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Marketers’ crystal ball: How to Use Predictive LTV Modeling to Improve Performance Marketing ROI
The digital advertising & marketing space has become even more complex: the pace that campaigns are being changed has accelerated, pushing advertisers to test and run more campaigns for shorter periods. Together with the new regulations that limit the use of external data it has become much more challenging to run a campaign with positive returns.
Imagine you could have a crystal ball, allowing you to take a peek into the future and see which of your campaigns is going to be successful? How much more effective could you have been?
This is exactly what predictive LTV modeling is aiming to achieve.
LTV modeling is a statistical technique that can be used to predict the total amount of revenue that a customer will generate over their lifetime. By understanding the LTV of your customers, you can make better decisions about how to allocate your marketing resources, set prices, and develop customer retention strategies.
LTV models are typically built using historical data on customer behavior, such as purchase history, product engagement data, and churn rates. Once an LTV model is trained, it can be used to predict the LTV of new or existing customers.
LTV modeling can help marketers optimize their marketing resources and achieve higher returns by signaling ad networks the highest LTV users, and by investing in each of its marketing assets according to its LTV, breaking the commonly used methodology of using the same average CAC (Customer Acquisition Costs).
Second, LTV modeling can help you to set prices that are more likely to generate a profit. By understanding the LTV of your customers, you can segment them better, build tailored packages according to the value that each segment receives from the company’s offering. This tailored packages approach will help optimizing the profit from each segment.
Third, LTV modeling can help you to develop customer retention strategies. By understanding the factors that influence customer churn, you can develop strategies to keep your customers coming back for more.
AI can be used to improve LTV modeling by collecting and analyzing large amounts of data, beyond simple segmentation. Instead of a simple statistical model of the type “users from the US that added 3 items to cart are worth $15 on average”, it can look at thousands of data points and identify statistical connections that cannot be identified analytically by a human and predict customer lifetime value more accurately.
There are a number of ways to get started with optimizing your marketing campaigns with LTV modeling. One way is to use a simple do-it-yourself (DIY) approach. This involves collecting data on customer behavior and using a spreadsheet or statistical software to create a simple segmentation-based LTV model that would rely on a few (2-5) data points per user.
Another way to get started with LTV modeling is to use an AI-powered platform, like Voyantis. These type of platforms use machine learning to collect and analyze data, and automatically generate LTV predictions, while processing thousands of relevant data points, for each user.
No matter which approach you choose, you can report back the LTV modeling to the ad networks, and use the LTV modeling results to track and monitor your campaigns performance, and make adjustments to your campaigns budgets and tCPA/tROAS bid targets as needed.
Here are some additional tips for using predictive LTV modeling to improve your performance marketing ROI:
By following these tips, you can use predictive LTV modeling to improve your performance marketing ROI and achieve your business goals.
Voyantis is helping marketers overcome the complexity of building, maintaining, and monitoring their own LTV modeling by tailoring a custom model for each individual customer/user using an AI-powered platform. , Our platform’s predictions allow marketers to evaluate the future impact of ad spend in real time, as well as report the user-level predicted LTV back to the ad networks through server-side APIs, to run predictive Value Optimization strategies in the networks. Voyantis’ platform learns users’ history to predict CAC , LTV and Conversions, allowing companies to acquire customers of the highest value and improve their overall return levels.
Predictive LTV modeling is a powerful method that can help you improve your performance marketing ROI. By using LTV predictions of your customers, you can attract better users, make better decisions about how to allocate your marketing resources, set prices, and develop customer retention strategies. This can lead to increased profits and improved customer satisfaction. However, creating an accurate LTV Model is not a simple task and requires integration of several sources, constant updates of the model and feeding the insights back to the marketing engine.