來自 SocialHub.AI 的產品巡覽、解釋和故事 — 探索 Consumer World Model、AI 同事和完整的行銷循環。
造訪我們的 YouTube 頻道 ↗來自我們的官方頻道 · @socialhubai
最新視頻
An invitation-only afternoon for registered NRF 2027 attendees: panels on customer intelligence and moving from AI pilots to growth. Independently organized. Full video on our channel: "Meet Socialhub.AI at NRF 2027"
打開並分享 →產品展示、講解和故事。最新發布優先呈現。
43 影片
An invitation-only afternoon for registered NRF 2027 attendees: panels on customer intelligence and moving from AI pilots to growth. Independently organized. Full video on our channel: "Meet Socialhub.AI at NRF 2027"
打開並分享 →I'm Bill from SocialHub.AI. This year we went through NRF's interview and screening process for the Innovators Showcase, and we passed. Come meet us at NRF 2027: Retail's Big Show, Jan 10–12, 2027, Javits Center, New York. Book a Showcase Meeting: https://socialhub.ai/NRF2027 Full video: https://youtu.be/e3P8Wye2bGQ AI disclosure: Bill Huang's on-screen likeness and voice in this video are AI-generated (digital twin and synthetic voice). Stock footage from Pexels. SocialHub.AI was selected to exhibit at the NRF Innovators Showcase. This video is not produced or endorsed by NRF. *NRF figures are as stated by NRF. Event details may change; see the organizer's announcements for the latest. #Shorts #NRF2027 #RetailTech
打開並分享 →I'm Bill Huang, founder & CEO of SocialHub.AI. This year we went through NRF's interview and screening process for the Innovators Showcase, and we passed. SocialHub.AI is now part of the class of 2027. What is the Innovators Showcase? According to NRF, it's an invite-only, immersive exhibit at NRF's Retail's Big Show. Each year, NRF selects about 50 retail tech companies worldwide. Their technology must already be live in a retail environment or in a paid pilot. Selected companies give live, hands-on demonstrations. Come meet us at NRF 2027: Retail's Big Show, January 10–12, 2027, Javits Center, New York. Talk with our team and explore the Consumer World Model with us. Book a Showcase Meeting: https://socialhub.ai/NRF2027 SocialHub.AI — The Customer Intelligence for Agentic Commerce. Chapters 0:00 Hi, I'm Bill 0:10 What is the NRF Innovators Showcase? 0:37 How we got selected 0:59 Meet us at NRF 2027 AI disclosure: Bill Huang's on-screen likeness and voice in this video are AI-generated (digital twin and synthetic voice). Stock footage from Pexels. SocialHub.AI was selected to exhibit at the NRF Innovators Showcase. This video is not produced or endorsed by NRF. *NRF figures are as stated by NRF. Event details may change; see the organizer's announcements for the latest.
打開並分享 →I read BCG's new report on agentic commerce, plus two related BCG articles. First I walk through what BCG found, then the six changes I see. BCG's findings and my own take are kept separate. What BCG found: a retailer can make a sale without a visit; most brands BCG surveyed expect to spend more on retail media, not less; losing the customer relationship is retailers' most common concern; and 76% of marketers in a BCG study say cutting brand spending hurts more than it did five years ago. My take, six changes: 1. the platform model 2. agent-friendly sites 3. ad budgets (my view: they shrink over time; that's not BCG's finding) 4. brand building 5. customer relationships 6. martech for people and agents. 💬 Which change are you working on first? Tell me in the comments. Chapters 0:00 Which BCG report I read 0:20 What BCG found 2:02 My take: 6 changes 3:23 Where SocialHub.AI fits The BCG research discussed: • "How Retailers Can Reinvent the Digital Flywheel for Agentic Commerce" (Oct 21, 2026): https://www.bcg.com/publications/2026/retailers-win-ai-shopping-agents • "Are CPG Companies Ready for Agentic Commerce?" (Oct 30, 2026): https://www.bcg.com/publications/2026/how-cpg-companies-win-agentic-commerce • "Agentic Scenarios Every Marketer Must Prepare For" (Mar 25, 2026): https://www.bcg.com/publications/2026/agentic-scenarios-every-marketer-must-prepare-for Loyalty Concierge in the member portal: https://socialhub.ai/platform/ai-frontier Learn more: https://www.socialhub.ai Disclaimer: This video is Bill Huang's independent commentary on publicly available BCG research. SocialHub.AI is not affiliated with or endorsed by BCG. Charts were redrawn by us from numbers published in the reports; each is labelled with its source. Statements introduced as "my take" or "I think" are Bill's opinion, not BCG's. Notes: AI shopping agents are discussed as an industry trend. The SocialHub.AI product screen is an illustrative mock-up with demo data (tagged ILLUSTRATIVE and DEMO DATA); graphics tagged ILLUSTRATIVE were made for this video; stock footage is from Pexels. Bill's on-camera segments and narration are an AI-generated likeness of Bill Huang (a digital twin of his face and voice). Credits Music: "Motivating Mornings" by Ahjay Stelino, via Mixkit (https://mixkit.co), Mixkit Stock Music Free License. Stock footage: Pexels (https://www.pexels.com), Pexels License. Clip IDs: 39238830, 5981972, 6667264, 6169987, 7647627, 14513394, 13983394, 8088612, 853782, 13983397, 6602861, 5738746, 6667244, 8388368. Voice: all narration uses an AI-generated likeness of Bill Huang's voice; on-camera segments use an AI-generated likeness of his face and voice. #AgenticCommerce #RetailAI #CustomerLoyalty
