{"id":4855,"date":"2026-08-08T22:48:38","date_gmt":"2026-08-08T19:48:38","guid":{"rendered":"https:\/\/ivitskiy.com\/blog\/adworld-experience-2024\/"},"modified":"2026-08-09T00:32:33","modified_gmt":"2026-08-08T21:32:33","slug":"adworld-experience-2024","status":"publish","type":"post","link":"https:\/\/ivitskiy.com\/blog\/en\/adworld-experience-2024\/","title":{"rendered":"30x Conversions with N-Gram Negative Keywords: Igor Ivitskiy at Adworld Experience 2024"},"content":{"rendered":"\n<div class=\"tldr\"><strong>In short:<\/strong> A B2C SaaS client had been stuck at 50 conversions per day for two years. Igor Ivitskiy exported the full search-term report, ran an n-gram profitability analysis in Excel, excluded thousands of unprofitable words as <a href=\"https:\/\/support.google.com\/google-ads\/answer\/2453972\" target=\"_blank\" rel=\"noopener\">negative keywords<\/a> and re-launched on <a href=\"https:\/\/support.google.com\/google-ads\/answer\/2497828\" target=\"_blank\" rel=\"noopener\">broad match<\/a>. Within one week, the account reached 1,500 conversions per day \u2014 a 30x jump at a lower CPA. The case won first place in the PPC Caesars Award 2024.<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.adworldexperience.it\/en\/\" target=\"_blank\" rel=\"noopener\">Adworld Experience 2024<\/a> took place in Bologna, Italy, on October 17\u201318, 2024. Igor presented this English-language PPC case at Europe\u2019s largest conference of real PPC cases.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The case study, <strong>\u201c30x Conversions for B2C SaaS Through <a href=\"https:\/\/ivitskiy.com\/blog\/en\/keyword-research-for-google-ads\/\">In-Depth Keyword Research<\/a>\u201d<\/strong>, shows how word-level profitability analysis of search terms \u2014 n-grams, broad match and thousands of negative keywords \u2014 broke a two-year growth ceiling in one week. The speaker, <a href=\"https:\/\/ivitskiy.com\/en\/ivitskiy\/\">Igor Ivitskiy<\/a>, is a Google Ads expert and \u201cThe Google Ads Scientist\u201d \u2014 a PhD mathematician who applies optimization science to Google &amp; YouTube Ads.<\/p>\n\n\n\n<div class=\"stat-bars\">\n<div class=\"stat-bar\">\n<div class=\"stat-bar__label\"><strong>Before<\/strong><\/div>\n<div class=\"stat-bar__track\"><div class=\"stat-bar__fill stat-bar__fill--muted\" style=\"width:3%\"><\/div><\/div>\n<div class=\"stat-bar__val\">50 conversions\/day<\/div>\n<\/div>\n<div class=\"stat-bar\">\n<div class=\"stat-bar__label\"><strong>After<\/strong><\/div>\n<div class=\"stat-bar__track\"><div class=\"stat-bar__fill\" style=\"width:100%\"><\/div><\/div>\n<div class=\"stat-bar__val\">1,500 conversions\/day<\/div>\n<\/div>\n<\/div>\n<div class=\"viz-src\">Source: Adworld Experience 2024 case, Igor Ivitskiy.<\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/ivitskiy.com\/blog\/wp-content\/uploads\/2026\/08\/igor-ivitskiy-adworld-experience-2024-bologna.jpg\" alt=\"Igor Ivitskiy speaking at Adworld Experience 2024 in Bologna, Italy\" width=\"800\" loading=\"lazy\"\/><figcaption class=\"wp-element-caption\">Igor Ivitskiy presenting the award-winning PPC case at Adworld Experience 2024, Bologna<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">How the n-gram method works<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The starting hypothesis was that only a small fraction of potential users purposely search for services such as car-plate, phone-number or property-address reports, although the service can be useful to almost anyone. About 70% of search volume sits in the long tail \u2014 terms so rare that they barely show up in reports.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Instead of bidding on particular queries, Igor bid on the broad market and excluded irrelevant traffic with negative keywords. The analysis turns the search-term report into word-level profitability data, then uses the unprofitable words as negatives and the most profitable words as new <a href=\"https:\/\/ivitskiy.com\/blog\/en\/broad-match-keywords\/\">broad-match keywords<\/a>.<\/p>\n\n\n\n<div class=\"diag-tree\">\n<div class=\"diag-branch\"><span class=\"diag-branch__num\">1<\/span><strong>Export search terms<\/strong><br>Export all search terms with their cost and profit data.<div class=\"diag-check\">Check: include cost and profit for the full report.<\/div><\/div>\n<div class=\"diag-branch\"><span class=\"diag-branch__num\">2<\/span><strong>Split into n-grams<\/strong><br>Break every term down into individual words, so each word becomes a unit of analysis.<div class=\"diag-check\">Check: separate every term into individual words.<\/div><\/div>\n<div class=\"diag-branch\"><span class=\"diag-branch__num\">3<\/span><strong>Calculate profitability<\/strong><br>For every word, total its contribution to cost and revenue, then sort by profitability.<div class=\"diag-check\">Check: identify both unprofitable and most profitable words.