<div class="sub-cat-page-holder"><div class="sub-cat-page-img"><img src="https://persona.ly/glossary/wp-content/uploads/2020/02/group-5.svg"/></div><div class="sub-cat-page-h-holder"><h2 class="sub-cat-page-title">Real-time bidding</h2><h4 class="sub-cat-page-subtitle"><span class="sub-cat-page-subtitle">Data Activation in Mobile UA</span><span class="csr-page-counter"> -> Page <span class="max-for-title">1 of <span class="sub-cat-max-page"></span></span></span></h4></div></div><div class="search-sidebar-holder"><a href="/glossary/"><h3 class="search-sidebar-holder-title">UA Glossary</h3></a>[vc_widget_sidebar sidebar_id="single-page-sidebar" el_class="front-page-sidebar-holder"]<div class="search-sidebar-footer-holder"></div></div>{"id":971,"date":"2020-09-22T12:04:40","date_gmt":"2020-09-22T12:04:40","guid":{"rendered":"https:\/\/persona.ly\/glossary\/?page_id=971"},"modified":"2020-09-27T06:13:38","modified_gmt":"2020-09-27T06:13:38","slug":"data-activation-in-mobile-ua","status":"publish","type":"page","link":"https:\/\/persona.ly\/glossary\/real-time-bidding\/data-activation-in-mobile-ua\/","title":{"rendered":"Data Activation in Mobile UA"},"content":{"rendered":"<p>[vc_row el_class=&#8221;front-hero-section&#8221;][vc_column width=&#8221;1\/2&#8243; css=&#8221;.vc_custom_1582730807176{background-image: url(https:\/\/persona.ly\/glossary\/\/wp-content\/uploads\/2020\/02\/group-19.svg?id=58) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: contain !important;}&#8221; el_class=&#8221;hero-first-section&#8221;][vc_column_text el_class=&#8221;page-heading&#8221; css=&#8221;.vc_custom_1593096796228{margin-top: 30px !important;}&#8221;]<\/p>\n<h1><a href=\"\/glossary\/\">UA <span class=\"color-ancient\">Glossary<\/span><\/a><\/h1>\n<p>[\/vc_column_text][vc_column_text]<p>Your source for everything mobile UA, from the basics to contentious standards, the glossary can help and inform both aspiring growth managers and experienced mobile app developers<\/p>\n[\/vc_column_text]<div class=\"ult-spacer spacer-69d0d8e9a9fc3\" data-id=\"69d0d8e9a9fc3\" data-height=\"105\" data-height-mobile=\"0\" data-height-tab=\"\" data-height-tab-portrait=\"\" data-height-mobile-landscape=\"0\" style=\"clear:both;display:block;\"><\/div>[\/vc_column][vc_column width=&#8221;1\/2&#8243;][vc_single_image image=&#8221;60&#8243; img_size=&#8221;full&#8221; alignment=&#8221;center&#8221; el_class=&#8221;hero-section-image&#8221;][\/vc_column][\/vc_row][vc_section css=&#8221;.vc_custom_1583429011820{background-image: url(https:\/\/persona.ly\/glossary\/\/wp-content\/uploads\/2020\/03\/bg_repeater_with_bg_svg.svg?id=320) !important;}&#8221; el_class=&#8221;post-content-section archive-page-custom single-subcat&#8221;][vc_row el_class=&#8221;covered-grace-font&#8221;][vc_column width=&#8221;2\/3&#8243; el_class=&#8221;gap-in-end-single&#8221;][vc_column_text][\/vc_column_text][vc_column_text]<style>.wp-show-posts-columns#wpsp-974 {margin-left: -2em; }.wp-show-posts-columns#wpsp-974 .wp-show-posts-inner {margin: 0 0 2em 2em; }<\/style><section id=\"wpsp-974\" class=\" wp-show-posts\" style=\"\"><article class=\" wp-show-posts-single wpsp-clearfix post-976 post type-post status-publish format-standard hentry category-data-activation-in-mobile-ua tag-balckbox tag-data-activation tag-mobile-ua tag-performance-metrics tag-user-acquisition tag-user-level-targeting\" itemtype=\"http:\/\/schema.org\/CreativeWork\" itemscope><div class=\"wp-show-posts-inner\" style=\"\">\t\t\t\t\t\t<header class=\"wp-show-posts-entry-header\">\n\t\t\t\t\t\t\t<h1 class=\"wp-show-posts-entry-title\" itemprop=\"headline\"><a href=\"https:\/\/persona.ly\/glossary\/data-activation-in-mobile-ua\/\" rel=\"bookmark\">Data Activation in Mobile UA<\/a><\/h1><div class=\"wp-show-posts-entry-meta wp-show-posts-entry-meta-below-title post-meta-inline\"><span class=\"wp-show-posts-posted-on wp-show-posts-meta\">\n\t\t\t\t\t<a href=\"https:\/\/persona.ly\/glossary\/data-activation-in-mobile-ua\/\" title=\"12:09 pm\" rel=\"bookmark\"><time class=\"wp-show-posts-entry-date published\" datetime=\"2022-09-22T12:09:11+00:00\" itemprop=\"datePublished\">September 22, 2022<\/time><time class=\"wp-show-posts-updated\" datetime=\"2022-12-11T12:41:18+00:00\" itemprop=\"dateModified\">December 11, 2022<\/time><\/a>\n\t\t\t\t<\/span><\/div>\t\t\t\t\t\t<\/header><!