{"id":20763,"date":"2025-02-07T16:03:14","date_gmt":"2025-02-07T16:03:14","guid":{"rendered":"https:\/\/www.equalexperts.com\/?p=20763"},"modified":"2025-02-26T13:43:56","modified_gmt":"2025-02-26T13:43:56","slug":"we-built-a-personalisation-engine-why-isnt-it-working-lessons-from-ctos-and-cdos","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/data\/we-built-a-personalisation-engine-why-isnt-it-working-lessons-from-ctos-and-cdos\/","title":{"rendered":"\u201cWe built a personalisation engine\u2014why isn\u2019t it working?\u201d Lessons from CTOs and CDOs"},"content":{"rendered":"<p>Many e-commerce companies invest heavily in personalisation engines, expecting AI-driven recommendations, dynamic content, and tailored experiences to drive higher conversion rates. But often, after months (or even years) of development, the results are underwhelming\u2014engagement remains stagnant, revenue uplift is marginal, and customer frustration grows.<\/p>\n<p>So, what\u2019s going wrong?<\/p>\n<p>We spoke to CTOs, CDOs, and Heads of Engineering who have been through this journey. Their insights reveal common pitfalls and critical lessons for turning struggling personalisation initiatives into real business impact.<\/p>\n<h1>1. The problem isn\u2019t your AI\u2014it\u2019s your data<\/h1>\n<p>&nbsp;<\/p>\n<p>Many organisations blame their recommendation engines when personalisation falls flat. The reality? Bad data is the real bottleneck.<br \/>\nCommon issues we\u2019ve seen are:<\/p>\n<ul>\n<li>Data silos \u2013 Customer data is scattered across platforms, preventing a unified personalisation strategy<\/li>\n<li>Duplicate and inconsistent records \u2013 A customer might be treated as multiple different users across touchpoints<\/li>\n<li>Lack of real-time data \u2013 Personalisation models rely on outdated batch-processed insights, leading to irrelevant recommendations.<\/li>\n<\/ul>\n<p><strong>Lesson: <\/strong>Personalisation is only as good as the data behind it.<\/p>\n<h1>2. The tech is built, but the business can\u2019t use it<\/h1>\n<p>&nbsp;<\/p>\n<p>One CTO shared how their engineering team built a personalisation engine, but marketing and product teams couldn\u2019t easily access or test recommendations. The result? Underutilisation and minimal impact.<\/p>\n<p>Common missteps are:<\/p>\n<ul>\n<li>No A\/B testing framework \u2013 Without proper experimentation, it\u2019s hard to measure whether personalisation is actually working.<\/li>\n<li>No clear ownership \u2013 Is personalisation owned by engineering, data, marketing, or product teams? Often, it\u2019s unclear.<\/li>\n<\/ul>\n<p><strong>Lesson:<\/strong> Personalisation needs cross-functional collaboration, not just a strong tech stack.<\/p>\n<p><center><a href=\"https:\/\/www.equalexperts.com\/ecommerce-personalisation-john-lewis-partners\/\"><img decoding=\"async\" class=\"aligncenter wp-image-20726 size-full\" src=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2025\/01\/EE_Blog_JLP_ADcreative_500x250.jpg\" alt=\"John Lewis\" width=\"500\" height=\"250\" srcset=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2025\/01\/EE_Blog_JLP_ADcreative_500x250.jpg 500w, https:\/\/www.equalexperts.com\/wp-content\/uploads\/2025\/01\/EE_Blog_JLP_ADcreative_500x250-300x150.jpg 300w\" sizes=\"(max-width: 500px) 100vw, 500px\" \/><\/a><\/center>&nbsp;<\/p>\n<h1>3. The infrastructure can\u2019t handle scale<\/h1>\n<p>&nbsp;<\/p>\n<p>A CDO at a leading e-commerce retailer described how their personalisation model performed well in a test environment but crashed under real-world traffic. The culprit? Bottlenecks in the data pipeline and inefficient query execution.<\/p>\n<p>Common scalability pitfalls are:<\/p>\n<ul>\n<li>Recommendation engines querying live databases instead of using precomputed embeddings<\/li>\n<li>Inefficient data pipelines causing latency in real-time personalisation<\/li>\n<li>Infrastructure not optimised for high-traffic events (e.g., Black Friday).<\/li>\n<\/ul>\n<p><strong>Lesson<\/strong>: A system that works in a pilot phase can still fail in production if scalability isn\u2019t considered.