<\/div><\/div>\n<div class=\"diag-branch diag-branch--wide\"><span class=\"diag-branch__num\">4<\/span><strong>Build and launch<\/strong><br>Use unprofitable words as negatives, profitable words as broad-match keywords, then launch the broadest possible combinations.<div class=\"diag-check\">Check: the account bids on the broad market while the waste is already excluded.<\/div><\/div>\n<\/div>\n<div class=\"viz-src\">Source: Adworld Experience 2024 case, Igor Ivitskiy.<\/div>\n\n\n\n<section class=\"first-hand-note\"><p><strong>How I filter <a href=\"https:\/\/ivitskiy.com\/blog\/en\/negative-keywords-wont-cut-your-waste\/\">negative keywords<\/a><\/strong><\/p><p>I select negative keywords by money and by audience response, not by intuition. Spend and response determine whether a word stays, while the final decision follows the project\u2019s overall profitability rather than the cosmetic appeal of any single metric.<\/p><\/section>\n\n\n\n<p class=\"wp-block-paragraph\">This method works not as a one-off trick, but as a repeatable discipline of learning from the account\u2019s own data. Our school\u2019s free webinar is devoted to this same way of working.<\/p>\n\n\n\n<!-- cta-anchor -->\n\n\n\n<h2 class=\"wp-block-heading\">Key insights<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>From 50 to 1,500 conversions per day in one week (30x).<\/strong> For two years, the client believed the market was capped at 50 conversions a day. Word-level profitability analysis broke that ceiling in the first iteration.<\/li>\n<li><strong>70% of the market hides in the long tail.<\/strong> Broad match reaches queries that appear too rarely to bid on directly; thousands of n-gram-derived negative keywords carve away the waste.<\/li>\n<li><strong>The analysis is a monthly loop, not a one-off.<\/strong> Each month brings new search terms; another n-gram analysis keeps cutting CPA and growing conversions.<\/li>\n<li><strong>Target CPA on broad match beat manual CPC on exact match<\/strong> in a head-to-head test inside the same account.<\/li>\n<li><strong>Hands-on analysis reveals intent.<\/strong> Igor tried automated n-gram tools, but recommends working through the data in Excel for big budgets.<\/li>\n<li><strong>Expensive products need financing.<\/strong> High-ticket items worked only with payments split into 12\u201318 instalments; without financing, they did not.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Key moments from the talk<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">The ceiling the client believed in<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The case began with a B2C SaaS company selling information as a service: reports built from a car plate, phone number or property address. It had a mass-market product, yet for two years every agency, freelancer and in-house specialist had run into the same ceiling of 50 conversions a day. The client had come to treat that number as the natural size of the market. His request was therefore conservative: reduce the cost per conversion to 3\u20133.5 dollars and, if possible, add a few more conversions. Even the way the relationship started challenged that caution. An assistant had rejected the project as too small, but the client kept contacting Igor until they signed a contract. \u201cNobody ever said no before.\u201d The remark mattered because the campaign that followed also began by refusing the client\u2019s inherited assumption about what was possible.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Where the hidden demand was<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The growth hypothesis came from a mismatch between usefulness and expressed demand. Only a small group deliberately searches for a service that checks a plate, phone number or address, although almost anyone could benefit from the answer. That left most demand hidden in three kinds of searches. The first came from people with implicit intent: someone researching car-sale scams before a purchase might need a vehicle-history report without naming that service. The second came from people who typed the data itself into Google, such as a particular plate or phone number. Every value created a unique query, so a conventional keyword list could never anticipate the whole market. The third came from people who did not know that such a service existed and therefore could not search for it directly. The strategic question was not how to win more of the small, obvious category, but how to reach all three without paying indefinitely for irrelevant traffic.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The method answered that question by changing the unit of the bet. Instead of bidding on selected queries one by one, the campaign bid on the broad market and used negative keywords to cut away waste. Those negatives were not chosen because a term looked strange or felt irrelevant. They came from the profitability of individual words across the project\u2019s search traffic: spending and response supplied evidence, and total business profit decided what belonged. This made broad match a controlled way to discover demand rather than an invitation to buy everything Google could send. The distinction is important. The edge did not come from predicting the full range of rare queries in advance; it came from allowing the system to find them while a growing evidence-based exclusion layer protected the economics. The detailed spreadsheet procedure remains part of the talk, but the governing idea is simple: buy access to the wide market, then remove what the project\u2019s own results identify as waste.