{"id":2000,"date":"2019-03-19T21:54:19","date_gmt":"2019-03-19T20:54:19","guid":{"rendered":"https:\/\/www.appagent.co\/blog\/?p=2000"},"modified":"2024-02-26T19:10:38","modified_gmt":"2024-02-26T19:10:38","slug":"complete-guide-on-predictive-ltv-modeling","status":"publish","type":"post","link":"https:\/\/webrixstudio.online\/aa\/blog\/complete-guide-on-predictive-ltv-modeling\/","title":{"rendered":"Summary of the Complete Guide on Predictive LTV modeling"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"2000\" class=\"elementor elementor-2000\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cf838a3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cf838a3\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-67b0670\" data-id=\"67b0670\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6bc5629 elementor-widget elementor-widget-text-editor\" data-id=\"6bc5629\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>AppsFlyer, AppAgent, and Incipia have collaborated to create a\u00a0<a href=\"https:\/\/www.appsflyer.com\/resources\/gaming\/predictive-modeling-app-marketers-guide\/basic-concepts-measurement\/\"><b>comprehensive guide<\/b><\/a><b>\u00a0on predictive LTV modeling<\/b>. This must-read resource caters to mobile marketers, UA managers, and marketing analysts.<\/p><p>Drawing insights from experts representing companies like Rovio, Hutch Games, Wargaming, Joom, Wolt, Blinkist, Kiwi.com and Boombit, this guide offers a holistic view on how LTV modeling differs across various business models.\u00a0\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5299fcb elementor-widget elementor-widget-text-editor\" data-id=\"5299fcb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>In this guide, you will learn:<\/strong><\/p><ul><li>The 3 main approaches to lifetime value (LTV) prediction.<\/li><li>Methods for assessing marketing profitability using Excel.<\/li><li>How to predict in-app ad LTV.<\/li><li>Best practices shared by top experts.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fb2730 elementor-widget elementor-widget-text-editor\" data-id=\"3fb2730\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><em>If you prefer a video format, you can watch my talk from MGS Berlin: <a href=\"https:\/\/appagent.com\/blog\/2020\/05\/20\/how-do-real-life-companies-use-lifetime-value-predictions\/\" target=\"_blank\" rel=\"noopener\">How Do Real-Life Companies Use LTV Predictions<\/a>?<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-eee19c0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"eee19c0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1b21ecf\" data-id=\"1b21ecf\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2a883a5 elementor-widget elementor-widget-heading\" data-id=\"2a883a5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">PREDICTIVE MODELING: BASIC CONCEPTS AND MEASUREMENT SET-UP\u200b<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e0a51e2 elementor-widget elementor-widget-heading\" data-id=\"e0a51e2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">WHY BUILD Prediction MODELS?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1be5236 elementor-widget elementor-widget-text-editor\" data-id=\"1be5236\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>There are numerous benefits to predictive modeling in mobile marketing. Knowing your typical user behaviour and the early milestones that separate users with high potential and users with low potential can be useful especially during an acquisition and re-engagement fronts.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1b88bf1 elementor-widget elementor-widget-heading\" data-id=\"1b88bf1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">SO, WHAT SHOULD I MEASURE?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a8f63b0 elementor-widget elementor-widget-text-editor\" data-id=\"a8f63b0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>To understand what you need to measure to get your predictions right and what\u2019s not necessary, let\u2019s briefly explore which data points are useful.\u00a0<\/p>\n<p>The complete scope is in the\u00a0<a href=\"https:\/\/www.appsflyer.com\/resources\/gaming\/predictive-modeling-app-marketers-guide\/basic-concepts-measurement\/\" target=\"_blank\" rel=\"noopener\">full version<\/a>\u00a0of this article.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5138f13 elementor-widget elementor-widget-heading\" data-id=\"5138f13\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Metrics<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8bda200 elementor-widget elementor-widget-text-editor\" data-id=\"8bda200\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ol>\n<li>Legacy metrics such as CTR and CTI (low confidence in predicting profit, fastest availability).