Use
Cases
Powering your strategic decisions with real-world data
01
Set wise strategies in shifting digital markets
How confident are you that your biggest product and strategy decisions reflect what people really want and need?
Set wise strategies in shifting digital markets
App platforms operate in a complex ecosystem where critical parts of the user journey sit outside their control; discovery happens on other platforms, competitors shape expectations, and new surfaces (like AI) change how users access and engage with categories.
Yet most decisions are still made using a partial, 1st party view of the marketplace and user experience. That’s where risk creeps in – investment in features that don’t move demand, reacting to competitors in the wrong way (or too late), and expansion into areas that don’t deliver incremental growth.
Validate whether product bets are actually driving incremental growth or just redistributing demand
See how competitor moves impact your acquisition, engagement, and conversion
Understand where demand is shifting and what is driving it
Identify emerging threats and substitutes before they impact your metrics
Identify emerging threats and substitutes before they impact your metrics
02
Acquire & retain high-value app users
Are you actually retaining your most valuable customers, or are they quietly spending elsewhere?
Leverage rich competitive intelligence to meet emerging app user wants and needs
In fiercely competitive categories, app users can easily switch, divide attention, and spend across multiple services with little friction.
Yet decisions on retention, pricing, promotions, and growth are often based only on internal data, leaving blind spots around how your competitive apps influence your customers.
As a result, teams risk misreading churn, underestimating competitive pressure, and investing in strategies that fail to protect or grow share-of-wallet.
Identify which customers are shifting spend and engagement to competitors
Understand exactly which in-app mechanics are pulling them away (features, incentives, SLA performance, pricing)
Compare how different platforms win orders, engagement, and loyalty in practice
Identify patterns of defection and return across real user journeys
Evaluate whether your retention, pricing, and growth strategies are working as intended
Reclaim customer value that would otherwise be lost to competitors
03
Train AI systems on real human behavior
As AI systems become core to discovery, recommendation, and decision-making, their performance is only as strong as the data they are trained on.
Ground your models and agents in how people actually act - not synthetic assumptions
Most models today rely on scraped web data, synthetic training loops, or highly constrained interaction environments. These sources do not reflect how people actually navigate apps, compare options, switch between platforms, or make decisions in real-world contexts.
This creates a growing gap between how models are trained and how users actually behave.
That gap shows up in underperformance; recommendations that don’t reflect real intent, ranking systems that optimise for the wrong signals, and models that reinforce bias or drift away from what users actually do.
Train models on real sequences of discovery, comparison, switching, and purchase behavior
Ensure automated decisions reflect how users actually evaluate and choose between options
Improve recommendation and ranking systems using observed behavioural patterns
Reduce reliance on synthetic or self-reinforcing training loops
Strengthen model alignment with real-world usage and intent
04
Track how AI engines are impacting your brand
Measure how your brand, products, and platform are represented - and acted on - inside AI systems
Measure how your brand, products, and platform are represented - and acted on - inside AI systems
AI is rapidly becoming a primary interface for discovery, comparison, and recommendation. Users are no longer just searching - they are asking. And increasingly, AI systems determine what gets seen, considered, and chosen.
But most organizations have no visibility into how their brand, products, or platform are represented inside these systems, how they are positioned relative to competitors, or whether that visibility translates into spend and engagement.
This creates a new blind spot: your brand is being interpreted and recommended by AI without a clear way to measure or influence it.
See how your brand, products, and platform are positioned within AI-driven recommendations
Understand how users interact with AI-generated suggestions in practice
Identify which competitors are being surfaced and in what context
Measure whether AI-driven discovery leads to engagement, switching, or conversion
Evaluate whether your retention, pricing, and growth strategies are working as intended
Reclaim customer value that would otherwise be lost to competitors