{"id":64990,"date":"2026-04-23T15:39:39","date_gmt":"2026-04-23T15:39:39","guid":{"rendered":"https:\/\/www.elixirr.com\/en-gb\/?p=64990"},"modified":"2026-04-23T15:39:42","modified_gmt":"2026-04-23T15:39:42","slug":"the-rise-of-synthetic-insight-how-to-move-faster-without-losing-confidence","status":"publish","type":"post","link":"https:\/\/www.elixirr.com\/en-gb\/the-rise-of-synthetic-insight-how-to-move-faster-without-losing-confidence\/","title":{"rendered":"The rise of synthetic insight: How to move faster without losing confidence"},"content":{"rendered":"<section\n    class=\"wp-block-text-block edwp-block layout--spacing-y bg--none\"\n    >\n    <div class=\"wp-block-text-block__container container fade-in\">\n        <div class=\"wp-block-text-block__content wysiwyg\">\n            <p><span data-contrast=\"auto\">Organisations are under pressure to move from insight to action faster than ever. Marketing teams want quicker answers. Innovation teams want to test more ideas. Leadership teams want better decisions without increasing research budgets.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This is where synthetic insight is starting to gain real traction.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">But despite the\u00a0momentum, there is still confusion. What is synthetic insight? Is it replacing traditional research? And how should organisations\u00a0actually use\u00a0it?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">We see synthetic insight not as a replacement for human research, but as a new layer in the insight ecosystem. Used well, it increases speed and focus. Used poorly, it creates false confidence.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2>What synthetic insight\u00a0really\u00a0is<\/h2>\n<p><span data-contrast=\"auto\">Synthetic insight uses artificial intelligence to\u00a0predict\u00a0likely\u00a0consumer\u00a0responses, behaviours, or preferences\u00a0based on patterns in training data. It can take several forms:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><strong>Synthetic personas<\/strong><span data-contrast=\"auto\"><strong>:<\/strong> AI-generated profiles\u00a0representing\u00a0consumer types based on aggregated data.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><strong>Synthetic panels<\/strong><span data-contrast=\"auto\"><strong>:<\/strong> Groups of synthetic respondents that mimic structured research samples to screen and prioritise ideas quickly.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><strong>Digital twins<\/strong><span data-contrast=\"auto\"><strong>:<\/strong>\u00a0More advanced\u00a0models\u00a0of real consumers or segments, built from first- and third-party data, used for scenario testing and forecasting.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">These approaches are not interchangeable.\u00a0They support different use cases, but all aim to improve speed,\u00a0efficiency\u00a0and decision-making.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Synthetic personas are typically used for early thinking and exploration, while digital twins are used for scenario testing and forecasting, often requiring stronger data foundations,\u00a0governance\u00a0and ongoing data inputs.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Understanding when to use each approach and how to combine them effectively, is critical.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2>Where synthetic insight works best<\/h2>\n<p><span data-contrast=\"auto\">We are seeing the strongest early adoption in organisations with more mature data,\u00a0insight\u00a0and marketing capabilities, where teams\u00a0operate\u00a0at high velocity and manage large portfolios of brands and campaigns.\u00a0The ability to screen ideas quickly before investing in full research is compelling.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">But this is not limited to one sector. Organisations that test multiple propositions or campaigns,\u00a0operate\u00a0in fast-moving markets, face pressure to reduce time to market and\u00a0have access to strong first-party data for more advanced or predictive applications can\u00a0benefit\u00a0from introducing synthetic insight deliberately.\u00a0The key word\u00a0being\u00a0deliberately.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Synthetic insight is most powerful in\u00a0early stages\u00a0of decision-making. It can help teams:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Narrow options before commissioning large studies<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">Pressure-test assumptions in briefs<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"auto\">Explore emerging opportunities<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"4\" data-aria-level=\"1\"><span data-contrast=\"auto\">Increase the throughput of ideas<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"5\" data-aria-level=\"1\"><span data-contrast=\"auto\">Rapidly screen concepts and messages<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"6\" data-aria-level=\"1\"><span data-contrast=\"auto\">Boost understanding of hard-to-reach or underrepresented groups<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"7\" data-aria-level=\"1\"><span data-contrast=\"auto\">Monitor consumer sentiment and\u00a0validate\u00a0emerging trends<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">It does not replace deep qualitative exploration, statistically robust quantitative research, or regulatory validation. Instead, it helps organisations use those investments more intelligently and can support quantitative approaches by strengthening samples and improving coverage.