Customer Acquisition

The ultimate goal in customer acquisition is to acquire the users with the highest possible lifetime value (LTV) at the lowest possible customer acquisition cost (CAC). However, because ad platforms only attribute conversions that occur within a short conversion window, it is not possible to optimize against actual lifetime value. As a result, you probably optimize against proxy events like registrations, free trials, subscriptions, or initial purchases. This is suboptimal however, and a better approach is to predict LTV within the conversion window, and optimize against these predictions. 

We send our LTV predictions directly to the main ad platforms via their APIs, enabling you to use them as an optimization objective. For Meta, this enables predicted LTV based conversion optimization, value optimization, and bid multipliers. For Google, this enables Target ROAS and Max Conversion Value based Smart Bidding.

Incorporating LTV predictions into your ad campaign optimization is crucial for maximizing ROAS and fostering profitable long-term growth. Get in touch to reap the benefits of predicted LTV.

When evaluating campaign performance, you can't afford to only focus on customer acquisition cost. You also need to consider the LTV of the acquired customers. Unfortunately actual LTV only reveals itself months or years after acquisition.

If you want to react to campaign performance quickly, you need to be able to predict customer value early. When aggregated on the campaign level our LTV predictions allow you to identify the total value you can expect to acquire from every campaign within hours of the campaign launching.

By allocating spending based on the predicted LTV to CAC ratio, you can ensure that your money goes where it has the biggest impact.

We use permission based integration to ensure a light and easy data onboarding process requiring less than an hour of your time. Thanks to our proprietary data model our predictions are integrated live with your systems within a week of data access.

Integrating directly with your selected data warehouse sources allows our pLTV models to learn from the entire interaction sequence in your customer’s journey. Using advanced causal machine learning and AI, our pLTV models incorporate the sequential and temporal dependencies between events to learn highly accurate and robust LTV predictions, while requiring only hours of customer data.

We integrate directly with Meta's CAPI, the Google Ads API and Measurement Protocol, Adjust, Appsflyer, and Segment.