LTV prediction platform powered by AI
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Chosen to power LTV predictions for the largest Fintech, SaaS and D2C brands
“We saw a 36% increase in team conversion rate and a 63%
6-month increase in ROAS. We’re excited to expand our growth strategy with Voyantis.”
“We had a significant LTV variance so Voyantis created a prediction model to optimize our acquisition investment. Using it, we were able to improve the 12-month return on ad spend by 30%.”
“Voyantis enables us to scale our UA budget while getting more confidence and better unit economics. Voyantis is quite the game changer for me and my team”.
"Partnering with Voyantis allowed us to acquire higher-value users at a lower cost. Voyantis predictions are being used as a key decision metric in our day-to-day decision-making".
Decision using indirect KPIs leads to under optimized results
Today, ad campaigns are managed based on indirect indicators such as CAC of first purchase which is not an optimized way to make marketing decisions
Turn your data into a predictive LTV of your users to get a direct indicator of your goals
Voyantis provides the direct KPI for these decisions with its predictive AI engine that creates an accurate future LTV for each customer within the first website visit
Leveraging future LTV for decisions yields optimized campaign results
Adjust your campaign budget, shifting more of your spend
towards the ad sets that are predicted to better perform
Voyantis integrates different data sources provided by the company. No Personally Identifiable Information (PII) is needed or is processed by Voyantis.
Prediction model generation
The Voyantis AI engine learns the unique nature of the company’s customers and their purchases and creates a custom prediction model tailored to the company.
Alignment to campaigns for decision making
Based on each company’s attribution, users LTV are aggregated to reflect the future value of the ad, ad set or campaign.
The model is tested and verified by the Voyantis AI engine to generate the highest accuracy of future predictions.
Focus on your most valuable future customers, today.Book a demo
How do you calculate predictive LTV?
Building an accurate model to calculate the LTV of a specific customer is not a simple task since there are many factors that should be taken into consideration. It is recommended to factor into the model historical data customers from their first purchase and throughout their interaction with the company and to compare it to the behavior of the specific customer that requires the prediction. It is recommended to use statistical tools or machine learning models based on AI to represent these models and to test the models for accuracy using historical data.
What is LTV prediction?
LTV Prediction is a method to accurately estimate the total revenue of a customer during their interaction with a company. In order to generate an accurate prediction, a model should integrate historical data including churn, retention, and revenue and to be able to model the behavior of different customers. Once accurate models were trained and tested, they could be used for prediction of LTV of new customers/ads/campaigns, etc…
What is a customer lifetime value model?
Lifetime value (LTV) model is a statistical representation of the total value (revenue) that a customer will generate throughout their entire interaction with a company. LTV models are typically built using historical data on customer behavior, such as purchase history, engagement, and churn rates. Once an LTV model is trained and its accuracy was validated using historical data, it can be used to predict the LTV of new or existing customers.