<\/p>\n<h1>4. Lack of flexibility stifles innovation<\/h1>\n<p>&nbsp;<\/p>\n<p>Several CTOs we spoke to described how early technology choices limited their ability to innovate later. Many personalisation platforms were designed with specific use cases in mind but couldn\u2019t adapt as customer expectations evolved.<\/p>\n<p>Signs of an inflexible system:<\/p>\n<ul>\n<li>Personalisation logic is hardcoded into the platform, making it difficult to test new models.<\/li>\n<li>Scaling personalisation globally requires expensive rewrites due to tightly coupled architectures.<\/li>\n<li>Integrating new AI models or third-party data sources is slow and complex.<\/li>\n<\/ul>\n<p><strong>Lesson<\/strong>: Rigid architectures and legacy data systems slow down personalisation efforts.<\/p>\n<h1>4. ROI expectations are misaligned<\/h1>\n<p>&nbsp;<\/p>\n<p>Many CDOs highlighted a disconnect between leadership expectations and real-world personalisation ROI timelines. Executives expect immediate revenue lift, but in reality:<\/p>\n<ul>\n<li>Personalisation takes time to optimise\u2014data models need continuous tuning<\/li>\n<li>Success metrics must be realistic\u2014personalisation might improve customer lifetime value (CLV) more than immediate conversions<\/li>\n<\/ul>\n<p>Long-term investment is key\u2014AI-driven personalisation isn\u2019t just about quick wins but creating sustained differentiation.<\/p>\n<p><strong>Lesson<\/strong>: Personalisation is not a one-time implementation\u2014it requires ongoing optimisation.<\/p>\n<p><center><a href=\"https:\/\/www.equalexperts.com\/ecommerce-personalisation-john-lewis-partners\/\"><img decoding=\"async\" class=\"aligncenter wp-image-20726 size-full\" src=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2025\/01\/EE_Blog_JLP_ADcreative_500x250.jpg\" alt=\"John Lewis\" width=\"500\" height=\"250\" srcset=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2025\/01\/EE_Blog_JLP_ADcreative_500x250.jpg 500w, https:\/\/www.equalexperts.com\/wp-content\/uploads\/2025\/01\/EE_Blog_JLP_ADcreative_500x250-300x150.jpg 300w\" sizes=\"(max-width: 500px) 100vw, 500px\" \/><\/a><\/center>&nbsp;<\/p>\n<h1>Final takeaways for CDOs and CTOs<\/h1>\n<p>&nbsp;<\/p>\n<p>If your personalisation engine isn\u2019t delivering results, the problem isn\u2019t the algorithm\u2014it\u2019s the data, infrastructure, and operational alignment.<\/p>\n<ul>\n<li>Fix your data first\u2014invest in real-time, high-quality, unified customer data<\/li>\n<li>Make personalisation accessible\u2014give marketing and product teams the tools to optimise and measure results<\/li>\n<li>Design for scale\u2014ensure your infrastructure can handle real-world e-commerce demands<\/li>\n<li>Future-proof your architecture\u2014adopt a modular, flexible data infrastructure that allows personalisation strategies to evolve without costly rework<\/li>\n<li>Align ROI expectations\u2014personalisation is a long-term investment, not a quick win.<\/li>\n<\/ul>\n<p>By applying these lessons, e-commerce leaders can turn underperforming personalisation into a true competitive advantage.<\/p>\n<p>Want to accelerate your data readiness for personalisation? <a href=\"https:\/\/www.equalexperts.com\/ecommerce-data-accelerator\/\">See how we can help.<\/a><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Many e-commerce companies invest heavily in personalisation engines, expecting AI-driven recommendations, dynamic content, and tailored experiences to drive higher conversion rates. But often, after months (or even years) of development, the results are underwhelming\u2014engagement remains stagnant, revenue uplift is marginal, and customer frustration grows. So, what\u2019s going wrong? We spoke to CTOs, CDOs, and Heads [&hellip;]<\/p>\n","protected":false},"author":133,"featured_media":20764,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[179],"tags":[],"location":[],"class_list":["post-20763","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u201cWe built a personalisation engine\u2014why isn\u2019t it working?\u201d | Equal Experts<\/title>\n<meta name=\"description\" content=\"Discover why personalisation efforts fail and how CTOs &amp; CDOs can fix data, scalability, and flexibility issues to drive real business impact.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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