AI-based bid optimization to maximize your Apple Ads performance
And Many More
Powerful analytics and campaign management at your fingertips
Get a comprehensive overview of all your Apple Ads campaigns. Monitor performance metrics, adjust budgets, and track spending in real-time with our intuitive dashboard.
Dive deep into your campaign performance with detailed analytics. Track key metrics like impressions, taps, installs, and conversion rates across all your campaigns and ad groups.
A highly adaptive, AI-driven solution for Apple Ads
Continuously analyze and rank keywords to maximize relevance and performance.
Use advanced machine learning models to find the optimum bid for all your keywords.
Adapt and optimize your budgets in real time for maximum impact.
Discover how Meditation Moments boosted ASA efficiency with ASAi+, driving 47% more downloads and lowering cost per subscription by 66%.
Read more →Pinger uses Phiture's ASAi+ tool to optimize Apple Ads campaigns across their portfolio of apps — leading to less wasted budgets.
Read more →onX Backcountry employed Phiture's ASAi+ models to optimize keyword bids across several Apple Ads campaigns.
Read more →Proprietary models dynamically adjust keyword bids based on real-time market data.
A simulated auction environment (ASASim) trains the models before real-world deployment.
ASAi+ models competitor behavior during simulations, and supports dynamic budget adjustments.
Pre-trained keyword clusters based on historical performance for tailor-made strategies.
Trains models using data specific to your app, ensuring no data from other apps is used.
Enhanced targeting by automatically discovering new keywords and probing them for value.
ASAi+ features bid exploration to find the optimal bids, escaping the limits of preset rules.
Comprehensive analytics and KPI reports to track your campaign performance.
ASAi+ seamlessly integrates with major Mobile Measurement Platforms like AppsFlyer, Adjust, and more — making your Apple Ads optimization smarter and easier.
Optimize your bidding strategy based on precise event-level insights directly from your MMP.
Choose specific apps and events from your MMP, easily set filters, and manage integrations smoothly.
Accurate attribution and consistent data across all your Apple Ads campaigns, enabling better marketing decisions.
Feature | ASAi+ by Phiture | Other Systems |
---|---|---|
Reinforcement Learning (RL) | Utilizes RL to dynamically adjust keyword bids based on real-time market data for optimal performance. | ! |
Simulated Environment & Keyword Clustering | Employs a simulated auction environment and uses pre-trained keyword clusters for accurate training and optimization. | No campaign simulation capabilities; does not emphasize pre-trained clusters for optimization. |
Dynamic Budget Adjustments & Optimization | Supports dynamic budget adjustments with automated bidding, leveraging RL to optimize budget allocation within CPA and budget targets. | ! |
Automatic Keyword Discovery & Probing | Automatically discovers and tests new keywords to enhance targeting and selection. | ! |
Adaptive Campaign Strategies | Classifies campaigns into focus states for targeted strategies and improved performance. | Does not offer campaign state analysis or adaptive campaign strategies. |
Data Privacy | Trains models using data specific to the app we're working on, ensuring no data from other apps is used. | ! |
Performance Reporting | Offers detailed analytics and KPI reports. | Offers detailed analytics and KPI reports. |
Yes, there is a monitoring dashboard which shows all keywords you are bidding on, with labels for "prioritized", "paused" and "removed".
Brand campaigns with enough impressions usually start seeing trends change around the two week mark. For other campaigns this might vary. After around four weeks, you should be seeing changes in performance.
ASAi+ is built around on "installs" as a KPI, but lower-funnel events can also be optimized towards — think: Purchase, subscription, etc. However, there needs to be sufficient data for this to work.
The reinforcement learning algorithms will pick up on these factors very quickly and adapt strategies accordingly.
The models utilize historical performance data in existing campaigns to speed up the training phase. If no data exists because the campaign is new, the model will start to explore bids and collects data on its own.