[ { "observation_id": "RTB-M001", "bin": 2, "metric": "TikTok Shop US GMV", "exact_wording": "US reached $11.8 billion in the first half of 2026, up 103% year-on-year (search-summary)", "value": "11.8", "unit": "USD bn", "definition": "GMV as estimated by Tabcut/Momentum Works", "denominator": "", "population": "TikTok Shop US", "sample_size": "not stated", "recruitment": "not applicable", "geography": "US", "period": "2026-H1", "publication_date": "2026-08", "currency": "USD", "gross_net": "gross (GMV)", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "vendor-estimate", "commercial_interest": "Momentum Works sells research; Tabcut sells TikTok analytics", "uncertainty": "third-party scraping-based estimate; not TikTok-reported", "comparability": "not comparable with TikTok-reported figures or other trackers (Dashboardly conflicts)", "source_ids": [ "RTB-S013" ], "claim_ids": [ "RTB-C011" ], "check_level": "search-level", "stream": "RTB" }, { "observation_id": "RTB-M002", "bin": 2, "metric": "Share of TikTok Shop US attributed GMV by content type", "exact_wording": "Shop tab rose to 51.4% of attributed GMV, while Live narrowed to 8.2% (search-summary)", "value": "Shop tab 51.4%; video 40.4%; LIVE 8.2%", "unit": "of attributed GMV", "definition": "tracker attribution", "denominator": "attributed US GMV", "population": "TikTok Shop US", "sample_size": "not stated", "recruitment": "not applicable", "geography": "US", "period": "2026-H1", "publication_date": "2026-08", "currency": "", "gross_net": "gross", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "vendor-estimate", "commercial_interest": "as RTB-M001", "uncertainty": "attribution method undisclosed", "comparability": "Dashboardly gives LIVE 26% (2025) — different period/method", "source_ids": [ "RTB-S013" ], "claim_ids": [ "RTB-C011" ], "check_level": "search-level", "stream": "RTB" }, { "sample_size": "not stated", "recruitment": "not applicable", "currency": "", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "check_level": "search-level", "comparability": "Not comparable with US/UK figures; China-specific", "observation_id": "RTB-M201", "bin": 6, "metric": "Orders placed via Qwen app during Lunar New Year coupon campaign", "exact_wording": "nearly 200 million orders during the two-week campaign (search-summary); 10 million in first nine hours", "value": "~200 million (two weeks); 10 million (first 9 hours)", "unit": "orders", "definition": "orders placed through Qwen prompts across Alibaba services", "denominator": "none", "population": "Qwen users, China", "geography": "China", "period": "2026-02 (two weeks from 6 Feb)", "publication_date": "2026-02 to 2026-03", "method": "vendor-estimate", "commercial_interest": "Alibaba (operator) claim relayed by Reuters / The Information", "uncertainty": "Vendor-claimed; coupon-subsidised; includes food delivery, tickets, travel", "source_ids": [ "RTB-S221", "RTB-S222" ], "claim_ids": [ "RTB-C223" ], "stream": "RTB-2" }, { "sample_size": "not stated", "recruitment": "not applicable", "currency": "RMB", "gross_net": "gross", "returns_treatment": "gross of returns (source notes high return rate)", "attribution_rule": "not stated", "check_level": "search-level", "comparability": "Not comparable with US/UK figures; China-specific", "observation_id": "RTB-M202", "bin": 2, "metric": "Douyin e-commerce GMV 2025 (expected)", "exact_wording": "2025 GMV expected above RMB 4 trillion; >30% growth first ten months (search-summary)", "value": ">RMB 4 trillion (expected)", "unit": "RMB GMV", "definition": "GMV (payment-based); returns high relative to Taobao", "denominator": "none", "population": "Douyin e-commerce", "geography": "China", "period": "2025", "publication_date": "2025", "method": "forecast", "commercial_interest": "LatePost reporting relayed by a subscription newsletter", "uncertainty": "Second-hand projection", "source_ids": [ "RTB-S224" ], "claim_ids": [ "RTB-C228" ], "stream": "RTB-2" }, { "observation_id": "ECO-M001", "bin": 1, "metric": "Retail returns as share of annual sales (all channels, estimate)", "exact_wording": "Retailers estimate that 15.8% of their annual sales will be returned", "value": "15.8% ($849.9bn)", "unit": "of annual retail sales; USD", "definition": "retailer-estimated returns", "denominator": "annual US retail sales", "population": "large US merchants (>$500m revenue)", "sample_size": "358 e-commerce professionals; 2,006 consumers", "recruitment": "not stated in reviewed snippet", "geography": "US", "period": "2025 (estimate made summer 2025)", "publication_date": "2025", "currency": "USD", "gross_net": "gross merchandise returned", "returns_treatment": "this is the returns measure", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "NRF trade association + Happy Returns (returns services)", "uncertainty": "estimate from respondent expectations", "comparability": "not comparable with Appriss $706bn (ECO-M003)", "source_ids": [ "ECO-S042", "ECO-S043" ], "claim_ids": [ "ECO-C050" ], "check_level": "search-level", "stream": "ECO" }, { "observation_id": "ECO-M002", "bin": 1, "metric": "Online return rate (estimate)", "exact_wording": "19.3% of online sales will be returned in 2025", "value": "19.3%", "unit": "of online sales", "definition": "projected share of online sales returned", "denominator": "online sales", "population": "US retail (large merchants surveyed)", "sample_size": "see ECO-M001", "recruitment": "not stated", "geography": "US", "period": "2025", "publication_date": "2025", "currency": "USD", "gross_net": "gross", "returns_treatment": "this is the returns measure", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "NRF + Happy Returns", "uncertainty": "derivation not shown", "comparability": "online vs all-channel differ", "source_ids": [ "ECO-S042", "ECO-S043" ], "claim_ids": [ "ECO-C050" ], "check_level": "search-level", "stream": "ECO" }, { "observation_id": "ECO-M003", "bin": 1, "metric": "Merchandise returned and fraud/abuse share", "exact_wording": "$706 billion in merchandise returned in 2025 ... 14.2% ($100 billion) ... fraud and abuse", "value": "$706bn; 14.2%", "unit": "USD; % of returns", "definition": "returns processed and loss from fraud/abuse", "denominator": "returns", "population": "US retail", "sample_size": "not stated", "recruitment": "not stated", "geography": "US", "period": "2025", "publication_date": "2026", "currency": "USD", "gross_net": "gross", "returns_treatment": "this is the returns measure", "attribution_rule": "not applicable", "method": "vendor-estimate", "commercial_interest": "Appriss Retail sells returns-fraud software", "uncertainty": "secondary only", "comparability": "different definition from NRF/Happy Returns", "source_ids": [ "ECO-S044" ], "claim_ids": [ "ECO-C051" ], "check_level": "search-level", "stream": "ECO" }, { "observation_id": "ECO-M004", "bin": 5, "metric": "AI-referred vs non-AI conversion (relative)", "exact_wording": "In March 2026, AI traffic converted 42 per cent better", "value": "+42% (Mar 2026); -38% (Mar 2025)", "unit": "difference in conversion rate", "definition": "orders per visit, AI-referred vs non-AI channels", "denominator": "visits", "population": "Adobe Analytics client US retail sites", "sample_size": "panel of >1 trillion visits (reported)", "recruitment": "Adobe client base (self-selected)", "geography": "US", "period": "2025-03; 2026-03", "publication_date": "2026-04", "currency": "n/a", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "referral classification by Adobe", "method": "vendor-estimate", "commercial_interest": "Adobe sells analytics/commerce software", "uncertainty": "selection effects; no volumes", "comparability": "relative rate, not volume; excludes agent-completed checkouts", "source_ids": [ "ECO-S037", "NFA-S001" ], "claim_ids": [ "ECO-C040" ], "check_level": "search-level", "stream": "ECO" }, { "observation_id": "ECO-M005", "bin": 6, "metric": "In-surface (ChatGPT Instant Checkout) conversion relative to click-through to retailer site", "exact_wording": "converted at about one-third the rate of click-throughs to Walmart's website", "value": "about one-third", "unit": "ratio of conversion rates", "definition": "not published", "denominator": "not published", "population": "Walmart products in ChatGPT Instant Checkout (~200,000 items)", "sample_size": "not published", "recruitment": "not applicable", "geography": "US", "period": "2025-11 to 2026-03", "publication_date": "2026-03", "currency": "n/a", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "vendor-estimate", "commercial_interest": "Walmart (retailer with own assistant Sparky; interest in its own channel)", "uncertainty": "executive remark", "comparability": "one retailer; not comparable with Adobe referral rates", "source_ids": [ "ECO-S040", "ECO-S041" ], "claim_ids": [ "ECO-C044" ], "check_level": "search-level", "stream": "ECO" }, { "observation_id": "ECO-M006", "bin": 5, "metric": "Shopify AI-driven orders growth", "exact_wording": "AI-driven traffic and orders to Shopify stores both tripled year over year", "value": "~3x", "unit": "YoY multiple", "definition": "orders attributed to AI-driven traffic (rule not stated)", "denominator": "not stated", "population": "Shopify merchants", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "global", "period": "Q2 2026", "publication_date": "2026-08", "currency": "n/a", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "filing", "commercial_interest": "Shopify (commerce platform)", "uncertainty": "secondary relay of results", "comparability": "growth multiple only", "source_ids": [ "ECO-S053" ], "claim_ids": [ "ECO-C047" ], "check_level": "search-level", "stream": "ECO" }, { "observation_id": "ECO-M201", "bin": 4, "metric": "AI-referred share of total site traffic", "exact_wording": "AI-referred sources at about 0.2% of total traffic in Q4", "value": "about 0.2%", "unit": "of sessions", "definition": "sessions from AI referral sources", "denominator": "all sessions in Contentsquare panel", "population": "Contentsquare client sites, all industries", "sample_size": "99bn sessions", "recruitment": "vendor client base", "geography": "global", "period": "2025-Q4", "publication_date": "2026-01-29", "currency": "", "gross_net": "", "returns_treatment": "not applicable", "attribution_rule": "referrer-based; AI sessions logged as direct/organic missed", "method": "vendor-estimate", "commercial_interest": "Contentsquare sells analytics", "uncertainty": "undercount likely", "comparability": "not retail-only; not comparable with Adobe growth figures", "source_ids": [ "ECO-S233", "ECO-S234" ], "claim_ids": [ "ECO-C218" ], "check_level": "search-level", "stream": "ECO-2" }, { "observation_id": "ECO-M202", "bin": 4, "metric": "ChatGPT share of retailer referral clicks", "exact_wording": "20% of the people who landed on Walmart.com by clicking a link on another site got there from ChatGPT", "value": "Walmart ~20%; Etsy >20%; Target ~15%; eBay ~10%; Amazon <3%", "unit": "of referral visits", "definition": "referral (link-click) visits", "denominator": "referral visits (<5% of total visits)", "population": "named US retailer sites", "sample_size": "not stated", "recruitment": "Similarweb panel", "geography": "US", "period": "2025-08", "publication_date": "2025", "currency": "", "gross_net": "", "returns_treatment": "not applicable", "attribution_rule": "referrer", "method": "vendor-estimate", "commercial_interest": "Similarweb sells traffic intelligence", "uncertainty": "second-hand", "comparability": "share of referrals, not of all visits", "source_ids": [ "ECO-S235" ], "claim_ids": [ "ECO-C221", "ECO-C222" ], "check_level": "search-level", "stream": "ECO-2" }, { "observation_id": "ECO-M203", "bin": 4, "metric": "US web referrals from GenAI platforms (count)", "exact_wording": "reached 226.8 million in the United States in January 2026", "value": "226.8 million", "unit": "referral visits per month", "definition": "web referrals from GenAI platforms", "denominator": "none (count)", "population": "all destination sites", "sample_size": "not stated", "recruitment": "Similarweb panel", "geography": "US", "period": "2026-01", "publication_date": "n.d.", "currency": "", "gross_net": "", "returns_treatment": "not applicable", "attribution_rule": "referrer", "method": "vendor-estimate", "commercial_interest": "Similarweb; Statista resale", "uncertainty": "paywalled; not seen", "comparability": "all sites, not retail", "source_ids": [ "ECO-S237" ], "claim_ids": [ "ECO-C224" ], "check_level": "search-level", "stream": "ECO-2" }, { "observation_id": "ECO-M204", "bin": 5, "metric": "Conversion rate of ChatGPT-referred visits vs organic search", "exact_wording": "conversion rate for visits referred by ChatGPT is 11.4%, compared with 5.3% for organic search", "value": "11.4% vs 5.3%", "unit": "of visits", "definition": "not seen", "denominator": "referred visits", "population": "e-commerce sites in Similarweb panel", "sample_size": "not stated", "recruitment": "panel", "geography": "global/not stated", "period": "2025", "publication_date": "2025-09-15", "currency": "", "gross_net": "", "returns_treatment": "not stated", "attribution_rule": "referrer", "method": "vendor-estimate", "commercial_interest": "Similarweb", "uncertainty": "method unseen", "comparability": "not comparable with Contentsquare 1.3% or Adobe ratios", "source_ids": [ "ECO-S236" ], "claim_ids": [ "ECO-C223" ], "check_level": "search-level", "stream": "ECO-2" }, { "observation_id": "ECO-M205", "bin": 1, "metric": "Retail returns share of sales (2024 edition)", "exact_wording": "16.9% of retailers' annual sales", "value": "16.9% ($890bn)", "unit": "of annual retail sales; USD", "definition": "retailer-estimated returns", "denominator": "annual US retail sales", "population": "US retailers surveyed", "sample_size": "not seen", "recruitment": "not seen", "geography": "US", "period": "2024", "publication_date": "2024", "currency": "USD", "gross_net": "gross", "returns_treatment": "this is the returns measure", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "NRF + Happy Returns (returns services)", "uncertainty": "respondent estimate", "comparability": "prior edition of ECO-M001; not a measured series", "source_ids": [ "ECO-S219", "ECO-S223" ], "claim_ids": [ "ECO-C215" ], "check_level": "search-level", "stream": "ECO-2" }, { "observation_id": "CON-M001", "bin": 3, "metric": "Share of US consumers who have used generative AI for online shopping", "exact_wording": "search-summary: 39 percent have used generative AI for online shopping", "value": "39%", "unit": "of respondents", "definition": "used genAI for online shopping (any task)", "denominator": "all respondents", "population": "US consumers", "sample_size": ">5,000 (reported as 5,000)", "recruitment": "not seen", "geography": "US", "period": "not confirmed (published 2025-03-17)", "publication_date": "2025-03-17", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Adobe (commerce/analytics software)", "uncertainty": "Stated recall; online panel likely; no margin given", "comparability": "Not comparable with orders or traffic; 'shopping' includes research", "source_ids": [ "ECO-S232" ], "claim_ids": [ "CON-C005" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M002", "bin": 4, "metric": "YoY growth in AI-referred traffic to US retail sites, holiday 2025", "exact_wording": "search-summary: AI-tool referrals to retail sites up 693.4% year over year", "value": "693.4% YoY (Nov 769%, Dec 673%)", "unit": "change in visits", "definition": "visits referred by genAI tools to retail sites in Adobe Analytics", "denominator": "prior-year AI referrals", "population": "Adobe Analytics client retail sites", "sample_size": "not applicable (traffic panel)", "recruitment": "Adobe client sites", "geography": "US", "period": "2025-11-01..2025-12-31", "publication_date": "2026", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "transaction-panel", "commercial_interest": "Adobe", "uncertainty": "Growth from small base; panel coverage not disclosed", "comparability": "Traffic, not shoppers or orders", "source_ids": [ "CON-S006" ], "claim_ids": [ "CON-C006" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M003", "bin": 5, "metric": "Online holiday sales attributed to AI and agents", "exact_wording": "search-summary: AI and agents drove 20% of all retail sales and fueled $262 billion", "value": "$262 billion; ~20%", "unit": "USD, global online sales", "definition": "Salesforce 'influenced by AI and agents' incl. recommendations and service agents", "denominator": "global online holiday sales $1.29tn", "population": "Salesforce Shopping Index (1.5bn shoppers)", "sample_size": "not applicable", "recruitment": "Salesforce commerce clients", "geography": "global", "period": "2025-11-01..2025-12-31", "publication_date": "2026-01", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "vendor-estimate", "commercial_interest": "Salesforce (sells AI agents to retailers)", "uncertainty": "Attribution rule undisclosed; vendor model", "comparability": "Mixes merchant-side AI with consumer AI; not orders through AI interfaces", "source_ids": [ "CON-S007" ], "claim_ids": [ "CON-C007" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M004", "bin": 3, "metric": "Used or open to using AI to complete a purchase", "exact_wording": "search-summary: 64% said they have used or are open to using AI to complete a purchase", "value": "64%", "unit": "of respondents", "definition": "used or would be open to", "denominator": "all respondents", "population": "consumers (US per summary)", "sample_size": "2,016", "recruitment": "not seen", "geography": "US (unconfirmed)", "period": "2025-03", "publication_date": "2025", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Bain (consulting)", "uncertainty": "Combines use and willingness", "comparability": "Not observed use", "source_ids": [ "CON-S008" ], "claim_ids": [ "CON-C008" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M005", "bin": 3, "metric": "Cautious about fully autonomous AI purchases", "exact_wording": "search-summary: around half still say they are cautious", "value": "~50%", "unit": "of respondents", "definition": "cautious about agents handling purchases start to finish", "denominator": "not stated", "population": "consumers", "sample_size": "not seen", "recruitment": "not seen", "geography": "US (unconfirmed)", "period": "2025", "publication_date": "2025-11-13", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Bain", "uncertainty": "Rounded; method unseen", "comparability": "Stated attitude", "source_ids": [ "CON-S009" ], "claim_ids": [ "CON-C009" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M006", "bin": 3, "metric": "Comfortable with AI suggesting options but human makes final decision", "exact_wording": "search-summary: 56% ... comfortable with AI suggesting options, but with humans making final decisions", "value": "56%", "unit": "of respondents", "definition": "stated comfort", "denominator": "all respondents", "population": "consumers in FR, DE, UK", "sample_size": "749", "recruitment": "not seen", "geography": "FR, DE, UK", "period": "not seen", "publication_date": "2026-03", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "McKinsey", "uncertainty": "Small multi-country sample (~250/country if equal)", "comparability": "Europe, not US", "source_ids": [ "CON-S010" ], "claim_ids": [ "CON-C010" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M007", "bin": 3, "metric": "Uncomfortable letting AI manage recurring purchases automatically", "exact_wording": "search-summary: 47% are uncomfortable letting AI manage recurring purchases automatically", "value": "47%", "unit": "of respondents", "definition": "stated discomfort", "denominator": "all respondents", "population": "consumers in FR, DE, UK", "sample_size": "749", "recruitment": "not seen", "geography": "FR, DE, UK", "period": "not seen", "publication_date": "2026-03", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "McKinsey", "uncertainty": "as above", "comparability": "Bears on replenishment-delegation assumption", "source_ids": [ "CON-S010" ], "claim_ids": [ "CON-C010" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M008", "bin": 3, "metric": "Have used an AI shopping assistant", "exact_wording": "search-summary: only 14% had used one", "value": "14% (Gen Z 24%; boomers 7%)", "unit": "of US adults", "definition": "ever used an AI shopping assistant", "denominator": "all respondents", "population": "US adults", "sample_size": "not seen", "recruitment": "YouGov online panel (assumed; not confirmed)", "geography": "US", "period": "2025 (mid-year)", "publication_date": "2025-07", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "YouGov (research sales)", "uncertainty": "Sub-group sizes unknown", "comparability": "Narrower 'assistant' wording than 'used AI for shopping'", "source_ids": [ "CON-S011", "CON-S012" ], "claim_ids": [ "CON-C011" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M009", "bin": 3, "metric": "Would trust an AI agent with weekly food shop", "exact_wording": "search-summary: 15% ... would already trust an AI agent to handle their weekly food