<\/li>\n<li>\u00a0Early indicator metrics such as CPI and retention rate (medium confidence in predicting profit, fast availability).<\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-37675b8 elementor-widget elementor-widget-heading\" data-id=\"37675b8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">KPIS<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7399cab elementor-widget elementor-widget-text-editor\" data-id=\"7399cab\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ol>\n<li>Tier 2 KPI confident predictors such as CAC (a great article on <a href=\"https:\/\/appagent.com\/blog\/2019\/08\/07\/how-to-calculate-the-customer-acquisition-cost-cac-for-your-mobile-application\/\" target=\"_blank\" rel=\"noopener\">customer acquisition cost<\/a> by my colleague Vit), CPA and many others (medium-high confidence in predicting profit, slow availability).<\/li>\n<li>Tier 1 KPI confident predictors: early revenue (LTV) and consequent ROAS as an indication of long term success (high confidence in predicting profit, slowest availability).<\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-89c26bb elementor-widget elementor-widget-text-editor\" data-id=\"89c26bb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Metrics are easier to calculate and mature much sooner than KPIs, which tend to take longer or involve complex formulas.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9903f58 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9903f58\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4b6d706\" data-id=\"4b6d706\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bba0bc5 elementor-widget elementor-widget-heading\" data-id=\"bba0bc5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Pros and Cons of Different LTV-Based Predictive Models: Insights from Top Marketers<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8383676 elementor-widget elementor-widget-text-editor\" data-id=\"8383676\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Building an LTV model to predict Return on Ad Spend (ROAS) could be overwhelming due to obvious differences in the way different types of apps retain and monetize users; just think of how distinct in-app purchase games, subscription-based apps and e-commerce businesses are. It\u2019s clear that there cannot be a one-size-fits-all LTV model.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4157c95 elementor-widget elementor-widget-heading\" data-id=\"4157c95\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">LIFETIME VALUE MODELS<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9d7eaf3 elementor-widget elementor-widget-text-editor\" data-id=\"9d7eaf3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><b>1. <\/b><strong>Retention-driven \/ ARPDAU Retention Model<\/strong><\/p>\n<p>Model a retention curve based on a couple of initial retention datapoints, then calculate the average number of active days per user (for Day 90, D180, etc.) and multiply that by an Average Revenue Per Daily Active User (ARPDAU) to get the predicted LTV. Good for high-retention apps.<\/p>\n<p><b>2. <\/b><strong>Ratio-driven<\/strong><\/p>\n<p>Calculating a coefficient (D90 LTV \/ D3 LTV) from historical data and then for each cohort, applying this coefficient to multiply the real D3 LTV to get a D90 LTV prediction. Good for \u201cStandard\u201d types of apps including many game genres.<\/p>\n<p><b>3. <\/b><strong>Behavior-driven \/ user-level predictions<\/strong><\/p>\n<p>Collecting a significant volume of data from app\u2019s users (session and engagement data, purchases, geo \/ device type, etc.) and processing them using machine learning to define which actions or action combinations are the best \u201cpredictors\u201d of a new user\u2019s value. Good for any app with access to an experienced data science team, engineering resources and lots of data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d9f1f0a elementor-widget elementor-widget-heading\" data-id=\"d9f1f0a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">FROM MVP TO COMPLEX MODELS<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9cd6b57 elementor-widget elementor-widget-text-editor\" data-id=\"9cd6b57\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>A good way to start your prediction path is with a simple \u201cMinimum Viable Product\u201d (MVP). The idea is to verify initial assumptions, learn more about the data and gradually build a model. That cost\/benefit ratio of more complex models is not necessarily better than simpler ones which was proven by the fact that companies confessed that they tend to stick to conceptually simple models.