It's the (AI) prescription to maximize your growth potential.
Does this sound familiar to you?
Voyantis can help you see the future!
We spent 3 years designing predictive LTV models and integrations for value-based bidding (VBB) ad campaigns with Meta and Google to save you time.
Predict
We develop customized predictive models for your unique customer journey, enabling Google and Meta to identify your ideal customers with precision.
Acquire
We deliver predictive lifetime value AI scores to Google and Meta Ad Manager via server API and help you target and acquire high-value customers. It’s called Ad Network Signal Optimization.
Retain
You get predictive signals into your BI from day one, helping you plan the best experience and monetization path for each customer. It’s called Foresight.
Repeat
We are here for the long haul. We'll keep monitoring data and network models to improve predictions for you so you don’t have to.
We proudly support Value-Based Bidding for Google and Meta
How it works
Our secret sauce for your brighter future.
Data processing
Voyantis collects and processes anonymized zero and first-party data directly from data sources like Snowflake, BigQuery, or Redshift. We do not collect PII data, EVER.
Predictive
model design
Our AI learns your core business objectives and historical customer behavior, creating bespoke models to predict lifetime value, helping you acquire the most profitable audiences. Again and again.
Value-based
bidding
Your Google and Meta ad campaigns are continuously trained by Voyantis on future customer value, making sure your marketing budget is buying you the highest value customers.
Model Optimization
24-7, 365
Voyantis watches your modes and data, so no data drifting, outages, or anomalies can negatively impact your campaigns. We work with Meta and Google to ensure our Network Orchestration models evolve alongside theirs. We give you a significant cost of ownership savings while achieving substantial gains.
Pre-built connectors for Seamless Integrations
In your future
Opting for Value-Based Bidding goes beyond enhancing ROAS and making more of your budget – it also boosts your Retention and ARR.
You can achieve these solid numbers too.
What’s Next
FAQ
Are all predictive models Voyantis develops the same?
Each model is unique. Voyantis takes into account each client's unique business circumstances, business goals, and available anonymized data points to craft a bespoke model. In addition, we develop a strategy on how to best train each ad network to deliver optimal results based on our client's unique needs. Sometimes we create hundreds of models to deliver the best results for our clients.
Does Voyantis use PII data in creating predictive models?
Voyantis develops bespoke prediction models using strictly anonymized data such as fully anonymized engagement telemetry, transaction, quizzes, and onboarding inputs.
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.