shop", "value": "15% (21% of 25-34)", "unit": "of respondents", "definition": "stated trust", "denominator": "all respondents", "population": "UK and US consumers", "sample_size": "not seen", "recruitment": "not seen", "geography": "UK, US", "period": "2025", "publication_date": "2025-12", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Checkout.com (payments)", "uncertainty": "Unknown n", "comparability": "Stated willingness, grocery replenishment", "source_ids": [ "CON-S013" ], "claim_ids": [ "CON-C013" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M010", "bin": 3, "metric": "Want agents to complete purchases for them (Ecommpay)", "exact_wording": "reviewer retrieval: Just 13.9% want agents to complete purchases for them", "value": "13.9%", "unit": "of respondents", "definition": "stated preference", "denominator": "not found (full report form-gated)", "population": "European consumers", "sample_size": "1,750+ consumers plus 16 merchant and payment leaders", "recruitment": "not seen", "geography": "Europe (five countries; Spain and UK named)", "period": "2026", "publication_date": "2026-07-02", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Ecommpay (payment platform; report lead generation)", "uncertainty": "External reviewer retrieval 10 Oct 2026; question, approval condition and item base not recovered", "comparability": "Stated willingness", "source_ids": [ "EXT-S013", "EXT-S014" ], "claim_ids": [ "EXT-C027", "EXT-C028", "EXT-C029" ], "check_level": "external-retrieval", "stream": "CON" }, { "observation_id": "CON-M011", "bin": 3, "metric": "Not comfortable with AI agents making purchases on their behalf", "exact_wording": "search-summary: 55.0% are not comfortable", "value": "55.0%", "unit": "of respondents", "definition": "stated discomfort", "denominator": "all respondents", "population": "US and UK consumers", "sample_size": "2,000", "recruitment": "not seen", "geography": "US, UK", "period": "2026-Q1", "publication_date": "2026-04-27", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Riskified (fraud prevention)", "uncertainty": "Sponsor interest in fraud risk", "comparability": "Not comparable to Q4 2025 70% 'at least somewhat comfortable' (different sample/framing)", "source_ids": [ "CON-S016" ], "claim_ids": [ "CON-C015", "CON-C016" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M012", "bin": 3, "metric": "Used AI to assist a shopping decision in last six months", "exact_wording": "search-summary: 77.6% of respondents had used AI to assist with a shopping or purchasing decision", "value": "77.6%", "unit": "of respondents", "definition": "any AI assistance", "denominator": "all respondents", "population": "US consumers", "sample_size": "1,009", "recruitment": "not seen", "geography": "US", "period": "six months to ~Apr 2026", "publication_date": "2026-04-27", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Exploding Topics / Semrush (AI-visibility tools)", "uncertainty": "Secondary; recruitment unknown", "comparability": "Broad wording inflates vs YouGov 14%", "source_ids": [ "CON-S017" ], "claim_ids": [ "CON-C017" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M013", "bin": 3, "metric": "Want generative AI integrated into shopping", "exact_wording": "search-summary: 71 per cent of consumers want it to be integrated", "value": "71%", "unit": "of respondents", "definition": "stated preference", "denominator": "not seen", "population": "consumers (multi-country)", "sample_size": "not seen", "recruitment": "not seen", "geography": "multi-country", "period": "2024", "publication_date": "2025-01", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Capgemini (IT services)", "uncertainty": "Secondary", "comparability": "Stated preference", "source_ids": [ "CON-S018" ], "claim_ids": [ "CON-C018" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M014", "bin": 3, "metric": "Used AI tool in past three months; used AI to discover/decide on products", "exact_wording": "search-summary: 68% ... at least one AI tool; 19% discover and/or decide", "value": "68%; 19%", "unit": "of respondents", "definition": "AI tool use; shopping-decision task", "denominator": "68% all; 19% base unclear", "population": "US consumers", "sample_size": "4,008", "recruitment": "not seen", "geography": "US", "period": "2026-02", "publication_date": "2026", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "McKinsey ConsumerWise", "uncertainty": "Denominator of 19% unclear", "comparability": "", "source_ids": [ "CON-S019" ], "claim_ids": [ "CON-C019" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M015", "bin": 3, "metric": "Used AI to complete a purchase", "exact_wording": "search-summary: 30 percent had used it to complete a purchase", "value": "30%", "unit": "of respondents", "definition": "self-reported; 'complete a purchase' undefined", "denominator": "all respondents", "population": "US adults", "sample_size": "2,000", "recruitment": "not seen", "geography": "US", "period": "2026", "publication_date": "2026-07-15", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "LDWW (marketing agency)", "uncertainty": "Secondary relay", "comparability": "Not observed orders; not bin 6", "source_ids": [ "CON-S022" ], "claim_ids": [ "CON-C026" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M016", "bin": 3, "metric": "Trust AI to place an order vs compare prices", "exact_wording": "search-summary: 65% trust AI to compare prices, but only 14% trust it to place an order", "value": "65% compare; 14% place order", "unit": "of respondents", "definition": "stated trust", "denominator": "all respondents", "population": "US adults", "sample_size": "not seen", "recruitment": "YouGov panel (assumed)", "geography": "US", "period": "2025-12", "publication_date": "2026", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "YouGov via secondary", "uncertainty": "Secondary", "comparability": "Stated trust", "source_ids": [ "CON-S023" ], "claim_ids": [ "CON-C027" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M017", "bin": 3, "metric": "Comfortable letting AI assistant shop on their behalf", "exact_wording": "search-summary: only 16% say they're comfortable", "value": "16%", "unit": "of respondents", "definition": "stated comfort", "denominator": "not seen", "population": "consumers", "sample_size": "not seen", "recruitment": "not seen", "geography": "not stated", "period": "2026", "publication_date": "2026", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Coveo (AI search vendor)", "uncertainty": "Secondary", "comparability": "Stated willingness", "source_ids": [ "CON-S024" ], "claim_ids": [ "CON-C028" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M018", "bin": 3, "metric": "Relied on AI tools to compare prices/brands/models/reviews", "exact_wording": "search-summary: Nearly two-thirds (62%) ... relied on AI tools to compare", "value": "62%", "unit": "of respondents", "definition": "stated use", "denominator": "all respondents", "population": "US consumers", "sample_size": "not seen", "recruitment": "not seen", "geography": "US", "period": "not seen", "publication_date": "2026", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "McKinsey/ICSC", "uncertainty": "Secondary", "comparability": "", "source_ids": [ "CON-S025" ], "claim_ids": [ "CON-C029" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M019", "bin": 3, "metric": "Forecast US agentic commerce market by 2030", "exact_wording": "search-summary: projected to reach $1 trillion", "value": "$1 trillion (2030)", "unit": "USD", "definition": "'agentic commerce market' — not defined by the source", "denominator": "not applicable", "population": "not applicable", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2030 (forecast)", "publication_date": "2026", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "forecast", "commercial_interest": "McKinsey/ICSC", "uncertainty": "Forecast", "comparability": "Forecast; definition unknown; not revenue of AI platforms; never combine with other bins", "source_ids": [ "CON-S025" ], "claim_ids": [ "CON-C029" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M020", "bin": 3, "metric": "Expect >=10% of purchases AI-driven within a year", "exact_wording": "search-summary: 33 per cent of consumers expect at least 10 per cent of their purchases to be driven by AI", "value": "33%", "unit": "of respondents", "definition": "stated expectation", "denominator": "all consumer respondents", "population": "consumers", "sample_size": "not seen", "recruitment": "not seen", "geography": "not stated", "period": "2026", "publication_date": "2026-06", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Checkout.com (payments)", "uncertainty": "Secondary", "comparability": "Expectation, not behaviour", "source_ids": [ "CON-S027" ], "claim_ids": [ "CON-C030" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M021", "bin": 6, "metric": "Merchant-reported share of transactions by AI agents", "exact_wording": "search-summary: AI agents account for only 3% of transactions today", "value": "3%", "unit": "of transactions (merchant self-report)", "definition": "'AI agent transaction' — not defined by the source", "denominator": "merchant transactions", "population": "merchants surveyed", "sample_size": "not seen", "recruitment": "not seen", "geography": "not stated", "period": "2026", "publication_date": "2026-06", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Checkout.com (payments)", "uncertainty": "Self-report by merchants; definition unknown; may include AI-referred orders", "comparability": "Not a measured transaction share; label as merchant survey estimate", "source_ids": [ "CON-S015" ], "claim_ids": [ "CON-C030" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M022", "bin": 3, "metric": "Millennials comfortable allowing AI agent purchases", "exact_wording": "search-summary: 72% of Millennials ... comfortable allowing an AI agent to make purchases", "value": "72% Millennials; ~25% aged 55+", "unit": "of age sub-sample", "definition": "stated comfort", "denominator": "age sub-samples", "population": "UK and US consumers", "sample_size": "not seen", "recruitment": "not seen", "geography": "UK, US", "period": "2025", "publication_date": "2025-12", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Checkout.com", "uncertainty": "Sub-sample n unknown", "comparability": "Age cohort evidence; not Gen Z specifically", "source_ids": [ "CON-S013" ], "claim_ids": [ "CON-C031" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M023", "bin": 3, "metric": "Use AI for shopping at least weekly", "exact_wording": "search-summary: 43.21% of