Cross-Border Selling Challenges

The U.S. grocery shopper.   Is no longer segmented only by age.   It is increasingly shaped by socioeconomic profile. Many brands entering U.S. retail still underestimate this reality. Because succeeding in American retail is no longer just about having a good product. It is about understanding: ↳ Who is shopping. ↳ What they prioritize. ↳ How they define value. ↳ And which retailers fit their lifestyle. The reality is that Working Class, Middle Class, Upper Middle Class and Affluent consumers often shop very differently. Different priorities. Different expectations. Different relationships with: price, quality, convenience, wellness and discovery. For example: Working Class shoppers are often focused on: ↳ Budget control. ↳ Basket affordability. ↳ Promotions. ↳ Private label. ↳ Practical value. Which helps explain the relevance of retailers such as Walmart, ALDI USA and Dollar General. Middle Class consumers often prioritize: ↳ Value over time. ↳ Family budgets. ↳ Bulk buying. ↳ Smart spending. ↳ Balanced convenience. Which is why retailers such as Walmart, Costco Wholesale and Kroger continue to perform strongly. Meanwhile, Upper Middle Class shoppers are increasingly driven by: ↳ Product quality. ↳ Convenience. ↳ Freshness. ↳ Better ingredients. ↳ Curated assortments. Making retailers such as Costco Wholesale, Trader Joe's and Publix Super Markets particularly relevant. And Affluent consumers often over-index in: ↳ Wellness. ↳ Premium experiences. ↳ Specialty products. ↳ Health-focused purchasing. ↳ Convenience and exclusivity. But there is an important nuance. Today's U.S. consumer is increasingly hybrid. Many affluent households still buy staples at Walmart. Many upper-middle-income shoppers regularly purchase from ALDI USA. And many value-focused consumers occasionally trade up to premium retailers for specific categories. In other words: Retail loyalty is becoming more fluid. The shopper journey is increasingly fragmented. Consumers are building their baskets across multiple retailers depending on the mission. That is why understanding retail through a single demographic lens is becoming less effective. The real opportunity is understanding: ↳ Shopping missions. ↳ Value perception. ↳ Retailer roles. ↳ And cross-shopping behavior. Because the future of U.S. grocery retail is not only about income. It is about how different consumers define value. And increasingly, every consumer defines value differently. Which retailer do you think best serves its target shopper today? And follow me if you want to understand how retail actually works in the U.S.

If your CX Program simply consists of surveys, it's like trying to understand the whole movie by watching a single frame. You have to integrate data, insights, and actions if you want to understand how the movie ends, and ultimately be able to write the sequel. But integrating multiple customer signals isn't easy. In fact, it can be overwhelming. I know because I successfully did this in the past, and counsel clients on it today. So, here's a 5-step plan on how to ensure that the integration of diverse customer signals remains insightful and not overwhelming: 1. Set Clear Objectives: Define specific goals for what you want to achieve. Having clear objectives helps in filtering relevant data from the noise. While your goals may be as simple as understanding behavior, think about these objectives in an outcome-based way. For example, 'Reduce Call Volume' or some other business metric is important to consider here. 2. Segment Data Thoughtfully: Break down data into manageable categories based on customer demographics, behavior, or interaction type. This helps in analyzing specific aspects of the customer journey without getting lost in the vastness of data. 3. Prioritize Data Based on Relevance: Not all data is equally important. Based on Step 1, prioritize based on what’s most relevant to your business goals. For example, this might involve focusing more on behavioral data vs demographic data, depending on objectives. 4. Use Smart Data Aggregation Tools: Invest in advanced data aggregation platforms that can collect, sort, and analyze data from various sources. These tools use AI and machine learning to identify patterns and key insights, reducing the noise and complexity. 5. Regular Reviews and Adjustments: Continuously monitor and review the data integration process. Be ready to adjust strategies, tools, or objectives as needed to keep the data manageable and insightful. This isn't a "set-it-and-forget-it" strategy! How are you thinking about integrating data and insights in order to drive meaningful change in your business? Hit me up if you want to chat about it. #customerexperience #data #insights #surveys #ceo #coo #ai

𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗶𝗻𝘃𝗲𝗻𝘁𝗲𝗱 "𝗽𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘁𝗮𝗿𝗴𝗲𝘁𝗶𝗻𝗴," 𝗯𝘂𝘁 𝘁𝗵𝗲 𝗮𝗶𝗺 𝘄𝗮𝘀 𝘄𝗿𝗼𝗻𝗴. 𝘋𝘦𝘮𝘰𝘨𝘳𝘢𝘱𝘩𝘪𝘤 𝘴𝘦𝘨𝘮𝘦𝘯𝘵𝘴 𝘥𝘰𝘯'𝘵 𝘱𝘳𝘦𝘥𝘪𝘤𝘵 𝘸𝘩𝘰 𝘣𝘶𝘺𝘴. 𝘛𝘩𝘦𝘺 𝘱𝘳𝘦𝘥𝘪𝘤𝘵 𝘸𝘩𝘰 𝘭𝘰𝘰𝘬𝘴 𝘭𝘪𝘬𝘦 𝘴𝘰𝘮𝘦𝘰𝘯𝘦 𝘸𝘩𝘰 𝘮𝘪𝘨𝘩𝘵. I have made a new article in Greenbook's Behavioral Insights Academy, link below. Here's the number no one wants to see in a targeting brief: 𝘿𝙚𝙢𝙤𝙜𝙧𝙖𝙥𝙝𝙞𝙘𝙨 𝙚𝙭𝙥𝙡𝙖𝙞𝙣 𝙧𝙤𝙪𝙜𝙝𝙡𝙮 𝟰–𝟲% 𝙤𝙛 𝙘𝙤𝙣𝙨𝙪𝙢𝙚𝙧 𝙗𝙚𝙝𝙖𝙫𝙞𝙤𝙧 𝙫𝙖𝙧𝙞𝙖𝙣𝙘𝙚. Not 40%. Not 15%. Four to six percent. Rachel Kennedy and colleagues analyzed 110,000+ brand user profile comparisons across 40 industries. Ford buyers and Chevrolet buyers looked statistically identical. Nike and Adidas buyers? Same. (https://lnkd.in/eU894pAx) Catalina Marketing's analysis of $415 million in TV ad spend found 53% of brand sales came from entirely outside the conventional demographic target. 𝘛𝘩𝘦𝘴𝘦 𝘢𝘳𝘦 𝘞𝘐𝘓𝘋 𝘯𝘶𝘮𝘣𝘦𝘳𝘴 𝘵𝘩𝘢𝘵 𝘴𝘩𝘰𝘶𝘭𝘥 𝘮𝘢𝘬𝘦 𝘦𝘷𝘦𝘳𝘺 𝘮𝘢𝘳𝘬𝘦𝘵𝘦𝘳 𝘴𝘵𝘰𝘱 𝘦𝘷𝘦𝘳 𝘢𝘴𝘬𝘪𝘯𝘨 𝘧𝘰𝘳 𝘨𝘦𝘯𝘥𝘦𝘳, 𝘢𝘨𝘦, 𝘨𝘦𝘰𝘨𝘳𝘢𝘱𝘩𝘺, 𝘢𝘧𝘧𝘭𝘶𝘦𝘯𝘤𝘦 𝘦𝘵𝘤 𝘦𝘷𝘦𝘳 𝘢𝘨𝘢𝘪𝘯! However, the brain doesn't check your age when it processes an ad. It asks three questions, almost entirely below conscious awareness: • 𝗗𝗼 𝗜 𝗸𝗻𝗼𝘄 𝘁𝗵𝗶𝘀 𝗯𝗿𝗮𝗻𝗱? Knowledge determines whether your ad is processed smoothly or with skepticism. • 𝗛𝗼𝘄 𝗱𝗼 𝗜 𝗳𝗲𝗲𝗹 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁? Strong brand attitudes activate instantly, filtering attention before deliberation begins. • 𝗗𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝘁𝗼 𝗺𝗲, 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄? Self-referential encoding produces better memory than any other strategy — confirmed across 129 studies. Two consumers with identical demographic profiles but different answers to those three questions will process the same ad as if they were different species. 𝗗𝗲𝗺𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰𝘀 𝗱𝗲𝘀𝗰𝗿𝗶𝗯𝗲 𝘄𝗵𝗼 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝘀𝗲𝗲𝗻 𝗮𝘀 𝗳𝗿𝗼𝗺 𝗮𝗳𝗮𝗿. 𝗨𝗽 𝗰𝗹𝗼𝘀𝗲, 𝘁𝗵𝗲 𝗯𝗿𝗮𝗶𝗻 𝗿𝗲𝘀𝗽𝗼𝗻𝗱𝘀 𝘁𝗼 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗸𝗻𝗼𝘄, 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝘄𝗮𝗻𝘁, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘁𝗵𝗲𝗺 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄. Those are not the same thing. And we have been optimizing for the wrong one for decades! One thing I have learned over the past years is that there is plenty of room to challenge even the most firmly held assumptions in marketing. Read the article here: https://lnkd.in/ese5YCW5 #neuromarketing #consumerneuroscience #marketingscience #behavioralinsights #advertising

