{"id":31784,"date":"2026-02-09T17:40:46","date_gmt":"2026-02-09T17:40:46","guid":{"rendered":"https:\/\/www.intercom.com\/blog\/?p=31784"},"modified":"2026-02-13T15:00:42","modified_gmt":"2026-02-13T15:00:42","slug":"cx-scores-how-we-built-a-metric-support-leaders-can-defend","status":"publish","type":"post","link":"https:\/\/www.intercom.com\/blog\/cx-scores-how-we-built-a-metric-support-leaders-can-defend\/","title":{"rendered":"CX Score: How we built a metric support leaders can defend"},"content":{"rendered":"<p>&#8220;We treated trust as a design principle from day one&#8221;<\/p>\n<p>We recently shared how we redesigned CX Score to deliver deeper, more actionable insights across every conversation. The most common follow-up from support leaders was the one that matters most: \u201c<em>Can I trust it?<\/em>\u201d<\/p>\n<p><span style=\"font-weight: 400;\">It&#8217;s the right question.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">CS teams are the subject matter experts on customer experience. They understand the nuance of what customers feel, the context behind every interaction, the difference between a technically resolved issue and a genuinely satisfied customer. Until now, there hasn\u2019t been a metric that could reliably match that expertise at scale.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-31794\" src=\"https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_BoostedResolution.png\" alt=\"fullresolution_CXScore\" width=\"1411\" height=\"785\" srcset=\"https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_BoostedResolution.png 1411w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_BoostedResolution-300x167.png 300w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_BoostedResolution-700x389.png 700w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_BoostedResolution-768x427.png 768w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_BoostedResolution-1320x734.png 1320w\" sizes=\"auto, (max-width: 1411px) 100vw, 1411px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">We built CX Score to give support teams a complete view of the customer experience across every conversation. It surfaces what\u2019s working, what\u2019s not, and why\u2014so leaders can communicate impact clearly and drive change across support, product, and the wider business.<\/span><\/p>\n<div class=\"wistia_responsive_padding\" style=\"padding: 50.83% 0 0 0; position: relative;\">\n<div class=\"wistia_responsive_wrapper\" style=\"height: 100%; left: 0; position: absolute; top: 0; width: 100%;\"><iframe loading=\"lazy\" class=\"wistia_embed\" title=\"cxscore_v2_MarkODonovan Video\" src=\"https:\/\/fast.wistia.net\/embed\/iframe\/3oz28vfrpd?web_component=true&amp;seo=true\" name=\"wistia_embed\" width=\"100%\" height=\"100%\" frameborder=\"0\" scrolling=\"no\"><\/iframe><\/div>\n<\/div>\n<p><script src=\"https:\/\/fast.wistia.net\/player.js\" async><\/script><\/p>\n<p>&nbsp;<\/p>\n<h2 id=\"five-steps-to-building-a-trustworthy-metric\"><b>Five steps to building a trustworthy metric<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">We treated trust as a design principle from day one\u2014starting with how support professionals actually evaluate conversations, validating every decision against human judgment, and creating a system transparent enough to inspect and defend.<\/span><\/p>\n<h3><b>1. It&#8217;s grounded in how support teams define quality<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">We started with how experienced support professionals actually evaluate conversations\u2014collecting real examples of strong, mixed, and poor interactions across industries, identifying the specific factors that shape overall experience, and writing plain-English rules for each.<\/span><\/p>\n<p><b>The result:<\/b><span style=\"font-weight: 400;\"> CX Score applies the same criteria a trained support professional would use, not generic LLM assumptions.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-31793\" src=\"https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_Description.png\" alt=\"\" width=\"1164\" height=\"216\" srcset=\"https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_Description.png 1164w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_Description-300x56.png 300w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_Description-700x130.png 700w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/CXScore_Description-768x143.png 768w\" sizes=\"auto, (max-width: 1164px) 100vw, 1164px\" \/><\/p>\n<h3><b>2. It&#8217;s aligned with human judgment<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">We created a dataset of thousands of real customer conversations spanning multiple industries, languages, channels, and agent types. Each was manually reviewed by experienced support professionals\u2014with two reviewers per conversation where possible and disagreement resolution to create stable consensus labels.<\/span><\/p>\n<p><b>The result:<\/b><span style=\"font-weight: 400;\"> CX Score is trained and tested to behave like an expert reviewer, not a language model making broad guesses.<\/span><\/p>\n<h3><b>3. It&#8217;s engineered by AI specialists<\/b><\/h3>\n<p>CX Score is more than a single prompt. It\u2019s a production system with specialized prompt engineering, infrastructure for scale, and a rigorous evaluation pipeline. It\u2019s built and maintained by Intercom\u2019s AI Group.<\/p>\n<p><span style=\"font-weight: 400;\">The systems model configuration is fine-tuned for support language and subtle sentiment, and it was rigorously tested across many different LLM models. It uses specialized prompt engineering to conservatively evaluate individual quality signals, defaulting them to neutral when evidence is ambiguous, and subjects every model revision to a multi-stage evaluation process that measures precision, recall, and agreement with human judgment before release.