Amazon and Walmart are no longer just retail competitors; they’re battling to control broader life infrastructures — shopping, healthcare, media and cloud. From AI-powered experiences to healthcare integration and strategic real estate moves to navigating economic uncertainty, the strategies of Amazon and Walmart are converging while remaining distinct. Their maneuvers paint a picture of a future where retail is not just about transactions, but about ecosystems. In a move that signals a future beyond visual browsing, Amazon recently began testing AI-generated audio summaries for products in its mobile app, a clear signal Amazon wants to reduce friction in product discovery, especially on mobile and voice-enabled platforms. This dovetails with the broader strategy of turning the shopping experience into a more passive, streamlined and personalized interaction, keeping users within its ecosystem. Amazon is also investing heavily in AI-enhanced developer tools (e.g., through Amazon Q Developer), suggesting that innovations like the audio summaries are only the beginning of a larger internal transformation. Amazon is diversifying revenue through digital services and AI tools, potentially offsetting retail volatility. Walmart is leveraging scale and supply chain efficiencies, aggressively expanding beyond its traditional domain, opening its largest centralized prescription fulfillment center. In tandem, Walmart is integrating Medicare Advantage benefits into its stores, allowing customers to use their supplemental health funds on wellness products. The aim to combine retail footprint, pharmacy logistics and insurance-based wellness programs represents a model that could rival traditional healthcare providers, especially in underserved areas. These developments signal a fork in retail strategy. For its own part, Walmart’s volume-based physical retail play is one that increasingly focuses on infrastructure and logistics (healthcare, fulfillment and real estate). On the other hand, Amazon’s experience- and tech-driven retail pushes automation, personalization and AI integration.
Discord’s new virtual reward system allows users to redeem exclusive digital items from Discord’s Shop using the rewards earned by watching product videos or playing games
Discord is experimenting with a new virtual reward system aimed at encouraging more users to engage with its interactive ads. This new feature, called “Orbs,” comes on the heels of the company preparing for a potential IPO. Users can earn Orbs by completing “Quests,” Discord’s ad format where advertisers incentivize users to watch product videos or play games by rewarding them with virtual items. The addition of Orbs gives users the chance to redeem exclusive digital items from Discord’s Shop, including Nitro credits, profile badges, avatar decorations, profile effects, and items from main collections. The goal of Orbs is to encourage more interaction with Quests as Discord seeks to demonstrate to partners that it can offer a scalable ad business. The Orbs experiment may also entice users who don’t have a subscription to try Nitro, as they can spend Orbs on credits instead of using a payment method. For instance, users need 1,400 Orbs to get three free days of Nitro, which typically costs $10 per month.
Pairzon’s AI platform uses actual in-store and online transactional data to generate predictive audience segments in real time and anonymously and securely shares personalized audiences with brands to enable precise targeting
Pairzon, an AI-powered marketing intelligence platform, launched its advanced Retail Media Solution, built to help retailers transform their first-party data into a powerful engine for growth, precision targeting, and revenue generation. Pairzon introduces a unique approach: enabling retailers to anonymously and securely share personalized audiences with brands. This unlocks AI-powered retail media campaigns that maximize ROAS across both in-store and online channels through precise shopper targeting. Unlike traditional solutions that rely on modeled or third-party data, Pairzon uses actual in-store and online transactional data to generate predictive audience segments in real time. These segments are seamlessly activated across major platforms including Meta, Google, TikTok, and Others—delivering performance at scale while maintaining strict data privacy and compliance (GDPR, CCPA). Retailers also benefit from closed-loop attribution, finally connecting ad impressions to physical or digital purchases. This transparency allows both retailers and brands to understand what truly drives performance, making each campaign fully measurable.
Microsoft’s marketplace allows users to browse, install and try from over 70 AI agents tailored for specific tasks and come auto-embedded within the user’s work environment
Microsoft has unveiled the Agent Store, a centralized marketplace where users can “shop” for AI agents tailored for specific tasks. Integrated into Microsoft 365 Copilot, the Agent Store serves both developers and non-technical users who want to use artificial intelligence (AI) agents to streamline workflows, automate tasks and enhance productivity across the workplace. “The Agent Store is your one-stop shop for the next generation of AI assistants,” Principal Product Manager Siffat Hingorani and Senior Product Manager Olive Hu said. Through the marketplace, users can browse, install and try agents from Microsoft and its partners and customers. OpenAI has also introduced a similar marketplace for GPTs that offer bots designed to perform specific tasks. Users can pick from a selection of GPTs or make their own. What’s different between the two is that Microsoft’s agents are embedded within the user’s work environment, with the user’s data as context. OpenAI’s GPT marketplace offers more general tasks to the public. Microsoft’s Agent Store launched with more than 70 agents, which it said would grow over time. Developers can build their own agents and publish to the store using Microsoft Copilot Studio and Microsoft 365 Agents Toolkit.
