Data
Every CSV behind every chart. Free to reuse under CC BY 4.0 with credit to Strandline Research.
- Cheapest plan at each volume One row per vendor and monthly session volume (1,000 to 100,000): the cheapest published plan that includes that many sessions, its monthly price, whether the page shows a currency, how it is billed, recording retention in days and the pricing page. UserProof rows are marked same_owner_as_strandline. From No. 10: Microsoft Clarity is the only one of 11 session replay tools whose free hosted plan still records every session at 50,000 a month.
- Every plan read One row per plan on the 11 vendors' pricing pages plus UserProof's: currency, price, billing, sessions or other units included, retention, the share of sessions recorded where the plan samples, and notes. From No. 10: Microsoft Clarity is the only one of 11 session replay tools whose free hosted plan still records every session at 50,000 a month.
- Which tools print a price at 50,000 sessions One row per vendor: whether its pricing page prints a price that records every session at 50,000 sessions a month, and if not, why. From No. 10: Microsoft Clarity is the only one of 11 session replay tools whose free hosted plan still records every session at 50,000 a month.
- Chart data Monthly price by session volume for the three tools with a US dollar price at every volume, as plotted. From No. 10: Microsoft Clarity is the only one of 11 session replay tools whose free hosted plan still records every session at 50,000 a month.
- Cost per Reddit comment One row per service that prices Reddit comments: who posts, the cost per Reddit comment in US dollars, how it was derived from the published price, the minimum term and the source page. From No. 09: Reddit comment prices run from $3 to $67 across 6 services that price comments.
- Chart data Cost per Reddit comment by service, as plotted. From No. 09: Reddit comment prices run from $3 to $67 across 6 services that price comments.
- CommunityMentions cost per placement CommunityMentions' monthly price divided by its median verified placements across all channels, from its delivery records. From No. 09: Reddit comment prices run from $3 to $67 across 6 services that price comments.
- Services and criteria One row per service: who posts the comments, each of the 7 criteria (1 met, 0 not met or not stated), the total, the price as published and the source page. From No. 07: 3 of 11 done-for-you Reddit marketing services give credit back when a comment is removed.
- Criteria met by service Total criteria met out of 7 for each service. From No. 07: 3 of 11 done-for-you Reddit marketing services give credit back when a comment is removed.
- Services meeting each criterion For each criterion, how many of the 11 services meet it. From No. 07: 3 of 11 done-for-you Reddit marketing services give credit back when a comment is removed.
- What the published price buys For the services that publish a price, the price and what it buys in their own words or ours. From No. 07: 3 of 11 done-for-you Reddit marketing services give credit back when a comment is removed.
- Sample totals and outcomes One row: comments, subreddits and brands in the sample, posting dates, comments removed at the same-day check, passed it, removed later, removed within a week and still up after a week, the matching rates, how many of the comments still up were confirmed at the sixth-night check, and how many failed the same-day check but reappeared later. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Removal by month of posting One row per posting month (16 to 31 July, August, 1 to 22 September): comments, subreddits, brands, share removed at the same-day check, comments that passed it, share of those removed later, share of all comments removed later, removed within a week and still up after a week. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Removal by day of posting One row per posting day from 16 July to 22 September 2026: comments, share removed by the same-day check and share removed within a week. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Share still up at each check One row per check (the same-day check, then nights 1 to 6): share of comments still up, for comments posted 16 to 31 July, in August, 1 to 22 September, and all months together. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Removal by subreddit group Subreddits grouped by name (patterns in queries.sql), reported where comments came from at least 5 brands and no brand had more than half: subreddits, brands, comments and share removed within a week, 16 July to 31 August and 1 to 22 September. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Subreddit groups with 95% intervals The rates in topics.csv with 95% Wilson intervals for each period. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Subreddit groups not reported Groups defined in queries.sql but left out of topics.csv: comments, brands, the largest brand's share of comments, and the reason. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Brands and subreddits compared across periods Brands with 20 or more comments, and subreddits with 5 or more, in both 16 July to 31 August and 1 to 22 September: how many removed a larger share in September, and the median share removed in each period. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Posting accounts compared across periods Posting accounts with 5 or more comments that passed the same-day check both from 16 July to 31 August and from 1 to 22 September: how many had a larger share of those comments removed later in September, the median share in each period, and how many passing comments have a recorded account. No account names. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Labels Reddit showed on 7 September From the service's 7 September 2026 snapshot of 1,210 comments posted 26 August to 6 September: comments fetched, still up, no longer up, shown as removed, shown as removed by Reddit, shown as deleted, not returned, and the share of comments no longer up that carried Reddit's own removal label. