{"id":180,"count":0,"description":"","link":"https:\/\/tenjin.com\/zh\/glossary\/predictive-analysis\/","name":"\u9884\u6d4b\u5206\u6790","slug":"predictive-analysis","taxonomy":"glossaries","parent":0,"meta":{"status":["1","1"],"order":["0","0"],"glossary_term_description":["\r\n\r\nPredictive analysis at Tenjin inclues (Predictive LTV) The N-Day All pLTV (Ad Mediation + IAP) shows Tenjin's combined prediction of Ad Mediation (ILRD) and IAP revenue, helping you estimate total LTV up to 30 days after install on the Tenjin dashboard, you can learn more about it here<\/a>.\r\n\r\n\r\n\r\n\r\n

What is predictive analysis?<\/h2>\r\n\r\n\r\n\r\n\r\nPredictive analysis (analytics) in mobile marketing means to predict certain events and metrics that will happen in the future; it responds to the question \"What will happen?\" For instance, how much LTV<\/a> and ROI<\/a> a marketing campaign will generate in the next 90 days.\r\n\r\n\r\n\r\n\r\n\r\nPredictive analytics is a set of data analysis methods that allow marketers to predict future trends. Such analytics is based on historical data. As an example, if we know what revenue a certain campaign or channel has generated previously, or is generating during the last seven days, we can prognose the revenue that this campaign or channel will generate in the next few weeks.\r\n\r\n\r\n\r\n\r\n\r\nTo deal with large data volumes and to interpret them into predictive metrics, advertisers utilize machine learning tools. Machine learning increases the speed of data analytics and helps to build up predictive models.\r\n\r\n\r\n\r\n\r\n

Why is predictive analysis used?<\/h2>\r\n\r\n\r\n\r\n\r\nLet's imagine the daily routine of a user acquisition manager or a UA team. They use marketing budgets to run user acquisition campaigns on different advertising channels. The main goal is to attract valuable users, and to make UA campaigns profitable.\r\n\r\n\r\n\r\n\r\n\r\nTo analyze the effectiveness of marketing campaigns, they look at different metrics such as ROI, ROAS<\/a>, LTV, IAA and IAP revenue<\/a>. And obviously, some campaigns and channels may appear ineffective and unprofitable. So UA managers constantly try to optimize campaigns in order to maximize profit and minimize losses. But what if they could predict the performance of a campaign? It would allow them to increase revenue and stop wasting money on unprofitable campaigns. That is when predictive analytics can be used. Historical data is used to predict the performance of campaigns or channels which helps with using the advertising budget more efficiently. The more historical data you have, the better, as it increases the accuracy of predictions made by machine learning models.\r\n\r\n\r\n\r\n\r\n\r\nAs an example, in mobile marketing predictive analytics can be used to predict ad or IAP revenue, LTV, ARPU.\r\n\r\n\r\n\r\n\r\n

