In the competitive landscape of mobile gaming, understanding the dynamics between Cost Per Install (CPI) and Lifetime Value (LTV) is crucial for optimizing user acquisition (UA) strategies and maximizing revenue.
This article delves into how CPI influences UA spend, the calculation of LTV, decision-making based on the CPI vs. LTV equation, the ideal payback periods for different game genres, the use of LTV prediction in UA, and setting up benchmarks versus utilizing LTV predictions.
How CPI Influences User Acquisition Spend
Cost Per Install (CPI) is the average cost incurred by a game developer or publisher every time a user installs their game through paid advertising efforts. CPI is a critical metric because it directly impacts the overall budget required for UA campaigns.
Budget Allocation: A higher CPI means acquiring each user is more expensive, requiring a larger budget to meet UA goals. Conversely, a lower CPI allows more users to be acquired within the same budget.
Market Competitiveness: CPI rates are influenced by market demand and competition. Popular genres or keywords may have higher CPIs due to increased bidding from multiple advertisers.
Targeting and Quality: Broad targeting may result in a lower CPI but might attract less engaged users. Narrow targeting can increase CPI but potentially attract higher-value users.
Influence on UA Spend:
Scaling Campaigns: Understanding CPI helps in scaling campaigns efficiently. If the CPI is low and the quality of users is high, it makes sense to increase UA spend.
ROI Considerations: UA spending should be calibrated so that the CPI does not exceed the LTV of the users acquired, ensuring a positive return on investment (ROI).
Scaling a game is not only a function of a killer user acquisition operation. It is also a function of an LTV. You can only scale your budget until the LTV allows you. Eg. If your LTV is $5, you can run profitable campaigns until you hit $4.5 CPI (or any other CPI that you calculate based on your margins).
This is my favorite CPI graph from the past. Do you remember Frozen City? It's a blast from the past, but it serves this purpose well!
Idle games generally have lower CPIs than other categories. What is important here is the low-poly visual design.
In this case, someone realized why hypercasual games always use(d) low poly. It drives low CPIs. I was in a lot of discussion about how “fancy or more quality” visual style drives higher IAPs, because of the premium feel of the game. Seriously WTF?
The visual style doesn’t impact IAP. If you have a different opinion, please message me, and we can discuss it. Why would it have?
How Is LTV Calculated?
Lifetime Value (LTV) represents the total revenue a game can expect to earn from a user over the entire period they play the game. Calculating LTV accurately is essential for making informed UA decisions.
LTV Calculation Methods:
Historical Data Analysis:
Average Revenue Per User (ARPU): Calculated by dividing total revenue by the number of users over a specific period.
Churn Rate: Understanding how quickly users stop playing the game helps in projecting future revenue.
Retention Rates: Higher retention rates generally indicate a higher LTV.
Predictive Modeling:
Cohort Analysis: Grouping users based on the time they started playing to identify patterns in spending and retention.
Machine Learning Models: Utilizing algorithms to predict future user behavior based on historical data.
LTV predictions take this concept to the next level by leveraging AI to find potential users with the highest LTV. This feature allows user acquisition managers to optimize their strategies by targeting users likely to yield the highest returns.
What’s under the hood
LTV predictions evaluate each user within 24 hours of joining the app and form an LTV prediction for 28 days. Based on these forecasts, you can send postbacks into your ad network directly from AppMetrica and optimize campaigns for topLTV users with just a few clicks.
In contrast to classic optimization suggestions based on traditional metrics like ‘time spent’ and ‘engagement’, the new AI-based predictive model collects and analyzes massive amounts of data around every user’s potential LTV to find the highest-quality leads for your ad campaigns. It uses the formula to ensure accuracy.
LTV = Revenue All x P (LTV > 0) + Revenue (first day) x 1 — P (LTV > 0)
LTV predictions also let you segment users into various LTV cohorts (like top 5, top 20, top 50, and bottom 50) and compare them.
To test the accuracy of the predictions and evaluate which method attracted a more engaged audience, the founding team ran an A/B test for a gaming app:
The test compared two options:
Optimization based on the «User played for at least 10 minutes» event
Optimization based on the predictive model for the top 20% of paying users (top20LTV)
The campaign settings and budgets were the same. The first ten days of the campaigns were spent learning and building up the data set. Within the next week, AppMetrica experts collected installation data to use in the predictive model.
AppMetrica’s A/B tests showed an overwhelming advantage of optimizing campaigns based on the LTV reports.
Churn Predictions
User churn is a common challenge in mobile app marketing. Identifying users likely to leave the app and implementing proactive measures to retain them is vital for long-term success.
How it works:
Churn predictions enable app owners and marketing teams to identify new users who will likely churn over time as soon as they install the app.