-- .entry-header -->\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"wp-show-posts-entry-content\" itemprop=\"text\">\n\t\t\t\t\t\t\t<p>[vc_row][vc_column el_class=&#8221;normal-text&#8221;][vc_custom_heading text=&#8221;What is Data Activation?&#8221; use_theme_fonts=&#8221;yes&#8221; el_class=&#8221;styled-heading blue-back&#8221; link=&#8221;url:%23|||&#8221;][vc_column_text]<span style=\"font-weight: 400;\">Data activation, at its broadest, means using data by making actions based on its implications. In other words, generating value and insights, recognizing behaviors and trends in the data, and using them to lead to improved performance, whatever the task may be.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">In our field, we refer to data activation as the use of aggregated data for purposes of user acquisition, which translates into having the ability to extrapolate data effectively. Since user acquisition is at the forefront of every app (and any business for that matter) and the competition is constantly growing, the challenge to acquire new users grows.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">As the competition for acquiring new users increases, so does the cost associated with it. <\/span><a class=\"custom-link-content\" href=\"https:\/\/www.adjust.com\/blog\/retargeting-vs-new-user-acquisition\/\"><span style=\"font-weight: 400;\">It\u2019s much more expensive to acquire new users than it is to retain existing ones<\/span><\/a><span style=\"font-weight: 400;\">. Overcoming the challenge of acquisition costs offers great rewards, such as scale, increased revenues, and eventual company growth.\u00a0<\/span>[\/vc_column_text]<div class=\"aio-icon-component   info-noty blue-noty style_1\"><div id=\"Info-box-wrap-4472\" class=\"aio-icon-box left-icon\" style=\"\"  ><div class=\"aio-icon-left\"><div class=\"ult-just-icon-wrapper  \"><div class=\"align-icon\" style=\"text-align:center;\">\n<div class=\"aio-icon-img \" style=\"font-size:71px;background:rgba(255,255,255,0.01);display:inline-block;\" >\n\t<img class=\"img-icon\" alt=\"Info Container\" src=\"https:\/\/persona.ly\/glossary\/wp-content\/uploads\/2020\/03\/combined-shape-2.svg\"\/>\n<\/div><\/div><\/div><\/div><div class=\"aio-ibd-block\"><div class=\"aio-icon-header\" ><h3 class=\"aio-icon-title ult-responsive\"  data-ultimate-target='#Info-box-wrap-4472 .aio-icon-title'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}'  style=\"color:#191536;\"> Retargeting Alongside UA<\/h3><\/div> <!-- header --><div class=\"aio-icon-description ult-responsive\"  data-ultimate-target='#Info-box-wrap-4472 .aio-icon-description'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}'  style=\"color:#191536;\"><span style=\"font-weight: 400;\">Since retargeting existing users is cheaper and can<\/span><a class=\"custom-link-content\" href=\"https:\/\/persona.ly\/glossary\/attribution\/retargeting-in-the-programmatic-ecosystem\/?utm_source=glossary&amp;utm_medium=post&amp;utm_campaign=data_activation\"><span style=\"font-weight: 400;\"> improve performance metrics <\/span><\/a><span style=\"font-weight: 400;\">such as ROAS and LTV, it should be included in your marketing strategy, but it\u2019s not a replacement for user acquisition. UA campaigns provide scale and further growth opportunities that cannot be reached without growing your existing users\u2019 pool.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The only way to deal with the high cost of acquisition and still reach quality users is by improving your acquisition efforts. Making the most out of the data at your disposal, and shortening the time between collecting and using the data gathered, is the way to reach this promised land.\u00a0<\/span><\/div> <!-- description --><\/div> <!