{"id":4375497,"date":"2018-08-08T05:57:14","date_gmt":"2018-08-08T03:57:14","guid":{"rendered":"https:\/\/www.deptagency.com\/insight\/the-trade-desks-new-audience-predictor-early-performance-findings\/"},"modified":"2025-12-08T13:44:44","modified_gmt":"2025-12-08T11:44:44","slug":"the-trade-desks-new-audience-predictor-early-performance-findings","status":"publish","type":"article","link":"https:\/\/www.deptagency.com\/en-in\/insight\/the-trade-desks-new-audience-predictor-early-performance-findings\/","title":{"rendered":"The Trade Desk\u2019s new audience predictor: Early performance findings"},"content":{"rendered":"<div class=\"block-article-post-header js-block-article-post-header block-article-post-header__theme--onyxGrey\">\n\t<button\tclass=\"cta-button block-article-post-header__back-button cta-button__color--white cta-button__style--back\" aria-label=\"Back\" >\n\t<svg width='41' height='41' viewBox='0 0 41 41' fill='none' xmlns='http:\/\/www.w3.org\/2000\/svg'><circle cx='20.5' cy='20.5' r='19.5' transform='rotate(-90 20.5 20.5)' stroke='var(--dept-button-icon-outline-color)' stroke-width='1.3' stroke-miterlimit='10' fill='none'\/><circle class='icon-backplate' cx='20.5' cy='20.5' r='19.5' transform='rotate(-90 20.5 20.5)' fill='var(--dept-button-icon-backplate-color)' fill-opacity='1'\/><path class='icon-center' d='M21.174 30 11 20.674l10.174-9.326m-9.326 9.328H30.5' stroke='var(--dept-button-icon-color)' stroke-width='1.3' stroke-miterlimit='10' fill='none'\/><\/svg>\n\t\t\t<span>\n\t\t\tBack to all articles\t\t<\/span>\n\t\n<\/button><h1\t\tclass=\"typography block-article-post-header__title typography__color--white typography__size--h1 typography__align--left\"\n\t\t\t>\n\t\tThe Trade Desk\u2019s new audience predictor: Early performance findings\t<\/h1><div\n\t\tclass=\"image-wrap block-article-post-header__image-wrap\"\n\t\tdata-align=\"center center\"\n\t\t\t>\n\n\t\t\t<picture\n\t\tclass=\"image__picture\"\n\t\t\t\t>\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t<img\n\t\t\t\tsrc=\"https:\/\/www.deptagency.com\/wp-content\/uploads\/2023\/03\/audience-band-bright-1047442-1024x719-1.jpg\"\n\t\t\t\tclass=\"image block-article-post-header__image\"\n\n\t\t\t\t\n\t\t\t\t\t\t\t\t\tloading=\"lazy\"\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\/>\n\t\t<\/picture>\n\t\n\t<\/div><div class=\"author block-article-post-header__author\" data-animation=\"slide-fade\">\n\t\t\t<img\n\t\t\tclass=\"author__portrait\"\n\t\t\tsrc=\"https:\/\/www.deptagency.com\/wp-content\/uploads\/2023\/03\/Feliks-Malts-Dept-150x150.jpg\"\n\t\t\talt=\"Feliks Malts\"\n\t\t\/>\n\t\n\t<div class=\"author__text\">\n\t\tFeliks Malts\t\t<br \/>\n\t\t \n\t\t\t<\/div>\n<\/div>\n\t<div class=\"block-article-post-header__meta\">\n\t\t\t\t\t\t\t<div class=\"block-article-post-header__meta-date\">\n\t\t\t\t<div class=\"block-article-post-header__date-description\">\n\t\t\t\t\tDate\t\t\t\t<\/div>\n\t\t\t\t<div class=\"block-article-post-header__published-date\">\n\t\t\t\t\t8 August 2018\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n<\/div>\n\n<div class=\"block-title-and-content js-block-title-and-content block-title-and-content__theme--white\">\n\t<div class=\"block-title-and-content__layout\">\n\t\t\n\t\t\t\t\t<div class=\"block-title-and-content__content\">\n\t\t\t\t\n<div class='block-title-and-content__item'><div class='block-title-and-content__item-inner'><p\t\tclass=\"typography block-typography__typography typography__color--onyxGrey typography__size--regular-body typography__align--left\"\n\t\t\t>\n\t\tRecently, our preferred DSP (Demand Side Platform), The Trade Desk (TTD), upgraded a targeting LaL (lookalike) feature that we previously tested with decidedly mixed results for clients. So as you can imagine, we were excited to test out these new enhancements as TTD offered the potential to scale our buys with precision, accuracy, and better performance.\t<\/p><\/div><\/div>\n\n<div class='block-title-and-content__item'><div class='block-title-and-content__item-inner'><p\t\tclass=\"typography block-typography__typography typography__color--onyxGrey typography__size--regular-body typography__align--left\"\n\t\t\t>\n\t\tThe featured product in question is TTD\u2019s Audience Predictor. TTD builds its LaL by indexing known converters against 3rd-party data and then seeing which 3rd-party data segments over-index. The engineering team at TTD really nailed this one by upgrading the following:\t<\/p><\/div><\/div>\n\n<div class='block-title-and-content__item'><div class='block-title-and-content__item-inner'><ul class=\"lists lists__color--onyxGrey lists__type--circle lists__size--default lists__align--left block-lists__lists\">\n\t<li><strong>Better matching:<\/strong><span style=\"font-weight: 400\"> You can now create LaL models that closely resemble converters by including the highest-value segments while excluding the lowest-performing segments.