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- The 7 September snapshot against the tracked week The same 1,210 comments joined by ID to the delivery records: how many are in this report's sample, how many were removed within the tracked week, how many were still up after it, and how many of those were gone in the 7 September snapshot. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Coverage of the 7 September snapshot When the snapshot was taken, eligible placements, comments fetched with a full comment link, placements submitted as share links and not fetched, and API objects requested, received and failed. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Timing of the same-day check Comments matched to a same-day check run, the share checked on the posting day, and the 10th, 50th and 90th percentile hours from assignment to the check. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Nightly re-check coverage by week of posting One row per week of posting from 25 June to 28 September 2026: comments, comments still up after their last check, the share of those whose last check came on the sixth night, and comments found removed at a nightly re-check. It shows why the sample starts on 16 July and ends on 22 September. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- SQL queries The read-only query behind every CSV in this report, and the steps behind removal-labels.csv. From No. 06: 81.9% of the brand-mention comments one Reddit marketing service posted from 1 to 22 September 2026 were removed within a week, up from 45.6% in August.
- Dataset totals One row: properties listed and with data access, the property left out because it belongs to another company, portfolio properties with data access, sites included and excluded, date range, total and visible impressions and clicks with their shares, queries, the lowest, median and highest share visible per site, quartiles of the long conversational share per site, sites with long conversational impressions, and sites that first appeared after the start date. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Query classes One row per class and subgroup, plus all visible queries: queries, impressions, share of visible impressions, clicks, CTR, average position and share of impressions at position 10 or better. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Query classes by site type One row per site type plus all sites: sites, total and visible impressions, share visible, and for each class the impressions, share of visible impressions, clicks, CTR and average position, with the number of sites that had long conversational impressions. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Sites, anonymized One row per site under a label (S01 to S27): site type, band of total impressions, share visible, long conversational and question shares of visible impressions, and the average position and desktop share of long conversational and keyword queries where both classes have at least 50 impressions. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Sites by long conversational share Number of sites in each band of long conversational share of visible impressions. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Comparisons within each site For sites with at least 30, 50 or 100 impressions in both classes: sites compared, sites where the first class ranks higher (average position) or has the higher desktop share, sites where keyword queries do, and the median gap (keyword position minus class position, or class desktop share minus keyword desktop share). From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Queries by number of words One row per word count: queries, impressions, share of visible impressions, clicks, CTR, average position, share of impressions at position 10 or better, and the impressions and share of the 3 queries with the most impressions. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Device split by class One row per site group (all sites and each site type) and class: impressions and the desktop, mobile and tablet shares. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Weekly shares One row per week, Monday to Sunday, labeled by its Monday, from the partial week of 22 June (24 to 28 June in the window) to the week of 14 September: days in the window, sites with long conversational impressions, visible impressions, long conversational and question impressions and shares, keyword share, average positions of long conversational and keyword queries, long conversational clicks, and the impressions of the week's top long conversational query. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Weekly long conversational impressions by site type One row per week, as in weekly.csv: days in the window and long conversational impressions for each site type and for all sites. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- First and last four weeks Visible and long conversational impressions and the long conversational share in the first and last four full weeks, for all 27 sites, for the 24 sites left after removing the 3 with the most long conversational impressions, and for the 21 sites with visible impressions in all 12 full weeks. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Concentration of long conversational impressions Long conversational impressions and their share for the top 10 queries, the top 1% of queries, queries with a single impression, and the one and three sites with the most. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Prompt text in queries Queries that contain a prompt phrase, grouped by the prompt they open with (generic text only, the questions left out): queries, impressions, clicks and average position. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Average position by class and site type Data for Exhibit 2: average position of long conversational and keyword queries for all sites and each site type, with the chart label, site group, class and the impressions behind each figure. From No. 05: Queries that look like AI fan-out made up 6.1% of 27 sites' visible Google impressions, ranked higher than keywords and drew 3 clicks.