-- aio-ibd-block --><\/div> <!-- aio-icon-box --><\/div> <!-- aio-icon-component -->[vc_column_text]<!-- Widget Shortcode --><div id=\"custom_html-7\" class=\"widget_text widget widget_custom_html widget-shortcode area-sidebar-form-mobile \"><div class=\"textwidget custom-html-widget\"><div class=\"subscribe-footer-from-holder sidebar-form-intouch\">\r\n<h3>Looking for app growth opportunities?<\/h3>\r\n<p>Leave your email and we'll get \r\nback to you!<\/p>\r\n<form class=\"subscribe-footer-from\">\r\n\t<div class=\"subscribe-footer-from-email-holder\"><input type=\"email\" class=\"subscribe-footer-email\" placeholder=\"Enter your email\"><\/div>\r\n<button class=\"subscribe-footer-submit\">\r\n\tGet in Touch\r\n<\/button>\r\n<\/form>\r\n<\/div>\r\n<div class='subscribe-footer-from-thankyou gsifthy'>\r\n\t<img alt=\"\" src=\"\/glossary\/wp-content\/uploads\/2020\/08\/02.svg\">\r\n<h3>Thank you for your interest!<\/h3>\r\n<p>Our team will contact you with more details.<\/p>\r\n<\/div>\r\n<div class=\"search-sidebar-footer-holder gisf\"><\/div><\/div><\/div><!-- \/Widget Shortcode -->[\/vc_column_text][vc_custom_heading text=&#8221;Why is Data Activation Vital for Your User Acquisition?&#8221; use_theme_fonts=&#8221;yes&#8221; el_class=&#8221;styled-heading purple-back&#8221; link=&#8221;url:%23|||&#8221;][vc_column_text]<span style=\"font-weight: 400;\">These days there are infinite data. Running successful UA\u00a0 is rooted in the way you use yours.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">When bidding on OpenRTB inventory, in a single bid request, you can get data on the type and model of the user\u2019s device, OS version, location, local time, and much more. Using data enrichment and combining aggregated data makes your options virtually limitless. The central question, and how you differentiate yourself from the competition, is in <\/span><b>the way you use, or more accurately, <\/b><b><i>look <\/i><\/b><b>at that data<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span>[\/vc_column_text]<div class=\"aio-icon-component   error-noty style_1\"><div id=\"Info-box-wrap-1335\" class=\"aio-icon-box left-icon\" style=\"\"  ><div class=\"aio-icon-left\"><div class=\"ult-just-icon-wrapper  \"><div class=\"align-icon\" style=\"text-align:center;\">\n<div class=\"aio-icon-img \" style=\"font-size:71px;background:rgba(255,255,255,0.01);display:inline-block;\" >\n\t<img class=\"img-icon\" alt=\"null\" src=\"https:\/\/persona.ly\/glossary\/wp-content\/uploads\/2020\/03\/combined-shape.svg\"\/>\n<\/div><\/div><\/div><\/div><div class=\"aio-ibd-block\"><div class=\"aio-icon-header\" ><h3 class=\"aio-icon-title ult-responsive\"  data-ultimate-target='#Info-box-wrap-1335 .aio-icon-title'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}'  style=\"color:#191536;\">User-level Targeting in iOS14 <\/h3><\/div> <!-- header --><div class=\"aio-icon-description ult-responsive\"  data-ultimate-target='#Info-box-wrap-1335 .aio-icon-description'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}'  style=\"color:#191536;\"><span style=\"font-weight: 400;\">With the iOS14 update and the removal of the IDFA, targeting specific users and understanding their behavior is virtually impossible. Targeting in iOS14 is only possible by context. You can read more about how we do it in our <\/span><a class=\"custom-link-content\" href=\"https:\/\/persona.ly\/glossary\/attribution\/user-acquisition-and-retargeting-in-ios-14\/?utm_source=glossary&amp;utm_medium=post&amp;utm_campaign=data_activation\"><span style=\"font-weight: 400;\">UA and Retargeting in iOS14 entry<\/span><\/a><span style=\"font-weight: 400;\"> and try our <\/span><a class=\"custom-link-content\" href=\"https:\/\/persona.ly\/context-distance-calculator\"><span style=\"font-weight: 400;\">context calculator<\/span><\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/div> <!-- description --><\/div> <!-- aio-ibd-block --><\/div> <!-- aio-icon-box --><\/div> <!