<\/span><\/li><li><strong>Improved sample rates:<\/strong> <span style=\"font-weight: 400\">Instead of looking at 1 in 1000 converters to find attributes to build a LAL off of, TTD makes it possible to look at 1 in 10 converters, allowing us to sample a larger scope of people<\/span><\/li><li><strong>Faster audience creation:<\/strong><span style=\"font-weight: 400\"> The system looks for new audiences every 5 minutes.<\/span><\/li><li><strong>Better optimization:<\/strong><span style=\"font-weight: 400\"> The system optimizes automatically toward top-performing segments.<\/span><\/li><\/ul><\/div><\/div>\n\n<div class='block-title-and-content__item'><div class='block-title-and-content__item-inner'><p\t\tclass=\"typography block-typography__typography typography__color--onyxGrey typography__size--regular-body typography__align--left\"\n\t\t\t>\n\t\tI was lucky enough to test the new Audience Predictor on a client and have seen some really promising results so far. Here\u2019s a summary of the test:<br><br><strong>Client<\/strong>: A cloud-based software company looking to attain aggressive growth goals for paying users, while maintaining CPA goals.<br><br><strong>Challenge<\/strong>: The previous targeting practices, such as reaching users by job functions that typically convert, had limited scale and weren\u2019t converting at the rates the client wanted.<br><br><strong>Solution<\/strong>: We began looking for more creative ways to build custom audiences and decided to tap into the wealth of 1st-party data within TTD by indexing converted users against 3rd-party data to build a LAL audience. Working within the DSP gave us full transparency into which data elements were and weren&#8217;t included in the LAL, as well as which elements contributed most to performance.<br><br><strong>Results<\/strong>: By targeting based on behaviors rather than job function, we were able to greatly expand the universe of who was being targeted, and meet our client&#8217;s CPA goals. Although only 8% of the total spend was allocated to this test, 13% of conversions were generated. With these results we can confidently ramp up spend.<br><br>Our CPA dropped 52% from April to May while spend doubled during that time. From May to June, our CPA continued to drop with more budget allocated MoM.<br><br>With our client\u2019s long sales cycle and seasonal fluctuations, it\u2019s too soon to analyze the real success of this test. But I\u2019m impressed so far and look forward to continuing testing this functionality.<br><br><em>Ready for better audience targeting? <a href=\"https:\/\/www.deptagency.com\/service\/growth\/data-and-intelligence\/\">The DEPT\u00ae team can help! Reach out to us with any questions.<\/a><\/em>\t<\/p><\/div><\/div>\n\t\t\t<\/div>\n\t\t\t<\/div>\n\n\t\n\t<div class=\"block-title-and-content__social-links\">\n\t\t\t\t\t<a\n\t\t\t\thref=\"https:\/\/www.deptagency.com\/en-in\/insight\/the-trade-desks-new-audience-predictor-early-performance-findings\/\"\n\t\t\t\tdata-share-url=\"https:\/\/www.deptagency.com\/en-in\/insight\/the-trade-desks-new-audience-predictor-early-performance-findings\/\"\n\t\t\t\tdata-share-title=\"The Trade Desk\u2019s new audience predictor: Early performance findings\"\n\t\t\t\tdata-share-type=\"copy\"\n\t\t\t\tclass=\"block-title-and-content__social-links-item js-social-link\"\n\t\t\t\taria-label=\"Copy page link\"\n\t\t\t\t\n\t\t\t\t\t\t\t\t\tdata-tooltip=\"true\"\n\t\t\t\t\tdata-tooltip-text=\"Link copied to clipboard.\"\n\t\t\t\t\t\t\t>\n\t\t\t\t<svg fill='none' xmlns='http: \/\/www.w3.org\/2000\/svg' width='22' height='22' viewBox='0 0 22 22'><path d='M6.832 15.167l8.333-8.333M9.332 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center\"\n\t\t\t>\n\n\t\t\t<picture\n\t\tclass=\"image__picture\"\n\t\t\t\t>\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t<img\n\t\t\t\tsrc=\"https:\/\/www.deptagency.com\/wp-content\/uploads\/2026\/02\/Forrester-_-2026-_-Insight-_-Phrase-16_9.png\"\n\t\t\t\tclass=\"image universal-item-card__image\"\n\n\t\t\t\t\t\t\t\talt=\"DEPT\u00ae named a Strong Performer in The Forrester Wave\u2122: Commerce Services, Q1 2026\"\n\t\t\t\t\n\t\t\t\t\t\t\t\t\tloading=\"lazy\"\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\/>\n\t\t<\/picture>\n\t\n\t<\/div><p\t\tclass=\"typography universal-item-card__meta typography__color--darkGrey typography__size--eyebrow-lc typography__align--left\"\n\t\t\t>\n\t\tCOMMERCE&emsp;\u2022&emsp;Nitin Kediyal\t<\/p><h3\t\tclass=\"typography universal-item-card__title typography__color--onyxGrey typography__size--h6 typography__align--left\"\n\t\t\t>\n\t\t<span>DEPT\u00ae named a Strong Performer in The Forrester Wave\u2122: Commerce Services, Q1 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