Google LTV Capabilities

Google recently rolled out LTV reporting in Google Ads. We think the next step should be to provide advertisers with the tools to allocate marketing resources based on CLV. If advertisers can optimize their budgets by CLV, they can improve ad relevance, targeting, and conversion rates.

Current State

Currently, Google allows advertisers to extract insights from 12+ months of conversion data and bucket customers into high, medium, and low value users. This product phase is used for customer value reporting for now.

Many advertisers are passing email addresses to Google Ads using customer match to build lookalike audience lists. Advertisers are also importing this information into Google Analytics to build a deeper understanding of CLV and customer behavior.

Another, more sophisticated approach to using CLV within Google is similar audiences. Marketers have the option to manually adjust audience bids or follow value rules if they choose auto bidding.

Advertisers are beginning to provide LTV values to Google through conversion tags, although they are having difficulty in scaling this solution. Similar audiences have great initial returns, but most advertisers are only using the top 5% of their customer base to build similar audiences. This value should be closer to 30-40% to achieve scale. There are a finite number of “whales” in any industry.

Customer use cases for this new capability include:

  • Feeding change in LTV to Google Ads to use as the conversion value
  • Decreasing ad spend on lower CLV cohorts
  • Optimizing landing page content, creative, and copy to match the needs of high-value customers by weighting A/B tests

Future State

In the future, advertisers should be able to use predictive customer lifetime value for ad and campaign budget optimization

The current space needs to advance to offer individual level customer lifetime value predictions earlier in the lifecycle. Currently, marketers need to wait for three or four transactions to predict CLV.

With a shorter CLV prediction time, advertisers can optimize their ads in near real-time. A model that accounts for volatility will be important, especially in this environment.

At Retina, we accurately predict customer lifetime value early in the customer journey, at or before first transaction. With this powerful metric, advertisers can improve ad relevance, optimize campaigns in real-time, and increase brand equity.