{"id":82590,"date":"2025-05-28T11:47:20","date_gmt":"2025-05-28T09:47:20","guid":{"rendered":"https:\/\/www.techbarcelona.com\/posting\/como-prever-el-impacto-de-las-variaciones-de-precio-en-las-ventas\/"},"modified":"2025-05-28T11:52:40","modified_gmt":"2025-05-28T09:52:40","slug":"como-prever-el-impacto-de-las-variaciones-de-precio-en-las-ventas","status":"publish","type":"posting","link":"https:\/\/www.techbarcelona.com\/en\/posting\/como-prever-el-impacto-de-las-variaciones-de-precio-en-las-ventas\/","title":{"rendered":"How to foresee the impact of sales prices variation"},"content":{"rendered":"<p data-start=\"2373\" data-end=\"2796\">A large company in the food sector was facing a critical challenge: it didn\u2019t know precisely how its prices were affecting consumer purchasing behavior across different regions and sales channels.<br data-start=\"2569\" data-end=\"2572\" \/>Without understanding how much sales volume reacted to price changes, adjustments were made with a high degree of uncertainty\u2014leading to volume losses, margin erosion, and risks to brand competitiveness at the point of sale.<\/p>\n<p data-start=\"2798\" data-end=\"3069\">Moreover, the scenario was complex: hundreds of SKUs with different formats, weights, and market positions, and no structured criteria to compare them to direct competitors. This made it even harder to build a strong, agile pricing strategy responsive to market dynamics.<\/p>\n<p data-start=\"3071\" data-end=\"3087\"><strong data-start=\"3071\" data-end=\"3087\">What we did:<\/strong><\/p>\n<ul data-start=\"3088\" data-end=\"3684\">\n<li data-start=\"3088\" data-end=\"3229\">\n<p data-start=\"3090\" data-end=\"3229\">We used actual point-of-sale data, complemented with competitive intelligence, to calculate price elasticity by SKU, channel, and region.<\/p>\n<\/li>\n<li data-start=\"3230\" data-end=\"3356\">\n<p data-start=\"3232\" data-end=\"3356\">We created a detailed product categorization, grouping SKUs based on attributes like format, size, and market positioning.<\/p>\n<\/li>\n<li data-start=\"3357\" data-end=\"3529\">\n<p data-start=\"3359\" data-end=\"3529\">We built highly accurate statistical models (with an average precision of 91%) capable of reliably predicting how each SKU\u2019s market share varies in response to pricing.<\/p>\n<\/li>\n<li data-start=\"3530\" data-end=\"3684\">\n<p data-start=\"3532\" data-end=\"3684\">We integrated these elasticities directly into the company\u2019s platform, enabling dynamic volume and revenue simulations across various pricing scenarios.<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"3686\" data-end=\"3821\"><strong data-start=\"3686\" data-end=\"3705\">Results so far:<\/strong><br \/>\nThe validated elasticities were integrated directly into the margin use case across the chain with a clear logic:<\/p>\n<ul data-start=\"3822\" data-end=\"3911\">\n<li data-start=\"3822\" data-end=\"3846\">\n<p data-start=\"3824\" data-end=\"3846\">Estimated elasticity<\/p>\n<\/li>\n<li data-start=\"3847\" data-end=\"3870\">\n<p data-start=\"3849\" data-end=\"3870\">Estimation accuracy<\/p>\n<\/li>\n<li data-start=\"3871\" data-end=\"3911\">\n<p data-start=\"3873\" data-end=\"3911\">Projected volume change based on price<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"3913\" data-end=\"4252\"><strong data-start=\"3913\" data-end=\"3946\">What was the business impact?<\/strong><br data-start=\"3946\" data-end=\"3949\" \/>More than just predictability: the company gained the ability to simulate before acting, optimizing margins, protecting volumes, and making pricing decisions strategically and in line with competitive realities. This translates into greater control over outcomes and faster responsiveness to the market.<\/p>\n<p data-start=\"4254\" data-end=\"4398\"><strong data-start=\"4254\" data-end=\"4323\">Want to turn your pricing decisions into a competitive advantage?<\/strong><br data-start=\"4323\" data-end=\"4326\" \/>Talk to us and let\u2019s explore your company\u2019s pricing challenges together.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A large company in the food sector was facing a critical challenge: it didn\u2019t know precisely how its prices were affecting consumer purchasing behavior across different regions and sales channels.Without understanding how much sales volume reacted to price changes, adjustments were made with a high degree of uncertainty\u2014leading to volume [&hellip;]<\/p>\n","protected":false},"template":"","class_list":["post-82590","posting","type-posting","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - 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