools. The survey of over 1,500 U.S. consumers in the summer of 2025 found that a majority have not used these tools and do not plan to. This contrasts with other research, such as data from Capgemini, which indicates a higher adoption rate and influence of these technologies on purchase decisions. The KPMG survey highlights consumer discomfort, particularly around generative AI analyzing their personal data, attributing this to concerns about privacy and trust. The article suggests that brands need to focus on transparency and privacy controls to build consumer confidence in these technologies. While social shopping sees some resonance in areas like apparel, particularly among younger demographics, overall adoption remains lukewarm according to the KPMG findings.
Detailed Findings:
KPMG Survey Findings:
Nearly two-thirds of consumers have not used AI shopping tools and do not plan to.
Over half (56%) of respondents have not used social shopping tools and don’t plan to.
When social shopping, respondents frequently purchase apparel, personal care products, and groceries.
Only 34% of respondents are comfortable with generative AI analyzing their personal online data, while 43% are uncomfortable.
Over half of respondents prefer conducting their own research for products and are not influenced by online ads.
Contrasting Capgemini Data:
Both generative AI and social media are top sources for purchase decisions.
Nearly a third of shoppers reported using social media to buy products and identify new brands.
Nearly 3 in 5 consumers say they’ve replaced traditional engines with generative AI search tools.
Reasons for Hesitancy (KPMG):
Consumers prefer to shop independently and aren't influenced by online ads.
Concerns about privacy and trust in technology regarding generative AI analyzing personal data.
Expert Opinion (Sam Ganga, KPMG):
Attributes hesitancy towards generative AI to consumer concerns about privacy and trust.
Recommends brands increase transparency and privacy controls to show the benefits of data sharing.
Believes the visual nature of fashion makes social shopping resonate for apparel.
Social Shopping Adoption:
Social shopping sees more traction in apparel due to its visual nature and influencer recommendations.
Younger demographics, particularly Gen Z, show higher adoption of social shopping. Over half of Gen Z report making online purchases via social media.
TikTok is a dominant social shopping platform, with users averaging $708 in spending annually on the app's e-commerce offerings.
Qurate is shifting its strategy to target social shopping and livestreaming across various platforms.
Key Takeaway:
Despite the hype around social shopping and generative AI, a KPMG survey reveals that many consumers remain hesitant to adopt these technologies, primarily due to a preference for independent research and concerns about privacy and trust in AI's use of personal data, contrasting with other research indicating higher levels of adoption.
Main Trend:
The Cautious Consumer in the Age of AI-Enhanced and Social Commerce: Consumers are approaching new shopping technologies like generative AI and social shopping with caution, indicating a need for greater transparency, trust-building, and demonstration of value from brands before widespread adoption occurs.
Description of the Trend (please name it):
The Tech-Hesitant Shopper: This trend describes the cautious and measured adoption of new shopping technologies, particularly generative AI and social commerce, by consumers who prioritize privacy, trust, and their own research over algorithmic recommendations and social influence.
What is consumer motivation:
Privacy Concerns: Hesitation towards sharing personal data and allowing AI to analyze their online activity.
Trust Issues: Lack of confidence in the security and ethical use of their data by technology and brands.
Preference for Autonomy: Many consumers prefer to conduct their own research and make independent purchase decisions.
Skepticism Towards Advertising: A significant portion of consumers report not being influenced by online advertisements.
Uncertainty about Value Proposition: Some consumers may not yet see the clear benefits of using generative AI or social shopping tools.
What is driving trend:
Public Awareness of Data Privacy Issues: Increased media coverage and awareness of data breaches and privacy violations.
Desire for Control Over Personal Information: Consumers want to maintain control over how their data is collected and used.
Established Shopping Habits: Many consumers are comfortable with traditional methods of online shopping and information gathering.
Information Overload: Consumers may be overwhelmed by the increasing number of shopping tools and platforms.
What is motivation beyond the trend:
Consumers ultimately seek convenience, value, and a positive shopping experience.
They may be open to adopting new technologies if their concerns about privacy and trust are adequately addressed and the benefits are clear.
Description of consumers article is referring to:
Age: The KPMG survey covered a broad range of U.S. consumers, but the article highlights higher adoption of social shopping among Gen Z. The hesitancy towards AI is also noted across a significant portion of respondents.