打開並分享 →Same points-only rewards for everyone, the same promo calendar, the same coupon. Research by Frederick Reichheld of Bain & Company, published in Harvard Business Review, found that a 5% lift in customer retention can raise profits by 25% to 95% (Reichheld & Schefter, "E-Loyalty: Your Secret Weapon on the Web," HBR, July–August 2000; also cited in HBR, 2014). So why do so many loyalty programs still treat every member the same? In this video we follow a hypothetical North American retailer, with stores, an online shop and an app, through five ways loyalty programs that stand out work differently: 1. Every purchase leads to the next: members sit in different tiers, each with its own perks, and every purchase moves them toward the next unlock: a reward, a coupon in their wallet, a stamp card. 2. The member's clock, not the promo calendar: an average can hide very different buying rhythms. The Consumer World Model (CWM) can read each member's own purchase rhythm once it's trained and validated on your own data. Until then, its behavior reads are uncalibrated, so treat them as reference signals. 3. Different members, different offers: a blanket coupon lifts sales, but much of the discount goes to people who'd have bought anyway. Instead: a reminder with no discount for someone about to buy, a small nudge for a coupon-sensitive member on the fence, and a bigger welcome-back offer for a lapsed high-value member, all inside a budget ceiling your team sets. 4. The nearby moment: a lightweight kit added to your own app, behind your own consent screen. When an opted-in member comes near a store, your app records it as your own first-party data, which can trigger a welcome nudge or a win-back offer. 5. Prove it: a simulation estimates what happens if you do nothing and compares options (forecasts, not results), and a holdout group gets no message, so the program only takes credit for sales it caused. 💬 Which of the five does your program do today? Tell us in the comments. Chapters 0:00 Same rewards, same coupon. Why? 0:25 1. Every purchase leads to the next 0:46 2. The member's clock, not the promo calendar 1:14 3. Different members, different offers 1:37 4. The nearby moment 1:55 5. Prove what it caused 2:18 Recap 2:32 Over to you Learn more: https://www.socialhub.ai Consumer World Model: https://cwm.socialhub.ai Notes: The retailer is hypothetical. Screens tagged SOCIALHUB.AI PRODUCT VIDEO are SocialHub.AI product recordings with demo data. Graphics tagged ILLUSTRATIVE are made for this video. CWM behavior reads are uncalibrated reference signals; purchase-rhythm reads are available only once the model is trained and validated on your own data. Simulation outputs are forecasts, not results. Location features apply only to opted-in members in the brand's own app. The closing message from Bill Huang uses an AI-generated likeness of Bill (a digital twin of his face and voice, made with his approval); it is labelled on screen and this video is marked as altered or synthetic content. Harvard Business Review and Bain & Company are cited as sources only; no endorsement or partnership is implied. Credits Music: "Talent in the Air" by Diego Nava, via Mixkit (https://mixkit.co), Mixkit Stock Music Free License. Stock footage: Pexels (https://www.pexels.com), Pexels License. Voice: synthetic narration; closing segment uses an AI-generated likeness of Bill Huang's voice and face. #LoyaltyMarketing #CustomerRetention #RetailAI
打開並分享 →A lot of retail AI looks great in a slide deck. The real test is a retailer saying: show me. That's why SocialHub.AI applied to the NRF 2027 Innovators Showcase at NRF 2027: Retail's Big Show, Javits Center, New York, January 10–12, 2027. In this video, Bill Huang, Founder & CEO of SocialHub.AI, shares three reasons we applied: 1. Learn from retailers. Three days face to face with retail teams, to hear where customer data stops being useful and where marketing decisions still feel like guesswork. 2. Show, don't tell. Per NRF, the program looks for technology already running in retail or in paid pilots, with a live demo. So you'll see the Consumer World Model (CWM) working on screen: a model of consumer behavior that suggests a member's likely next events. 