What is predictive analytics?
Predictive analytics in marketing is a data-driven approach to campaign optimization that leverages predictive modeling–a statistical and computational technique that uses historical digital marketing datasets to make informed predictions about future outcomes.
Data analytics and marketing go hand in hand. By forecasting future marketing trends, advertising performance, customer behaviors, and more, predictive analytics is used by organizations to make informed decisions, allocate resources more effectively, and optimize marketing strategies.
Vs. machine learning
Predictive analytics vs. machine learning
Predictive analytics and machine learning are closely related but differ in their primary objectives. While predictive forecasting focuses on accurately estimating future outcomes based on historical data, machine learning is a broader field that involves training algorithms to learn from data and make decisions without explicit programming. In marketing, predictive analytics often leverages machine learning techniques to build predictive models.
Why is predictive analytics important?
Predictive analytics can improve performance measurement by helping app marketers set clear, data-driven performance metrics and KPIs. Accurately predicting customer behaviors and campaign outcomes facilitates a more precise measurement of marketing efforts and return on investment (ROI).
Benefits of predictive analytics include:
Predictive analytics use cases
In the world of mobile app marketing, predictive analytics models can be used to assist in many data-informed tasks. Here are just a few examples.
For app marketers:
- Predictive marketing automation: Data-driven algorithms and machine learning are used to anticipate and respond to the needs, behaviors, and preferences of individual customers or segments with a high degree of precision, and automatically respond accordingly. For example, predictive analytics tools like Adjust’s Campaign Automation can automatically adjust budget and bid optimization for live ads. This approach enhances customer engagement while simultaneously streamlining marketing processes and reducing resourcing requirements.
- Predictive segmentation: Dividing a customer base into groups based on predictive factors. For example, predictive analytics could be used to identify users who are likely to adopt specific features within the app or to pinpoint users who are likely to have a lower cost per action (CPA) or have higher engagement.
- Predictive retargeting: Predictive models are used for retargeting campaigns to identify users who have previously interacted with a brand and did not initially convert but have a high potential of installing your app.
- User acquisition forecasting: Anticipating which acquisition channels will yield the most valuable users and allocating marketing resources accordingly.
- Customer churn prediction: Predicting which customers are likely to churn in order to implement retention strategies at the optimal point in the user journey.
- Sales forecasting: Forecasting future in-app purchases (IAPs) based on historical data helps with inventory management and revenue planning.
For app developers:
- App usage behavior forecasting: Anticipating how users interact with an app and tailoring the user interface and features accordingly to enhance engagement.
- App performance prediction: Using predictive analytics to forecast app performance issues and bugs, allowing for proactive maintenance.
- Load balancing and scalability: Forecasting peak usage times to scale server resources to ensure optimal app performance.
- Recommendation engines: Recommending relevant products or services to customers based on their browsing and purchase history.
Adjust and predictive analytics
In addition to our Campaign Automation solution, Adjust also offers Audience Builder, a predictive segmentation tool, and raw data reporting, which can be plugged into your business’ existing predictive models for actionable insights.
Predictive analytics is an invaluable tool in marketing that enables mobile apps to make more informed and efficient marketing decisions, ultimately leading to improved customer satisfaction and increased profitability.
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