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Segwise
AI agents maximize ROAS for mobile games
Segwise
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AI agents maximize ROAS for mobile games
Most popular alternative: AdSpawn (1 saves)
By unverified author Claim this AI
Generated by ChatGPT
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.
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May 2, 2024
Initial release of Segwise.
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Segwise was manually vetted by our editorial team and was first featured on August 23rd 2024.
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12,55495v2.1 released 2mo agoFree + from $45/mo
This is the first AI marketing tool I’ve used that actually helps me get things done, not just suggest generic ideas. The mix of AI-powered strategy and real experts executing the work makes it feel like having a high-performance marketing team without the overhead -
11,21422Released 24d agoFree + from $5/mo
Pros and Cons
Pros
Maximizes ROAS
Optimizes in-app purchases
Boosts ad revenue
Aid in user retention
Reduces CPI
No-code integration
Targets mobile games
Assists gaming studios
MELRON feature
Always-on ROAS analyst
Causal inference models usage
Terabytes of player data
Detects player demographics
Identifies influencing in-app behaviors
Hyper-segments player data
Anomaly detection for insights
Ability to query ROAS
Integration with all data sources
Integration with data warehouses
Built by industry expert creators
Spot hidden ROAS opportunities
Root cause ROAS inputs
Capable of data hyper-segmentation
Fine-tuned LLMs
Boosts D7 ROAS & Retention
Saves weekly operational hours
No MAU based pricing
Transforms data analysis
View 23 more pros
Cons
Limited to mobile gaming
Focused on ROAS only
No-code may limit customizations
Dependent on data warehouses
May miss non-digital metrics
Bias towards in-app revenue
Options for anomaly detection unclear
MELRON -- usage complexity
Specific data sources integration
Depends on large user dimensions
View 5 more cons
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Q&A
What is Segwise?
Segwise is an AI tool developed for mobile games to optimize in-app purchases, ad revenue, user retention, and to maximize Return on Ad Spend (ROAS). Developed to help gaming studios, it reduces the cost per installation (CPI) and requires no coding for integration. Segwise employs AI agents to uncover hidden opportunities in campaigns, in-game events, and app performance data.
How can Segwise boost the Return on Ad Spend (ROAS) for my mobile game?
Segwise utilizes AI agents to uncover hidden opportunities across in-game events, campaigns, and app performance data. Its feature, MELRON, acts as a perpetual ROAS analyst, using Causal Inference Models to detect player demographics and behaviors which can enhance ROAS. Plus, the tool hyper-segments player data across numerous user dimensions and values, running anomaly detection models to deliver insights on metric movement.
Can Segwise help me to reduce the cost per installation (CPI) of my app?
Yes, Segwise can aid in reducing the Cost Per Installation (CPI) of your app. It does this by leveraging AI agents to identify hidden opportunities across your in-game events, campaigns, and app performance data, thereby enhancing the effectiveness of your ad spend.
What is the function of MELRON in Segwise?
MELRON is a feature in Segwise that serves as a continuous ROAS analyst. It leverages Causal Inference Models on terabytes of player data to identify player demographics and in-app behaviors which can positively impact ROAS. MELRON is instrumental in spotting hidden opportunities for optimizing ROAS.
How does Segwise use Causal Inference Models?
Segwise employs Causal Inference Models through its MELRON feature to analyze terabytes of player data. The function is used to detect player demographics and in-app behaviors which can have an impact on ROAS. By running these models, Segwise can identify key demographics and behaviors that leverage ROAS.
How does Segwise segment the player data?
Segwise hyper-segments player data across millions of user dimensions and values. It then deploys anomaly detection models to deliver insights on metric movement, allowing to detect changes and alterations in player behavior and campaign effectiveness.
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