Segwise is a tool designed to help maximize Return on Ad Spend (ROAS), and optimize in-app purchases, ad revenue, and user retention for mobile games. The tool is developed to aid gaming studios in reducing their cost per installation (CPI) and enhance the integration process by eliminating the need for coding. Segwise uses AI agents to identify and exploit hidden opportunities across campaigns, in-game events, and app performance data. One of the key features of the tool, MELRON, serves as an always-on ROAS analyst. It uses Causal inference models on terabytes of player data to detect player demographics and in-app behaviors which affect ROAS. The tool also hyper-segments player data across millions of user dimensions and values, running anomaly detection models to provide insights on metric movement. Additionally, Segwise offers the capability to ask any questions about ROAS metrics for instant answers tailored to specific data. Finally, Segwise provides a seamless, no-code integration with all data sources and warehouses.
Description
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Pros
Ability to query ROAS
Aid in user retention
Always-on ROAS analyst
Anomaly detection for insights
Assists gaming studios
Boosts ad revenue
Boosts D7 ROAS & Retention
Built by industry expert creators
Capable of data hyper-segmentation
Causal inference models usage
Detects player demographics
Fine-tuned LLMs
Hyper-segments player data
Identifies influencing in-app behaviors
Integration with all data sources
Integration with data warehouses
Maximizes ROAS
MELRON feature
No MAU based pricing
No-code integration
Optimizes in-app purchases
Reduces CPI
Root cause ROAS inputs
Saves weekly operational hours
Spot hidden ROAS opportunities
Targets mobile games
Terabytes of player data
Transforms data analysis
Aid in user retention
Always-on ROAS analyst
Anomaly detection for insights
Assists gaming studios
Boosts ad revenue
Boosts D7 ROAS & Retention
Built by industry expert creators
Capable of data hyper-segmentation
Causal inference models usage
Detects player demographics
Fine-tuned LLMs
Hyper-segments player data
Identifies influencing in-app behaviors
Integration with all data sources
Integration with data warehouses
Maximizes ROAS
MELRON feature
No MAU based pricing
No-code integration
Optimizes in-app purchases
Reduces CPI
Root cause ROAS inputs
Saves weekly operational hours
Spot hidden ROAS opportunities
Targets mobile games
Terabytes of player data
Transforms data analysis
Cons
Bias towards in-app revenue
Dependent on data warehouses
Depends on large user dimensions
Focused on ROAS only
Limited to mobile gaming
May miss non-digital metrics
MELRON — usage complexity
No-code may limit customizations
Options for anomaly detection unclear
Specific data sources integration
Dependent on data warehouses
Depends on large user dimensions
Focused on ROAS only
Limited to mobile gaming
May miss non-digital metrics
MELRON — usage complexity
No-code may limit customizations
Options for anomaly detection unclear
Specific data sources integration
Alternatives
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