Diverse Consumer Behavior Analysis

We analyzed consumer spending patterns across three major marketplaces heading into Q4. The data reveals a fundamental shift in buyer behavior: FINDING #1: High-income shoppers are trading down across categories Consumer sentiment dropped to near-record lows despite 4% GDP growth. Even households earning $100K+ are cutting holiday spending by double digits. This isn't temporary belt-tightening. FINDING #2: Gen Z adoption of AI shopping tools jumped to 43% Nearly half of younger consumers now use AI to validate purchases before checkout. Traditional product detail pages alone no longer close the sale. The decision happens before they reach your listing. FINDING #3: Buy-now-pay-later usage crossed 75% penetration Over three-quarters of shoppers plan to use payment flexibility options this season. Brands without BNPL integration are leaving revenue on the table before Black Friday even starts. FINDING #4: Early shopping behavior accelerated by two full weeks 58% of consumers started holiday purchasing before November. The old playbook of launching promotions Thanksgiving week is now arriving after peak traffic already converted elsewhere. FINDING #5: Basket sizes contracted while transaction volume increased Shoppers are making more frequent, smaller purchases. Average order values dropped across apparel, electronics, and grocery categories. Your unit economics need recalibration. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Brands optimizing for last year's consumer behavior will underperform competitors who adapted to these five shifts. The marketplace doesn't reward nostalgia. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗰𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁: → Test promotional calendars starting two weeks earlier than 2024 → Add BNPL options to high-ticket SKUs before Cyber Week → Build content strategy around AI discovery patterns, not just human search 𝗬𝗼𝘂𝗿 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: Pick one finding above and stress-test your Q4 strategy against it this week. 𝗥𝗲𝗺𝗶𝗻𝗱𝗲𝗿: These trends accelerate heading into 2026. What worked during the last holiday cycle is already outdated.

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

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.

We are excited to introduce 🚀 𝘿𝙞𝙫𝙚𝙧𝙨𝙞𝙩𝙮𝙊𝙣𝙚 𝘿𝙖𝙩𝙖𝙨𝙚𝙩 🚀, one of the largest and most geographically diverse real world datasets for modeling everyday life behavior with smartphone sensor data. 📜 Our paper “DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling” that was recently accepted at the Proceeding of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT/ UbiComp/ISWC Conference) 2025, provides a comprehensive description of this dataset. The dataset was collected as part of the WeNet – The Internet of Us project. 📜 Paper: https://lnkd.in/drq79-9W 🌐 DiversityOne Website: https://lnkd.in/dHf-yqCH 📊 Dataset Catalogue: https://lnkd.in/duaJ8U4V 🚀 🔍 DiversityOne at a Glance ✅ Unlike prior behavior modeling datasets that focus on single-country cohorts, limited sample sizes, or preprocessed sensor features, DiversityOne provides raw, multimodal smartphone sensor data from 8 countries spanning both global north and south, with a large cohort of 782 young adults. ✅ Data collected from eight countries—China 🇨🇳, India 🇮🇳, Mongolia 🇲🇳, Italy 🇮🇹, Denmark 🇩🇰, the UK 🇬🇧, Paraguay 🇵🇾, and Mexico 🇲🇽—captures diverse lifestyles and cultural variations, enabling cross-country and cross-cultural studies. ✅ Data collection from 782 participants over four weeks generated 26 smartphone sensor time series (e.g., accelerometer, GPS, app usage, screen time) and over 350,000 self-reports, providing a rich behavioral context. This makes it one of the most comprehensive and openly available time series sensor datasets. Additionally, the dataset includes psycho-social surveys and demographic information, allowing for high-resolution insights into human behavior across different cultural contexts. ✅ The dataset also serves as a testbed for AI researchers working on time series modeling, domain adaptation / generalization across countries, pushing the boundaries of ubiquitous computing, HCI, responsible AI, and machine learning research. Excited to see what the community would do with this unique dataset! Università di Trento, ETH Zürich, EPFL, Idiap Research Institute, IT-Universitetet i København, Amrita Vishwa Vidyapeetham, Fondazione Bruno Kessler - FBK, Jilin University, Jilin University, IPICyT, The London School of Economics and Political Science (LSE), National University Of Mongolia, National University of Mongolia, Universidad Católica "Nuestra Señora de la Asunción", Universidad Católica Nuestra Señora de la Asunción, Aalborg University Matteo Busso, Andrea Bontempelli, Leonardo Javier Malcotti, Peter Kun, PhD, Shyam Diwakar, Chaitanya Nutakki, Marcelo Rodas Britez, Donglei Song, Salvador Ruiz-Correa, George Gaskell, Miriam Bidoglia, Amarsanaa Ganbold, Altangerel Chagnaa, Luca Cernuzzi, Alethia Hume, Ronald Chenú Abente Acosta, Asiku Roy Alia, Daniel Gatica-Perez, Amalia de Götzen, Ivano Bison, Fausto Giunchiglia