3. AI agents for marketing. Many teams are asking where agents fit and who stays in control. We'll show how CWM-enabled Loop Marketing connects insight to campaigns (capture, decision, activation, learning), with Harness adding approvals and an audit trail. If you're at NRF 2027, book a Showcase meeting at https://socialhub.ai/NRF2027 Retail leaders can also request an invitation to our invitation-only Executive Pre-Day on January 9. 💬 What's one thing you wish AI understood about your customers? Tell me in the comments. Chapters 0:00 Why we applied 0:24 Reason 1: Learn from retailers 0:50 Reason 2: Show, don't tell 1:28 Reason 3: AI agents for marketing 1:57 What you'll see in New York 2:27 Your turn Notes: Bill appears as an AI digital twin, with an AI-generated voice and lip-sync made from his own recordings. Product screens are a SocialHub.AI demo recording with demo data. NRF names and logo are shown for information only; no endorsement or partnership is implied. Credits Music: "Lo-Fi 04" by Lily J, via Mixkit (https://mixkit.co), Mixkit Stock Music Free License. Stock footage: Pexels (https://www.pexels.com), Pexels License. Learn more about SocialHub.AI: https://www.socialhub.ai #NRF2027 #RetailAI #AIAgents
打開並分享 →Innovators Showcase at the Javits Center (Jan 10–12) or the invitation-only Executive Pre-Day on Jan 9. Full video on our channel: "Meet Socialhub.AI at NRF 2027"
打開並分享 →Agentic commerce may change how brands win the sale: when AI shopping agents search, compare and buy for us, paying for visibility may no longer translate into influence. How brands can shift budget from buying attention to earning a place in an AI agent's decision, through brand trust, reliable product information and real customer intent. Search ads and sponsored listings were built around human attention. But an AI agent's job is to find the best fit for a shopper's needs, budget and preferences. In this video we follow two hypothetical people: Sarah, a runner who hands her shoe purchase to an AI agent, and Maya, the CMO of a running shoe brand, who asks a different question while reviewing next year's media plan: how do we earn a place in an agent's decision? Her answer is to stop pouring more money into bought attention and invest in two directions instead: the brand itself (a clear promise, real value, and accurate, complete, structured product information) and the customer, both human and AI (understanding the intent behind each purchase, building real loyalty, and making the brand easy for agents to read and trust). Advertising will adapt. But the economics of buying attention may have to be rebuilt. The battle is shifting from attention to intent. 💬 When an AI agent shops for your customer tomorrow, will it choose you? Tell us in the comments. Chapters 0:00 From attention to intent 0:14 The shopper delegates 0:48 Paying for visibility may not buy influence 1:01 A different question 1:19 Two better places to invest 1:55 Understanding intent 2:09 The battle is shifting 2:19 Over to you Notes: Sarah, Maya and the brand are hypothetical. Screens tagged ILLUSTRATIVE are mock-ups made for this story. The screen tagged DEMO is a SocialHub.AI product demo recording with demo data. Credits Music: "Piano Reflections" by Ahjay Stelino, via Mixkit (https://mixkit.co), Mixkit Stock Music Free License. Stock footage: Pexels (https://www.pexels.com), Pexels License. Voice: synthetic narration. Learn more about SocialHub.AI: https://www.socialhub.ai #AgenticCommerce #AIAgents #RetailAI #FutureOfCommerce
打開並分享 →Most retailers use AI tools by now. But using AI is not the same as changing how the business works. In this video we follow one hypothetical North American retailer, with stores, an online shop and a loyalty program, and two people on its team: Dana, a merchandiser, and Leo, who runs loyalty marketing. It starts with individuals working faster with AI. Next, digital workers take on defined jobs within clear boundaries. Then Leo's small team orchestrates specialized agents around one goal. Finally, the organization shares context within clear permissions, keeps agent actions traceable, and learns from every execution. These stages can coexist in one company, and human judgment and accountability remain essential throughout. The real distinction is not how many AI tools a company uses. It is how work is delegated, how decisions are made, and how the organization changes as a result. 💬 So, where is your organization today, and what would it take to move beyond individual productivity? Tell us in the comments. Chapters 0:00 One retailer's AI journey 0:17 It starts with: people working faster with AI 0:37 Next: digital workers take on defined jobs 0:55 Then: human–AI teams redesign the work 1:24 Finally: a new way of operating 1:58 What really changes 2:16 Over to you Learn more about SocialHub.AI: https://www.socialhub.ai Notes: The retailer, Dana and Leo are hypothetical. Screens tagged ILLUSTRATIVE are mock-ups made for this story. Screens tagged DEMO are SocialHub.AI product demo recordings with demo data. Harness is rolling out. Credits Music: "Wyungamesh" by Eugenio Mininni, via Mixkit (https://mixkit.co), Mixkit Stock Music Free License. Stock footage: Pexels (https://www.pexels.com), Pexels License. Voice: synthetic narration. #RetailAI #AIAgents #FutureOfWork