consumers use AI to help with shopping at least once a week", "value": "43.21%", "unit": "of respondents", "definition": "self-reported frequency", "denominator": "not seen (all or AI users)", "population": "US consumers", "sample_size": "1,009", "recruitment": "not seen", "geography": "US", "period": "to 2026-04", "publication_date": "2026-04-27", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Exploding Topics / Semrush", "uncertainty": "Secondary; denominator ambiguous", "comparability": "", "source_ids": [ "CON-S017" ], "claim_ids": [ "CON-C033" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M024", "bin": 3, "metric": "AI shoppers using AI for grocery", "exact_wording": "search-summary: Grocery shopping was reported by 44.62% of AI shoppers", "value": "44.62%", "unit": "of AI shoppers", "definition": "category of AI-assisted shopping", "denominator": "AI shoppers", "population": "US consumers who use AI to shop", "sample_size": "subset of 1,009", "recruitment": "not seen", "geography": "US", "period": "to 2026-04", "publication_date": "2026-04-27", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Exploding Topics / Semrush", "uncertainty": "Secondary", "comparability": "Category evidence for assisted use, not delegation", "source_ids": [ "CON-S017" ], "claim_ids": [ "CON-C033" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M025", "bin": 4, "metric": "YoY growth in genAI traffic to US retail sites, holiday 2024", "exact_wording": "search-summary: increased by 1,300% year-over-year", "value": "1,300%", "unit": "change in visits", "definition": "Adobe Analytics genAI referrals", "denominator": "prior-year", "population": "Adobe client retail sites", "sample_size": "not applicable", "recruitment": "Adobe clients", "geography": "US", "period": "2024-11-01..2024-12-31", "publication_date": "2025", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "transaction-panel", "commercial_interest": "Adobe", "uncertainty": "Small base", "comparability": "Traffic", "source_ids": [ "CON-S006" ], "claim_ids": [ "CON-C034" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M026", "bin": 5, "metric": "Conversion of AI referrals vs other traffic, holiday 2025", "exact_wording": "search-summary: AI referrals converted 31 per cent more than other traffic sources", "value": "+31% (Thanksgiving +54%, Black Friday +38%)", "unit": "relative conversion rate", "definition": "Adobe Analytics", "denominator": "non-AI traffic conversion", "population": "Adobe client retail sites", "sample_size": "not applicable", "recruitment": "Adobe clients", "geography": "US", "period": "2025-11..2025-12", "publication_date": "2026", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "transaction-panel", "commercial_interest": "Adobe", "uncertainty": "Selection effects; not causal", "comparability": "Contradicts earlier 'convert less' coverage (CON-C035)", "source_ids": [ "CON-S006" ], "claim_ids": [ "CON-C006", "CON-C035" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M027", "bin": 3, "metric": "At least somewhat comfortable with AI agents making purchases (Riskified Q4 2025 wave, different sample and framing from Q1 2026)", "exact_wording": "search-summary: 70 percent expressing at least some comfort", "value": "70% at least somewhat comfortable", "unit": "of respondents", "definition": "stated comfort", "denominator": "all respondents", "population": "consumers in multiple markets", "sample_size": "5,400", "recruitment": "not seen", "geography": "multi-market", "period": "2025-Q4", "publication_date": "2026", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Riskified", "uncertainty": "Different framing from Q1 2026", "comparability": "Not comparable with CON-M011", "source_ids": [ "CON-S016" ], "claim_ids": [ "CON-C016" ], "check_level": "search-level", "stream": "CON" }, { "observation_id": "CON-M201", "bin": 3, "metric": "Gen Z used (or considered) AI tools to help with a purchase, past year", "exact_wording": "search-summary: 61% of Gen Z shoppers used AI tools to help with a purchase in the last year", "value": "61%", "unit": "of respondents", "definition": "used/considered AI tools for a purchase", "denominator": "Gen Z respondents (sub-sample n not disclosed)", "population": "US adults", "sample_size": "1,000 total (Talker Research); Gen Z n not disclosed", "recruitment": "Talker Research, 'nationally representative'", "geography": "US", "period": "2025-09/10", "publication_date": "n.d.", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "PayPal (payments; agentic checkout); relayed by eMarketer", "uncertainty": "Secondary relay; 'used or considered'", "comparability": "Not comparable with delegation measures", "source_ids": [ "CON-S201" ], "claim_ids": [ "CON-C201" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M202", "bin": 3, "metric": "Would allow an AI agent to choose and buy a product without approval", "exact_wording": "search-summary: 27% of Gen Z respondents said they would allow an AI agent to choose and buy a product without approval", "value": "Gen Z 27%; Gen X and younger Boomers 4%", "unit": "of respondents", "definition": "allow agent to choose and buy without approval", "denominator": "generational sub-samples (bases displayed in Ipsos’s presentation; not transcribed by this study)", "population": "US adults aged 18–65", "sample_size": "1,500 total", "recruitment": "not seen", "geography": "US", "period": "2026-02", "publication_date": "2026-03-24", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Ipsos (research sales); relayed by eMarketer", "uncertainty": "Primary presentation retrieved by an external reviewer on 10 Oct 2026; item wording differs between relays", "comparability": "Strictest delegation wording among Gen Z figures", "source_ids": [ "CON-S202", "EXT-S011", "EXT-S012" ], "claim_ids": [ "CON-C202", "EXT-C025", "EXT-C026" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M203", "bin": 3, "metric": "At least somewhat likely to let AI agents buy for them", "exact_wording": "search-summary: 47% of Gen Zers and 48% of millennials say they are at least somewhat likely to let AI agents buy things for them", "value": "Gen Z 47%; Millennials 48%", "unit": "of respondents", "definition": "at least somewhat likely", "denominator": "generational sub-samples", "population": "US adults (assumed)", "sample_size": "not seen", "recruitment": "not seen", "geography": "US", "period": "not seen", "publication_date": "n.d.", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "YouGov (research sales); relayed by eMarketer", "uncertainty": "Loose wording", "comparability": "Not comparable with CON-M202", "source_ids": [ "CON-S202" ], "claim_ids": [ "CON-C203" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M205", "bin": 3, "metric": "Prefer AI platforms for product research (vs search engines)", "exact_wording": "search-summary: 33% of Gen Z prefer AI platforms for product research, compared with 37% who prefer search engines", "value": "Gen Z 33% (vs 37%); Millennials 26% (vs 40%); Gen X 13%; Boomers 3%", "unit": "of respondents", "definition": "channel preference for product research", "denominator": "generational sub-samples", "population": "consumers", "sample_size": "1,000 total (US, UK, AU/NZ)", "recruitment": "not seen", "geography": "US, UK, AU/NZ", "period": "not seen (published 2025-09-15)", "publication_date": "2025-09-15", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Commerce (BigCommerce; commerce software) + Future Commerce (research/media)", "uncertainty": "Small pooled multi-country sample; sub-sample n undisclosed", "comparability": "Preference, not use or delegation", "source_ids": [ "CON-S203" ], "claim_ids": [ "CON-C204" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M206", "bin": 3, "metric": "Used AI platforms to search for products, past 12 months, by age", "exact_wording": "search-summary: around one in five Americans used AI platforms to search for products", "value": "all ~1 in 5; 18-39 24%; 40-64 18%", "unit": "of respondents", "definition": "used AI platforms to search for products while shopping", "denominator": "US adults", "population": "US adults", "sample_size": "not seen", "recruitment": "not seen", "geography": "US", "period": "2025-06/2025-09", "publication_date": "n.d.", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Statista (data sales)", "uncertainty": "n not seen", "comparability": "Age bands are not generations", "source_ids": [ "CON-S204" ], "claim_ids": [ "CON-C205" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M207", "bin": 3, "metric": "Gen Z expect to use an AI agent to complete shopping tasks in future", "exact_wording": "search-summary: 41% of Gen Z expect to use an AI agent to complete shopping tasks on their behalf", "value": "41%", "unit": "of respondents", "definition": "future expectation", "denominator": "Gen Z", "population": "not confirmed", "sample_size": "not seen", "recruitment": "not seen", "geography": "not confirmed", "period": "not seen", "publication_date": "n.d.", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Synchrony (consumer credit)", "uncertainty": "Expectation", "comparability": "Not use", "source_ids": [ "CON-S217" ], "claim_ids": [ "CON-C206" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M208", "bin": 3, "metric": "Would outsource household shopping to AI", "exact_wording": "search-summary: 21% would outsource household shopping (vs. 11%)", "value": "Gen Z 21%; all adults 11%", "unit": "of respondents", "definition": "would outsource", "denominator": "Gen Z / all adults", "population": "US adults (assumed)", "sample_size": "not seen", "recruitment": "not seen", "geography": "US (assumed)", "period": "not seen", "publication_date": "n.d.", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Harris Poll sponsor not identified; relayed by Substack", "uncertainty": "Tertiary relay", "comparability": "Hypothetical", "source_ids": [ "CON-S218" ], "claim_ids": [ "CON-C207" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M209", "bin": 3, "metric": "Use AI for help during buying journey", "exact_wording": "search-summary: Almost half (45%) turn to AI for help during their buying journeys", "value": "45% (research 41%, interpret reviews 33%, deals 31%)", "unit": "of respondents", "definition": "use AI for help during buying journeys", "denominator": "consumers", "population": "consumers in 23 countries", "sample_size": "18,000+", "recruitment": "not seen", "geography": "23 countries", "period": "2025-Q3", "publication_date": "2026-01-07", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "IBM (AI/commerce consulting) with NRF (retail trade body)", "uncertainty": "Global pooled; weighting unknown", "comparability": "Assistance, not delegation", "source_ids": [ "CON-S206" ], "claim_ids": [ "CON-C210" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M210", "bin": 3, "metric": "Used an AI tool for at least one shopping task", "exact_wording": "search-summary: nearly half of US shoppers (47 percent) surveyed have used an AI tool for at least one shopping task", "value": "47%", "unit": "of respondents", "definition": "any AI tool, any shopping task", "denominator": "US shoppers surveyed", "population": "US shoppers", "sample_size": "not seen", "recruitment": "not seen", "geography": "US", "period": "not seen (published 2025-12-02)", "publication_date": "2025-12-02", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Visa (agentic payments)", "uncertainty": "n not seen", "comparability": "Broad use wording", "source_ids": [ "CON-S208" ], "claim_ids": [ "CON-C212" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M211", "bin": 3, "metric": "GenAI users open to AI agents as personal shoppers", "exact_wording": "search-summary: three-quarters of people who use genAI are open to deploying AI agents as personal shoppers", "value": "about 75%; 83% rely on genAI when choosing products", "unit": "of respondents", "definition": "openness among genAI users", "denominator": "genAI users only", "population": "consumers in 15 countries", "sample_size": "18,214 total", "recruitment": "not seen", "geography": "15 countries", "period": "2025-01", "publication_date": "2025-06", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Accenture (consulting); relayed by eMarketer", "uncertainty": "Conditional denominator; secondary", "comparability": "Not all-consumer base", "source_ids": [ "CON-S213" ], "claim_ids": [ "CON-C213" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M212", "bin": 3, "metric": "Trust AI agents to make routine purchases on their behalf", "exact_wording": "search-summary: only 24% of US online adults trust AI agents to act on their behalf to make routine purchases", "value": "24% (US); 24% (UK)", "unit": "of respondents", "definition": "trust agents for routine purchases", "denominator": "online adults", "population": "US and UK online adults", "sample_size": "not seen", "recruitment": "not seen", "geography": "US; UK", "period": "2025-03", "publication_date": "2025-11", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Forrester (research sales)", "uncertainty": "Paywalled; n not seen", "comparability": "Online adults only", "source_ids": [ "CON-S210", "CON-S211" ], "claim_ids": [ "CON-C214" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M213", "bin": 3, "metric": "Willing to let an AI agent complete a purchase on their behalf", "exact_wording": "search-summary: 30% of Consumers Willing to Let an AI Agent Complete Their Purchases", "value": "30%", "unit": "of respondents", "definition": "willing", "denominator": "consumers", "population": "not confirmed", "sample_size": "not seen", "recruitment": "not seen", "geography": "not confirmed", "period": "not seen", "publication_date": "2025-12-08", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Contentsquare (merchant analytics)", "uncertainty": "n, geography unknown", "comparability": "Contrasts with 8.29%/13.9%/16% narrow wordings", "source_ids": [ "CON-S214" ], "claim_ids": [ "CON-C216" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M214", "bin": 3, "metric": "Fully comfortable with AI completing online purchases", "exact_wording": "search-summary: only 8.29% were \"fully comfortable\" with AI completing online purchases", "value": "8.29%", "unit": "of respondents", "definition": "fully comfortable", "denominator": "US shoppers", "population": "US shoppers", "sample_size": "not seen", "recruitment": "not seen", "geography": "US", "period": "not seen (reported 2026-01)", "publication_date": "n.d.", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Omnisend (e-commerce marketing software); relayed by Practical Ecommerce", "uncertainty": "Secondary", "comparability": "Top-box only", "source_ids": [ "CON-S215" ], "claim_ids": [ "CON-C217" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M215", "bin": 3, "metric": "Use AI tools while shopping online / to research products / weekly", "exact_wording": "search-summary: Seventy percent of respondents said they use AI tools while shopping online", "value": "70% / 65% / 32%", "unit": "of respondents", "definition": "use of AI tools", "denominator": "respondents", "population": "not confirmed", "sample_size": "not found", "recruitment": "not seen", "geography": "not confirmed", "period": "not seen", "publication_date": "2026-01-22", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Clutch (agency marketplace)", "uncertainty": "n not found", "comparability": "Use, not delegation", "source_ids": [ "CON-S209" ], "claim_ids": [ "CON-C219" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M216", "bin": 3, "metric": "Generally trust AI recommendations without verification", "exact_wording": "search-summary: only 17% generally trust AI recommendations without checking them", "value": "17% (95% have some concern)", "unit": "of respondents", "definition": "trust without checking", "denominator": "respondents", "population": "not confirmed", "sample_size": "not found", "recruitment": "not seen", "geography": "not confirmed", "period": "not seen", "publication_date": "2026-01-22", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Clutch", "uncertainty": "n not found", "comparability": "Trust measure", "source_ids": [ "CON-S209" ], "claim_ids": [ "CON-C219" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M217", "bin": 3, "metric": "Comfort using AI by product category", "exact_wording": "search-summary: household items (40%) and electronics (37%) ... health and wellness products (16%)", "value": "household items 40%; electronics 37%; health and wellness 16%", "unit": "of respondents", "definition": "comfort using AI for category", "denominator": "respondents", "population": "not confirmed", "sample_size": "not found", "recruitment": "not seen", "geography": "not confirmed", "period": "not seen", "publication_date": "2026-01-22", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Clutch", "uncertainty": "n not found; 'use AI' may be assistance", "comparability": "Only 2026 category split found", "source_ids": [ "CON-S209" ], "claim_ids": [ "CON-C219" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "CON-M218", "bin": 3, "metric": "Could not name an impressive AI shopping experience", "exact_wording": "search-summary: 68% could not name a single AI shopping experience that impressed them", "value": "68% (over 60% use AI when shopping)", "unit": "of respondents", "definition": "dissatisfaction proxy", "denominator": "UK consumers", "population": "UK consumers", "sample_size": "not seen", "recruitment": "not seen", "geography": "UK", "period": "not seen", "publication_date": "2025-12-01", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "CI&T (retail AI consulting)", "uncertainty": "n not seen", "comparability": "Not churn", "source_ids": [ "CON-S216" ], "claim_ids": [ "CON-C221" ], "check_level": "search-level", "stream": "CON-2" }, { "observation_id": "MKT-M001", "bin": 1, "metric": "US retail e-commerce sales (seasonally adjusted)", "exact_wording": "Seasonally adjusted e-commerce sales were $340.2 billion (search summary)", "value": "340.2", "unit": "USD billion per quarter", "definition": "Census MRTS: sales of goods/services where order/price/terms negotiated over an online system; payment may be offline", "denominator": "not applicable (level)", "population": "US retail firms sampled in Monthly Retail Trade Survey", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026-Q2", "publication_date": "2026-08-18 (reported)", "currency": "USD", "gross_net": "sales as reported by retailers (MRTS basis; net of returns per Census retail definitions—not confirmed this run)", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "official-statistic", "commercial_interest": "none (official statistics)", "uncertainty": "sampling error; e-commerce YoY change margin reported as +/-0.9pp at 90% confidence; subject to revision", "comparability": "Current dollars; not inflation-adjusted; excludes food services; not comparable to Adobe/Salesforce panels.", "source_ids": [ "MKT-S001", "MKT-S002" ], "claim_ids": [ "MKT-C001" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M002", "bin": 1, "metric": "E-commerce share of total US retail sales (SA)", "exact_wording": "e-commerce was 17.1% of total retail (search summary)", "value": "17.1", "unit": "% of total retail sales", "definition": "Census e-commerce / total retail (NAICS 44-45 excl. food services)", "denominator": "Total US retail sales (~$1.99tn Q2 2026 as reported)", "population": "US retailers", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026-Q2", "publication_date": "2026-08-18 (reported)", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "official-statistic", "commercial_interest": "none", "uncertainty": "sampling error; revisions", "comparability": "Ratio of value; not a share of shoppers or of orders.", "source_ids": [ "MKT-S001", "MKT-S002" ], "claim_ids": [ "MKT-C002" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M003", "bin": 1, "metric": "UK internet sales as % of retail (ONS J4MC, via third party)", "exact_wording": "Internet sales stood at 27.3% of total UK retail in April 2026 (search summary)", "value": "27.3", "unit": "% of retail sales value", "definition": "ONS RSI: internet sales (web/app) as share of total retail sales, Great Britain", "denominator": "Total GB retail sales value", "population": "GB retailers in ONS Retail Sales Inquiry", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "Great Britain", "period": "2026-04", "publication_date": "2026-06 (data hub)", "currency": "GBP", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "official-statistic", "commercial_interest": "none (ONS); secondary publisher commercial", "uncertainty": "unverified restatement; ONS revisions", "comparability": "Different denominator and definition from US Census and NBS; do not chart as one series.", "source_ids": [ "MKT-S005", "MKT-S004" ], "claim_ids": [ "MKT-C005" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M004", "bin": 1, "metric": "China online retail sales of physical goods share of total retail sales of consumer goods", "exact_wording": "accounting for 25.0% of the total retail sales of consumer goods (search summary)", "value": "25.0", "unit": "% (cumulative YTD)", "definition": "NBS online retail sales of physical goods", "denominator": "Total retail sales of consumer goods incl. catering", "population": "China enterprises/platforms", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "China", "period": "2026-01 to 2026-04", "publication_date": "2026-05-19", "currency": "CNY", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "official-statistic", "commercial_interest": "none", "uncertainty": "methodology change reported mid-2026", "comparability": "Denominator includes catering; cumulative YTD; not comparable to US/UK shares.", "source_ids": [ "MKT-S007" ], "claim_ids": [ "MKT-C006" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M005", "bin": 1, "metric": "China online physical goods / total retail of consumer goods (derived ratio)", "exact_wording": "Retail sales of online products amounted to 7396.5 billion yuan ... total ... 