Hello LinkedIn Network: I just published my latest article on Forbes, and it reveals a troubling paradox in today's marketing landscape. Here are the facts: ✓ Multicultural households represent 46 million U.S. households (33% of all households) ✓ They spent $3.3 trillion in 2024—31% of ALL household spending ✓ They drove 43% of ALL spending growth from 2017-2024 ✓ In categories like vehicle insurance (+203%), medical supplies (+135%), and eggs (+123%), multicultural consumers are the dominant growth engine. Yet some of the largest advertisers are moving in the opposite direction: decreasing investments in the very segments driving growth. This isn't just a missed opportunity; it's leaving money on the table. The message is clear: growth isn't found in broad, undifferentiated "one-size-fits-all" approaches. It's found in understanding the sophisticated, diverse multicultural consumer, and investing accordingly. To truly choose growth, you need resources, expertise, and the willingness to challenge outdated assumptions. Read the full analysis: https://lnkd.in/eXt5yr7t #Marketing #Growth #MulticulturalMarketing #ConsumerInsights #Strategy

Tag someone who needs to hear this rant 🤐 One of the biggest mistakes brands make when engaging with the UK’s Muslim community is treating them as a monolithic group. The reality is far more complex—and far more exciting 😎 1️⃣ For Starters The 4 million Muslims in the UK are ridiculously diverse. They come from a wide range of ethnic backgrounds—Black, White, Somali, Yemeni, Pakistani, Bangladeshi, Turkish...you get the idea. Each group brings its own unique cultural traditions, languages, and experiences, which shape their consumer behaviours in different ways. 2️⃣ For Mains Despite their shared faith, Muslims are not one-size-fits-all. Here’s why it’s crucial for brands to stop lumping them together (lost count of how many times I have heard 'the Muslim consumer is worth'): 👨🎨 Cultural nuances matter: A campaign that resonates with Somali Muslims might not connect with British-Pakistani audiences. Understanding these differences allows your brand to create more meaningful and authentic messages. We don't all eat chicken curries btw. 👩🎤 Different consumer preferences: From fashion to food, each group has its own tastes and trends. For example, halal beauty might be more relevant to one group, while modest fashion might resonate stronger with another. Catering to these differences can set your brand apart. 🕋 Faith remains the common thread: While the Muslim community is diverse, their faith is a unifying force. Shared values like ethical consumption, modesty, and community still play a significant role in purchasing decisions, but they manifest in unique ways depending on cultural background. 3️⃣ For Dessert If you’re serious about engaging with Muslim consumers, you need to move beyond a one-size-fits-all approach. Recognise the diversity within the community, and tailor your messaging to reflect the rich mosaic of cultures that make up the UK’s Muslim population. Not only will you create more impactful campaigns, but you’ll also build deeper, longer-lasting connections with one of the country’s most dynamic and growing markets. 💪 #MuslimConsumer #CulturalDiversity #InclusiveBranding #UKMarketing #FaithAndCommunity #RespectTheDifference