🗣️ "Should we launch our product in Spanish or English for the US Hispanic market?" 👀 After helping launching 10+ tech products in this market, here's what the data actually shows: 📱 Language Preferences by Generation: 👨 👩 Gen Z & Millennials: ▶ 76% prefer English for tech products ▶ BUT 82% want customer support in both languages ▶ 91% appreciate bilingual marketing 👴 👵 Gen X & Boomers: ▶ 65% prefer Spanish interfaces ▶ 89% need Spanish support ▶ 72% make purchase decisions in Spanish 🔑 Key Insights: 1️⃣ Interface ≠ "Support Your product can be in English", but support MUST be bilingual. 2️⃣ Marketing = Both Campaigns should run in both languages, with cultural nuance. 3️⃣ Documentation Hierarchy: ▶ Technical docs → English ▶ How-to guides → Both ▶ Customer support → Spanish first #HispanicMarketing #ProductStrategy #Localization #TechInclusion

The consumer who asked for more Change is the only constant in a VUCA world. That with changing times, consumers’ purchase behaviour too evolve is a known fact. However, what if consumer behaviour seems to be witnessing change in two opposite directions - at the same time!! A few years ago, McKinsey had highlighted an interesting and paradoxical consumer behaviour taking shape. Consumers are living in a world of ‘ands’. A phrase used to describe the contradictory shopping behaviours of people across the globe. As explained by McKinsey, “Consumers don’t want one thing or another. They want both, but in different ways.” Here are some behavioural patterns being shaped by consumers preference for ‘ands’ Trading down and splurging selectively: Consumers are both flocking to value and buying premium products and services.  Shoppers who splurge in some categories may seek value in others. Finding comfort in familiarity and brand experimentation: While big and established brands emerge as tried and tested choice for consumers during uncertain times, they are also trying new brands. Additionally, consumers are now purchasing a repertoire of products to fill specific needs, instead of purchasing just one product. Demanding sustainability and affordability: Consumers are gravitating towards sustainable products but are also not willing to pay a premium for sustainable products in times of inflation. Maybe the real question isn't whether consumers are becoming more contradictory. But whether brands are becoming flexible enough to serve those contradictions. #MarketerDiaries #ConsumerInsight Link to the article: https://lnkd.in/gws4P7Ze