Gender: Not specified in the article.
Income: Not specified in the article.
Lifestyle: Includes consumers who value their privacy, prefer to be self-directed in their purchase decisions, and may be skeptical of new technologies without clear benefits and safeguards.
Conclusions:
While social shopping is gaining some traction, particularly among younger demographics, and other research suggests a growing use of generative AI in shopping, the KPMG survey indicates a significant portion of consumers remain hesitant, primarily due to concerns about data privacy and a preference for independent research. Brands need to build trust and transparency to encourage wider adoption of these technologies.
Implications for Brands:
Prioritize Transparency and Privacy: Clearly communicate how consumer data is used and implement strong privacy controls.
Build Trust: Focus on demonstrating the benefits of AI shopping tools while ensuring data security.
Understand Consumer Shopping Preferences: Recognize that many consumers prefer independent research and are not easily influenced by ads.
Targeted Approach to Social Shopping: Focus on visually driven products like apparel and engage younger demographics effectively on platforms like TikTok and Instagram.
Educate Consumers on Value Proposition: Clearly articulate the advantages of using generative AI and social shopping tools.
Implication for Society:
Highlights the ongoing tension between technological advancement and consumer concerns about privacy and data security.
Indicates a need for responsible development and deployment of AI technologies in the retail sector.
Implications for Consumers:
Consumers have a choice in whether or not to adopt these new shopping technologies.
Those who are cautious are seeking more information and assurances about privacy and trust.
Implication for Future:
Widespread adoption of AI in shopping may take longer than anticipated due to consumer hesitancy.
Brands that prioritize transparency and trust will likely have more success with these technologies in the long run.
Social shopping will likely continue to grow, especially with younger generations and visually driven product categories.
Consumer Trend (name, detailed description):
The Privacy-Focused Digital Shopper: This trend describes consumers who are increasingly aware of and concerned about their online data privacy when shopping online and are hesitant to use technologies like generative AI that require the analysis of their personal information.
Consumer Sub Trend (name, detailed description):
Selective Social Shopper: While cautious overall, some consumers, particularly younger demographics and those interested in visually driven products like apparel, are selectively engaging with social shopping platforms, often influenced by peer recommendations and influencer content.
Big Social Trend (name, detailed description):
Growing Awareness and Concern Over Data Privacy: Across various aspects of online life, there is a rising public consciousness and apprehension regarding the collection and use of personal data by companies and technologies.
Worldwide Social Trend (name, detailed description):
Global Debate on the Ethics and Regulation of AI: The ethical implications and the need for regulation around artificial intelligence are being discussed and considered globally across various sectors.
Social Drive (name, detailed description):
The Desire for Security and Control Over Personal Information: Consumers have a fundamental desire to protect their personal data and maintain control over how it is used by businesses and technologies.
Learnings for brands to use in 2025: (bullets, detailed description)
Transparency is Non-Negotiable: Be upfront and clear about how consumer data is used.
Build Robust Privacy Protections: Implement strong security measures to safeguard customer data.
Focus on User Benefits: Clearly communicate the value proposition for using new technologies.
Strategy Recommendations for brands to follow in 2025: (bullets, detail description)
Invest in Transparent Data Policies: Make your data usage policies easily understandable and accessible to consumers.
Offer Clear Opt-In Options: Ensure consumers have control over whether their data is analyzed by AI tools.
Highlight Success Stories and Benefits: Showcase how these technologies can improve the shopping experience in a trustworthy manner.
Final sentence (key concept) describing main trend from article (which is a summary of all trends specified):
In 2025, "The Tech-Hesitant Shopper" trend reveals that consumers are approaching AI-enhanced and social commerce with caution, prioritizing privacy, trust, and independent research, signaling a need for brands to build confidence and demonstrate clear value in these emerging shopping technologies.
What brands & companies should do in 2025 to benefit from trend and how to do it:
In 2025, brands and companies should prioritize building consumer trust and ensuring transparency around data privacy as they implement social shopping and generative AI tools. This involves clearly communicating the benefits to shoppers, implementing robust privacy controls, and offering clear opt-in options for data analysis to alleviate consumer concerns and encourage greater adoption of these emerging shopping technologies.
Final Note:
Core Trend: The Tech-Hesitant Shopper: Cautious adoption of AI and social shopping due to privacy and trust concerns.
