What’s new in Apphud: SplitMetrics Acquire Integration, Experiments Improvements, SDK UpdatesLet’s see
Apphud
Why Apphud?PricingContact
Nataly
Nataly
May 14, 2024
20 min read

LTV Prediction Use Cases for Subscription Apps

In this article, we will provide LTV prediction use cases and strategies that subscription application managers can implement to enhance their revenue growth strategy and stay ahead of the curve.

LTV Prediction Use Cases for Subscription Apps

In the fast-paced world of subscription applications, staying ahead of the competition and maximizing revenue is critical. One of the keys to success in this space is using predictive tools to optimize decision-making and drive growth. However, for these tools to be truly effective, access to high-quality, accurate data is essential.

By the end of this article, you will have a better understanding of how predictive tools can revolutionize your subscription app business and help you reach new heights of success. Let's dive in and discover the power of predictive analytics to drive subscription app revenue growth.


The most informative and helpful predictive metrics

Three of the most informative and useful predictive metrics for subscription applications are lifetime value (LTV), churn rate, and revenue projections.

Lifetime Value (LTV)

LTV is a critical metric that helps companies understand the total revenue they can expect from an individual customer throughout their relationship. By accurately predicting LTV, companies can make informed decisions about customer acquisition costs, marketing strategies, and revenue forecasts. This metric is especially valuable for subscription applications, allowing them to identify their most valuable customers and tailor their offerings to meet their needs.

Churn Rate 

Churn rate, on the other hand, measures the percentage of customers who cancel their subscriptions within a given period. By predicting churn rates, subscription applications can implement proactive strategies to reduce customer churn and improve retention rates. This metric helps companies identify potential problems with their product or service offerings, improve customer satisfaction, and ultimately increase revenue potential.

Revenue

Revenue forecasting is critical for subscription applications to effectively plan and manage their finances. By predicting future revenue streams, companies can make informed decisions about investments, growth strategies, and budget allocation. This metric allows companies to set realistic revenue goals, monitor their progress toward those goals, and make adjustments as needed to ensure long-term success.

Predictive LTV, churn, and revenue forecasts can significantly benefit subscription-based applications by providing valuable insights into customer behavior, financial performance, and growth opportunities. By leveraging these predictive metrics, companies can optimize operations, drive sustainable growth, and stay competitive in the ever-evolving digital marketplace.


LTV Prediction Use Cases

In this chapter, we will describe how Apphud and AppMetrica LTV Prediction data can be implemented in your subscription app growth tactics.

LTV (Lifetime Value) prediction data is critical for subscription apps to effectively understand and segment their user base. First, we need to identify user groups with high growth potential. In Apphud, by leveraging LTV predictive data and using specific filtering parameters such as location, device platform, search ad campaign, ad group, or even keyword, managers can apply these insights to activities across multiple channels.

Look at the filtration system in the Apphud LTV Prediction Chart. 

Segmentation in the Cumulative LTV Charts, ApphudSegmentation in the Cumulative LTV Charts, Apphud

Before we start, we have some recommendations for the most productive utilization of the LTV Predictions:

  • Date range selection: Opt for broader date ranges for more accurate predictions. However, avoid overly extensive periods.
  • Filter usage: Limit the depth of the filter application as it can reduce the cohort data size, affecting prediction accuracy.
  • Volume consideration: For apps with high daily transaction volumes, shorter date ranges, even a day, can be effective. Conversely, for lower volumes, choose intervals covering at least 200 subscribers for better accuracy.

1. Advertising campaign optimization 

LTV prediction data helps identify high-value users who are more likely to convert or remain subscribers. Let's select a cohort of users from search ad campaigns or compare LTV predictions for different search ad groups. The only requirement is that your cohorts have enough data to make a prediction.

For the most relevant prediction, you should have 500 subscriptions. As a general rule, the number of renewals of the oldest cohort must be greater than 2, but not greater than the maximum number of subscriptions per year. For example, for monthly subscriptions, the number of renewals of the oldest cohort should be less than 12.

Based on the data Apphud provides after filtering and comparison, you can more effectively allocate ad spending and tailor your marketing strategy to the most profitable user segments. By aligning the marketing budget with predicted lifetime value, you can optimize spending for the best return on investment. 

By understanding the lifetime value of each customer, you can tailor advertising efforts to focus on acquiring and retaining the customers who are likely to generate the most revenue over time.

ARPAS metric

Apphud's aggregated data shows that analyzing the ARPAS metric holds the potential for your growth strategy. It's obvious that paying users generate more revenue, but don't ignore the ARPAS metric that includes trial users. By analyzing the LTV of paying and trial users, comparing different cohorts, and understanding the longest lifetime, you can calculate the optimal bid range for user acquisition techniques and optimize ad campaigns more effectively.

