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ClickMint — Post-click behavioural panel for AI-referred ecommerce traffic: session-level records across 90+ DTC brands identifying the AI source of each visit, the landing page, scroll/dwell/exit behaviour, and attributed revenue, paired with a second and rarer layer — controlled A/B experiment records showing which post-click storefront experience (copy, UX, design variant) converted AI-referred visitors better, with measured lift in conversion rate, revenue per user and GMV. Also holds a normalised source-level taxonomy of AI referral channels that most brands cannot see because the traffi…
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Post-click behavioural panel for AI-referred ecommerce traffic: session-level records across 90+ DTC brands identifying the AI source of each visit, the landing page, scroll/dwell/exit behaviour, and attributed revenue, paired with a second and rarer layer — controlled A/B experiment records showing which post-click storefront experience (copy, UX, design variant) converted AI-referred visitors better, with measured lift in conversion rate, revenue per user and GMV. Also holds a normalised source-level taxonomy of AI referral channels that most brands cannot see because the traffic is buried in referral/direct.
From 2 yearsCoverage Information Technology · Consumer DiscretionaryAsset class Equities
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ClickMint — Post-click behavioural panel for AI-referred ecommerce traffic: session-level records across 90+ DTC brands identifying the AI source of each visit, the landing page, scroll/dwell/exit behaviour, and attributed revenue, paired with a second and rarer layer — controlled A/B experiment records showing which post-click storefront experience (copy, UX, design variant) converted AI-referred visitors better, with measured lift in conversion rate, revenue per user and GMV. Also holds a normalised source-level taxonomy of AI referral channels that most brands cannot see because the traffi…
ClickMint offers (Alternative, Sentiment) — $315M+ of ecommerce revenue analysed across 90+ brands; at a typical DTC AOV of roughly $70-120 this implies on the order of 2.6M-4.5M attributed orders, and therefore tens of millions of behavioural sessions (each order preceded by many tracked sessions with scroll/dwell/exit events).
Randomised controlled-trial corpora for conversion-optimisation and storefront-design models (variant plus measured outcome is direct supervision, not correlation); preference/RLHF data on which product presentation converts; consumer purchase-intent structure (budget, constraint, comparison set) for demand and product-attribute models; channel-attribution ground truth for marketing-mix and media-mix models; evaluation sets for agentic-shopping post-click experiences.
The data is with 2 years of history.
Coverage spans US; Application Software, Multiline & Direct Marketing Retail; alternative, sentiment; equities.
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