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Predictive Analysis

< 1 min read

Definition:

Predictive analysis, also called predictive analytics, is a set of data analysis methods that use historical data to forecast future outcomes. In mobile marketing, it answers the question: what will happen next? UA teams use predictive analysis to estimate metrics like LTV, ROAS, and campaign revenue before those outcomes have fully materialized, so they can make faster and more confident budget decisions.

What Is Predictive Analysis?

Predictive analysis uses patterns in historical data to generate forward-looking estimates. If you know how a campaign or channel has performed over the last seven days, or across previous cohorts, you can build a reasonable projection of where it is heading.

In mobile marketing, that forward visibility matters. UA teams cannot always wait 90 days to know whether a campaign is profitable. Decisions about scaling, cutting, or reallocating budget need to happen earlier. Predictive analysis gives teams an early signal on campaign quality and user value, so those decisions are based on data rather than guesswork.

The more historical data you have, the more accurate those predictions become. Machine learning models improve as they process more data, which means predictive analysis gets more reliable as your data set grows over time.

What Is the Purpose of Predictive Analysis?

Consider the daily workflow of a UA manager. They are running campaigns across multiple ad networks, tracking metrics like ROI, ROAS, LTV, IAA revenue, and IAP revenue. Some campaigns are performing well. Others are not. The goal is to maximize return and minimize wasted spend, but evaluating campaign performance takes time.

Predictive analysis shortens that cycle. Instead of waiting for full data to accumulate, historical patterns are used to project where each campaign is heading. That means:

  • Reallocating budget toward better-performing campaigns earlier
  • Stopping spend on campaigns unlikely to deliver profitable returns
  • Setting more accurate bids based on predicted user value rather than early proxy metrics
  • Planning ahead with greater confidence about revenue outcomes

The result is a more efficient use of advertising budget across the board.

How Does Predictive Analysis Work?

Predictive analysis works by identifying patterns in historical data and applying them to current data to generate a forecast. Here is a simplified breakdown:

  1. Collect historical data. The model learns from past campaign performance, user behavior, revenue figures, and retention rates.
  2. Identify patterns. Machine learning algorithms find relationships between early signals and long-term outcomes. For example, users who complete a specific in-app action in their first three days may consistently generate more revenue over 90 days.
  3. Generate predictions. The model applies those patterns to new data, producing a forward-looking estimate such as a predicted 90-day LTV for a current cohort.
  4. Refine over time. As actual outcomes become available, they are compared against predictions. This feedback loop improves accuracy continuously.

Machine learning is what makes this scalable. Handling large data volumes and identifying complex patterns across many variables is not practical manually. Machine learning tools do this automatically, and they get better the more data they have to work with.

Predictive Analysis in Mobile Marketing

Predictive analysis can be applied to several key metrics in a mobile marketing workflow:

LTV Prediction 

A UA team is running campaigns across multiple networks. Rather than waiting 90 days to compare LTV by channel, a predictive model generates an early estimate based on the first seven days of user behavior. High-value channels are identified early, and budget is shifted accordingly.

Revenue Forecasting 

A campaign has been running for two weeks. Based on early revenue patterns and historical data from similar campaigns, a predictive model estimates total revenue over the next 30 days. The team uses that forecast to decide whether to scale or reallocate.

ARPU and IAP Prediction 

For apps with hybrid monetization, predictive analysis can estimate both ad revenue and IAP revenue from a given cohort, giving UA teams a clearer picture of combined user value across monetization streams.

Predictive Analysis Tools

Predictive analysis at scale requires tools that can process large data volumes and surface predictions in a usable format.

MMP-Integrated Prediction 

Some MMPs, including Tenjin, offer built-in predictive LTV features that generate forward-looking estimates directly from attribution and revenue data. This is the most accessible starting point for teams that want predictive insights without building custom infrastructure.

Data Warehouse and BI tools 

Teams with more advanced setups can build predictive models in their data warehouse using tools like BigQuery ML, or connect data to BI platforms that support predictive modeling. This offers more flexibility but requires more engineering resources.

ETL and Data Pipeline Tools 

Predictive models are only as good as the data feeding them. Clean, connected data from across your marketing stack is the foundation. Tenjin partners with Growth Fullstack, which helps mobile developers build reliable data pipelines that can serve as the basis for LTV prediction models from basic to advanced. You can reach out to them directly at info@growthfullstack.com.

Predictive Analysis with Tenjin

Tenjin's dashboard includes N-Day Total Predicted LTV, which combines predicted ad mediation revenue (ILRD) and IAP revenue to estimate total LTV up to 30 days after install. This gives your UA team an early read on campaign and channel quality without waiting for the full measurement window to close.

For teams that want to go further, Tenjin's partnership with Growth Fullstack makes it possible to build more advanced LTV prediction models on top of your Tenjin data. You can learn more about N-Day pLTV directly in the Tenjin documentation.

Related Terms

Frequently Asked Questions

What is predictive analysis in mobile marketing? 

Predictive analysis uses historical data and machine learning to forecast future outcomes like LTV, ROAS, and campaign revenue. It helps UA teams make faster budget decisions by generating early estimates of campaign performance before the full data window has closed.

How does predictive analysis work? 

Predictive models identify patterns in historical data and apply them to current data to generate forecasts. Machine learning handles the heavy lifting, processing large data volumes and improving prediction accuracy over time as more data becomes available.

Can I use predictive analysis with Tenjin? 

Yes. Tenjin's dashboard includes N-Day Total Predicted LTV, combining predicted ad mediation and IAP revenue to estimate total LTV up to 30 days after install. For more advanced LTV prediction models, Tenjin partners with Growth Fullstack. Reach out at info@growthfullstack.com to learn more.