28774.4 billion yuan (search summary)", "value": "about 25.7 (study ratio)", "unit": "%", "definition": "Derived by study from NBS levels", "denominator": "Total retail sales of consumer goods", "population": "China", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "China", "period": "2026-01 to 2026-07", "publication_date": "2026-08-19", "currency": "CNY", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "derived by this study", "commercial_interest": "none", "uncertainty": "derived; NBS-stated share not seen", "comparability": "Derived ratio; label as calculation.", "source_ids": [ "MKT-S006" ], "claim_ids": [ "MKT-C007" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M006", "bin": 1, "metric": "EU internet users who bought online in past 12 months", "exact_wording": "77% of internet users bought or ordered goods or services for personal use online in the previous 12 months (search summary)", "value": "77", "unit": "% of internet users aged 16-74", "definition": "Eurostat ICT household survey: e-buyers in last 12 months", "denominator": "Internet users aged 16-74", "population": "EU individuals aged 16-74", "sample_size": "~330,000 (2025 wave, secondary)", "recruitment": "national statistical household surveys", "geography": "EU-27", "period": "2024", "publication_date": "2025-02-20", "currency": "not applicable", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "official-statistic", "commercial_interest": "none", "uncertainty": "survey sampling; ~330,000 respondents reported for 2025 wave", "comparability": "Share of people, not value; incomparable with sales shares.", "source_ids": [ "MKT-S009", "MKT-S010" ], "claim_ids": [ "MKT-C009" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M008", "bin": 4, "metric": "Generative-AI referral traffic growth, Prime Day 2026 Day 1", "exact_wording": "increasing by 98.3% (search summary of secondary)", "value": "98.3", "unit": "% YoY change in visits", "definition": "Visits from clicks on genAI links", "denominator": "Prior-year comparable period", "population": "Adobe panel US retail sites", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026-06-23", "publication_date": "2026-06-24 (reported)", "currency": "not applicable", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "transaction-panel", "commercial_interest": "Adobe sells Adobe Analytics / Experience Cloud commerce and marketing software; benefits from AI-commerce narrative", "uncertainty": "secondary only; Day-1 only", "comparability": "Growth rate only.", "source_ids": [ "ECO-S055" ], "claim_ids": [ "MKT-C014" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M009", "bin": 4, "metric": "Forecast AI-referred traffic growth, holiday 2026", "exact_wording": "AI traffic to U.S. retail sites to rise 130% year-over-year (search summary of secondary)", "value": "130", "unit": "% YoY (forecast)", "definition": "Clicks on links from AI chat and browser services", "denominator": "Holiday 2025 AI-referred visits", "population": "Adobe panel", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026-11-01 to 2026-12-31", "publication_date": "2026-09-29", "currency": "not applicable", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "forecast", "commercial_interest": "Adobe sells Adobe Analytics / Experience Cloud commerce and marketing software; benefits from AI-commerce narrative", "uncertainty": "forecast", "comparability": "Forecast; do not mix with measured series.", "source_ids": [ "MKT-S013" ], "claim_ids": [ "MKT-C017" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M011", "bin": 5, "metric": "Conversion advantage of AI-referred visits (Prime Day 2026 Day 1)", "exact_wording": "converted 50.7% better than non-AI channels (search summary of secondary)", "value": "50.7", "unit": "% higher conversion rate", "definition": "as above", "denominator": "non-AI channels", "population": "Adobe panel", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026-06-23", "publication_date": "2026-06 (reported)", "currency": "not applicable", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "transaction-panel", "commercial_interest": "Adobe sells Adobe Analytics / Experience Cloud commerce and marketing software; benefits from AI-commerce narrative", "uncertainty": "secondary; one day", "comparability": "Rate ratio only.", "source_ids": [ "ECO-S055" ], "claim_ids": [ "MKT-C015" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M014", "bin": 6, "metric": "Merchants live on ChatGPT Instant Checkout (Shopify cohort)", "exact_wording": "only about 30 Shopify merchants had participated in Instant Checkout as of February 2026 (search summary of tertiary)", "value": "about 30", "unit": "merchants", "definition": "Shopify merchants participating in in-chat checkout", "denominator": "not applicable", "population": "Shopify merchants", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026-02", "publication_date": "2026", "currency": "not applicable", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "vendor-estimate", "commercial_interest": "analyst (Forrester sells research) via commercial blog", "uncertainty": "single analyst remark, relayed", "comparability": "Merchant count, not orders; not a market size.", "source_ids": [ "MKT-S020" ], "claim_ids": [ "MKT-C024" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M017", "bin": 3, "metric": "McKinsey 2030 forecast: US agentic-commerce orchestrated B2C retail revenue", "exact_wording": "as much as $1 trillion in orchestrated U.S. retail revenue by 2030 (search summary)", "value": "up to 1 (US); 3 to 5 (global)", "unit": "USD trillion", "definition": "Retail revenue 'orchestrated' by agentic commerce; goods only; per secondary reading includes AI-guided human purchases", "denominator": "not applicable", "population": "US B2C retail", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US; global", "period": "2030", "publication_date": "2025-10", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "forecast", "commercial_interest": "McKinsey consulting", "uncertainty": "forecast; method not seen", "comparability": "Not comparable to Bain/MS; not AI-platform revenue; an influence-type measure, binned 3 for lack of a forecast bin.", "source_ids": [ "MKT-S023", "MKT-S024" ], "claim_ids": [ "MKT-C029", "MKT-C030" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M018", "bin": 7, "metric": "Morgan Stanley 2030 forecast: US e-commerce spend by agentic shoppers", "exact_wording": "base case near $190 billion and the bull case near $385 billion (search summary)", "value": "190 to 385", "unit": "USD billion", "definition": "US e-commerce spending via AI agents ('agentic shoppers')", "denominator": "US e-commerce (10-20%)", "population": "US consumers", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2030", "publication_date": "2025-12-08", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "forecast", "commercial_interest": "Morgan Stanley research", "uncertainty": "base/bull scenario range", "comparability": "Consumer spend forecast; mixes delegation and AI-assisted purchase per survey basis; not platform revenue.", "source_ids": [ "MKT-S025" ], "claim_ids": [ "MKT-C031" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M019", "bin": 6, "metric": "Bain 2030 forecast: US agentic commerce", "exact_wording": "roughly 15% to 25% of overall e-commerce (search summary)", "value": "300 to 500 (15-25%)", "unit": "USD billion (% of US online retail)", "definition": "Transactions AI agents initiate, influence or complete, third-party or retailer-hosted", "denominator": "US online retail sales 2030", "population": "US", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2030", "publication_date": "2025-12", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "forecast", "commercial_interest": "Bain consulting", "uncertainty": "range as published", "comparability": "Includes 'influence'; overlaps bins 5-7; not platform revenue.", "source_ids": [ "MKT-S026" ], "claim_ids": [ "MKT-C033" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M020", "bin": 3, "metric": "US adults who made a purchase using AI in the past month (AlphaWise, cited by Morgan Stanley)", "exact_wording": "Roughly 23% of Americans made purchases using AI in the past month (search summary)", "value": "about 23", "unit": "% of US respondents", "definition": "Self-reported purchase 'using AI' (wording not seen)", "denominator": "US survey respondents", "population": "US adults (definition not seen)", "sample_size": "not stated in reviewed snippet", "recruitment": "not stated in reviewed snippet", "geography": "US", "period": "2025 (field dates not seen)", "publication_date": "2025-12-08", "currency": "not applicable", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "survey", "commercial_interest": "Morgan Stanley research", "uncertainty": "sample and margin not seen", "comparability": "Stated behaviour; not orders; must not be multiplied by spend.", "source_ids": [ "MKT-S025" ], "claim_ids": [ "MKT-C032" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M021", "bin": 2, "metric": "Mobile share of US retail e-commerce (EMARKETER)", "exact_wording": "Mobile Will Account for the Majority of US Retail Ecommerce Sales Starting in 2027 (chart title)", "value": "nearly half (2026); majority (2027+)", "unit": "% of US retail e-commerce sales", "definition": "EMARKETER m-commerce: retail sales ordered via mobile devices (incl. tablets per EMARKETER convention—not confirmed)", "denominator": "US retail e-commerce sales", "population": "US", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026-2027", "publication_date": "2026-02-01", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "forecast", "commercial_interest": "EMARKETER