How Tradedoubler is Quantifying Client Conversations to Power AI and Product decisions
Corin Ward, Director of AI, shares how AI is helping standardize global operations, quantify client feedback, and turn conversations into structured data for product and business decisions.
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Tradedoubler operates across multiple markets, with local teams delivering tailored services to clients in each region.
This decentralized model is a key strength. It enables strong local relationships. Many businesses face fragmented data sources across regions.
But it also creates a challenge for AI leaders:
How do you standardize data and decision-making across a global organization while maintaining a holistic view without losing local nuance?
In this case study, Corin Ward explains how implementing Kaizan AI has enabled Tradedoubler to unify client conversation data, quantify insights at scale, and turn them into actionable insights that support data driven decision making and AI-driven decisions.
Key Results: Actionable Insights
Using Kaizan, Tradedoubler is now able to:
Standardize data across global teams while maintaining local nuance. Create a consistent, comparable view of client interactions across markets with reliable data.
Quantify qualitative client feedback at scale. Turn conversations into relevant data, measurable themes, trends, and signals.
Turn conversations into product and business decisions. Give decision makers real client insight they can turn into meaningful actions across roadmap, prioritization, and strategy.
The Challenge: Global Scale Without Standardized Insight
Tradedoubler operates across multiple countries, each with its own processes and client relationships.
While this enables high-quality local service, it introduces complexity when trying to scale insight across the organization and can create data silos.
“Each country is its own entity… the more local processes you have, you move away from globalization efficiencies.”
From an AI leadership perspective, this creates a core challenge:
how to maintain local flexibility while achieving global standardization.
At the same time, client insight was largely anecdotal, increasing the risk of poor data quality.
“You hear a lot of word-of-mouth about issues and positive things, but quantifying that is very difficult.”
Without structured data, it was difficult to determine:
how widespread an issue actually was
which themes mattered most
where to prioritize action
When teams rely on inconsistent inputs, getting a clear understanding from available data becomes much harder.
The Shift: From Conversations to Structured Data Analysis
Kaizan enables Tradedoubler to transform unstructured client conversations into structured, analysable information.
Instead of relying on subjective feedback loops, teams can now quantify what is being said across thousands of interactions.
“You can see how many times an issue is mentioned and how much positive feedback is coming in.”
This allows AI and product leaders to:
identify patterns across markets
validate insights with real data
track key metrics to support data driven decisions
prioritize based on scale, not opinion
This kind of structured insight helps teams monitor performance and understand customer behavior.
What was previously anecdotal becomes measurable and actionable.
Structured data points also create deeper insights.
From Admin Burden to Strategic Data Driven Decision Making Layer
Before implementing Kaizan, transcripts and recordings already existed, but were time-consuming to use.
“Transcripts and video recordings are nothing new… but they were very admin-heavy.”
Kaizan removes that burden by automatically generating summaries, follow-ups, and structured outputs that are easier to use in tracking tools.
More importantly, it changes how this data is used.
“We can move beyond just recording to actually building business decisions and business cases off the data.”
What was once documentation becomes a strategic data layer for the business.
Data accuracy is what makes that layer useful.
Turning Conversations into Product Development Signals
One of the most powerful outcomes has been the ability to use conversation data to directly inform product development and shape data driven strategies.
With access to a growing dataset of client interactions, Tradedoubler can now:
identify recurring feature requests across markets by analyzing patterns in customer interactions
build data-backed product business cases to better allocate resources
“We can query how many times something has been mentioned… which helps build a business case for a product.”
This removes guesswork and enables faster, more confident decision-making.
Scaling Actionable Insights Across Time and Teams
As adoption has grown, so has the dataset.
Tradedoubler now has access to years of meeting data, enabling deeper analysis of market trends over time and helping teams spot future trends.
“We have this huge dataset now… we can start making not just client decisions, but product decisions.”
Insights can be structured at multiple levels, with performance metrics viewed across each:
client level
country level
team or user level
This historical view helps teams respond quickly when patterns change.
This flexibility allows the organization to scale insight generation across the entire business.
Building for an Agentic Future
What began as a client-facing tool has evolved into a broader data foundation for AI.
The structured dataset created by Kaizan can now be used to power internal AI initiatives.
“We can feed this data into agents and proofs of concept… to empower them and get better results.”
This opens up new possibilities for:
AI agents trained on real client interactions
automated decision support systems
more advanced internal AI workflows
For AI leaders, this represents a shift from isolated tools to connected, data-driven systems.
The Impact
Since implementing Kaizan, Tradedoubler has transformed how it captures, structures, and uses client data.
Instead of relying on fragmented, anecdotal insight, the organization now has access to a scalable dataset built from real conversations. Kaizan captures every client interaction to create structured memory and health scores.
This enables them to:
standardize insight across global teams and support a data driven culture
quantify client feedback at scale with a data driven approach
make more informed product and business decisions
reduce manual administrative work
build a foundation for AI and agent-based systems
For AI leaders, the ability to turn unstructured communication into structured intelligence is becoming a key competitive advantage.
That creates stronger strategies built from actionable insights. Kaizan is designed for client service and account management teams and integrates with existing tools like CRMs and meeting platforms.
“The scope for the future is very exciting… we can now use this data to power the next generation of AI and automation.”