Levi’s reports 12 consecutive quarters of double-digit growth driven by 2X rise in e-commerce business to 10% of total net revenue in 2014; DTC take 52% share in total revenue
Levi Strauss & Co. is citing efforts to develop next-generation e-commerce solutions as a key component of its financial success. The denim giant has recorded 12 consecutive quarters of global double-digit growth, including a strong first quarter of fiscal 2025 it credited in part to global direct-to-consumer net revenues increasing 9% on a reported basis and 12% on an organic basis, as well net revenues from e-commerce growing 13% on a reported basis and 16% on an organic basis. DTC comprised 52% of total net revenues in the quarter. In addition, during the last five years, Levi’s e-commerce business has doubled, growing from 5% of total net revenue in 2019 to 10% in 2024. Jason Gowans, chief digital and technology officer, Levi Strauss & Co., said “We’re creating a global digital flagship experience that not only is expanding our loyal fan base but also is giving them a reason to return, steadily improving the overall health of our e-commerce business with global net revenues and EBIT margins improving double-digits on a compound annual growth rate over the last three years.” Gowans cited several specific digital commerce solutions it has developed, including a new search tool, high-quality content images, and more videos on product pages designed to create a dynamic online shopping experience and showcase items as they look in real life. Visitors to the site can also use a new fit quiz tool, which lets shoppers answer a series of questions on fit preferences, returning a number of personalized recommendations.
Comscore adds consumer AI tool usage data to its reporting suite; data shows >30% of the U.S. online population uses AI tools actively each month, reflecting the rapid rise of this category
Comscore announced the addition of consumer AI tool usage data to its industry-leading suite of reporting. This new data set captures site visitation metrics for 117 AI tools and features across nine distinct categories, spanning both PC and mobile platforms. With this launch, Comscore is providing advertisers, agencies, and brands with a clearer picture of how consumers are interacting with AI tools, from fully AI-powered platforms like ChatGPT and Microsoft Copilot to mainstream applications with AI features, like Canva. This data set is designed to track real-world usage, providing actionable insights into how these tools are reshaping consumer behavior. Key Insights from the new data Include: Widespread Adoption: Over 30% of the U.S. online population uses AI tools actively each month, reflecting the rapid rise of this category. Top AI tools include Open AI Gen AI, Microsoft Gen AI and Canva Gen. Cross-Platform Growth: 67 million U.S. consumers engage with AI on mobile devices, indicating strong momentum beyond desktop. Category Leaders: Beyond AI assistants, creative tools led the top categories with Audio (23.8 M projected visitors), Image Generation (23 M), Design (23 M), and Video Generation (22.4 M).
Authenticity, trust and relatability outweighing aesthetics and clout in influencer marketing with 40% of consumers picking relatability as the top reason they trust an influencer, as against their fame or follower count
Typeform, the conversational data collection platform, today released Get Real: The Data on Influencer Marketing, a first-of-its-kind report blending data and video storytelling from more than 1,300 influencers, marketers, and consumers. The report reveals a clear shift in audience expectations: authenticity outweighs aesthetics, and connection matters more than clout and curated content. Key findings include: Reach doesn’t equal resonance: As marketers shift from chasing scale to building connection, trust is becoming the new currency. To mitigate the credibility crisis, brands should prioritize creators with resonance and relatability over follower count. Half of consumers would unfollow an influencer if they knew they bought followers. One-third of influencers admit to buying followers or engagement. Nearly 40% of consumers say relatability is the top reason they trust an influencer—not their fame or follower count. Authenticity over AI: Consumers are no longer swayed by high production value or overly polished content, and the surge of AI-generated content is accelerating the credibility crisis. 81% of influencers are using AI to assist with content creation, yet 35% of consumers distrust AI-generated influencer content. 61% of consumers believe influencers should disclose when they use AI. Trust can’t be scripted: When brands hand influencers rigid scripts or products they don’t believe in, the content can feel forced. Audiences tune out, and creators feel frustrated and creatively constrained. Consumers’ number one “ick” when watching influencer content is inauthentic engagement, followed closely by lack of transparency. Influencers say the most frustrating part of working with brands is being forced to sound inauthentic, with 1 in 4 influencers citing it as their biggest challenge. Only 1 in 3 consumers believe influencers use the products they promote, and 56% of influencers admit to promoting products they don’t actually like. 71% of consumers have regretted a purchase based on an influencer’s recommendation.
