{"id":3585,"date":"2023-12-27T17:11:24","date_gmt":"2023-12-27T17:11:24","guid":{"rendered":"https:\/\/nilg.ai\/?p=3585"},"modified":"2025-03-17T11:55:06","modified_gmt":"2025-03-17T11:55:06","slug":"ditch-the-crystal-ball-reverse-engineering-with-machine-learning","status":"publish","type":"post","link":"https:\/\/nilg.ai\/en_us\/202312\/ditch-the-crystal-ball-reverse-engineering-with-machine-learning\/","title":{"rendered":"Ditch the Crystal Ball: Reverse-Engineering with Machine Learning"},"content":{"rendered":"<p>&nbsp;<\/p>\n<p>Machine Learning models are estimators &#8211; which means they can be used not only to predict unknowns in your business but also to reverse-engineer complex business processes.<\/p>\n<p>As part of this blog post, you will learn how to identify these potential points of improvement, prioritize them, and create models to estimate them.<\/p>\n<h2><b>Identification<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">How do you figure out your current dependencies on external market indicators? You start by listing all the third-party\/external services you use to get external indicators, the volume of queries, the associated costs of each, a qualitative score of how much value it\u2019s generating for you and what are the business processes that depend on those indicators. This will help you determine what are the services that are most relevant for your business.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Some examples include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Real Estate and Construction: <\/b><span style=\"font-weight: 400;\">\u00a0listing prices per area segment, points of interest<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\"><b>Retail<\/b><span style=\"font-weight: 400;\">: item depreciation per segment (e.g. car or mobile phone per year make model, mobile phone per make)<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\"><b>FinTech: <\/b><span style=\"font-weight: 400;\">expected growth per company<\/span><\/li>\n<\/ul>\n<h2><b>Data Prioritization<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Your next objective is to understand how you can stop depending on these third parties, and which ones are easier to replace. For this, for each process, identify the key features and variables that influence the outcome of the process. List all the data sources you have available internally, the ones you can acquire, and the ones you can purchase (and what are the costs to do so).\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Some examples of data sources that can be used to determine the services listed above are:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Real Estate and Construction: <\/b><span style=\"font-weight: 400;\">real estate listings pages, open geographical data (OpenStreetMaps)<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\"><b>Retail<\/b><span style=\"font-weight: 400;\">: listings pages, auctions, classified advertisements (e.g. Craigslist) \u2026<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\"><b>FinTech: <\/b><span style=\"font-weight: 400;\">social media, news, Linkedin job posts<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">We dive deeper into the identification and prioritization of data sources as part of our Data Ignite methodology.<\/span><\/p>\n<div class=\"course-cta\">\n\t\t<div class=\"course-cta-img\"><img decoding=\"async\" width=\"582\" height=\"903\" src=\"https:\/\/nilg.ai\/wp-content\/uploads\/2023\/09\/2-194x301-1-1.jpg\" class=\"attachment-full size-full\" alt=\"Building Great Datasets Course\" srcset=\"https:\/\/nilg.ai\/wp-content\/uploads\/2023\/09\/2-194x301-1-1.jpg 582w, https:\/\/nilg.ai\/wp-content\/uploads\/2023\/09\/2-194x301-1-1-193x300.jpg 193w, https:\/\/nilg.ai\/wp-content\/uploads\/2023\/09\/2-194x301-1-1-300x465.jpg 300w\" sizes=\"(max-width: 582px) 100vw, 582px\" \/><\/div>\n\t\t<div class=\"course-cta-content\"><h6>Course<\/h6><h3>Building Great Datasets<\/h3>\n\t\t\t<p>Learn more about how to identify and prioritize data sources<\/p>\n\t\t\t<a href=\"https:\/\/nilg.ai\/en_us\/product\/building-great-datasets\/\" class=\"cta_btn\">Learn More<\/a>\n\t\t<\/div>\n\t<\/div>\n<h2><b>Determine the opportunity size<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Afterward, measure the trade-offs of continuing to use a 3rd party solution or building your own custom. Here, you\u2019ll want to understand what the realistic money gain you\u2019ll get out of using your custom-built solution in a fixed time frame, assuming you can only replicate the performance up to a certain degree.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To ensure an efficient and cost-effective transition, we recommend spending <\/span><b>as much as 10% of that value<\/b><span style=\"font-weight: 400;\"> to create a Proof of Concept. This will allow you to further check for the viability of the idea in a controlled environment. You should also prioritize the list of ideas to tackle by their expected value, technical feasibility, ease of business integration, and potential risks of failure. If you&#8217;re wondering about how to do so, we have the perfect tool to help you. Book a meeting with us to further discuss this process!