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0909552 elementor-widget elementor-widget-heading\" data-id=\"0909552\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">TEAMS &amp; RESPONSIBILITIES<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e1d7d91 elementor-widget elementor-widget-text-editor\" data-id=\"e1d7d91\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Ideally, there are two roles: an experienced analyst with an overreach to marketing that can advise on the strategy and tactical levels as well as decide which model should be used in addition to how it should be used; and a dedicated analyst which then \u201cowns\u201d LTV calculations and predictions on a day-to-day basis.<\/p>\n<p>Outsourcing can certainly jumpstart the process, especially if a company has limited knowledge of the topic. However, in the longer term, given the product is found to be viable and more advertising dollars are being spent on more networks, an internal team should take over.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-20d5de7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"20d5de7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ea00c3e\" data-id=\"ea00c3e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-30db175 elementor-widget elementor-widget-heading\" data-id=\"30db175\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">METHODS FOR ASSESSING MOBILE MARKETING PROFITABILITY WITH EXCEL\u200b<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd6f269 elementor-widget elementor-widget-text-editor\" data-id=\"cd6f269\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Excel is more powerful than you think. By using a scatter plot and bit of algebra, you can turn an Excel trendline equation into a powerful tool for identifying early on the point at which your marketing campaigns prove they are likely to turn a profit.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f32efe elementor-widget elementor-widget-text-editor\" data-id=\"3f32efe\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><em>Access the <a href=\"https:\/\/appagent.com\/blog\/user-acquisition-viability-calculator\/\">User Acquisition Viability Calculator<\/a> to model your entire UA funnel and predict profitability.<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ffe5663 elementor-widget elementor-widget-heading\" data-id=\"ffe5663\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">HERE IS A VERY QUICK GO-THROUGH GUIDE:\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f67dd77 elementor-widget elementor-widget-text-editor\" data-id=\"f67dd77\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The\u00a0<strong>first step<\/strong>\u00a0is to ensure you have enough Week 0 and 6-month data points. A rule of thumb for Week 0 ROAS-based predictions, you should shoot for at least 60 pairs of Week 0 and 6-month ROAS observations.<\/p>\n<p>The\u00a0<strong>second step<\/strong>\u00a0is to split your data set into two groups, one for training and one for prediction. Place the lion\u2019s share of the data (~80%) in the training group.<\/p>\n<p>The\u00a0<strong>third step<\/strong>\u00a0is to use a scatter plot to graph the data, with the Week 0 ROAS on the x-axis and 6-month ROAS on the y-axis. Add a trendline and add the equation and R-squared settings.<\/p>\n<p><strong>Step four<\/strong>\u00a0involves using the y = mx + b linear equation to solve for the equation\u2019s x value (Week 0 ROAS) when the y value (6-month ROAS) is 100%. The answer to the question of how to predict profit at 6-month is that your ROAS must be greater than x in the first week.<\/p>\n<p>S<strong>tep five<\/strong>\u00a0is where you use your prediction segment of the full data set to assess how well your model was able to predict actual outcomes. This can be assessed using the mean absolute percentage error (MAPE). You may even try out a few more trendlines (exponential, logarithmic, etc).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-18b43df7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"18b43df7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-156448\" data-id=\"156448\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0c50b83 elementor-widget elementor-widget-heading\" data-id=\"0c50b83\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">ADDING ANOTHER PIECE TO THE PIE: PREDICTING IN-APP AD LTV<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f16df07 elementor-widget elementor-widget-text-editor\" data-id=\"f16df07\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong data-gtm-vis-recent-on-screen-47723816_29=\"832624\" data-gtm-vis-first-on-screen-47723816_29=\"832624\" data-gtm-vis-total-visible-time-47723816_29=\"100\" data-gtm-vis-has-fired-47723816_29=\"1\">In-app advertising (IAA)<\/strong>\u00a0has become increasingly popular, accounting for at least 30% of app revenue in 2018. Even developers who had been completely reliant on\u00a0<strong data-gtm-vis-recent-on-screen-47723816_29=\"832625\" data-gtm-vis-first-on-screen-47723816_29=\"832625\" data-gtm-vis-total-visible-time-47723816_29=\"100\" data-gtm-vis-has-fired-47723816_29=\"1\">in-app purchases (IAP)<\/strong>\u00a0have started monetizing with ads.