- Samples and exclusions One row per sample: source list, date window, domains drawn, domains that answered, home pages that were not 200, redirected to another domain or were not HTML, live HTML sites, sites excluded as parked, for sale or placeholder (with the split), other exclusions, and sites analyzed. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Newly registered domain files used One row per WhoisDS daily file: file date, domains listed, .com domains, and domains drawn into the new-site sample. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Files for AI agents by sample One row per file type and sample: sites analyzed, sites where the file is present, share, and Wilson 95% interval. Covers llms.txt, llms.txt without platform defaults, llms-full.txt, index.md, Markdown alternate links, /.well-known/api-catalog and robots.txt. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- robots.txt rules for AI crawlers One row per AI crawler and sample: sites analyzed, sites whose robots.txt blocks the crawler by name, sites that name it and allow the root, sites that block it only through a rule for all crawlers, and shares. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- robots.txt summary by sample Sites with a valid robots.txt, sites that name any AI crawler, sites that block one by name, Cloudflare managed robots.txt files, Content-Signal and Content-Usage lines, and blanket blocks of all crawlers. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Where llms.txt files come from The llms.txt files in each sample split by source: Shopify, GoDaddy Website Builder and Wix defaults, other site builders, WordPress SEO plugins, and other or not attributable. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- llms.txt with and without platform defaults Per sample: sites analyzed, sites with llms.txt, how many of those files are platform defaults, and the share of sites with an llms.txt that is not a platform default. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Size and links of llms.txt files Per sample: number of llms.txt files, median size in bytes, median number of Markdown links, files with one link or none, and files that start with a heading. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Site platforms and their files Per sample and platform (detected from home page markers): sites on the platform, share of the sample, sites with llms.txt and llms-full.txt, and sites whose robots.txt names or blocks an AI crawler. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Exclusion rules and counts Every parked, for-sale, placeholder, redirect and bot-check rule, with the number of live HTML sites it excluded in each sample. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Excluded pages that still serve llms.txt Per sample: excluded sites, how many of them serve a valid llms.txt, and how many of those are GoDaddy for-sale landers or GoDaddy launching-soon pages. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- API catalog files Per sample: sites serving a valid /.well-known/api-catalog linkset, and how many of those link to a Universal Commerce Protocol endpoint. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Chart data: files for AI agents The shares plotted in Exhibit 1, taken from ai-files.csv. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Chart data: llms.txt sources on new sites The shares plotted in Exhibit 2, taken from llms-txt-sources.csv. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Chart data: robots.txt blocks by crawler The shares plotted in Exhibit 3, taken from robots-ai-rules.csv. From No. 04: 17.5% of new .com websites serve an llms.txt file, and most of those files are Shopify, GoDaddy and Wix defaults.
- Sample totals and outcomes One row: comments, subreddits and brands in the sample, posting dates, comments removed by the first check, removed later, still visible, the matching rates, re-check coverage and the count of comments with a web link. From No. 03: 54.2% of the brand-mention comments one Reddit marketing service placed were removed within a week, and 47.1% of those removals came on the day of posting.
- When removals were found One row per check: removed comments first found at that check, share and cumulative share of all removals, and median hours from assignment to that check. From No. 03: 54.2% of the brand-mention comments one Reddit marketing service placed were removed within a week, and 47.1% of those removals came on the day of posting.
- Timing of the first check Comments matched to a first-check run, and the 10th, 50th and 90th percentile hours from assignment to the first check. From No. 03: 54.2% of the brand-mention comments one Reddit marketing service placed were removed within a week, and 47.1% of those removals came on the day of posting.
- Removal by week of posting One row per week: comments, subreddits, share removed by the first check and share removed within a week. From No. 03: 54.2% of the brand-mention comments one Reddit marketing service placed were removed within a week, and 47.1% of those removals came on the day of posting.