\u00a0<\/span><\/p>\n<p><b>The result:<\/b><span style=\"font-weight: 400;\"> A metric built by a team that understands LLM behavior in production support environments, where accuracy and consistency matter most.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-31804\" src=\"https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-1-scaled.png\" alt=\"\" width=\"2560\" height=\"1424\" srcset=\"https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-1-scaled.png 2560w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-1-300x167.png 300w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-1-700x389.png 700w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-1-768x427.png 768w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-1-1536x854.png 1536w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-1-2048x1139.png 2048w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-1-1320x734.png 1320w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><\/p>\n<h3><b>4. It&#8217;s validated statistically and qualitatively<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Trust requires measurement, not vibes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We tested CX Score across standard ML metrics<\/span><span style=\"font-weight: 400;\">: Precision (when the model flags a negative experience, how often do humans agree?), recall (how many human-identified issues does it catch?), and F1 score (the balance between both).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We set an explicit bar: <\/span><b>F1 above 0.8<\/b><span style=\"font-weight: 400;\">, representing high agreement with human judgment. We reran these evaluations through every revision, checking for regressions or biases. And we focused especially on negative experiences, because a false negative hides a real problem.<\/span><\/p>\n<p><b>The result:<\/b><span style=\"font-weight: 400;\"> CX Score meets a measurable standard before it ships\u2014not a gut check, a statistical requirement.<\/span><\/p>\n<h3><b>5. It was battle-tested with real customers<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Lab accuracy isn&#8217;t enough. Customer environments are messy: Varied ticket types, mixed languages, unpredictable edge cases.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Before release, we ran a multi-phase field test\u2014shadow-scoring conversations with both old and new models, validating sensible behavior across agent type and conversation length, then rolling out to a controlled customer group who confirmed the scores felt right, reasons were clear, and insights were actionable.<\/span><\/p>\n<p><b>The result:<\/b><span style=\"font-weight: 400;\"> CX Score shipped because real teams told us it made sense in practice, not because it passed internal tests.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-31802\" src=\"https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-2-scaled.png\" alt=\"\" width=\"2560\" height=\"1242\" srcset=\"https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-2-scaled.png 2560w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-2-300x146.png 300w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-2-700x340.png 700w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-2-768x372.png 768w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-2-1536x745.png 1536w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-2-2048x993.png 2048w, https:\/\/www.intercom.com\/blog\/wp-content\/uploads\/2056\/02\/Image-2-1320x640.png 1320w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><\/p>\n<h2 id=\"the-takeaway\"><b>The takeaway<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Support leaders asked the right question: &#8220;Can I trust it?&#8221; CX Score is designed to earn that trust \u2014 because it was built and validated as a metric you can inspect and defend, not a black-box model output. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">It&#8217;s grounded in how support teams define quality, aligned to expert human review, engineered for consistency in production, held to a clear evaluation bar, and battle-tested with real customers. If you&#8217;ve ever had to justify CS metrics to your exec team, CX Score is designed to be the single source of truth you can stand behind.<\/span><\/p>\n<p><b>Want to see CX Score in your workspace?<\/b> <a href=\"https:\/\/app.intercom.com\/a\/apps\/_\/reports\/overview\"><span style=\"font-weight: 400;\">Get started<\/span><\/a><span style=\"font-weight: 400;\"> \u2192<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">This is the second post in our CX Score series. Missed the first one? <\/span><\/i><a href=\"https:\/\/www.intercom.com\/blog\/the-new-cx-score-explained\/\"><i><span style=\"font-weight: 400;\">Read how CX Score evolved to provide deeper, more actionable insights<\/span><\/i><\/a><i><span style=\"font-weight: 400;\"> \u2192<\/span><\/i><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A CX metric grounded in expert judgment, proven with real-world testing, and built to be defensible.<\/p>\n","protected":false},"author":603,"featured_media":31799,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"category":[3,25421,5],"tags":[25020,259,547],"coauthors":[25469],"class_list":["post-31784","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-customer-support","category-guides-reports","category-product-and-design","tag-cx","tag-data","tag-product"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - 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