Are recommender systems truly capturing the full spectrum of user interests? Traditional recommendation approaches often optimize for a single behavior type, such as purchases in e-commerce or likes in social media, creating a limited view of user preferences. But what if we considered a broader range of user behaviors-such as watching, commenting, sharing, or favoriting? Researchers from Renmin University of China and Tencent introduced Tricolore, a novel framework that tackles this exact challenge by leveraging multi-behavior user profiling. Under the hood, Tricolore employs a versatile multi-vector learning strategy, grouping behaviors into distinct categories based on correlation. It then dynamically fuses these behavior categories with a behavior-wise multi-view fusion module, effectively handling data sparsity and better modeling user preferences. Tricolore also utilizes a popularity-balanced negative sampling approach, significantly reducing popularity bias-balancing recommendation accuracy with diversity. Its adaptive multi-task learning structure can be tailored to specific platform requirements, substantially improving performance for both mainstream and cold-start user scenarios. Extensive experiments on short-video and e-commerce platforms showcase its superiority over conventional methods. The approach, presented in the IEEE Transactions on Knowledge and Data Engineering, marks a promising advancement in creating more robust, nuanced, and effective recommendation systems.

One of the biggest challenges in UX research is understanding what users truly value. People often say one thing but behave differently when faced with actual choices. Conjoint analysis helps bridge this gap by analyzing how users make trade-offs between different features, enabling UX teams to prioritize effectively. Unlike direct surveys, conjoint analysis presents users with realistic product combinations, capturing their genuine decision-making patterns. When paired with advanced statistical and machine learning methods, this approach becomes even more powerful and predictive. Choice-based models like Hierarchical Bayes estimation reveal individual-level preferences, allowing tailored UX improvements for diverse user groups. Latent Class Analysis further segments users into distinct preference categories, helping design experiences that resonate with each segment. Advanced regression methods enhance accuracy in predicting user behavior. Mixed Logit Models recognize that different users value features uniquely, while Nested Logit Models address hierarchical decision-making, such as choosing a subscription tier before specific features. Machine learning techniques offer additional insights. Random Forests uncover hidden relationships between features - like those that matter only in combination - while Support Vector Machines classify users precisely, enabling targeted UX personalization. Bayesian approaches manage the inherent uncertainty in user choices. Bayesian Networks visually represent interconnected preferences, and Markov Chain Monte Carlo methods handle complexity, delivering more reliable forecasts. Finally, simulation techniques like Monte Carlo analysis allow UX teams to anticipate user responses to product changes or pricing strategies, reducing risk. Bootstrapping further strengthens findings by testing the stability of insights across multiple simulations. By leveraging these advanced conjoint analysis techniques, UX researchers can deeply understand user preferences and create experiences that align precisely with how users think and behave.

Think AI in CX is exclusive to big tech giants? That’s the trap. Small businesses adopting AI are outplaying the giants and leading the game. If you’re not innovating, you’re missing the revolution. As a consultant working with SMBs, I've seen firsthand how AI tools are leveling the playing field. Recently I was asked by one of my clients to come up with a solution for two challenges: Challenge 1: 24/7 customer support with limited staff and agent attrition Challenge 2: Customer retention in a highly competitive market One of the solutions I stumbled upon is Freshdesk by Freshworks. 1. 24/7 Support with Freddy AI Agents Freddy AI Agents can automate repetitive customer queries, providing round-the-clock support without additional staffing. They use natural language processing to understand customer intent, offering personalized responses across multiple channels like the web, social media, and messaging apps. They can handle customer inquiries across multiple languages, provide hyper-personalized responses, and seamlessly transfer more complex issues to human agents when needed. 2. AI-Generated Customer Retention Insights Freddy AI goes beyond basic support by analyzing customer interactions using machine learning algorithms. Freddy AI can: • Provide personalized customer experiences • Generate predictive analytics about customer trends • Provide recommendations to agents to help respond to tickets faster with Freddy AI Copilot • Create actionable insights that help businesses improve their customer retention strategies 3. Multilingual Support and Global Reach     Freshdesk enables businesses to break language barriers through: • Multi-lingual portals and knowledge base • Integration with messaging platforms like WhatsApp, Instagram, and Slack • Ability to understand and respond to customer queries across different languages Tailored support based on customer preferences and history, creating unique interactions Bonus Insight: When implementing an AI solution, focus on AI that is immediately usable and directly supports business objectives, ensuring your SMB can leverage advanced technology without complex implementation. Have you given AI a thought for your CX? Any additional tactics, thoughts or tools?