打開並分享 →Heading to NRF 2027 in New York? Socialhub.AI will be there January 9–12, 2027, with one theme: from consumer intelligence to autonomous growth. Follow a customer-engagement lead at a (hypothetical) North American retailer as they plan NRF week and pick the right way to connect. Book a Showcase meeting or request a Pre-Day invitation: https://www.socialhub.ai/NRF2027 Chapters 0:00 Meet Socialhub.AI at NRF 2027 0:24 Two ways to connect 0:47 Understand: the Consumer World Model 1:06 Activate, then connect 1:26 Book a Showcase meeting 1:45 The Executive Pre-Day 2:16 See you in New York Two ways to connect • NRF Innovators Showcase: Javits Center, River Pavilion, New York, Jan 10–12, 2027 (Jan 10–11, 9 AM–5 PM ET; Jan 12, 9 AM–4 PM ET). Product demonstrations and personalized conversations. Booth: pending confirmation. • Executive Pre-Day, "AI-Native Retail: From Intelligence to Growth": Saturday, Jan 9, 2027, 2–5 PM ET. Executive roundtable, panel discussions and networking. Open only to registered NRF 2027 attendees; invitation required. Independently organized; not an official NRF or Microsoft-hosted event. Tentative agenda. What you can explore 01 Understand: Consumer World Model 02 Activate: CWM-Enabled Loop Marketing (capture, decision, activation, learning) 03 Connect: AI Agents & Enterprise Integration (Microsoft and SAP integration opportunities) Meeting requests and invitation applications are reviewed by the team; a request is not a confirmed booking. Details may change. Product screens show demo data. Page imagery is AI-generated illustration. NRF and Retail's Big Show are marks of the National Retail Federation. Microsoft and SAP are trademarks of their respective owners. Mention does not imply endorsement. Credits Music: "Wyungamesh" by Eugenio Mininni (Mixkit, Mixkit Stock Music Free License). Stock footage: Pexels (Pexels License). #NRF2027 #RetailAI #Socialhub
打開並分享 →It's Monday morning. An engagement lead at a North American retailer has customer, order and behavioral data in Snowflake, and one question: which customers deserve a closer look, and why? This 2.5-minute integration preview shows how Consumer World Model (CWM) works with data your team already keeps in Snowflake. CWM connects to the export views your team approves, a customer-managed connector sends authorized records to the external CWM service, and analysis results are written back to Snowflake for review. Recorded live in Snowsight with a demo account using synthetic data: two members, two orders and eight behavioral events. Account identifiers and the service user name are masked. Overview panels are illustrative. Chapters 0:00 Intro: a Monday question 0:22 Step 1 · Start with approved data 0:37 Step 2 · Traceable, consented records 0:49 Step 3 · A round trip, back to Snowflake 1:15 Step 4 · Retries without duplicates 1:32 Step 5 · History across runs 1:45 Step 6 · Verified in Snowflake 1:58 Your team decides What you'll see • CWM reading only the export views your team approves • Members that keep a source identifier and explicit consent dates • Reference signals for purchase, long gap and cart removal written back to Snowflake. These are uncalibrated reference values, not purchase probabilities • Replay testing: four historical events, four unique event IDs, zero duplicates • Two CWM jobs with two results each, kept side by side for comparison • Snowflake query history showing a successful writeback by the restricted connector role • Joining returned results to your own data, for example for a win-back review, with people making the call Learn more: https://socialhub.ai CWM: https://cwm.socialhub.ai Important: Demo account with synthetic data. Signals shown are uncalibrated reference values, not purchase probabilities. Snowflake is a trademark of its respective owner; this video does not imply partnership or endorsement. Availability may vary. Stock footage: Pexels (Pexels License). Music: "Wyungamesh" by Eugenio Mininni, Mixkit (Mixkit Stock Music Free License). #Snowflake #CustomerData #MarketingAI
