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How Stake.com Turns 1.5M+ Monthly Player Conversations Into Actionable Intelligence
About the Company
Stake.com operates one of the world’s largest global iGaming platforms, spanning online casino, live dealer, and sportsbook experiences across dozens of markets.
The platform is crypto-native by design, supporting both digital and fiat payment flows across regions.
Stake consistently ranks among the top gambling websites globally, attracting over 100 million monthly visits and serving tens of millions of registered users worldwide. Customer support is treated as a strategic investment, with a strong emphasis on live-agent conversations to preserve context, quality, and player confidence. This operating model generates an enormous volume of first-party customer language across products, markets, and customer tiers—creating both a competitive advantage and a data challenge.
Before Dimension Labs
Handling 1M+ customer conversations each month, Stake utilizes the Intercom chat platform, with the majority of these conversations being managed by live agents. These conversations span the full customer journey, including registration, KYC verification, deposits and withdrawals, gameplay and bet placement, and VIP-related inquiries.
While these channels are critical to customer experience, the data they produce exists primarily as raw, unstructured text. Conversations could be reviewed individually or sampled, but analyzing them holistically across regions, products, and customer segments wasn’t possible to do at scale.
Before Dimension Labs:
Unstructured Language Data: Conversations lacked schema and could not be queried consistently
Slow Insight Cycles: Manual analysis was labor-intensive and limited with most data going unanalyzed
Non-Deterministic Insights: Results varied across analysts and time periods
Siloed Systems: Intercom and CRM data remained fragmented, preventing cross-product and tier-level analysis
Lagging Indicators: Early churn and friction signals were identified too late to act
Stake had access to vast amounts of customer data, but lacked the infrastructure required to consistently extract meaning from it. Decision-making at scale relied heavily on experience and intuition. As Stake described it, teams were often “putting a finger in the air and assuming what we were doing was right,” with limited ability to validate product or experience changes against real customer evidence.
With Dimension Labs
“Dimension Labs has become the default insight layer across the company.” — CX Manager, Stake.com
Stake implemented Dimension Labs to transform raw customer conversations from Intercom into structured, actionable intelligence. Before they were limited to small samples of data and Intercom’s limited high-level tagging. Now leadership is able to validate product, design, and CX decisions using the intelligence derived from all of their customer interactions—reducing the risk of misaligned investments.
Every support conversation is analyzed at the record level and enriched with governed dimensions that capture customer journey stage, product area, issue category, region, payment context, and customer segment. This creates a shared analytical schema that remains consistent across teams, markets, and time.
With Dimension Labs, Stake can now:
Structure over 1.5M monthly customer conversations into consistent signals
Unify analysis across 4 global regions, 18 customer tiers, and various products
Enable week-over-week comparability using the same definitions
Embed conversation insights directly into operational workflows
Eliminate manual tagging and ad hoc analysis
Organization-wide Adoption
Dimension Labs has already been adopted across 7 internal departments, with dashboards tailored to each team’s product area and responsibilities. New product and design hires are onboarded into the platform as part of their first weeks, and new teams are regularly requesting access. Adoption extends beyond a single function, positioning Dimension Labs as shared analytics infrastructure for the company rather than a standalone CX tool.
This adoption also reaches the executive level. Customer conversations are reviewed weekly in leadership pulse-check meetings, with insights presented by every major department. Leadership now has consistent, real-time visibility into customer friction across the business, allowing issues to be identified and prioritized within the same operating week rather than during periodic reporting cycles.
“We can finally understand why customers are contacting us, not just what category their issue falls into.” — Stake.com
Use Case 01
Deposits & Withdrawals Experience Intelligence
“We can move from question to insight fast enough that it actually changes what teams do that week.” — Stake.com
Deposits and withdrawals are among the most critical moments in the player experience, directly impacting trust, conversion, and retention. Stake uses Dimension Labs to analyze customer conversations related to these workflows across regions and payment methods. Rather than relying on aggregate metrics or reactive escalations, Stake structures deposit and withdrawal-related conversations to quickly identify where and why issues occur.
This enables Stake to:
Identify any recurring issues across regions or payment types
Compare fiat and crypto payment experiences
Detect early signs of dissatisfaction before churn appears in downstream metrics
Prioritize operational fixes based on real customer behavior
By turning financial support conversations into structured intelligence, Stake gains early visibility into issues that directly affect customer conversion and retention.
Use Case 02
Customer Segmentation & VIP Intelligence
“Dimension Labs lets our teams validate product and design decisions using real customer conversations.” — CX Manager, Stake.com
Stake serves a wide range of customer segments, from casual players to high-value VIPs. Dimension Labs allows Stake to analyze conversations by each customer tier, rather than treating all interactions equally.
Customer conversations are enriched with segmentation metadata, enabling teams to understand:
Segment-specific pain points and expectations
Questions and intent related to VIP programs and level progression
Emerging issues that disproportionately affect high-value customers
This insight allows Stake to tailor experience improvements and retention strategies by customer value, ensuring that VIP experience optimization is driven by data rather than anecdote.
Use Case 03
Customer Journey & Product Experience Analysis
“What used to be impossible or too manual to analyze is now available within the same week, directly inside dashboards our teams use every day.” — Stake.com
Across casino gameplay and sportsbook experiences, customer friction often concentrates at specific stages of the journey. Stake uses Dimension Labs to map conversations to key journey stages, including registration, KYC, deposits, gameplay, and withdrawals.
This customer journey structure enables Stake to:
See where friction concentrates across the end-to-end journey
Compare journey performance across products and regions
Track week-over-week shifts in customer issues and dissatisfaction drivers
Ground product and experience decisions in full-population data
The result is a shift from anecdotal transcript reviews to a complete, journey-level view of player experience at scale.
Conclusion
Conclusion
“Teams don’t wait for reports anymore. They explore insights themselves and use them to set priorities.” — Stake.com
Modern iGaming operators generate massive volumes of customer conversations across markets, products, and customer segments. These interactions contain critical signals related to trust, friction, and retention, but only when they are analyzed consistently and at scale.
By establishing Dimension Labs as the Meaning Layer for customer language, Stake transformed raw support conversations into structured, analytics ready intelligence. As the business continues to scale globally, this foundation ensures customer experience remains measurable, comparable, and continuously improvable across all of their customer support tools and channels.
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