- Removal by subreddit size Subreddits and comments in each member-count band, with the share removed by the first check and within a week. From No. 03: 54.2% of the brand-mention comments one Reddit marketing service placed were removed within a week, and 47.1% of those removals came on the day of posting.
- Removal by how many comments a subreddit received Subreddits grouped by their comment count in the sample, with comments and the share removed within a week. From No. 03: 54.2% of the brand-mention comments one Reddit marketing service placed were removed within a week, and 47.1% of those removals came on the day of posting.
- Named subreddits Subreddits with 30 or more comments from at least 3 brands, where no brand has more than half the comments: comments, brands, removed, removal rate with a 95% Wilson interval, removals found at the first check, and median hours from assignment to the check that found each removal. From No. 03: 54.2% of the brand-mention comments one Reddit marketing service placed were removed within a week, and 47.1% of those removals came on the day of posting.
- Spread across subreddits with 30 or more comments All 54 subreddits with 30 or more comments: pooled, lowest and highest removal rate, observed standard deviation against the one expected from sample size, and a chi-square test of equal rates. From No. 03: 54.2% of the brand-mention comments one Reddit marketing service placed were removed within a week, and 47.1% of those removals came on the day of posting.
- How widely the September rise spread Subreddits with at least 5 comments both from 30 July to 2 September and from 3 to 16 September: how many saw their removal rate rise, and the median rate in each period. From No. 03: 54.2% of the brand-mention comments one Reddit marketing service placed were removed within a week, and 47.1% of those removals came on the day of posting.
- SQL queries The read-only query behind every CSV in this report. From No. 03: 54.2% of the brand-mention comments one Reddit marketing service placed were removed within a week, and 47.1% of those removals came on the day of posting.
- Daily Google traffic and ChatGPT visitors One row per day, 1 July to 20 September 2026: whole-site Google clicks, impressions and average position (final data), and ChatGPT referral visitors from Plausible. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- ChatGPT referral visitors by week Sum of daily ChatGPT referral visitors for each Monday-to-Sunday week from 1 June to 20 September 2026. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Before and after windows Every before-and-after comparison in the report: metric, source, window dates, before and after values, change and percentage change. Main windows are 17 to 30 August and 7 to 20 September 2026. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- The 34 new pages by type By page type: pages, publication dates, pages indexed on 22 September, pages with a first Google impression within a day of publication, pages indexed within a day, Google clicks, impressions and average position (3 to 22 September and 7 to 20 September), visible and conversational query impressions, Bing pages, impressions and clicks through the week of 18 September, and ChatGPT entry visitors (3 to 22 September). From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Google impressions by type of new page Google impressions and clicks from 3 to 22 September 2026 for the four page types published 3 to 5 September, with impressions per page. Source of Exhibit 3. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Visible Google queries on the new pages by type Distinct queries, query and page pairs, impressions, share of visible impressions, clicks and average position for conversational and keyword queries on the 34 new pages, 3 to 22 September 2026. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Examples of conversational queries The 10 conversational queries with the most impressions on the new pages, plus every query containing a pasted AI instruction, with the page type, impressions and average position. Queries naming companies are left out. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Where the extra Google clicks came from Pages with impressions, clicks and impressions before (17 to 30 August) and after (7 to 20 September) for the new pages, older pages already in Google results, older pages with no August impressions, and the noindexed template pages, with older pages also split into product pages and other pages. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Entry pages of ChatGPT visitors ChatGPT referral visitors by the type of page they entered on, 1 June to 2 September and 3 to 22 September 2026. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Daily AI tracker runs One row per run of a daily tracker that asked 10 buyer prompts to up to 4 AI engines, 27 July to 9 September 2026, plus the re-run of 22 September: answers expected, returned and missing, answers naming the company, citing its site and citing a new comparison listicle, for all engines and for ChatGPT. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- AI answers by prompt type and engine, 22 September 2026 For each prompt type and engine: prompts, answers, answers naming the company and answers citing its site. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- ChatGPT API answers, 22 September 2026 The 13 ChatGPT answers from the 22 September scan: pass, prompt type, prompt with names replaced, number of citations, web searches reported by the API (re-asks only), whether the answer names the company, and what the answer did. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Prompts All 20 prompts used, with the company and competitor names replaced by placeholders, and the engines each was asked on. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Brand-name searches by week Google clicks, impressions and average position for searches of the company's current or former name, by Monday-to-Sunday week from 6 April to 20 September 2026. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Where the publisher ranked itself The 12 new comparison listicles grouped by the number of vendors listed and the publisher's own position. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- What the site changed, by date Dated list of the changes the site made from 3 to 22 September 2026, with the number of pages each change touched where it applies to a set of pages. From No. 02: A B2B site added 34 pages, 12 of them comparison listicles. ChatGPT referrals went from 2 to 46 per fortnight.