打開並分享 →It's Monday morning, and a CRM marketing lead has one question before her team meeting: where should we focus this week? Consumer World Model (CWM) is now available in ChatGPT, so she can ask it right there, in plain language. This 2-minute tour follows one marketer, Maya, whose CWM workspace already brings together commerce and engagement data from SAP, customer records from Salesforce and online orders from Shopify. She installs CWM Consumer Intelligence from ChatGPT's app directory, connects it with a read-only workspace key, and asks for a short summary of consumer signals with the source cutoff and a plan for the week. Recorded live in ChatGPT with a demo workspace using sample data. Overview panels are illustrative. Chapters 0:00 Intro: Maya's Monday question 0:18 Her data, already connected 0:36 Step 1 · Install from the app directory 0:46 Step 2 · Connect, read-only 1:04 Step 3 · Ask in plain language 1:15 Step 4 · Signals, with a source cutoff 1:37 Step 5 · A plan for the week 1:52 People stay in control What you'll see • Finding "CWM Consumer Intelligence, by SocialHub.AI" in ChatGPT's app directory • A read-only connection: it only reads existing data, and marketing actions are not supported • Aggregate behavioral signals shown as uncalibrated reference scores, not probabilities • Source, coverage and source cutoff, so you know how fresh the answer is • A short investigation plan for the week, with people reviewing and deciding every action in their own campaign tools Who it's for: existing CWM customers with linked workspace access. Learn more: https://socialhub.ai CWM: https://cwm.socialhub.ai Important: Demo workspace with sample data; scores shown are uncalibrated reference signals, not customer results. The ChatGPT connection is read-only and no marketing actions are executed from ChatGPT. ChatGPT is a separate service from SocialHub.AI. Availability may vary. Stock footage: Pexels (Pexels License). Music: "Wyungamesh" by Eugenio Mininni, Mixkit (Mixkit Stock Music Free License). #ChatGPT #CRM #MarketingAI
打開並分享 →How can a marketing team on SAP move from an existing audience to a reviewed campaign and a clear next step? This illustrative scenario follows Maya, a fictional retail marketer whose team uses SAP Engagement Cloud (formerly SAP Emarsys), as Techsun Retail AI Hub helps her read customer state, draft a campaign and email, compare options in a digital-twin simulation, hand the draft back to Emarsys, sync a multi-step journey into SAP Engagement Cloud as a draft automation program, and follow up with next-best-action and win-back steps. It closes with an illustrative Joule scenario and a practical place to start. Techsun Retail AI Hub works with SAP Emarsys. Consumer World Model outputs are reference signals until they are validated on your own data. The SAP review screens shown are illustrative mock-ups; the sync-to-SAP sequence is from a recorded demo environment with demo data. Figures on screen are demo data or forecasts, not results. Learn more: https://socialhub.ai Chapters 0:00 SAP customer data and the marketing task 0:42 From existing audience to customer state 1:35 Turning a business goal into a campaign 2:28 Brand content and customer relevance 3:13 Comparing options before sending 3:53 Handoff, sync, and review in SAP 5:43 Behavior and orders return to customer context 6:33 Next best action and win-back 7:20 Asking a question in an illustrative Joule scenario 8:13 Operational control and where to start #SAP #CustomerEngagement #MarketingAutomation
打開並分享 →Your team opens a customer record in Salesforce. The history is there, but what should the next conversation be about? This 2-minute tour shows how Consumer World Model for Salesforce brings customer understanding onto the Contact page, with the evidence behind it and an employee in charge of every action. Shown on genuine Salesforce screens from an independent test org (managed app 1.1.0) with synthetic test evidence. Overview panels are illustrative. Chapters 0:00 Intro: the next-conversation question 0:24 Step 1 · Understand the customer 0:39 Step 2 · Prepare the conversation 0:54 Step 3 · Inspect the evidence 1:10 Step 4 · Review before action 1:23 Step 5 · Keep results distinct 1:34 How it connects: admin setup and who does what What you'll see • A business brief on the Contact page: observed context, why it matters, and a proposed next step for human review • Discovery questions to adapt, a concern worth exploring, and risks to verify • Source coverage and limitations. Predictions that aren't available stay unavailable instead of being guessed • A Salesforce Flow that shows the recommendation and a follow-up due date before an employee confirms a native follow-up Task • Action progress tracked separately from the business outcome Setup: admins connect an authorized CWM tenant, map customer identities, choose permitted sources and assign access. Models and language model interpretation stay in CWM. Learn more: https://socialhub.ai Salesforce demo page: https://cwm.socialhub.ai/salesforce/demo Important: Illustrative. Genuine Salesforce screens from an independent test org with synthetic test evidence. Availability depends on entitlement and validated evidence. Salesforce is a separate provider; Marketplace publication and security review are pending. Customer-facing actions require employee confirmation. Stock footage: Pexels (Pexels License). #Salesforce #CRM #CustomerExperience
