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?
See Market Reality
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Make decisions based on how the market actually works - not just what you can see
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.
01
Validate
whether product bets are actually driving incremental growth or just redistributing demand
02
See
how competitor moves impact your acquisition, engagement, and conversion
03
Understand
where demand is shifting and what is driving it
04
Identify
emerging threats and substitutes before they impact your metrics
05
Assess
whether new surfaces and intermediaries are helping or hurting your position
->
See Market Reality
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02
Acquire & retain high-value app users
Are you actually retaining your most valuable customers, or are they quietly spending elsewhere?
Find Lost Revenue
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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.
01
Identify
which customers are shifting spend and engagement to competitors
02
Understand
exactly which in-app mechanics are pulling them away (features, incentives, SLA performance, pricing)
03
Compare
how different platforms win orders, engagement, and loyalty in practice
04
Identify
patterns of defection and return across real user journeys
05
Evaluate
whether your retention, pricing, and growth strategies are working as intended
06
Reclaim
customer value that would otherwise be lost to competitors
->
Find Lost Revenue
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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.
Train on Real Behavior
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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.
01
Train
models on real sequences of discovery, comparison, switching, and purchase behavior
02
Ensure
automated decisions reflect how users actually evaluate and choose between options
03
Improve
recommendation and ranking systems using observed behavioural patterns
04
Reduce
reliance on synthetic or self-reinforcing training loops
05
Strengthen
model alignment with real-world usage and intent
->
Train on Real Behavior
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04
Track how AI engines are impacting your brand
Measure how your brand, products, and platform are represented - and acted on - inside AI systems
See Your Brand in AI
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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.
01
See
how your brand, products, and platform are positioned within AI-driven recommendations
02
Understand
how users interact with AI-generated suggestions in practice
03
Identify
which competitors are being surfaced and in what context
04
Measure
whether AI-driven discovery leads to engagement, switching, or conversion
->
See Your Brand in AI
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