- Dataset totals One row: runs, projects, prompts, requests, answers, failed requests, citations, distinct URLs and domains, own-site citations and their share, and the first and last answer dates (US Eastern). From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Projects One row per project under an anonymous label (P01 to P12): scan schedule, runs, prompts, answers, failed requests, citations and share of all citations. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Prompt types One row per prompt type (discovery, competitor alternatives, problem): prompts, prompts per project, answers and citations. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Answers and citations by engine One row per engine: model id, requests, failed requests, answers, citations, mean, median and maximum citations per answer, answers with no citation and their share, citations per answer that cites at least one source, distinct domains and URLs, citations per answer relative to ChatGPT, and first and last answer dates. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Engine summary by part of the dataset The engine summary for the daily-scan project and for the 11 single-scan projects: answers, citations, mean and median citations per answer, and answers with no citation. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Answers with no citation, by prompt type One row per part of the dataset (all projects, daily-scan project, single-scan projects), engine and prompt type: answers, answers with no citation and their share. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Cross-engine overlap For prompt-runs in which all four engines answered: distinct URLs or domains cited, and how many were cited by one engine, by two or more and by all four, for all projects and each part of the dataset. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Overlap by engine For each engine: URLs and domains it cited in prompt-runs where all four engines answered, and how many another engine also cited for the same prompt in the same run. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Overlap by engine pair For each pair of engines: URLs and domains cited by either and by both for the same prompt in the same run, summed over prompt-runs. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Day-over-day retention Daily-scan project only. One row per engine: days answered, answers, URLs per answer, consecutive-day pairs, URLs cited on day one and still cited the next day, URL retention overall and when both days carried citations, domain retention, distinct URLs over the full period and the share cited on one day only. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Retention by days elapsed Daily-scan project only. One row per gap of 1 to 28 days: the share of cited URLs the same engine cited again for the same prompt after that many days, per engine, with the number of URLs behind each share. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Citations by source type One row per source type: citations and share for all engines, and citations and share for each engine. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Source types by prompt type One row per prompt type and source type: citations and share of that prompt type's citations. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Answers citing each source type One row per engine and source type: answers, answers citing at least one source of that type, and their share. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Top 50 cited domains The 50 most-cited domains, citations of brands' own sites excluded: rank, citations, share of all 22,141 citations, number of engines and of projects citing the domain, domain group, and the share of the domain's citations whose URL matches the listicle rule. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Domains by number of projects citing them One row per number of projects (1 to 12): domains cited for that many projects and their citations, with the domains named for 8 or more projects. Own-site citations excluded. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Concentration of citations For all engines and each engine: citations, distinct domains, citations to the top 10 domains and to the top 1% of domains with their shares, the number of domains that account for half of citations, and domains cited once. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Perplexity references by list rank One row per position in Perplexity's source list: sources listed, sources the answer text points to with a numbered marker, and their share. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Perplexity references by source type One row per source type plus all sources: sources listed by Perplexity, sources the answer text points to, their share and the median list rank. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.
- Perplexity references in the top five Sources listed in ranks 1 to 5 of Perplexity's source list, Reddit against all other domains: sources listed, referenced in the text, and share. From No. 01: ChatGPT, Perplexity, Gemini and Claude rarely cite the same page for the same buyer question.