打開並分享 →Most AI agent settings should be tunable. Five never should. In the Socialhub.AI Harness, they aren't more knobs, they're a fixed floor: 1. Approval gears: anything that messages a customer stays locked to human review. 2. Spend caps. 3. A token budget, charged before the AI acts. 4. A kill-switch. 5. An audit log of every step. They live in the agent runtime, not in the prompt. The agent can't edit them, and every call is checked first. If a check can't complete, the action doesn't run. Tune the behavior. Never the floor. Learn more: https://socialhub.ai Illustrative; product screens use demo data. Harness guardrails are rolling out with the enterprise agent runtime. Customer-facing messages require human review. Scene footage: Pexels (free license). #AIAgents #AIGovernance #MarketingAutomation
打開並分享 →Your AI can pick who needs a win-back offer in seconds. But if the write-back to loyalty is a nightly batch, the loop still turns once a day. Loop Marketing runs four steps: Capture → Decide → Activate → Accumulate. The slowest step sets the speed. Keeping all four on one member ID and one governed data layer lets the decision, the offer and the points land back on the member record instead of in tomorrow's batch. Anything that messages a customer still goes to a person for review, every step stays on the record, and each turn is measured against a holdout. Not a faster model. A faster write-back. Learn more: https://socialhub.ai Illustrative; product screens use demo data. Loop speed and results depend on your systems, data quality and validation. Customer-facing messages require human review. Scene footage: Pexels (free license). #LoopMarketing #MarketingAutomation #Loyalty
打開並分享 →If you run CRM or loyalty, generation got cheap; decisions didn't. Who should you contact, when, and with what, and how would you prove it worked? This explainer walks through the Consumer World Model (CWM): a small, brand-specific behavior model that reads member signals and suggests likely next steps. It runs inside a Harness of permissions, approvals and audit, with human review before anything reaches a customer. Chapters 0:00 Why this matters (intro) 0:34 Marketing has a model problem 1:15 Big brain, small brain 1:59 One brand, one Behavior Model 2:39 Three reads on every member 3:34 Guilty until proven useful 4:12 The do-nothing twin 5:00 From signal to action 5:47 Where to start What you'll learn: - Why a "big brain" language model and a "small brain" behavior model do different jobs - One brand, one Behavior Model: never pooled, versioned, and checked against a baseline - Three reads on every member, and why small segments are suppressed, not smoothed - The do-nothing Digital Twin: triage ideas before you spend; the experiment decides; the holdout proves - Loop Marketing: capture, decide, activate, accumulate, with people approving what goes out Note: product screens use demo data and graphics are illustrative. CWM outputs are reference signals, not calibrated purchase probabilities, until validated on your own data. Digital Twin outputs are forecasts, not results. Outcomes depend on data quality and validation. Start here: The Consumer World Model playlist: https://www.youtube.com/playlist?list=PLDwbRdXMysVU Learn more: https://socialhub.ai #CRM #LoyaltyMarketing #ConsumerWorldModel
打開並分享 →A shopping mall starts managing consumers alongside its merchants through one cross-tenant loyalty network. Follow a new Hong Kong resident and her friend from joining to earning, redeeming and inviting. Learn more: https://socialhub.ai/resources/videos/shopping-mall-cross-tenant-loyalty SocialHub.AI: https://www.socialhub.ai
打開並分享 →Three AI colleagues on one governed data layer: SoCode brings certified numbers into your IDE, SoTag makes Slack a shared workspace for people and agents, and SoMe gives every member a personal AI connection. Learn more: https://socialhub.ai/resources/videos/so-family-agents SocialHub.AI: https://www.socialhub.ai
打開並分享 →A tour of the live Win-Back console: guardrails you set once, AI employees with budgets and goals, and a real agent session that hands off to a human when needed. Every step stays on the record. Learn more: https://socialhub.ai/resources/videos/ai-employees-win-back SocialHub.AI: https://www.socialhub.ai
