Lifetime Value Predictions for Google Ads and Meta Advertising

Acquiring users who bring long-term value to your business is crucial in today's digital market. Platforms like Google Ads and Meta Advertising (formerly Facebook Ads) offer powerful tools to help reach potential customers. However, they need your guidance to focus on the users who matter most to your business. One effective way to do this is by using Lifetime Value (LTV) predictions.

In this blog post, we'll discuss the basics of using LTV in user acquisition and provide practical steps to optimize your campaigns on both Google Ads and Meta Advertising. For more technical deep dive read our blog post about predicting lifetime value for mobile games using Machine Learning.

Table of Contents

1. The Challenge of Predicting LTV Early

2. Using Predictive Analytics and Machine Learning

3. Optimizing Google Ads Campaigns with LTV Predictions

4. Implementing LTV in Meta Advertising

5. Conclusion

The Challenge of Predicting LTV Early

While Google Ads and Meta Advertising can help you reach a wide audience, your real goal is to attract users who will generate higher value. The challenge is that you often have a limited time—usually between 1 to 7 days—to report the lifetime value of new users. This short window makes it tough to predict which users will become valuable customers, especially if they don't make immediate purchases.

This chart illustrates how, for example, User A purchased only one item on the first day in your app. However, they returned to the app over the course of the first year and made additional purchases. User B, on the other hand, may have made two purchases on the first day but churned quickly thereafter. As a result, User A yielded a $40 ROI in the first year, while User B generated only $20. If we analyzed the data naively, we might have preferred to acquire more users like User B based solely on their first-day behavior.

Illustration of user LTV calculation and challanges

Using Predictive Analytics and Machine Learning

So, how can you estimate a user's LTV before they make a purchase?

The answer lies in leveraging predictive analytics powered by machine learning. By analyzing early user behaviors—such as app navigation patterns, time spent on key screens, engagement frequency, or even the specific items browsed—advanced algorithms can identify patterns and correlations linked to high-value customers. These models use historical data from existing customers to predict the likelihood of future purchases, churn, or repeat interactions, allowing you to estimate a user’s potential lifetime value even before they make their first purchase. See our article about Cohort based Forecasting for example. Lifetime Value prediction can be implemented in many ways, however once you achieve such predictions (either by leveraging SaaS solutions or building your own model) you can upload them to your marketing platforms and help them optimize the campaigns for you.

Using Lifetime-value predictions to optimize Google Ads and Meta Advertising Campaigns

The process of optimizing campaigns works similarly across platforms that support user acquisition:

1. Set a Budget and Targeting Rules: Define your budget and audience criteria to guide your campaign.

2. Generate Users and Installs: The ads platform runs your campaign, delivering users and app installs that meet your targeting parameters.

3. Track and Predict LTV: Monitor user behavior and use machine learning to predict their lifetime value based on early activity.

4. Upload Data for Optimization: Provide the platform with a CSV file containing tracking IDs and LTV predictions, allowing it to optimize acquisitions and focus on attracting high-value users over time.

Now, let's explore how we can do that specifically with Google Ads and Meta Advertising.

Optimizing Google Ads Campaigns with LTV Predictions

To optimize your Google Ads campaigns for high-value customers, you can incorporate your Lifetime Value (LTV) predictions into your advertising strategy. By integrating LTV predictions into your conversion values, you provide Google Ads with enhanced data, enabling its Smart Bidding algorithms to prioritize users who are likely to generate more long-term revenue.

Here's how you can implement this approach:

1. Integrate LTV into Conversion Values

   • Report Predicted LTV as Conversion Value: When a user converts—such as making a purchase or signing up—report their predicted LTV as the conversion value instead of just the immediate transaction amount. This informs Google Ads about the total expected value from each customer, allowing the system to focus on acquiring high-value users.

2. Use Value-Based Bidding Strategies [see here]

   • Select an Appropriate Bidding Strategy: Choose either Target ROAS (Return on Ad Spend) or Maximize Conversion Value as your bidding strategy. These strategies leverage the conversion values (now including LTV) to optimize bids toward users who are expected to bring higher long-term value.

3. Leverage Performance Max Campaigns

   • Utilize Performance Max Campaigns: Set up Performance Max campaigns to access all of Google's channels within a single campaign. With LTV-enhanced conversion values, Performance Max can efficiently identify and prioritize high-value customers across Search, Display, YouTube, and more.

4. Adjust Conversion Values with Value Rules (Optional)

   • Create Value Rules: In Google Ads, set up value rules to adjust conversion values for specific customer segments or behaviors indicative of high LTV. For example, you can increase the conversion value for users who engage with premium products or exhibit loyal behavior.

     • Example: If a user views high-priced items or subscribes to premium services, you might increase their conversion value by a certain percentage to reflect their higher potential value.

Implementing LTV in Meta Advertising

Meta Advertising also allows you to optimize for high-LTV users through value-based lookalike audiences.

Creating a custom list with your own data on Meta Advertising

Steps to Create a Value-Based Lookalike Audience [see here]

1. Prepare Your Customer List:

   • Include key identifiers like email or phone number.

   • Add a column for LTV with actual monetary values (not rankings or ratings).

2. Upload the List:

   • Go to Meta's Audience Manager.

   • Choose Create Audience > Custom Audience > Customer List.

   • Map your LTV column as the "value" field.

3. Create a Lookalike Audience:

   • Use your value-based custom audience as the source.

   • Target this lookalike audience to find new users similar to your high-value customers.

Best Practices for LTV Data

Do:

• Include a Range of LTVs: Use data from both average and high-value customers to give Meta a broader perspective.

• Keep Currency Consistent: Ensure all LTV values are in the same currency.

• Use Real Data: Provide actual revenue figures for each customer to improve targeting accuracy.

Don't:

• Avoid Rankings or Ratings: Don't use subjective scores like star ratings; they don't represent actual monetary value.

• Exclude Negative Values: Meta can't process negative LTV values.

Tips for Uploading Customer Lists

• Prepare the List Properly:

  • Include at least one unique identifier per customer.

  • Label the LTV column clearly (e.g., "value") and use positive numbers.

• Mapping Data:

  • Meta provides feedback during the upload:

    • A green checkmark means data is mapped correctly.

    • A yellow exclamation mark indicates errors or incomplete mapping.

• Security Measures:

  • Personal identifiers are hashed (encrypted) during upload to protect privacy.

Conclusion

Incorporating Lifetime Value predictions into your Google Ads and Meta Advertising campaigns can significantly improve your user acquisition efforts. By focusing on users likely to bring long-term value, you enhance immediate campaign performance and contribute to sustained business growth.

While there are technical challenges in using predictive analytics and machine learning, partnering with experts can make the process smoother and more effective.

At TensorOps, we specialize in AI consulting and can help you implement LTV prediction models tailored to your business needs. Want to learn more? [Discover our collaborative program with Google Cloud to build LTV prediction models using BigQuery ML, potentially with funding assistance.]

Ready to enhance your user acquisition strategy with LTV predictions? Contact us today to see how we can help you achieve lasting growth through advanced analytics and machine learning.