Can I do predictive analysis with Tenjin?<\/h2>\r\n\r\n\r\n\r\n\r\nAt Tenjin, we partnered with Growth FullStack to provide LTV prediction<\/a> for our customers. If you're interested in exploring or learning more about our LTV prediction models (basic<\/a> to advanced<\/a>), reach out to us at info@growthfullstack.com. You can also now see your N-Day Total Predicted LTV on the Tenjin dashboard directly. You can read more about it here<\/a>.\r\n\r\n"]},"acf":[],"yoast_head":"\nPredictive Analysis - Tenjin Glossary<\/title>\n<meta name=\"description\" content=\"Learn what Predictive Analysis is, how it forecasts user behavior & revenue potential. Make proactive UA & monetization decisions earlier in Tenjin.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/tenjin.com\/zh\/glossary\/predictive-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predictive Analysis - Tenjin Glossary\" \/>\n<meta property=\"og:description\" content=\"Learn what Predictive Analysis is, how it forecasts user behavior & revenue potential. Make proactive UA & monetization decisions earlier in Tenjin.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/tenjin.com\/zh\/glossary\/predictive-analysis\/\" \/>\n<meta property=\"og:site_name\" content=\"Tenjin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"CollectionPage\",\"@id\":\"https:\/\/tenjin.com\/glossary\/predictive-analysis\/\",\"url\":\"https:\/\/tenjin.com\/glossary\/predictive-analysis\/\",\"name\":\"Predictive Analysis - Tenjin Glossary\",\"isPartOf\":{\"@id\":\"https:\/\/tenjin.com\/ru\/#website\"},\"description\":\"Learn what Predictive Analysis is, how it forecasts user behavior & revenue potential. 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Make proactive UA & monetization decisions earlier in Tenjin.","breadcrumb":{"@id":"https:\/\/tenjin.com\/glossary\/predictive-analysis\/#breadcrumb"},"inLanguage":"zh-Hans"},{"@type":"BreadcrumbList","@id":"https:\/\/tenjin.com\/glossary\/predictive-analysis\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/tenjin.com\/"},{"@type":"ListItem","position":2,"name":"Predictive Analysis"}]},{"@type":"WebSite","@id":"https:\/\/tenjin.com\/ru\/#website","url":"https:\/\/tenjin.com\/ru\/","name":"Tenjin","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/tenjin.com\/ru\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"zh-Hans"}]}},"glossary_term_description":"<!-- wp:paragraph -->\r\n\r\nPredictive analysis at Tenjin inclues (Predictive LTV) The N-Day All pLTV (Ad Mediation + IAP) shows Tenjin's combined prediction of Ad Mediation (ILRD) and IAP revenue, helping you estimate total LTV up to 30 days after install on the Tenjin dashboard, you can learn more about it <a href=\"https:\/\/docs.tenjin.com\/docs\/revenue#predicted-ltv\">here<\/a>.\r\n\r\n<!-- \/wp:paragraph -->\r\n\r\n<!-- wp:heading -->\r\n<h2 class=\"wp-block-heading\">What is predictive analysis?<\/h2>\r\n<!-- \/wp:heading -->\r\n\r\n<!-- wp:paragraph -->\r\n\r\nPredictive analysis (analytics) in mobile marketing means to predict certain events and metrics that will happen in the future; it responds to the question \"What will happen?\" For instance, how much <a href=\"https:\/\/growthfullstack.com\/usecase\/simple-customer-lifetime-value-ltv-prediction-in-python\">LTV<\/a> and <a href=\"\/v1\/docs\/return-on-investment-roi\">ROI<\/a> a marketing campaign will generate in the next 90 days.\r\n\r\n<!-- \/wp:paragraph -->\r\n\r\n<!-- wp:paragraph -->\r\n\r\nPredictive analytics is a set of data analysis methods that allow marketers to predict future trends. Such analytics is based on historical data. As an example, if we know what revenue a certain campaign or channel has generated previously, or is generating during the last seven days, we can prognose the revenue that this campaign or channel will generate in the next few weeks.\r\n\r\n<!-- \/wp:paragraph -->\r\n\r\n<!-- wp:paragraph -->\r\n\r\nTo deal with large data volumes and to interpret them into predictive metrics, advertisers utilize machine learning tools. Machine learning increases the speed of data analytics and helps to build up predictive models.\r\n\r\n<!-- \/wp:paragraph -->\r\n\r\n<!-- wp:heading -->\r\n<h2 class=\"wp-block-heading\">Why is predictive analysis used?<\/h2>\r\n<!-- \/wp:heading -->\r\n\r\n<!-- wp:paragraph -->\r\n\r\nLet's imagine the daily routine of a user acquisition manager or a UA team. They use marketing budgets to run user acquisition campaigns on different advertising channels. The main goal is to attract valuable users, and to make UA campaigns profitable.\r\n\r\n<!-- \/wp:paragraph -->\r\n\r\n<!-- wp:paragraph -->\r\n\r\nTo analyze the effectiveness of marketing campaigns, they look at different metrics such as ROI, <a href=\"\/v1\/docs\/return-on-ad-spend-roas\">ROAS<\/a>, LTV, <a href=\"https:\/\/tenjin.com\/docs\/ad-monetization-ad-revenue?\">IAA and IAP revenue<\/a>. And obviously, some campaigns and channels may appear ineffective and unprofitable. So UA managers constantly try to optimize campaigns in order to maximize profit and minimize losses. But what if they could predict the performance of a campaign? It would allow them to increase revenue and stop wasting money on unprofitable campaigns. That is when predictive analytics can be used. Historical data is used to predict the performance of campaigns or channels which helps with using the advertising budget more efficiently. The more historical data you have, the better, as it increases the accuracy of predictions made by machine learning models.\r\n\r\n<!-- \/wp:paragraph -->\r\n\r\n<!-- wp:paragraph -->\r\n\r\nAs an example, in mobile marketing predictive analytics can be used to predict ad or IAP revenue, LTV, ARPU.\r\n\r\n<!-- \/wp:paragraph -->\r\n\r\n<!-- wp:heading -->\r\n<h2 class=\"wp-block-heading\">Can I do predictive analysis with Tenjin?<\/h2>\r\n<!-- \/wp:heading -->\r\n\r\n<!-- wp:paragraph -->\r\n\r\nAt Tenjin, we partnered with Growth FullStack to provide <a href=\"https:\/\/growthfullstack.com\/usecase\/advanced-customer-lifetime-value-ltv-prediction-in-python\">LTV prediction<\/a> for our customers. If you're interested in exploring or learning more about our LTV prediction models (<a href=\"https:\/\/growthfullstack.com\/usecase\/simple-customer-lifetime-value-ltv-prediction-in-python\">basic<\/a> to <a href=\"https:\/\/growthfullstack.com\/usecase\/advanced-customer-lifetime-value-ltv-prediction-in-python\">advanced<\/a>), reach out to us at info@growthfullstack.com. You can also now see your N-Day Total Predicted LTV on the Tenjin dashboard directly. You can read more about it <a href=\"https:\/\/docs.tenjin.com\/docs\/revenue#predicted-ltv\">here<\/a>.\r\n\r\n<!-- \/wp:paragraph -->","_links":{"self":[{"href":"https:\/\/tenjin.com\/zh\/wp-json\/wp\/v2\/glossaries\/180","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tenjin.com\/zh\/wp-json\/wp\/v2\/glossaries"}],"about":[{"href":"https:\/\/tenjin.com\/zh\/wp-json\/wp\/v2\/taxonomies\/glossaries"}],"wp:post_type":[{"href":"https:\/\/tenjin.com\/zh\/wp-json\/wp\/v2\/docs?glossaries=180"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}