research sales", "uncertainty": "forecast; paywalled detail", "comparability": "Different from Adobe device share of holiday spend.", "source_ids": [ "MKT-S033", "MKT-S034" ], "claim_ids": [ "MKT-C041" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M022", "bin": 2, "metric": "Mobile share of US online holiday spend (Adobe forecast)", "exact_wording": "Mobile is forecast at 57.4% of season spend (search summary)", "value": "57.4", "unit": "% of online spend", "definition": "Adobe: share of online spend on smartphones (definition not confirmed)", "denominator": "Adobe-measured US online holiday spend", "population": "Adobe panel", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026-11-01 to 2026-12-31", "publication_date": "2026-09-29", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "forecast", "commercial_interest": "Adobe sells Adobe Analytics / Experience Cloud commerce and marketing software; benefits from AI-commerce narrative", "uncertainty": "forecast", "comparability": "Seasonal panel measure; not comparable with EMARKETER annual share.", "source_ids": [ "MKT-S013" ], "claim_ids": [ "MKT-C042" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M023", "bin": 1, "metric": "Amazon share of US marketplace e-commerce (EMARKETER)", "exact_wording": "Will Slip to 63.3% in 2026 (chart title)", "value": "63.3", "unit": "% of US marketplace e-commerce sales", "definition": "Amazon marketplace GMV (1P+3P per EMARKETER) / total US retail marketplace e-commerce", "denominator": "US retail marketplace e-commerce", "population": "US", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026", "publication_date": "2025-10-01", "currency": "USD", "gross_net": "GMV (assumed by EMARKETER convention; not confirmed)", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "forecast", "commercial_interest": "EMARKETER research sales", "uncertainty": "forecast", "comparability": "Concentration context; marketplace denominator only.", "source_ids": [ "MKT-S030" ], "claim_ids": [ "MKT-C039" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M024", "bin": 7, "metric": "Retail orders executed by AI agents under prior delegation (observed total)", "exact_wording": "none: no comparable public total isolating completed consumer retail purchases made under prior delegation was found in the primary sources reviewed", "value": "not found within stated search", "unit": "not applicable", "definition": "Orders an agent places under a standing instruction without per-order human approval", "denominator": "not applicable", "population": "not applicable", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US; global", "period": "to 2026-10-09", "publication_date": "not applicable", "currency": "not applicable", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "absence record", "commercial_interest": "not applicable", "uncertainty": "not found in the sources reviewed; not a zero and not proof of absence; Visa’s controlled agent-initiated transaction counts and Checkout.com’s merchant-reported 3% are not such totals", "comparability": "Placeholder row so the bin is shown as empty-with-reason, never as 0.", "source_ids": [ "ECO-S039", "CON-S007", "MKT-S025", "MKT-S026" ], "claim_ids": [ "MKT-C043", "EXT-C032" ], "check_level": "search-level", "stream": "MKT" }, { "observation_id": "MKT-M201", "bin": 2, "metric": "US social commerce sales", "exact_wording": "US social commerce sales will amount to $87.02 billion in 2025 (search summary)", "value": "87.02", "unit": "USD billion per year", "definition": "EMARKETER: purchases made directly on a social platform or via social links to a retailer product page with immediate purchase option; incl. some deals paid elsewhere (C2C)", "denominator": "not applicable (level)", "population": "US social commerce buyers (modelled)", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2025", "publication_date": "2025", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "vendor-estimate", "commercial_interest": "Research sales", "uncertainty": "Model estimate; method not seen", "comparability": "Broad definition incl. click-outs; not comparable with Census e-commerce or in-app checkout-only GMV", "source_ids": [ "MKT-S201", "MKT-S202" ], "claim_ids": [ "MKT-C201", "MKT-C205" ], "check_level": "search-level", "stream": "MKT-2" }, { "observation_id": "MKT-M202", "bin": 2, "metric": "US social commerce sales forecast", "exact_wording": "surpassing $100 billion for the first time (search summary)", "value": "more than 100 (growth 18.0%)", "unit": "USD billion per year", "definition": "EMARKETER: purchases made directly on a social platform or via social links to a retailer product page with immediate purchase option; incl. some deals paid elsewhere (C2C)", "denominator": "not applicable", "population": "modelled", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026", "publication_date": "2025", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "forecast", "commercial_interest": "Research sales", "uncertainty": "Forecast", "comparability": "As M201", "source_ids": [ "MKT-S201" ], "claim_ids": [ "MKT-C202" ], "check_level": "search-level", "stream": "MKT-2" }, { "observation_id": "MKT-M203", "bin": 2, "metric": "Social commerce share of US retail e-commerce", "exact_wording": "at 6.9%, reaching 9.3% in 2029 (search summary)", "value": "6.9 (forecast 9.3 in 2029)", "unit": "percent", "definition": "EMARKETER: purchases made directly on a social platform or via social links to a retailer product page with immediate purchase option; incl. some deals paid elsewhere (C2C)", "denominator": "US retail e-commerce sales (EMARKETER estimate, not Census)", "population": "modelled", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "base year not stated (context 2025); 2029 forecast", "publication_date": "2025", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "vendor-estimate", "commercial_interest": "Research sales", "uncertainty": "Base year unconfirmed", "comparability": "Denominator is EMARKETER's own e-commerce estimate", "source_ids": [ "MKT-S201" ], "claim_ids": [ "MKT-C203" ], "check_level": "search-level", "stream": "MKT-2" }, { "observation_id": "MKT-M204", "bin": 2, "metric": "TikTok Shop share of US social commerce", "exact_wording": "18.2% of total social commerce in the US ... 24.1% by 2027 (search summary)", "value": "18.2 (forecast 24.1 by 2027)", "unit": "percent", "definition": "EMARKETER: purchases made directly on a social platform or via social links to a retailer product page with immediate purchase option; incl. some deals paid elsewhere (C2C)", "denominator": "US social commerce sales (EMARKETER)", "population": "modelled", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2025; 2027 forecast", "publication_date": "2025", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "vendor-estimate", "commercial_interest": "Research sales", "uncertainty": "Model estimate", "comparability": "Share of EMARKETER social commerce, not of e-commerce", "source_ids": [ "MKT-S201" ], "claim_ids": [ "MKT-C204" ], "check_level": "search-level", "stream": "MKT-2" }, { "observation_id": "MKT-M205", "bin": 8, "metric": "Salesforce Agentforce ARR", "exact_wording": "Agentforce ARR above $1.5 billion, up more than 240% (search summary)", "value": "more than 1.5", "unit": "USD billion ARR", "definition": "Salesforce Agentforce ARR; from Q2 FY27 includes AI offerings, Slackbot and Headless 360", "denominator": "not applicable", "population": "Salesforce customers, all industries", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "Global", "period": "Q2 FY27", "publication_date": "2026", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "filing", "commercial_interest": "Issuer sells the product", "uncertainty": "Issuer metric; ARR definition changed in this quarter", "comparability": "Cross-industry; not commerce-specific; not comparable with pre-Q2 FY27 Agentforce ARR", "source_ids": [ "MKT-S204" ], "claim_ids": [ "MKT-C209" ], "check_level": "search-level", "stream": "MKT-2" }, { "observation_id": "MKT-M206", "bin": 8, "metric": "Salesforce Agentforce + Data 360 ARR", "exact_wording": "combined Agentforce and Data 360 ARR near $3.9 billion, up more than 210% (search summary)", "value": "near 3.9", "unit": "USD billion ARR", "definition": "Issuer-defined combined ARR", "denominator": "not applicable", "population": "Salesforce customers, all industries", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "Global", "period": "Q2 FY27", "publication_date": "2026", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "filing", "commercial_interest": "Issuer", "uncertainty": "Issuer metric", "comparability": "Includes data platform; cross-industry", "source_ids": [ "MKT-S204" ], "claim_ids": [ "MKT-C210" ], "check_level": "search-level", "stream": "MKT-2" }, { "observation_id": "MKT-M207", "bin": 8, "metric": "Salesforce Agentforce ARR (prior quarter)", "exact_wording": "more than $1 billion in Agentforce ARR (search summary)", "value": "more than 1", "unit": "USD billion ARR", "definition": "Pre-redefinition Agentforce ARR", "denominator": "not applicable", "population": "all industries", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "Global", "period": "Q1 FY27", "publication_date": "2026", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "filing", "commercial_interest": "Issuer", "uncertainty": "Issuer metric", "comparability": "Not comparable with M205 (definition widened)", "source_ids": [ "MKT-S205" ], "claim_ids": [ "MKT-C211" ], "check_level": "search-level", "stream": "MKT-2" }, { "observation_id": "MKT-M208", "bin": 8, "metric": "Shopify Sidekick first-time users", "exact_wording": "750,000 shops used Sidekick for the first time (search summary)", "value": "750,000", "unit": "shops (first-time users in quarter)", "definition": "Shops using Sidekick for the first time in the quarter", "denominator": "not disclosed (total Shopify shops)", "population": "Shopify merchants", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "Global", "period": "2025-Q3", "publication_date": "2025", "currency": "not applicable", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "vendor-estimate", "commercial_interest": "Issuer sells the product", "uncertainty": "Issuer claim via transcript; not active or retained use", "comparability": "Not a revenue figure; no 2026 update found", "source_ids": [ "MKT-S207" ], "claim_ids": [ "MKT-C212" ], "check_level": "search-level", "stream": "MKT-2" }, { "observation_id": "MKT-M209", "bin": 8, "metric": "Worldwide