Core Strategy: Prioritize transparency, build trust, and clearly communicate user benefits.
Core Industry Trend: Disparity between the development of advanced shopping tech and consumer readiness.
Core Consumer Motivation: Desire for privacy, security, control, and demonstrable value.
Final Conclusion: Consumer hesitancy towards new shopping technologies underscores the critical need for brands to prioritize ethical data handling and transparent communication to foster trust and encourage adoption in the evolving retail landscape.
Core Trend Detailed (The Tech-Hesitant Shopper):
Description: This core trend describes the cautious and measured approach that many consumers are taking towards the adoption of new shopping technologies, specifically social shopping and generative artificial intelligence tools. These shoppers, while aware of technological advancements, are exhibiting a reluctance to fully embrace these platforms for their purchasing needs. This hesitancy is primarily fueled by concerns surrounding the privacy and security of their personal data, a preference for conducting their own independent research on products, and a general lack of complete trust in the algorithms and data analysis involved in these emerging technologies.
Key Characteristics of the Trend (summary):
Low Adoption Rates: A significant portion of consumers have not used and do not plan to use social shopping or generative AI tools.
Privacy Concerns: Discomfort with AI analyzing personal online data is a major deterrent.
Trust Deficit: Skepticism exists regarding the security and ethical use of personal information by these technologies and the companies providing them.
Preference for Independent Research: Many shoppers prefer to conduct their own product research rather than relying on AI recommendations or social influence.
Resistance to Online Ads: Over half of consumers report not being influenced by online advertisements, suggesting a broader skepticism towards digital marketing tactics.
Selective Adoption: While overall adoption is low, certain demographics (like Gen Z for social shopping) and product categories (like apparel on visual platforms) see slightly higher engagement.
Market and Cultural Signals Supporting the Trend (summary):
The KPMG survey findings indicating that nearly two-thirds of consumers haven't used AI shopping tools and over half haven't used social shopping tools and don't plan to.
The specific discomfort expressed by 43% of KPMG survey respondents regarding generative AI analyzing their personal data.
The finding that over half of KPMG respondents prefer their own research and are not influenced by online ads.
The contrast with Capgemini data showing higher adoption, suggesting a divergence in consumer experiences or survey methodologies that still points to a segment of hesitant shoppers.
The acknowledgement by KPMG's Sam Ganga of consumer concerns about privacy and trust as key factors driving this reaction.
How the Trend Is Changing Consumer Behavior (summary):
Delay in Widespread Adoption: The expected rapid uptake of AI-powered and social shopping methods is being slowed down by consumer caution.
Continued Reliance on Traditional Methods: Consumers are sticking with familiar online shopping practices and search engines for product discovery.
Increased Vigilance Over Data Sharing: Shoppers are likely to be more cautious about what personal information they share with online platforms.
Demand for Greater Transparency: Consumers are seeking more clarity from brands about how their data is being used by these new technologies.
Implications Across the Ecosystem (For Brands and CPGs, For Retailers, For Consumers, summary):
For Brands and CPGs: Need to temper enthusiasm for immediate large-scale integration of these technologies and focus on building consumer trust through transparent data policies and robust privacy measures. Marketing should emphasize user benefits while addressing privacy concerns.
For Retailers: Should consider a phased approach to rolling out AI and social shopping features, prioritizing clear communication and ensuring secure data handling. Understanding that a significant portion of their customer base may be hesitant is crucial for strategy.
For Consumers: Indicates that they have a level of control over the adoption of new shopping technologies and are actively evaluating the trade-offs between convenience and privacy. Highlights the importance of staying informed about data usage practices.
Strategic Forecast: The Tech-Hesitant Shopper trend is likely to persist in the short to medium term. Until brands can effectively address consumer concerns about privacy, security, and the value proposition of AI-driven and social commerce, widespread adoption will likely be gradual. Over time, as trust is built and the benefits become clearer, more consumers may embrace these technologies, but the initial caution signals a need for a thoughtful and responsible implementation.
Final Thought: The wariness of consumers towards AI-enhanced and social shopping serves as an important reminder that technological advancement in retail must be accompanied by a strong focus on ethics, transparency, and building genuine trust with shoppers. Convenience alone may not be enough to overcome fundamental concerns about data privacy and the evolving shopping experience.
Comentarios