<\/p>\n<p>Ideally, marketers would be able to understand the nominal value of every single impression; that would practically make it a \u201cpurchase\u201d. After gathering sufficient data, we\u2019d be able to create prediction models similar to in-app purchases. However, in the real world it\u2019s not that simple because of the volume and structure of provided revenue data. To list a number of issues &#8211; not one source of ads leads to different eCPM, some ad networks pay for different actions (install, click) etc.<\/p>\n<p>Among interviewed gaming app marketers, none had this actually figured out to a stage they\u2019d be happy using it. Calculating user-level in-app ad LTV at the level of precision we\u2019re used to having with in-app purchase LTV will likely continue to be a challenge, at least in the near future.<\/p>\n<p>It leads to another approach called \u201c<strong data-gtm-vis-recent-on-screen-47723816_29=\"832626\" data-gtm-vis-first-on-screen-47723816_29=\"832626\" data-gtm-vis-total-visible-time-47723816_29=\"100\" data-gtm-vis-has-fired-47723816_29=\"1\">The Contribution Method<\/strong>\u201d. Contribution margins work by converting a channel\u2019s contribution to overall user behavior into that channel\u2019s earning margin from the overall ad revenue generated by all users. The theory is that the more a channel\u2019s acquired users generate actions in an app, the more influential and deserving that channel\u2019s hand in claiming credit for advertising revenues from those users.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9a39546 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9a39546\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9c0321e\" data-id=\"9c0321e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-77bd3e8 elementor-widget elementor-widget-heading\" data-id=\"77bd3e8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">BEST PRACTICES FOR BUILDING MOBILE MARKETING PREDICTION MODELS<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f2d5307 elementor-widget elementor-widget-text-editor\" data-id=\"f2d5307\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>When building data models or systems that are used to guide significant decisions, it\u2019s not only important to build the best system possible but also to perform ongoing testing to ensure its effectiveness. For both purposes, make sure that you continuously feed your profit prediction model to keep it trained on the most relevant data. Also, put in enough effort to choose the right KPI for predicting profitability. For better accuracy, try to segment your users into more homogenous groups and remember to factor time.<\/p>\n<p><span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-weight: var( --e-global-typography-text-font-weight ); letter-spacing: 0px;\">If you are interested in learning more on how to calculate customer acquisition cost that is closely connected LTV in terms of business economics, check the article on &#8220;<\/span><a style=\"font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-weight: var( --e-global-typography-text-font-weight ); letter-spacing: 0px; background-color: #ffffff;\" href=\"https:\/\/appagent.com\/blog\/2019\/08\/07\/how-to-calculate-the-customer-acquisition-cost-cac-for-your-mobile-application\/\" target=\"_blank\" rel=\"noopener\">How To Calculate Customer Acquisition Cost (CAC) for your Mobile Application<\/a><span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-weight: var( --e-global-typography-text-font-weight ); letter-spacing: 0px;\">&#8221; by Vit Volsicka.