AI spending forecast (all industries)", "exact_wording": "worldwide AI spending will reach $632 billion by 2028 (search summary)", "value": "632", "unit": "USD billion per year", "definition": "IDC AI spending guide (software, services, hardware for AI)", "denominator": "not applicable", "population": "all industries", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "Global", "period": "2028 forecast (2024 vintage)", "publication_date": "2024-08-19", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "forecast", "commercial_interest": "Research sales", "uncertainty": "Forecast; 2024 vintage", "comparability": "Not retail; not merchant commerce AI; context only", "source_ids": [ "MKT-S211" ], "claim_ids": [ "MKT-C215" ], "check_level": "search-level", "stream": "MKT-2" }, { "observation_id": "MKT-M210", "bin": 8, "metric": "US retail technology budgets (not AI-specific)", "exact_wording": "US retailers will increase their technology budgets to $113 billion in 2026 (search summary)", "value": "113", "unit": "USD billion per year", "definition": "Forrester US retail tech spend (all technology)", "denominator": "not applicable", "population": "US retailers", "sample_size": "not applicable", "recruitment": "not applicable", "geography": "US", "period": "2026 forecast", "publication_date": "n.d.", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not stated", "method": "forecast", "commercial_interest": "Research sales", "uncertainty": "Forecast", "comparability": "Total tech budget; AI share not isolated; do not label as AI spend", "source_ids": [ "MKT-S212" ], "claim_ids": [ "MKT-C216" ], "check_level": "search-level", "stream": "MKT-2" }, { "observation_id": "HIS-M003", "bin": 1, "metric": "E-commerce share of US retail sales (first estimate)", "exact_wording": "only 0.64 percent of the total $821.2 billion in U.S. retail sales", "value": "0.64 (first release); 0.7 (revised)", "unit": "percent", "definition": "Census first release", "denominator": "Total retail sales $821.2bn (first) / $787.2bn (revised)", "population": "not applicable", "sample_size": "not stated", "recruitment": "not applicable", "geography": "US", "period": "1999-Q4", "publication_date": "2000-03", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "official-statistic", "commercial_interest": "none", "uncertainty": "Revised", "comparability": "Same series, revised basis", "source_ids": [ "HIS-S003", "HIS-S004" ], "claim_ids": [ "HIS-C003" ], "check_level": "search-level", "stream": "HIS" }, { "observation_id": "HIS-M005", "bin": 1, "metric": "Online retail sales of physical goods share (China)", "exact_wording": "26.1% of total retail sales of consumer goods", "value": "26.1", "unit": "percent", "definition": "NBS online physical goods / total retail sales of consumer goods", "denominator": "RMB 50.12 trillion", "population": "not applicable", "sample_size": "not stated", "recruitment": "not applicable", "geography": "China", "period": "2025", "publication_date": "2026-01-19", "currency": "CNY", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "official-statistic", "commercial_interest": "Government", "uncertainty": "Not stated", "comparability": "Excludes services; different denominator", "source_ids": [ "HIS-S008", "HIS-S009" ], "claim_ids": [ "HIS-C006" ], "check_level": "search-level", "stream": "HIS" }, { "observation_id": "HIS-M006", "bin": 2, "metric": "Mobile share of US online spending, Cyber Monday", "exact_wording": "capturing 57.5% of all online spending", "value": "57.5", "unit": "percent", "definition": "Adobe Analytics panel of US retail sites", "denominator": "Online spending on Cyber Monday ($14.25bn)", "population": "not applicable", "sample_size": "not stated", "recruitment": "not applicable", "geography": "US", "period": "2025-12-01", "publication_date": "2025-12-02", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "vendor-estimate", "commercial_interest": "Adobe sells analytics/commerce software", "uncertainty": "Panel coverage not stated", "comparability": "Single day; not annual", "source_ids": [ "HIS-S043" ], "claim_ids": [ "HIS-C042" ], "check_level": "search-level", "stream": "HIS" }, { "observation_id": "HIS-M007", "bin": 2, "metric": "Mobile share of US online transactions, Nov-Dec 2025", "exact_wording": "mobile shopping accounted for 56.4% of online transactions in the final two months of 2025", "value": "56.4", "unit": "percent", "definition": "eMarketer citing Adobe", "denominator": "Online transactions (panel)", "population": "not applicable", "sample_size": "not stated", "recruitment": "not applicable", "geography": "US", "period": "2025-11 to 2025-12", "publication_date": "2025-12", "currency": "", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "vendor-estimate", "commercial_interest": "eMarketer sells research; Adobe data", "uncertainty": "Not stated", "comparability": "Seasonal; transactions vs spend not reconciled", "source_ids": [ "HIS-S042" ], "claim_ids": [ "HIS-C043" ], "check_level": "search-level", "stream": "HIS" }, { "observation_id": "HIS-M008", "bin": 2, "metric": "China mobile payment transactions (PBOC)", "exact_wording": "over 4.52 billion mobile payment transactions ... RMB22.59 trillion", "value": "4.52 billion; RMB 22.59 trillion", "unit": "transactions; CNY", "definition": "Bank-processed mobile payments", "denominator": "n/a", "population": "not applicable", "sample_size": "not stated", "recruitment": "not applicable", "geography": "China", "period": "2014", "publication_date": "2015-02-17", "currency": "CNY", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "official-statistic", "commercial_interest": "Restated by trade press", "uncertainty": "Not stated", "comparability": "Payment flows, NOT commerce; do not compare with retail", "source_ids": [ "HIS-S041" ], "claim_ids": [ "HIS-C041" ], "check_level": "search-level", "stream": "HIS" }, { "observation_id": "HIS-M009", "bin": 2, "metric": "US livestream commerce (Coresight)", "exact_wording": "US livestream commerce was estimated at $31.7 billion in 2023, with a forecast of $67.8 billion by 2026", "value": "31.7 (2023 est.); 67.8 (2026 forecast)", "unit": "USD billion", "definition": "Coresight definition not opened", "denominator": "n/a", "population": "not applicable", "sample_size": "not stated", "recruitment": "not applicable", "geography": "US", "period": "2023; 2026", "publication_date": "2023", "currency": "USD", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "forecast", "commercial_interest": "Coresight sells research", "uncertainty": "High; other estimates far lower", "comparability": "Conflicts with eMarketer estimate (not logged as observation)", "source_ids": [ "HIS-S069" ], "claim_ids": [ "HIS-C063" ], "check_level": "search-level", "stream": "HIS" }, { "observation_id": "HIS-M010", "bin": 2, "metric": "Douyin e-commerce GMV", "exact_wording": "the gross merchandise volume (GMV) of Douyin E-commerce was approximately 3.5 trillion yuan", "value": "~3.5", "unit": "CNY trillion", "definition": "36Kr-reported GMV, definition not stated", "denominator": "n/a", "population": "not applicable", "sample_size": "not stated", "recruitment": "not applicable", "geography": "China", "period": "2024", "publication_date": "2025", "currency": "CNY", "gross_net": "gross", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "vendor-estimate", "commercial_interest": "Press report; company did not confirm", "uncertainty": "Unconfirmed", "comparability": "GMV not comparable with NBS retail", "source_ids": [ "HIS-S067" ], "claim_ids": [ "HIS-C061" ], "check_level": "search-level", "stream": "HIS" }, { "observation_id": "HIS-M011", "bin": 2, "metric": "Pinduoduo GMV and active buyers", "exact_wording": "more than 343.6 million active buyers with annual GMV of RMB 262.1 billion", "value": "343.6m buyers; RMB 262.1bn", "unit": "buyers; CNY", "definition": "Company-defined (F-1) per secondary", "denominator": "n/a", "population": "not applicable", "sample_size": "not stated", "recruitment": "not applicable", "geography": "China", "period": "12 months to mid-2018", "publication_date": "2018-07-26", "currency": "CNY", "gross_net": "gross", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "filing", "commercial_interest": "Issuer", "uncertainty": "F-1 not opened", "comparability": "GMV is gross", "source_ids": [ "HIS-S062" ], "claim_ids": [ "HIS-C057" ], "check_level": "search-level", "stream": "HIS" }, { "observation_id": "HIS-M012", "bin": 1, "metric": "Amazon third-party share of paid units", "exact_wording": "third-party sellers accounted for 61% of total paid units sold globally", "value": "61", "unit": "percent of paid units", "definition": "Amazon-reported", "denominator": "Amazon paid units", "population": "not applicable", "sample_size": "not stated", "recruitment": "not applicable", "geography": "Global", "period": "2025-Q1", "publication_date": "2025", "currency": "", "gross_net": "not stated", "returns_treatment": "not stated", "attribution_rule": "not applicable", "method": "filing", "commercial_interest": "Amazon", "uncertainty": "Secondary restatement", "comparability": "Units, not GMV", "source_ids": [ "HIS-S013" ], "claim_ids": [ "HIS-C020" ], "check_level": "search-level", "stream": "HIS" }, { "observation_id": "HIS-M201", "bin": 6, "metric": "Share of Alexa-enabled device owners who made a voice purchase (historical analogue)", "exact_wording": "merely 2% of consumers who own the 50 million Alexa-enabled devices have used the voice option to make a purchase so far this year (search summary)", "value": "2", "unit": "percent", "definition": "Owners of Alexa-enabled devices making at least one voice purchase in 2018 to date", "denominator": "owners of ~50 million Alexa-enabled devices (devices vs owners ambiguous)", "population": "Alexa device owners", "sample_size": "not stated", "recruitment": "not applicable (internal data per unnamed sources)", "geography": "US (implied)", "period": "2018 YTD (to ~Aug 2018)", "publication_date": "2018-08", "currency": "not applicable", "gross_net": "not applicable", "returns_treatment": "not applicable", "attribution_rule": "not stated", "method": "vendor-estimate", "commercial_interest": "The Information (subscription news); Amazon disputed", "uncertainty": "Anonymous sources; disputed by Amazon; denominator ambiguous", "comparability": "Historical (2018) voice-interface purchase rate; not comparable with 2025-26 AI-assistant metrics", "source_ids": [ "HIS-S201", "HIS-S202" ], "claim_ids": [ "HIS-C205", "HIS-C206" ], "check_level": "search-level", "stream": "HIS-2" } ]