How to optimize ad campaigns through LTV Predictions in AppMetrica

AppMetrica’s LTV Predictions feature helps app owners and acquisition teams identify, match, and optimize campaigns around users with the highest lifetime value. This AI-based feature works from Day 1 of your campaign, evaluating each user within 24 hours of them joining the app. Then it forms an LTV prediction for the next 28 days which you can use to send postbacks into your ad network directly from AppMetrica, optimize campaigns for similar topLTV users, and maximize your best-performing channels as soon as possible.

To set up campaign optimization with LTV Predictions in AppMetrica, you will first need to segment your users based on top LTV in the Audience report:

  • Choose any report — for example, the Audience report.
  • Select Segment by users + 
  • Choose the event tag EVENT_LTV_… and then include the LTV range:
  • EVENT_LTV_0_5 — top 5% of paying users
  • EVENT_LTV_0_20 — top 20% of paying users
  • EVENT_LTV_0_50 — top 50% of paying users
  • EVENT_LTV_50_100 — bottom 50% of paying users.
  • Select the date of the event: it corresponds to the date following the installation date.
Audience report segmentation at AppMetricaAudience report segmentation at AppMetrica
Audience report at AppMetricaAudience report at AppMetrica

From these reports, you can also determine the profit that each user cohort with a specific lifetime value brings to your app. 

Then, you can optimize your ad campaigns to target similar users with high LTV scores by sending postback signals to your ad networks directly from AppMetrica. 

If you use AppMetrica as your primary MMP, you can follow the postback setup instruments in this How-to-Guide. If you use other tracking systems as MMPs, you can also export events with logs API / data stream API for your own BI system or integrate AppMetrica with other solutions.

Integrate Apphud with Appmetrica to get deeper data about your subscription app.

Advice on how to use LTV predictions to optimize advertising campaigns:

1. Start by collecting as much data as possible on customer behavior, buying patterns, and preferences. This will help you better predict each customer's LTV.

2. Use advanced analytics tools and algorithms to analyze the data and create predictive models that can accurately forecast customer lifetime value. Apphud is a great example.

3. Segment your customer base based on their predicted LTV and tailor your advertising campaigns to target each segment accordingly.

4. Continuously monitor and adjust your campaigns based on the actual performance of your advertising efforts and the actual LTV of your customers.

Mistakes to avoid when using LTV predictions to optimize ad campaigns:

1. Relying too heavily on predictive models without considering other factors that may affect customer LTV, such as changes in market conditions or competitive activity.

2. Ignoring the importance of customer feedback and engagement in optimizing your campaigns for maximum ROI.

3. Ignoring the potential impact of external factors, such as economic downturns or industry disruptions, on customer LTV and advertising effectiveness.

4. Don't rely on segments that have too short a funnel, not enough subscriptions, and no consistent data.

2. Messaging and content personalization 

By using the geographic segmentation approach, app managers can pinpoint areas where their app is thriving and areas where growth opportunities exist. Let's say you've dug up the insights using the country filter in Apphud's LTV prediction chart and you want to confirm the hypotheses that have emerged.

Apphud allows you to track countries by user IP and by store country. 

For example, if the LTV prediction data indicates that users from a certain country have a high LTV, you can personalize the user experience for each group, e.g. by providing tailored content and spending more on better localization to realize the full revenue potential of that location.

Push notifications

With Apphud, it's possible to send push notifications based on the insights you've gained from the LTV prediction chart.

Automated push notifications (Rules) at ApphudAutomated push notifications (Rules) at Apphud
  • Create a custom audience that matches the segment of prospects with the highest LTV. Customize the communication strategy to provide them with premium content and early access to new features.
  • Target high-value customers with personalized offers and promotions to increase cross-sell and up-sell opportunities. Offer additional products after subscription and promote a more expensive plan to convert users.  
  • Re-engage subscribers who have a high predicted lifetime value but have stopped using the app. Send personalized messages to encourage them to return and continue using the app.
  • Don't forget about the low potential LTV audience - you can conduct surveys that will give you insight into how to turn them into profitable users.

Overall personalization advice 

Continuously monitor the impact of personalization tactics on user retention, engagement, and revenue generation:

  • Analyze user preferences and behavior to recommend personalized content, such as articles or videos, tailored to each user's predicted lifetime value.
  • Suggest relevant products or services to users based on their value potential, increasing the likelihood of upsells or cross-sells.
  • Customize app features, promotions, and imagery to match the cultural preferences and norms of high LTV regions, ensuring that the app resonates with users in those markets.
  • Launch geo-targeted marketing campaigns specific to each region, taking into account the preferences, trends, and behaviors of users in high LTV areas to maximize effectiveness and ROI.
  • If certain user segments have a lower predicted LTV, companies can investigate further to understand why and make the necessary adjustments. This can range from improving the user interface to enhancing customer support or even changing the pricing structure.