Walmart’s agentic AI strategy to follow ‘surgical’ approach where agents will have expertise at retail-specific tasks and their work outputs will be stitched together to solve complex workflows
Walmart U.S. Chief Technology Officer Hari Vasudev unveiled the retailer’s agentic AI strategy and implementation plans, preparing for an era where robot shoppers will buy products and services from robot sellers, accessing websites optimized for them with the goal of delivering fast, hyper-personalized experiences to human shoppers. First, Walmart identifies core agentic AI capabilities that would work best for the retailer, are cohesive and can scale globally, per the post. Next, it uses a “surgical” approach to agentic AI. That means its agents will be experts at specific tasks, unlike the more generic solutions from other providers. Finally, Walmart agents’ work outputs will be stitched together to solve complex workflows. As an example, Walmart taps its retail-expert large language model to build agents within its generative AI shopping assistant, which appears as a smiley face chatbot. These agents can do specific tasks such as deep personalization, item comparison and shopping journey completion, among others. The model is trained on the retailer’s data and can be combined with other models to contextually address the customer’s needs Walmart’s existing generative AI-powered tools are on their way to becoming fully autonomous agents. Walmart is also exploring using AI agents across the company, from doing in-store tasks to merchandising planning at the home office and beyond. Shoppers are already using Walmart’s shopping assistant to find products, and the next step is to let an agent do the research, make decisions and place the order. This autonomous task would be ideal for repeat purchases of everyday necessities. Walmart is aware of the risk of hallucinations, or AI models making things up. So, it is adding a layer of governance, checks and balances, as well as evaluating which parts of agentic AI need human oversight and approval
Experiential anchors- booksellers, fitness centers and food & beverage brands are serving to drive customer visits to malls, accounting for 8-16% of visitor share
Macy’s and JCPenney still play a key role in drawing customers to malls, but empty anchor implants like entertainment brands, fitness centers, and restaurants are increasingly building new traffic across longer ranges of hours, declares Placer.ai’s latest Mall Report. One key participant in this trending phenomenon is a brand that most retail experts thought had run its course: Barnes & Noble. In recent years, the land’s leading bookseller has reinvented itself in smaller stores (15,000 sq. ft. versus 25,000 sq. ft.) redesigned to be “hangouts” for local customers with better lighting, more open layouts, and opportunities for social interaction. At the Coronado Center in Albuquerque, N.M., the Barnes & Noble accounted for 7.9% in 2024, according to Placer.ai, outperforming both Macy’s and JCPenney. Key food-and-beverage brands, too, are now outpacing department stores with their traffic counts. At Northridge Fashion Center in Northridge, Calif., Porto’s Bakery & Café was the No. 1 customer draw with a 15.6% share of overall center visitor, a full 3.6 percentage point lead over No. 2 Dick’s Sporting Goods. And while Placer.ai had Target as the top tenant at Glendale Galleria in Glendale, Calif., Placer.ai with a 14.4% share of visitors, it was followed by by In-N-Out Burger’s 8.6% share in second place. “Increasingly, shopping centers are turning to fitness centers as experiential anchors,” according to the Placer.ai report. “And since many people work out early in the morning, these gyms are having a significant impact on the distribution of mall visits across dayparts.” At Northshore Mall in Peabody, Mass., where a Life Time gym opened in 2021, visits between 7:00 a.m. and 12:00 p.m. rose from 13% to 15% over the past five years. Similarly, at Jackson Crossing in Jackson, Mich., where Planet Fitness arrived in 2022, the morning visit share increased from 14% to 16%.
Microsoft proposes GenAI Intent-based routing (IBR)- Customer Intent Agent discovers and manages intents, while IBR uses those intents to route conversations, connecting customer needs to the right support resources with speed and precision
Intent-based routing (IBR) is a generative AI-powered capability that routes customer queries based on real-time intent recognition and dynamic user group assignment. It is enabled by the Customer Intent Agent, which autonomously discovers and manages customer intents by analyzing past interactions and builds an evolving intent library. Customer Intent Agent discovers and manages intents, while IBR uses those intents to route conversations, connecting customer needs to the right support resources with speed and precision. Once an intent and its group are identified, IBR routes the conversation to the appropriate user group based on the mapped intent group. Next, IBR assigns it to the best-suited support representative within the group, based on their capacity, presence, and other routing attributes. This enables faster, more accurate resolutions. By turning intent from a passive insight into an active, intelligent routing decision, IBR becomes the operational backbone of an intent-driven contact center. Subsequently, this results in better assisted and self-service experiences. Implementing intent-based routing in your contact center can offer numerous benefits: Enhanced precision and personalization; Dynamic intent discovery; Streamlined routing configuration; Smarter workforce management and load handling; and Scalable and adaptable.