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-46109c5 elementor-widget elementor-widget-spacer\" data-id=\"46109c5\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-20ee29ee elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"20ee29ee\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5fd9f149\" data-id=\"5fd9f149\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7cef3a3b elementor-align-center elementor-widget__width-inherit e-transform e-transform elementor-widget elementor-widget-button\" data-id=\"7cef3a3b\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateX_effect_hover&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_hover_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_hover_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_hover&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_hover_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_hover_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_skewX_effect_hover&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_skewX_effect_hover_tablet&quot;:{&quot;unit&quot;:&quot;deg&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_skewX_effect_hover_mobile&quot;:{&quot;unit&quot;:&quot;deg&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_skewY_effect_hover&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_skewY_effect_hover_tablet&quot;:{&quot;unit&quot;:&quot;deg&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_skewY_effect_hover_mobile&quot;:{&quot;unit&quot;:&quot;deg&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/appagent.com\/contact\/\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Let's grow your app!<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-778520a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"778520a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-26ef339\" data-id=\"26ef339\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-57b5162 elementor-widget elementor-widget-text-editor\" data-id=\"57b5162\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2>CONTRIBUTORS<\/h2>\n<p>Teemu Rautiainen, Elif Buyukcan, Kasim Zorlu and Leonard Seffer from\u00a0<a href=\"https:\/\/www.rovio.com\/\">Rovio Entertainment<\/a><br \/>Alexandra Lomakina from\u00a0<a href=\"https:\/\/www.joom.com\/\">Joom<\/a><br \/>Fredrik Lucander from\u00a0<a href=\"https:\/\/wolt.com\/\">Wolt<\/a><br \/>Matej Lancaric from\u00a0<a href=\"https:\/\/boombit.com\/our-games\/\" target=\"_blank\" rel=\"noopener\">Boombit<\/a>\u00a0(formerly at Pixel Federation)<br \/>Gessica Bicego from\u00a0<a href=\"https:\/\/www.blinkist.com\/\" target=\"_blank\" rel=\"noopener\">Blinkist<\/a><br \/>Anna Yukhtenko and Tim Mannveille from\u00a0<a href=\"https:\/\/www.hutch.io\/\" target=\"_blank\" rel=\"noopener\">Hutch Games<\/a><\/p>\n<p><a href=\"https:\/\/www.kiwi.com\/\" target=\"_blank\" rel=\"noopener\">Kiwi.com<\/a><\/p>\n<h2>AUTHORS<\/h2>\n<p>Martin Jel\u00ednek from AppAgent<br \/>Gabe Kwakyi from\u00a0<a href=\"https:\/\/incipia.co\/\" target=\"_blank\" rel=\"noopener\">Incipia<\/a><br \/>Shani Rosenfelder from\u00a0<a href=\"https:\/\/www.appsflyer.com\/hp2\/\" target=\"_blank\" rel=\"noopener\">AppsFlyer<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4da4805 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4da4805\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cac2e7c\" data-id=\"cac2e7c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\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},"excerpt":{"rendered":"<p>AppsFlyer, AppAgent, and Incipia have collaborated to create a\u00a0comprehensive guide\u00a0on predictive LTV modeling. This must-read resource caters to mobile marketers, UA managers, and marketing analysts. Drawing insights from experts representing companies like Rovio, Hutch Games, Wargaming, Joom, Wolt, Blinkist, Kiwi.com and Boombit, this guide offers a holistic view on how LTV modeling differs across various [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":13400,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[210],"tags":[65,76,61],"class_list":["post-2000","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data","tag-mobile-analytics","tag-mobile-marketing","tag-user-acquistion"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Summary of the Complete Guide on Predictive LTV modeling<\/title>\n<meta name=\"description\" content=\"What are the 3 main approaches to LTV predictions, what methods for assessing marketing profitability with Excel are, how to predict in-app ad LTV.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Summary of the Complete Guide on Predictive LTV modeling\" \/>\n<meta property=\"og:description\" content=\"What are the 3 main approaches to LTV predictions, what methods for assessing marketing profitability with Excel are, how to predict in-app ad LTV.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/webrixstudio.online\/aa\/blog\/complete-guide-on-predictive-ltv-modeling\/\" \/>\n<meta property=\"og:site_name\" content=\"AppAgent\" \/>\n<meta property=\"article:published_time\" content=\"2019-03-19T20:54:19+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-02-26T19:10:38+00:00\" \/>\n<meta property=\"og:image\" 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