-- aio-icon-component -->[vc_column_text]<span style=\"font-weight: 400;\">All marketing efforts, way before the digital and mobile apps world, focused on understanding the customer and its needs. Though the technology changed, the ideas remain the same. Understanding trends, patterns, and users\u2019 behaviors are key to running successful UA campaigns.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">This behavior can be understood by focusing your analysis on an existing pool of users to predict future behaviors and recognize potential new users. In digital marketing, it is often referred to as <\/span><b>lookalikes<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span>[\/vc_column_text]<div class=\"aio-icon-component   info-noty blue-noty style_1\"><div id=\"Info-box-wrap-8113\" class=\"aio-icon-box left-icon\" style=\"\"  ><div class=\"aio-icon-left\"><div class=\"ult-just-icon-wrapper  \"><div class=\"align-icon\" style=\"text-align:center;\">\n<div class=\"aio-icon-img \" style=\"font-size:71px;background:rgba(255,255,255,0.01);display:inline-block;\" >\n\t<img class=\"img-icon\" alt=\"Info Container\" src=\"https:\/\/persona.ly\/glossary\/wp-content\/uploads\/2020\/03\/combined-shape-2.svg\"\/>\n<\/div><\/div><\/div><\/div><div class=\"aio-ibd-block\"><div class=\"aio-icon-header\" ><h3 class=\"aio-icon-title ult-responsive\"  data-ultimate-target='#Info-box-wrap-8113 .aio-icon-title'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}'  style=\"color:#191536;\">A Sample is Enough<\/h3><\/div> <!-- header --><div class=\"aio-icon-description ult-responsive\"  data-ultimate-target='#Info-box-wrap-8113 .aio-icon-description'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}'  style=\"color:#191536;\"><span style=\"font-weight: 400;\">Contrary to popular belief, if data is used correctly, there\u2019s no need to gather data for months or from an outstanding amount of users. A sample of users, with features that relate specifically to them, can help predict and target effectively. Once this sample proves itself, the campaign can be scaled.\u00a0<\/span><\/div> <!-- description --><\/div> <!-- aio-ibd-block --><\/div> <!-- aio-icon-box --><\/div> <!-- aio-icon-component -->[vc_custom_heading text=&#8221;It\u2019s All About the Features&#8221; use_theme_fonts=&#8221;yes&#8221; el_class=&#8221;styled-heading blue-back&#8221; link=&#8221;url:%23|||&#8221;][vc_column_text]<span style=\"font-weight: 400;\">Depending on the technologies at use (with the year being 2020 we assume the technology is very much in existence and in use) you have a lot of data. You\u2019ve recognized patterns and behaviors within these data, and now it\u2019s time to act upon it and generate predictions or make decisions that would help acquire new users effectively.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">The question is how to derive specific insights and implement them from these immense quantities of data. There\u2019s a tendency to forget there are users behind the data and commit solely to the numbers at hand, recognizing trends and following them, without rationalizing them. Which, in the long-run, creates problems.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">When machine-learning algorithms are in use, the way to derive meaning from the mass is by creating <\/span><b>features that dictate the algorithms on how to use the data at hand<\/b><span style=\"font-weight: 400;\">. These features bridge between data and the users behind them. A feature can be as simple as the time of day or the day of the week, and complex as the current session length or the contextual relationship between the user and the promoted application.