打開並分享 →When a customer abandons a cart late at night, what should your AI agent do? The Skills Library writes your best play down as a managed, versioned asset with triggers, tool allowlists and machine-checked rules. Learn more: https://socialhub.ai/resources/videos/enterprise-skill-library SocialHub.AI: https://www.socialhub.ai
打開並分享 →Flash is a platform you build on: an MCP server so Claude or your own agent can operate it, a full REST API with webhooks and connectors, and embeddable pages behind SSO and SCIM. Every call is scope-checked and logged. Learn more: https://socialhub.ai/resources/videos/build-on-flash-open-platform SocialHub.AI: https://www.socialhub.ai
打開並分享 →An LLM can write a great email in seconds — but it can't tell you who should get it, or when. That takes a second model: a consumer world model trained on your own first-party behavior data. Big brain, small brain: the LLM reasons and writes; the world model estimates each member's likely next move and grounds it. And it has to earn its place — if it can't beat a simple baseline on held-out data, it doesn't go live. CWM outputs are uncalibrated reference signals until validated on your own data. Learn more: https://socialhub.ai
打開並分享 →Privacy as machinery, not a page in the footer: AI-drafted, versioned policies, consent bound to the exact published version, one-click opt-out, and member rights such as data export and erasure built into the platform. Learn more: https://socialhub.ai/resources/videos/privacy-consent-compliance SocialHub.AI: https://www.socialhub.ai
打開並分享 →Follow one campaign's coupon budget through three gates: tiered authorization, two-step approval, and a hard budget ceiling the system enforces at issuance. Then see in-flight monitoring and finance-ready exports. Learn more: https://socialhub.ai/resources/videos/campaign-cost-governance SocialHub.AI: https://www.socialhub.ai
打開並分享 →Tell the AI your goal in plain words and watch Flash draft a real, editable campaign: audience, offer, channels, and an on-brand email. Nothing sends without your approval. Learn more: https://socialhub.ai/resources/videos/campaign-ai-generated SocialHub.AI: https://www.socialhub.ai
打開並分享 →Location marketing done properly: a lightweight SDK in your own app, behind your consent screen. Store geofences turn visits into first-party behaviors that trigger campaigns, and lift is measured against a holdout. Learn more: https://socialhub.ai/resources/videos/lbs-store-location-marketing SocialHub.AI: https://www.socialhub.ai
打開並分享 →Stop guessing twice: who to target, and what happened after the click. Behavior-based predictions become consent-filtered audiences for Google and Meta, and results are measured against a holdout. Learn more: https://socialhub.ai/resources/videos/ads-on-real-behavior SocialHub.AI: https://www.socialhub.ai
打開並分享 →A blanket coupon lifts sales, but much of it goes to people who would have bought anyway. Smart Discounting uses purchase-imminence scores and discount-sensitivity tags to build a discount ladder, and pretests it on digital twins. Learn more: https://socialhub.ai/resources/videos/smart-discounting SocialHub.AI: https://www.socialhub.ai
打開並分享 →Follow one marketing manager through a complete cross-border campaign: audience insight, ads, a campaign generated from one business goal, digital-twin testing, next-best actions and win-back. Real product, start to finish. Learn more: https://socialhub.ai/resources/videos/the-full-loop-en SocialHub.AI: https://www.socialhub.ai
打開並分享 →Point an AI at raw tables and it's confidently wrong. Capture adds a governed semantic layer (business ontology, governed tags, certified metrics) so every agent and dashboard reads the same number. Learn more: https://socialhub.ai/resources/videos/capture-ai-ready-cdp SocialHub.AI: https://www.socialhub.ai
打開並分享 →Socialhub.AI has been selected for the NRF 2027 Innovators Showcase and is on the organizer's published exhibitor list. The Innovators Showcase is an invitation-only retail tech program within NRF's Retail's Big Show. Per NRF, about 50 retail tech companies are selected worldwide each year. Their technology must already run in retail environments or be in paid pilots, with live demo capability. 📍 NRF 2027: Retail's Big Show 📅 January 10–12, 2027 🏢 Javits Center, New York We'll be demoing there and meeting retailers and technology partners. Our focus is the Consumer World Model (CWM), which understands consumer behavior and predicts what comes next to support customer engagement decisions. It works together with Harness and Loop Marketing. Thank you to our team, customers, and partners. See you in New York! 🌐 https://socialhub.ai *NRF figures are as stated by NRF. Event details may change; see the organizer's announcements for the latest.