The AI model analyzes all active users over 3 weeks, scoring their activity daily. While the model requires no specific metrics, it accurately predicts users who are more likely to quit using your app depending on the typical user’s lifecycle in the particular app. The generated report splits all users into groups based on churn probability: >95%, 75-95%, 50-75%, and <50%, with a 99% accuracy rate as guided by the 3-sigma rule.
This feature empowers you to implement targeted retention strategies to prevent user dropoffs by leveraging predictive analytics, such as launching personalized push notifications, personalized incentives, and more.
Knowing which users are more likely to churn gives you an upper hand at engaging them and preventing their exit from the app as soon as possible. AppMetrica lets you reach your users directly from your account via personalized push notifications—quite handy!
Factors Influencing LTV:
In-App Purchases (IAP): Revenue from users purchasing virtual goods or premium features.
Advertising Revenue: Earnings from displaying ads to users within the game.
User Engagement: More engaged users are likely to spend more and stay longer.
Game Updates and Content: Regular updates can improve retention and increase LTV.
Decision-Making Based on the CPI vs. LTV Equation
The fundamental principle in UA is that the CPI should be less than the LTV of the acquired user. This ensures that the revenue they generate justifies the cost of acquiring a user.
Scenarios:
CPI < LTV: Profitable scenario. UA campaigns can be scaled up.
CPI > LTV: Unprofitable scenario. UA strategies need reevaluation.
Decision-Making Strategies:
Adjust Targeting: Refine audience segments to attract higher-LTV users.
Optimize Creatives: Improve ad creatives to increase conversion rates and lower CPI.
Enhance Monetization: Improve in-game monetization strategies to increase LTV.
What Is the Best Payback Period for Each Game/Genre?
The payback period is the time it takes for the revenue from a user to cover the cost of acquiring them.
Factors Affecting Payback Period:
Game Genre:
Hyper-Casual Games: Typically have lower LTVs and shorter payback periods (often within days or weeks).
Hybrid-Casual Games: Higher retention, bigger spend depth. Borrowing the hyper casual marketability but allowing players to spend more! (payback period usually 1-6 months)
Mid-Core to Hardcore Games: Higher LTVs with longer payback periods (several months to years).
Monetization Model:
Ad-Supported Games: Rely on ad revenue, which may require shorter payback periods due to lower per-user revenue.
Freemium Models: Can have longer payback periods as users may spend more over time.
Determining the Best Payback Period:
Financial Goals: Align with the company's cash flow requirements and investment capacity.
Market Standards: Benchmark against industry averages for similar game genres.
User Behavior: Analyze user spending and engagement patterns to set realistic payback periods.
How Do You Use LTV Prediction in User Acquisition?
Accurate LTV prediction enables game developers to make data-driven decisions in their UA strategies.
Using LTV Predictions:
Budget Allocation: Allocate more budget to channels or campaigns that attract higher-LTV users.
Bid Optimization: Adjust bids in advertising platforms to target users likely to have higher LTVs.
Personalized Marketing: Tailor marketing messages to segments predicted to generate more revenue.
Methods for LTV Prediction:
Cohort Analysis: Identifying patterns in user groups to forecast future behavior.
Predictive Analytics: Using statistical models and machine learning to predict LTV based on early user behavior indicators.
How to Set Up Benchmarks vs. When to Use LTV Prediction
Setting Up Benchmarks:
Industry Standards: Use average CPIs, LTVs, and payback periods from industry reports as initial benchmarks.
Historical Performance: Leverage your game's past performance data to set realistic benchmarks.
Competitor Analysis: Analyze competitor metrics where available.
When to Use LTV Prediction:
New Games or Updates: When historical data is limited, predictive models can help estimate LTV.
Dynamic Markets: In rapidly changing markets, real-time LTV predictions can adjust UA strategies promptly.
Personalization: For highly segmented UA campaigns, LTV predictions can optimize efforts at a granular level.
Balancing Benchmarks and Predictions:
Continuous Monitoring: Regularly compare actual performance against benchmarks and adjust predictions accordingly.
Flexible Strategies: Be prepared to pivot UA strategies based on predictive insights and benchmark comparisons.
Few last words
Understanding and effectively managing the CPI vs. LTV equation is essential for the financial success of mobile games. By carefully analyzing how CPI influences UA spending, accurately calculating and predicting LTV, and making informed decisions based on these metrics, developers can optimize their UA strategies for maximum ROI.
Additionally, setting appropriate payback periods and balancing benchmarks with predictive analytics further refine the approach, ensuring sustained growth and profitability in the competitive mobile gaming industry.
Thanks for the article.
Do you have any statistics which connect IAP and visual style?
Because my common sense is, that higher quality (and what a normal player would consider higher quality) assets contribute to higher IAP.
I have no data for this though.