\u00a0<\/span>[\/vc_column_text][vc_custom_heading text=&#8221;Time-of-day and Day of the week&#8221; font_container=&#8221;tag:h3|text_align:left|color:%23191536&#8243; use_theme_fonts=&#8221;yes&#8221; el_class=&#8221;styled-subheading&#8221;][\/vc_column][\/vc_row][vc_row][vc_column el_class=&#8221;normal-text&#8221;][vc_single_image image=&#8221;979&#8243; img_size=&#8221;full&#8221; alignment=&#8221;center&#8221; el_class=&#8221;single-post-image-back&#8221;][vc_column_text]<span style=\"font-weight: 400;\">To explain this point, we\u2019ll use the time-of-day data and show how you can create different features using the same data. You can create a time-of-day feature that refers to this information on an hourly basis (i.e there are 24 units a day) and let the algorithm do its thing (target users by their most active hour units, for example). This would be an example of a feature in a machine-learning algorithm that follows the data blindly, without accounting for actual user behavior.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">On the other hand, you can look at the data and<\/span><b> combine known human behaviors that correlate with the data.<\/b><span style=\"font-weight: 400;\"> For example, according to the data, users are mostly inactive during the night, while they are at their most active in the morning (while they are commuting to work), then they are somewhat active during the workday, and active again in their evenings.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">Instead of creating 24 single hour units, you can create, for example, 4 units, each representing a different level of activity during the day, and use these data when targeting. Feeding a model with this kind of feature makes a lot more sense, in the long run, than blindly following the most basic grouping of data (i.e, the 24 single-hour units).\u00a0<\/span>[\/vc_column_text][vc_single_image image=&#8221;980&#8243; img_size=&#8221;full&#8221; alignment=&#8221;center&#8221; el_class=&#8221;single-post-image-back&#8221;][vc_column_text]<span style=\"font-weight: 400;\">An example of how features can become more convoluted and interesting can be by creating a feature that combines the time of day unit with movement (i.e, a location change within a certain time of day). Assuming that if it\u2019s rush hour and users are in movement, they\u2019re probably commuting to work and are at their most active, at least for the morning portion of the day.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">This means that the feature not only follows the data and targets accordingly, but it also combines human behaviors and can make nuanced decisions (i.e targeting all users in rush hour is not as accurate as targeting all users at rush hour who are also in movement). <\/span><b>The better the features combine between data and behavior, the easier it is to understand user behavior and actually predict future behaviors.\u00a0<\/b>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">If we continue the line of time of day &#8211; building a feature regarding the time of day and basing it on data collected on weekdays, then running it on the weekends would result in very different outcomes.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">While midweek users were most active in the morning unit, when they were commuting to work, on the weekend they might be busy, asleep, with their family, or running errands these same hours.<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">Had the feature included a location change (or a day of the week grouping) &#8211; the results would have been better and lookalikes would play their part. Though being too specific also has its downfalls. The problem with creating specific features is having them become limiting, by overfitting the model.\u00a0<\/span>[\/vc_column_text][vc_custom_heading text=&#8221;The Problem With Overfitting&#8221; font_container=&#8221;tag:h3|text_align:left|color:%23191536&#8243; use_theme_fonts=&#8221;yes&#8221; el_class=&#8221;styled-subheading&#8221;][vc_column_text]<a class=\"custom-link-content\" href=\"https:\/\/towardsdatascience.com\/what-are-overfitting-and-underfitting-in-machine-learning-a96b30864690\"><span style=\"font-weight: 400;\">Overfitting <\/span><\/a><span style=\"font-weight: 400;\">is what happens when the model learns the details and noise in the training data to the extent that it negatively affects the performance of the model on <\/span><i><span style=\"font-weight: 400;\">new<\/span><\/i><span style=\"font-weight: 400;\"> data. Instead of correctly generalizing the data and following trends, it follows random fluctuations and noise and applies what it\u2019d learned on new data.