3. Pricing Optimization

As a result of the LTV prediction user segmentation, we have user groups to work with. Let's dive into the most effective tactics for subscription app monetization.

For example, suppose the LTV data shows that customers who pay a higher price upfront tend to generate a higher lifetime value. In this case, the app manager may decide to implement a higher initial price point for the app. On the other hand, if the data suggests that customers are more likely to churn if the price is too high, the developer may opt for a freemium model or a lower price point with in-app purchases.

Subscription application managers can consider implementing tiered or personalized pricing strategies to target high-value customers.

In addition, you can tailor your pricing strategy based on the user's device. In Apphud, you can filter users based on their device platform to determine which platform generates the highest LTV. By understanding which platform is more lucrative for the app, you can allocate resources accordingly to drive growth.

Similarly, you can track LTV not only for paying users but also for trial users. Apphud allows you to choose between ARPU (average revenue per user), ARPPU (average revenue per paying user), and ARPAS (average revenue per activated subscription) metrics for LTV calculation.

Understanding the most profitable subscription plans is a way to choose a better pricing strategy and improve app performance. That's why Apphud offers the possibility of long-term LTV analysis with a custom range of up to 999 days.

999 days cohort in the Cumulativa LTV Chart at Apphud999 days cohort in the Cumulativa LTV Chart at Apphud

 If you have different subscription types: weekly, monthly, and annual, we recommend analyzing the longest cohort period - 999 days, it allows you to see the big picture.

Paywall A/B experiments

Identify the customer segments that generate the most and least profit to find areas for improvement in the monetization strategy. Offer a special discount or upgrade to subscribers with a high predicted lifetime value to encourage additional purchases.

In Apphud, you can run targeted experiments tailored to custom audiences, such as specific countries, app versions, or other user segments.

Experiment settings at ApphudExperiment settings at Apphud

Then, test different subscription plans tailored to each cohort to see which one leads to higher retention and LTV.

1. Test different pricing models: Offer a discounted introductory price for a limited time to see if it increases conversion rates and ultimately boosts LTV.

2. Test different subscription lengths: Offer both monthly and annual subscription options to see if customers are more likely to subscribe for a longer duration with a discount.

3. Test different messaging: Experiment with different messages and call-to-action buttons to see which resonate with customers and lead to higher conversion rates.

4. Test targeted promotions: Use LTV prediction data to create personalized offers for different customer segments, such as a discount for customers with a high predicted LTV, to see if it increases conversion rates and LTV.


Challenges in Prediction for Subscription Apps

Predicting user behavior and engagement in subscription apps is a challenging task. There are several key challenges that app developers and marketers face when it comes to predicting user behavior and improving subscription retention rates.

First, one of the biggest challenges in predicting user behavior for subscription apps is the quality and availability of data. It can be difficult to collect and analyze relevant user data, especially when users have different preferences and behaviors. This can lead to incomplete or inaccurate insights, making it difficult to accurately predict user churn or engagement.

Another challenge is the complexity of analyzing user behavior. Subscribers can interact with the app in a variety of ways, from browsing content to making purchases or engaging with in-app features. Understanding and analyzing these behaviors can be complex, requiring advanced analytics tools and techniques.

Incorporating external factors such as market trends, competition, and seasonality adds another layer of complexity to predicting user behavior. These external factors can affect user engagement and retention rates, so it is essential to take them into account when making predictions. However, integrating external data sources and factors into predictive models can be challenging and may require additional resources and expertise.

By addressing these challenges and leveraging advanced analytics tools and techniques, app developers and marketers can improve predictive accuracy, increase subscription app revenue, and take the app to the next level of success.


Conclusion

In summary, predictive tools have the potential to significantly increase revenue for subscription applications by providing valuable insights and driving strategic decisions. Some of the most informative and useful predictive metrics include churn rate, customer lifetime value, and user engagement metrics. Use cases for predictive analytics in subscription applications range from personalizing the user experience to improving retention and upsell opportunities. However, challenges such as data quality and interpretation must be addressed to successfully implement predictive strategies.

Key takeaways for implementing predictive strategies include investing in high-quality data collection and analysis tools, leveraging machine learning algorithms to make accurate predictions, and constantly monitoring and adjusting predictive models to ensure their effectiveness. Ultimately, subscription apps that harness the power of predictive tools will be better equipped to drive revenue growth and improve user satisfaction in a competitive market.

With analytics platforms for apps like Apphud and Appmetrica, you will be able to gain data insights from the LTV prediction chart and implement your hypothesis of increasing subscription app revenue. Sign up to Apphud and let's grow your app revenue together!

Nataly
Nataly
Head of Marketing at Apphud
7+ years in product marketing. Nataly is responsible for marketing strategy development and execution. Committed adherent of the agile methodology.

Related Posts