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2>Why\u00a0blending\u00a0matters<\/h2>\n<p><span data-contrast=\"auto\">The most effective organisations do not choose between synthetic and traditional research. They design how the two work together.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Synthetic insight increases speed and focus. Human research provides nuance,\u00a0context\u00a0and confidence, including a deeper understanding of motivations and needs that are often not explicitly articulated.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Crucially, synthetic insight should not be accepted at face value. It requires expert interpretation,\u00a0validation\u00a0and benchmarking against real-world data to build confidence in its reliability.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Used together, they create a closed learning loop. Synthetic tools surface hypotheses and narrow choices. Human research\u00a0validates,\u00a0deepens\u00a0and challenges those findings, helping to explain the\u00a0\u2018why\u2019\u00a0behind behaviours. Performance data then feeds back into both.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This blended model reduces waste while protecting decision quality.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2>The limitations are real<\/h2>\n<p><span data-contrast=\"auto\">Synthetic insight is not magic. It provides directional learning, not definitive answers.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Its effectiveness depends on:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Data quality and representativeness\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">Transparency of\u00a0modeling\u00a0approaches\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"auto\">Clear governance and usage guardrails\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"4\" data-aria-level=\"1\"><span data-contrast=\"auto\">User understanding of what outputs mean\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">Just as importantly, it depends on starting with the right questions. As with any form of research, clear\u00a0objectives\u00a0and thoughtful framing are critical.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Synthetic outputs should not be accepted at face value. They require expert interpretation, critical\u00a0thinking\u00a0and benchmarking against real-world data to build confidence in their reliability.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Poorly calibrated models can amplify bias or create overconfidence. The organisations moving fastest are also the ones investing most heavily in data foundations,\u00a0governance\u00a0and the capability to interpret outputs effectively.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The technology is advancing quickly. But the differentiator will not be the tools alone. It will be how people use them. The shift is not just technical, but human. Teams must apply judgment, challenge\u00a0outputs\u00a0and bring a deep understanding of the consumer to turn data into meaningful insight.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2>A pragmatic path forward<\/h2>\n<p><span data-contrast=\"auto\">We see leading organisations following a similar\u00a0pattern:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ol>\n<li><strong>Start small<\/strong><span data-contrast=\"auto\"><strong>:<\/strong> Pilot synthetic personas or panels in\u00a0low-risk\u00a0use cases.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><strong>Blend intentionally<\/strong><span data-contrast=\"auto\"><strong>:<\/strong> Combine outputs with traditional research rather than replacing it.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><strong>Invest in data foundations<\/strong><span data-contrast=\"auto\"><strong>:<\/strong> Strengthen first-party data and integration capabilities,\u00a0while being mindful of biases in training data, cultural\u00a0context\u00a0and recency of inputs.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><strong>Scale gradually<\/strong><span data-contrast=\"auto\"><strong>:<\/strong> Introduce more advanced predictive capabilities, such as digital twins, as confidence grows.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">This is not about leaping ahead. It is about building capability in line with data maturity and organisational readiness, with a clear understanding of the limitations and risks.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Synthetic insight is here to stay. The question is not whether to adopt it, but how.\u00a0Organisations that move thoughtfully, rather than reactively, will increase speed and capacity without sacrificing confidence. And in a world where decision cycles continue to compress, that balance will become a competitive advantage.<\/span><\/p>\n        <\/div>\n            <\/div>\n<\/section>\n\n\n<section\n    class=\"wp-block-call-to-action edwp-block js-wp-block-call-to-action layout--none bg--none\"\n    >\n    <div class=\"container\">\n        <div class=\"wp-block-call-to-action__content\">\n            <div class=\"edwp-component edwp-fade-in\">\n                                        <div class=\"wp-component-content__buttons\">\n            <a href=\"https:\/\/www.elixirr.com\/en-gb\/industries\/retail-consumer-goods\/\" class=\"btn btn--secondary-orange\"><span>Learn more<\/span><\/a>        <\/div>\n        <\/div>        <\/div>\n\n            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