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">The process of building these features is challenging and requires expertise both in being able to connect the data points to the business problem at hand, as well as ensuring the model is representative and not overfitted. When done right, the fruits outweigh the labor by a significant margin.\u00a0<\/span>[\/vc_column_text][vc_custom_heading text=&#8221;Blackbox Approaches&#8221; use_theme_fonts=&#8221;yes&#8221; el_class=&#8221;styled-heading purple-back&#8221; link=&#8221;url:%23|||&#8221;][vc_column_text]<span style=\"font-weight: 400;\">The term Blackbox, in the context of machine-learning algorithms, refers to the type of models that are<\/span><b> uninterpretable by humans<\/b><span style=\"font-weight: 400;\"> (such as neural networks).\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">At its core, a Blackbox model works similarly to ML models that can be interpreted (like logistic regression) but they are exponentially harder to understand since their iterative and sometimes recursive nature learns complex feature relationships and estimating the importance of each feature and its relation to the other features is essentially impossible.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">The way to evaluate a Blackbox model is by its ability to predict &#8211; seeing if it actually is able to effectively make the predictions it was defined to, in your production setting.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">Interpretable models and their decisions, on the other hand, can be relatively easily analyzed to decide if they are making decisions that seem correct when considering the business problem at hand, and then further explained to decision-makers. They may rely on non-interpretable attributes, but can still be understood by their original attributes.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">In reality, Blackbox models, like neural networks (that were originally designed by computer scientists together with neurological scientists in order to mimic the way the human brain works), can definitely generate more accurate models than simpler, interpretable ones, but that isn\u2019t always the case.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">Sometimes, the simpler models outperform the complex ones due to the fact that the data scientist has more control over what happens behind the scenes and can make little patches and tweaks to fine-tune the model to the problem at hand.<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">When planning to tackle a business problem with machine learning, you\u2019ll need to first establish if it\u2019s important to you to be able to interpret the decisions guided by the model, and, of course, your production environment limitations, as the more advanced Blackbox models require stronger computational resources to infer.<\/span>[\/vc_column_text][vc_custom_heading text=&#8221;Predictive Performance Metrics &#8221; use_theme_fonts=&#8221;yes&#8221; el_class=&#8221;styled-heading blue-back&#8221; link=&#8221;url:%23|||&#8221;][vc_column_text]<span style=\"font-weight: 400;\">Using machine learning models, advertisers can predict the eventual lifetime value for each user, from their first day of using the app, and in many cases with incredible accuracy. Implementing this data can help stir campaign budgets towards the most successful marketing channels early on, allowing the UA team to effectively scan the entire ecosystem for any source that can meet goals and expedite growth.\u00a0<\/span>[\/vc_column_text][vc_column_text]<span style=\"font-weight: 400;\">Data activation, be it in mobile campaigns or traditional marketing campaigns, is based on current and past data to predict future outcomes. This idea relates to predictive KPIs. 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