Prediction: today’s decisions, with tomorrow’s data
The focus on the protection of personal information increases, but marketers still need to understand how to run successful ad campaigns. And we’ve great news: with prediction, marketers can get an accurate estimate of how their campaigns will perform, before wasting any budget or time. Even with limited data.
Where does prediction come from?
To be fair, prediction is no novelty for big app studios. Their teams usually design their own internal BIs. Most of them have data scientists working on predictions, because key decisions for the future should never be made blindfolded.
But here’s the thing. Any app studio, no matter the size, the language, the revenues, the structure, the MMP, etc., should have affordable and easy access to predictions. Not just the big guys. That’s why Tempr. – a predictive tool that adapts to each app vertical, and their KPIs – was created. Picture it: one big team helping UA Managers make today’s decisions, based on tomorrow’s data.
How does prediction work?
Prediction gives clarity on what’s going to happen next. The core principle is to use the past to predict the future.
This marketing approach uses data science to forecast the strategies that are the most likely to succeed. In a simple way, here’s how it works:
1: Historical data mapping
The past is what we call historical data. On the graph below, each black dot represents the revenues made on a specific date.
2: Historical data understanding
The aim is to find trends, seasonality, and any other metrics that affected the past revenues – taking into account bids, budgets, traffic, app verticals, KPIs, etc.
With machine learning, a mathematical formula that reflects the trends and seasonability of the graph in one curve is calculated. The objective is not to precisely follow each dot but come to a global result like the blue curve on the graph.
4: Data enrichment
The source’s data is enriched with data from MMPs such as MetricWorks for more accurate results.
The prediction model powered by machine learning create thousands of scenarios with changing parameters and suggests the best bid-, budget-, and creative combination that will achieve the highest results (ROAS or CPE) in the future.