"In our latest report, we make the case for cross-border AI incident infrastructure: institutional, technical, and legal arrangements that would allow national and international authorities to detect, share, and act on AI incidents and hazards together across borders. The six architectural components required for cross-border AI Incident infrastructure, along with concrete actions needed to secure them, are outlined below: Internationally interoperable protocols for incident documentation and reporting. Governments and industry actors should adopt incident documentation structures and protocols that are universally compatible with one another, so that incident data collected in one jurisdiction becomes evidence that other jurisdictions can use. Mandatory incident documentation, disclosure, and preparedness obligations on frontier AI companies. Governments should enact statutory incident reporting obligations on frontier AI companies and require them to publish policies for preventing, preparing for and responding to serious AI incidents. Domestic incident data access and institutional coordination. Governments should grant national AI Safety Institutes, or functionally equivalent bodies, statutory access to incident data held by AI deployers and sectoral regulators, paired with formal information-sharing arrangements between such bodies and regulatory authorities. Adequate international incident analysis capacity and expertise. International standards development organizations should develop a standardized methodology for AI incident root-cause and supply-chain analysis, with multilateral institutions such as the OECD and the United Nations (UN) advocating for its adoption and national bodies investing in the technical capacities needed to apply it. Cross-border incident-sharing channels and joint investigation mechanisms. National AI governance bodies, cybersecurity agencies, and sector-specific regulators should be empowered to share incident intelligence with counterparts abroad, such as through the International Network for Advanced AI Measurement, Evaluation and Science or through a new international mechanism that could be proposed via the UN’s Global Dialogue on AI Governance. Internationally distributed incident detection, analysis and response capacity. Multilateral institutions should help extend incident infrastructure to jurisdictions who have inadequate domestic incident monitoring and management capacities through the establishment of bilateral or multilateral intelligence-sharing, capacity-building, and incident response coordination partnerships between countries that already possess relatively mature incident infrastructure and those who do not." Caio Vieira Machado, George Gor and Omer Bilgin at The Future Society

Q4 isn’t just about holiday sales. It’s about imprinting on the next generation of buyers. Case study in real time: As mall culture fades, tweens are showing up in Sephora, mirroring parent behavior and what they see on social media. Meanwhile, Claire’s is shuttering locations - a reminder that the channel and context have changed. What’s driving the shift: → Less mall culture = fewer “default” tween brands → More social media = beauty content hitting kids at 9, not 15 → A tighter parent-influence loop (parents model, kids mirror) Growth signal: Today’s under 18-year-old population is 52.7% racially/ethnically diverse - the most multicultural consumer base in U.S. history (Census.gov)... AND the pipeline is accelerating: total U.S. births fell 2% in 2023, while births to Hispanic mothers rose 1% YoY (Reuters, citing CDC). Formula: **Parent relevance → Child imprint → Generational compounding.** This multicultural reality isn't coming in a decade - it's here now and the operating system of growth. The brands that recalibrate creative, media, and measurement today will own this loop and set the category standard as today's diverse kids become tomorrow's primary buyers. #ProgrammaticAdvertising #DigitalAdvertising #HolidayAdvertising #Q4 #Marketing #AudienceIntelligence #MulticulturalMarketing #Retail #DigitalDigital Culture Group, LLC