Types of Customer & thier Behaviour: In FMCG Context In the FMCG industry, understanding different types of customers and their behaviors is crucial for developing effective marketing and sales strategies. Here are common customer types and their behaviors: 1. Price-Sensitive Customers - Behavior: These customers prioritize price over other factors. They often look for discounts, promotions & the best deals. - Strategies: Attract them with competitive pricing, frequent promotions, bulk purchase discounts & loyalty programs. 2. Brand-Loyal Customers - Behavior: These customers prefer specific brands and are less likely to switch, even if alternatives are cheaper. - Strategies: Maintain high product quality, engage with loyalty programs & reinforce brand values through consistent marketing. 3. Convenience Seekers - Behavior: Convenience is a top priority for these customers. They prefer easily accessible products and value time-saving solutions. - Strategies: Ensure wide product availability, offer online shopping & home delivery options, and highlight the convenience of your products. 4. Impulse Buyers - Behavior: These customers make spontaneous purchases, often influenced by attractive packaging, in-store promotions, or point-of-sale displays. - Strategies: Use eye-catching packaging, place products strategically near checkout counters & offer limited-time promotions. 5. Quality-Conscious Customers - Behavior: These customers value product quality above all else. They are willing to pay a premium for high-quality products. - Strategies: Highlight product quality through advertising, offer samples & ensure positive reviews & testimonials are visible. 6. Trend-Followers - Behavior: These customers are attracted to the latest trends & innovations. They are often influenced by social media & influencer endorsements. - Strategies: Keep up with market trends, collaborate with influencers & use social media to launch new products. 7. Value-Conscious Customers - Behavior: These customers seek a balance between price & quality. They are not the cheapest option but want good value for their money. - Strategies: Highlight the value proposition, provide detailed product information & offer occasional discounts or bundles. Understanding Customer Behavior 1. Purchase Frequency - Frequent Shoppers: Regularly buy FMCG products due to their fast consumption rate. - Occasional Shoppers: Purchase less frequently, often for non-essential or luxury FMCG items. 2. Decision-Making Process - Rational Buyers: Make decisions based on detailed information, comparisons & logical reasoning. - Emotional Buyers: Make decisions based on emotions, brand attachment, or personal experiences. 3. Shopping Habits - In-Store Shoppers: Prefer the traditional shopping experience, interacting with products physically. - Online Shoppers: Prefer the convenience of online shopping, valuing easy access & home delivery.

Reflecting on my work closely involved in the quick commerce industry, I've come to appreciate just how distinctive regional behaviors can be. Take Riyadh, for instance, where the consumer base primarily consists of working population, including a significant chunk of expats. Here, the priority lies in ensuring a seamless delivery experience. However, shift your focus to the western regions, and you'll find a different emphasis altogether. Price sensitivity reigns supreme among consumers, driving a heightened focus on value. Venturing into the eastern regions presents yet another fascinating dynamic. Here, the platform experience takes center stage, with consumers placing great value on the ease of navigating through the purchasing journey. As I reflect on these regional nuances, it's clear that understanding them is paramount for businesses seeking to thrive in the diverse landscape of Saudi Arabia. HungerStation | هنقرستيشن II Jahez International Company II ToYou | تويو II MRSOOL | مرسول II Nana | نعناع #ConsumerBehavior #RegionalNuances #BusinessStrategy #SaudiArabia

💡 Why Personality Segmentation Is Reshaping Modern Marketing Today’s consumers are diverse—not just in age or income, but in how they think, feel, and behave. That’s why brands are moving beyond demographics and embracing personality segmentation to truly connect with their audience. At the heart of this approach lies the Big Five Personality Traits, a proven framework that helps marketers understand the psychology behind consumer decisions: 🔹 Openness – Curious, creative consumers who love new experiences 🔹 Conscientiousness – Organized, detail-oriented buyers who value reliability 🔹 Extraversion – Outgoing, social individuals who respond to energetic, engaging communication 🔹 Agreeableness – Cooperative, empathetic consumers who appreciate warmth and trust 🔹 Neuroticism – Emotionally sensitive individuals who seek security and reassurance By mapping audiences using these traits, brands can: ✨ Personalize content that speaks to individual motivations ✨ Improve product positioning with psychological insight ✨ Build deeper emotional resonance and authentic loyalty As AI and data analytics advance, personality-based segmentation will become an essential pillar of customer strategy. Brands that understand the “why” behind behavior will be the ones that stand out and scale. 🔍 Exploring personality-driven marketing for your brand? Let’s connect and share ideas! #marketingstrategy #consumerbehaviour #personalitysegmentation #bigfive #marketresearch #brandgrowth #digitalmarketing