To begin with, lifetime value (LTV) or customer lifetime value (CLV) indicates the measurement of the value an average user spends over their lifetime as a customer of a brand. LTV provides crucial insights on what business metrics to use, including user acquisition (UA) cost and cost per action (CPA). Marketers can refer to these metrics to set advertising strategies accordingly and forecast future actions.
Due to recent changes in privacy regulations like Apple’s App Tracking Transparency (ATT) and the introduction SKAdNetwork (SKAN), it has become unclear how LTVs can be calculated without the same amount of user-level data. pLTV provides a feasible alternative, in which lifetime values can be measured using machine learning systems and anonymized data that respects user privacy.
pLTV uses a machine learning (ML) model that is able to work with generalized postback information, historical data, and past user activity data to perform predictive analysis. These datasets are focused on “what” users are doing instead of “who” they are, and the information is completely anonymized.
Initially, marketers will use historical campaign data to form user segments based on outstanding behavioral traits and their likeliness to remain engaged with a brand. Afterward, the algorithm embedded within the pLTV model can rapidly examine large sets of data to separate users into the segment they best fit into. Once the groups are formed, marketers are easily able to determine each user’s potential value and loyalty to their brand, and they can formulate different strategies for each group, based on the predicted value.
To conduct pLTV measurements, marketers need to collect a lot of data that can accurately provide correlations and future recommendations. They should have access to historical datasets, and campaign activity should be measured early on to avoid having skewed or insufficient data. Furthermore, the compiled raw data must be refined, making sure that any duplicates or incorrect formats are fixed before inputting it into the machine learning model. Although using a machine learning system expedites the analytics process, it is only capable of processing the information as it is, meaning that marketers should be responsible for checking for any errors that may distort results.
pLTV matters because it is the most accurate predictive analysis known for forecasting future marketing strategies while staying in line with today’s privacy-centric environment. Being able to predict the potential value of a user brings noticeable improvements to a brand’s operations, as marketers can credibly allocate future budgets and strategies in a more profitable manner, ensuring that there is less wasted in money and resources.
Since pLTV models provide a data-driven segmentation of users, it is extremely useful for getting a birds-eye view of a brand’s marketing performance and seeing the general direction it is heading towards. These insights are necessary for establishing long-term marketing goals that will gradually move the needle for the brand.