<\/span><\/p>\n<h2><b>Collect data and train a model<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">At this point, you should start collecting or purchasing the data that you need to replicate the external process, as well as the associated market indicators. Focus on the data that\u2019s more easily available and more impactful first, to develop the Proof of Concept.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">When you have the data available, use Machine Learning to train a model based on the collected, purchased, and\/or internal data. This model learns the patterns and relationships within the data, effectively replicating the observed business process.<\/span><\/p>\n<h2><b>Run a business simulation<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">After having your first model, you should translate the model\u2019s performance into a business metric. Technical metrics &#8211; which we call \u201cSide-kicks\u201d, are good to estimate how good the replacement model is, but it\u2019s important to translate it to a KPI which the stakeholders can understand our \u201cHero\u201d.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You have built an approximator to your external market indicators which, without a doubt, won\u2019t be as accurate as the original value. What you need to consider at this point is if there\u2019s a possibility that your error is low enough to the point where you already have a positive outcome, or if you need to iterate further.\u00a0 You can <\/span><a href=\"https:\/\/nilg.ai\/202308\/boosting-profits-with-mediocre-ai-models\/\"><span style=\"font-weight: 400;\">easily make profit out of mediocre models<\/span><\/a><span style=\"font-weight: 400;\">!<\/span><\/p>\n<p>&nbsp;<\/p>\n<h2><b>Replace the original process<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">At this point, you can replace the original business process. Monitor the KPIs that depend on that external market indicator, and have a fallback solution if you notice that the business simulation and the real-life results are not properly aligned.<\/span><\/p>\n<p>You will no longer depend on a third party provider to give you the data that you were using to run your business &#8211; that&#8217;s already a great step forward!<\/p>\n<h2><b>Summing up<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">In this blog post, we covered the several steps to reverse engineer market indicators: identifying potential points of improvement, prioritizing them by opportunity size and effort, creating models to estimate them, and determining if the models are good enough to be replacements.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If a high dependency on external market indicators is something you\u2019ve been facing in your business, contact me and I can help you discover how to tackle it.<\/span><\/p>\n  \n\n <div class=\"author-cta\">\n\t\t<div class=\"author-cta-img\">\n\t\t    \n\t\t    <img decoding=\"async\" width=\"1024\" height=\"906\" src=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael.png\" class=\"attachment-full size-full\" alt=\"Rafael Cavalheiro NILG.AI\" srcset=\"https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael.png 1024w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael-300x265.png 300w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael-768x680.png 768w, https:\/\/nilg.ai\/wp-content\/uploads\/2022\/07\/Web-Rafael-600x531.png 600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t    <\/div>\n\n<div class=\"author-cta-content\">\n\t<h3>Do you want to further discuss this idea?<\/h3><p>Book a meeting with <strong>Rafael Cavalheiro<\/strong><\/p>\t<a class=\"cta_btn\" onclick=\"Calendly.showPopupWidget('');return false;\"  \n\">Meet Rafael<\/a>\n\t\t\t\n\t<a href=\"https:\/\/nilg.ai\/en_us\/?post_type=team&p=1650\" class=\"author-cta-link\">Learn More<\/a>\n\t\t\t<\/div>\n\t<\/div>\n\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; Machine Learning models are estimators &#8211; which means they can be used not only to predict unknowns in your business but also to reverse-engineer complex business processes. As part of this blog post, you will learn how to identify these potential points of improvement, prioritize them, and create models to estimate them. Identification How [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":3587,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[80],"tags":[44,45],"class_list":["post-3585","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-opinion","tag-ai4business","tag-machine-learning"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Ditch the Crystal Ball: Reverse-Engineering with Machine Learning - NILG.AI<\/title>\n<meta name=\"description\" content=\"In the fast-paced world of business, staying ahead often means relying on external market indicators. 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