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What the numbers changed<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The change appeared within a week. Daily conversions rose from the supposedly immovable 50 to 1,500, a 30x increase, while both cost per click and cost per conversion fell. The long tail was cheaper because other advertisers were not bidding directly on those rare searches, and the negative-keyword layer kept the wider reach economically useful. The account was no longer limited to people who already knew the product category and used the expected language. It could meet latent intent, searches made from raw data and users discovering the service for the first time. After two years of failed attempts to cross the old ceiling, the client reduced the contrast to one question. \u201cYesterday there were 1,540 conversions. How did you do it?\u201d The answer was not a larger bid on the old market, but a different definition of the market itself.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Several lessons survive the case. The analysis has to be repeated every month because new search terms continually reveal new negative keywords; the exclusion layer improves as the market supplies more evidence. In the same account, an automated target-CPA strategy on broad match beat manual bids on exact match, showing that tighter control at the query level did not guarantee a better business result. The larger data set then opened another level of optimization across audiences and demographics, including combinations of keywords, age, gender, household income and interests. Hands-on review also exposed intent that an automated report could conceal. \u201cFor big budgets I highly recommend working through the numbers yourself.\u201d Finally, the method has a commercial boundary: expensive products worked only when financing divided the price into 12\u201318 payments. Broad reach can uncover demand, but it cannot remove the customer\u2019s affordability constraint. Across all these lessons, the standard remains overall project profitability, not an isolated metric that merely looks efficient.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Full recording of this talk is part of our bonus pack for students.<\/p>\n\n\n\n<aside class=\"limits-note\"><p><strong>When this does not apply<\/strong><\/p><ul>\n<li><strong>When search ads are not the main source of leads.<\/strong> The talk defines the method\u2019s scope as niches where search ads are the main lead source.<\/li>\n<li><strong>For high-ticket items without financing.<\/strong> In the UK test, expensive products worked only when payment was split into 12\u201318 instalments; without financing, they did not.<\/li>\n<li><strong>As a fully automated shortcut.<\/strong> Igor selects negatives manually and recommends hands-on Excel analysis for big budgets because it exposes user intent.<\/li>\n<\/ul><\/aside>\n\n\n\n<h2 class=\"wp-block-heading\">Third-party traces<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.adworldexperience.it\/en\/ppc-caesars-award-2024\/\" target=\"_blank\" rel=\"noopener\">PPC Caesars Award 2024 \u2014 official award page<\/a><\/li>\n<li><a href=\"https:\/\/www.adworldexperience.it\/en\/2024-speakers-program\/\" target=\"_blank\" rel=\"noopener\">Adworld Experience 2024 \u2014 speakers &amp; program<\/a><\/li>\n<\/ul>\n\n\n\n<div class=\"key-takeaways\">\n<p><strong>Key takeaways<\/strong><\/p>\n<p>The first iteration took the account from 50 to 1,500 conversions per day in one week.<\/p>\n<p>Broad match found long-tail demand; n-gram negatives removed unprofitable traffic.<\/p>\n<p>Repeating the analysis every month kept pushing CPA down and conversions up.<\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Adworld Experience 2024 award case: word-level profitability of search terms took a B2C SaaS account from 50 to 1,500 conversions per day in one week.<\/p>\n","protected":false},"author":0,"featured_media":4856,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[204],"tags":[],"class_list":["post-4855","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-conference-talks","generate-columns","tablet-grid-50","mobile-grid-100","grid-parent","grid-50"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>N-gram negative keywords: the Google Ads case that won 30x<\/title>\n<meta name=\"description\" content=\"Adworld Experience 2024 award case: word-level profitability of search terms took a B2C SaaS account from 50 to 1,500 conversions per day in one week.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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