打開並分享 →Socialhub.AI has been selected for the NRF 2027 Innovators Showcase and is on the organizer's published exhibitor list. The Innovators Showcase is an invitation-only retail tech program within NRF's Retail's Big Show. Per NRF, about 50 retail tech companies are selected worldwide each year. Their technology must already run in retail environments or be in paid pilots, with live demo capability. 📍 NRF 2027: Retail's Big Show 📅 January 10–12, 2027 🏢 Javits Center, New York We'll be demoing there and meeting retailers and technology partners. Our focus is the Consumer World Model (CWM), which understands consumer behavior and predicts what comes next to support customer engagement decisions. It works together with Harness and Loop Marketing. Thank you to our team, customers, and partners. See you in New York! 🌐 https://socialhub.ai *NRF figures are as stated by NRF. Event details may change; see the organizer's announcements for the latest.
打開並分享 →After a campaign closes, how did it actually land? This video shows how NPS surveys, a member-facing concierge, and behavioral signals turn what customers say, ask and do into one feedback picture. Learn more: https://socialhub.ai/resources/videos/listening-to-customers SocialHub.AI: https://www.socialhub.ai
打開並分享 →Integration without copies: query Snowflake, Databricks and Salesforce Data Cloud where the data lives, through a governed, cost-checked, audited loop. Plus enterprise connectors for commerce, POS and ERP systems. Learn more: https://socialhub.ai/resources/videos/zero-copy-integrations SocialHub.AI: https://www.socialhub.ai
打開並分享 →The Consumer World Model flags members who are close to buying. Watch the live product turn that signal into an audience, pretest offers on digital twins, and launch an automated campaign. Learn more: https://socialhub.ai/resources/videos/consumer-world-model-product-tour SocialHub.AI: https://www.socialhub.ai
打開並分享 →What a Consumer World Model is: a behavioral model that learns from real customer behavior in time order, with a Behavior Model for the past and a Digital Twin for the future. The video maps its capabilities and walks through two cases from start to finish. Learn more: https://socialhub.ai/resources/videos/what-is-a-consumer-world-model SocialHub.AI: https://www.socialhub.ai
打開並分享 →An apparel chain sets one annual GMV goal, then breaks it down by time and by organization, all the way to each store. Every goal tracks the same certified metric your dashboard uses and rolls up automatically from real orders. Learn more: https://socialhub.ai/resources/videos/business-goals SocialHub.AI: https://www.socialhub.ai
打開並分享 →A tour of the loyalty module, led by the portal members hold in their hand: tiers and perks, points ledger, rewards mall, coupon wallet, referrals, stamp cards, wallet passes and more. Screens are built as blocks with AI and published without developers. Learn more: https://socialhub.ai/resources/videos/loyalty-member-portal SocialHub.AI: https://www.socialhub.ai
打開並分享 →A new CRM lead is asked how many members the brand is losing, and who. Flash answers on day one because preset metrics and tags ship with the platform on one governed semantic layer. Learn more: https://socialhub.ai/resources/videos/preset-metrics-and-tags SocialHub.AI: https://www.socialhub.ai
打開並分享 →The whole platform on one map: customer-lifecycle applications on top, a big brain (LLM) and a small brain (Consumer World Model) in the middle, and a governed first-party data foundation underneath. Part two follows one win-back as data flows through every layer. Learn more: https://socialhub.ai/resources/videos/system-architecture SocialHub.AI: https://www.socialhub.ai
打開並分享 →Bill Huang, founder and CEO of SocialHub.AI, explains why language models alone can't tell brands who is about to buy or leave, and what a Consumer World Model does differently. A 3-minute introduction to the idea behind the platform. Learn more: https://socialhub.ai/resources/videos/consumer-world-model-founder-message SocialHub.AI: https://www.socialhub.ai
打開並分享 →Three ways Flash reads your business, all on one governed semantic layer so they never disagree: live dashboards, plain-language insights answered from certified metrics, and an AI research agent for strategy. Learn more: https://socialhub.ai/resources/videos/decide-strategy-analytics-insights SocialHub.AI: https://www.socialhub.ai
打開並分享 →