Blog

This article discusses how predictive Lifetime Value (pLTV) targeting can enhance advertising strategies by focusing on long-term customer value rather than immediate returns. It outlines an experimental approach where existing campaigns are split into two: one using traditional optimization methods and the other employing pLTV-based optimization, with the goal of demonstrating that pLTV targeting leads to significant improvements in long-term Return on Ad Spend (ROAS) and can be effectively scaled. The article also details methods for measuring the success of pLTV implementation and explores what integrating pLTV as a core component of user acquisition strategies entails.

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Most ad networks focus on quick, short-term revenue from new users. This means they might overlook customers who could bring more value in the long run. If ad platforms knew a customer’s long-term potential upfront, they could better target high-value users. In this piece, we explore how pLTV helps you find and attract these valuable customers.

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Predicting LTV is a great tool for user acquisition. In this piece, we argue that budget allocation models are fundamentally different from those for ad-platform optimization.

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Marketers across industries have moments when they recognise their metrics misalign with their true business goals. Awareness begins by looking under the hood of commonly used metrics and questioning whether they still serve the purpose of directing the business. In this article, I describe the importance of aligning your marketing metrics with your business goals, the nuances between pLTV, CPA and ROAS, and share the incredible Esbjerg bridge fiasco as an example.

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We explore the best practices for evaluating predictive LTV models in advertising. First, we explain the problems of inflated reporting, seasonality, and end selection. For each, we show solutions such as using causal inference and modern marketing measurement.

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We highlight the issues of A/B testing for marketing purposes and give some of our experience on using causal AI to improve this process dramatically.

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Marketing leaders grapple with the challenge of optimizing revenue through data-driven strategies while protecting against data misuse and privacy breaches. To navigate this, this article outlines four principles to handle this complex trade-off.

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Inspired by a recent paper by Meta and Deloitte, this article discusses the importance of first-party data in a privacy-first world and how marketers are using it to improve ad targeting, personalisation, and measurement. It also provides recommendations on how to enable these use cases and a case study of a fashion brand that successfully used first-party data with the help of analytic partner.

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Here's how you can determine if your business case lends itself well for predictive LTV to improve your ad spend.

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Excessive churn is perhaps the greatest threat to the healthy growth and profitability of any recurring revenue company. More precisely, the real pain point occurs when your customer acquisition cost is greater than their lifetime value (LTV).

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This article is about some of the pitfalls of early lifetime value (LTV) prediction that should be avoided to get the most out of value-based event optimization or value-based-bidding strategies provided by companies such as Meta, Google, or TikTok.

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A churn curve is a handy tool to evaluate your churn in different cohorts/periods. BI tools are not great at collecting time-based data, such as subscription periods, and unifying them. This tutorial will demonstrate how to use a BI tool to arrive at a fully filterable churn curve. We choose PowerBI in this demo.

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