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title: '**Predicted lifetime value (pLTV)**'

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# **Predicted lifetime value (pLTV)**
![](https://i.imgur.com/xm3qv4m.jpg)

The anticipated or potential value of a customer, is calculated by combining historical data with the results of recent measurements. This provides marketers with the ability to construct and optimise campaigns based on their audience's anticipated consumption patterns.
# What exactly is meant by the notation "predicted lifetime value" (pLTV)?
To get a clearer picture of how [pLTV](https://top10livechat.com/how-to-create-a-pltv-model/) contributes to the achievement of your measurement and performance objectives, we must first get a firm grasp on what it means to measure lifetime value (LTV), the enormous additional value that can be derived from predictive analytics, and the enormous potential that can be unlocked by the combination of these two powerful tools, which is referred to as pLTV.
The term "lifetime value," which is also abbreviated as "LTV," refers to an estimate of the average amount of revenue that will be generated by a customer over the course of the time that they use your product or service. However, in light of the recent revolution in data privacy, how is LTV to be measured if one does not have the same level of access to granular performance data, and in particular, data that is measured over the long term?
This is where predictive analytics or modelling comes into play in the picture. It does this by utilising machine learning and artificial intelligence (AI) to analyse historical campaign data, past user behaviour data, and additional transactional data in order to predict future actions.
Your audience can then be segmented not by their actual identity, but by their interaction with your user funnel in its earliest stages, which can indicate their future potential to drive meaningful value to your business. This is accomplished by creating different clusters of behavioural characteristics, which can then be used to create different clusters of behavioural characteristics.
# What are the reasons behind its significance?
You are able to make quick decisions regarding the optimization of your campaign thanks to predictive modelling, which equips you with the knowledge you need to do so without skipping a beat. Stop campaigns in their tracks if they aren't producing the desired results, or quickly increase your spending on initiatives that have the potential to produce even better outcomes, all without compromising the privacy of your customers.
In a nutshell, [pLTV](https://top10livechat.com/how-to-create-a-pltv-model/) gives you the ability to leverage the power of data science so that you can estimate, based on the actions that your customers have taken in the past, how much money they will spend in your app over a set period of time in the future.
It also enables you to segment users according to their acquisition source and forecast projections accordingly, making it an ideal tool for determining which of your marketing channels will produce the highest spending users now — and in the future.
Understanding user behaviour patterns and the typical milestones that separate high-potential users from low potential users can be extremely valuable at any stage of the user lifecycle, but especially during the acquisition and re-engagement stages.
