{"id":3369,"date":"2023-08-31T10:59:22","date_gmt":"2023-08-31T10:59:22","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/6_applications_of_predictive_analytics_in_business_intelligence\/"},"modified":"2025-10-09T10:20:27","modified_gmt":"2025-10-09T08:20:27","slug":"6_applications_of_predictive_analytics_in_business_intelligence","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/6_applications_of_predictive_analytics_in_business_intelligence\/","title":{"rendered":"6 business intelligence applications: use cases & benefits of predictive analytics"},"content":{"rendered":"\t\t
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Predictive analytics extracts information from data sets<\/a> to discover relationships, recognize patterns, forecast trends, find associations, etc. This technique allows us to anticipate the future and make the right decisions.\u00a0The applications of machine learning in business intelligence are uncountable. In this post, we describe some of the most common ones:<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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  1. Customer targeting<\/a>.<\/li>
  2. Churn prevention<\/a>.<\/li>
  3. Sales forecasting<\/a>.<\/li>
  4. Market analysis<\/a>.<\/li>
  5. Risk assessment<\/a>.<\/li>
  6. Financial modeling<\/a>.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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    You can use the data science and machine learning platform Neural Designer<\/a> to easily build predictive models from your business data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t

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    \n\t\t\t\t\t\tCustomer targeting using machine learning\t\t\t\t\t<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
    \n\t\t\t\t\t\tCustomer targeting divides a customer base into groups of individuals similar in specific marketing-related ways, such as age, gender, interests, and spending habits.\n\nIt enables companies to target tailored marketing messages to customers who are likely to buy their products.\n\nIt has been proved that predictive analytics can identify potential customers much better than traditional techniques.\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
    \n\t\t\t\t\t\n\t\t\t\t\t\tRead article\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\tCustomer churn prediction using ML\t\t\t\t\t<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
    \n\t\t\t\t\t\tChurn prevention aims to predict which customers, when, and why end their relationship with our company.\n\nRetaining an existing customer is much cheaper than acquiring a new one. Therefore, customer churn can be very costly for companies.\n\nBy harnessing the power of big customer data sets, companies can develop predictive models that enable proactive intervention.\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
    \n\t\t\t\t\t\n\t\t\t\t\t\tRead article\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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    \n\t\t\t\t\t\tSales forecasting using machine learning\t\t\t\t\t<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
    \n\t\t\t\t\t\tSales forecasting analyzes prior sales, seasonality, market-moving events, etc. It results in a realistic prediction of the demand for a product or service. Sales forecasting can be applied to short-term, medium-term, or long-term forecasting.\n\nIn this regard, predictive analytics can anticipate customer responses and attitude changes by looking at all factors.\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
    \n\t\t\t\t\t\n\t\t\t\t\t\tRead article\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\tImprove wine quality using machine learning\t\t\t\t\t<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
    \n\t\t\t\t\t\tAnalysis of market surveys helps companies address customer requirements, increasing their profit and reducing attrition.\nOnce the company has designed the predictive model, it can search for those attributes that fit consumer tastes.\n\nAn example is to model wine quality based on physicochemical tests (e.g., pH values). Here, the output is based on sensory data, for instance, evaluations by wine experts.\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
    \n\t\t\t\t\t\n\t\t\t\t\t\tRead article\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\tCustomer risk assessment using machine learning\t\t\t\t\t<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
    \n\t\t\t\t\t\tRisk assessment helps companies identify and analyze potential problems in business operations. Predictive analytics builds decision support systems to estimate which operations are profitable and which are not.\n\nThe term \"risk assessment\" can vary in meaning depending on the context. For example, in banking, it involves evaluating the risk of customers defaulting on loans. By analyzing various factors, the goal is to predict whether a customer is likely to repay a loan, thus mitigating the impact of default risk.\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
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    Conclusions<\/h2>

    Machine learning doesn’t have only\u00a0business applications.\u00a0Many industries use predictive analytics to improve their results and anticipate future events to act accordingly.<\/span><\/p>

    You can find successful applications in retail, banking, insurance, telecommunications, energy, etc.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":13,"featured_media":2330,"template":"","categories":[],"tags":[47],"class_list":["post-3369","blog","type-blog","status-publish","has-post-thumbnail","hentry","tag-finance"],"acf":[],"yoast_head":"\n6 business intelligence applications: use cases & benefits of predictive analytics<\/title>\n<meta name=\"description\" content=\"Discover some typical business applications of machine learning, from customer targeting to financial modeling.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/blog\/6_applications_of_predictive_analytics_in_business_intelligence\/\" \/>\n<meta property=\"og:locale\" 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