Imagine discovering your business has 90,000 VIP customers. On paper, that might sound remarkable, even miraculous. But what happens when deeper analysis shows you only have 3,500 genuine VIPs, and the inflated figures are draining budgets and overstretching resources?
That's exactly the scenario Gali Hartuv from Warrior Lab encountered, and it highlights the serious risks of false positives in performance data.
What Are False Positives in Performance Data?
According to Gali, false positives arise primarily in two distinct areas: segmentation and financial performance.
In segmentation, false positives occur by incorrectly classifying players and inflating customer segments. Gali shared an operator’s specific story:
“They told me they had 90,000 VIPs, but upon reviewing data, it turned out they genuinely had just 3,500 VIPs.”
This misunderstanding significantly impacted operations and expenditures. The company was providing VIP bonuses to players who didn’t justify such rewards, which resulted in elevated costs and increased complexity. Correcting their VIP segmentation improved player activity dramatically, from mid-50s percentage level to the low 90s, and also cut bonus expenditures by a staggering 70%.
In terms of financial false positives, Gali identified unsustainable business practices stemming from celebrating short-term growth metrics without examining underlying behaviour. he explained:
“When we’re looking at false positives, we’re celebrating metrics while excluding the behaviour behind that metric.”
Misinterpreting data leads to decisions based on artificially inflated growth. This sets unrealistic expectations and unsustainable practices, inevitably harming the business over time.
Recognising Misleading Data and Its Effects
To challenge long-held assumptions about data accuracy, teams must first become comfortable questioning existing practices. Gali encourages businesses to critically assess historical data rather than assuming past successes are genuine indicators of future profitability.
Businesses must reverse-engineer their performance metrics, examining these critical questions:
- How many unique depositors contributed to revenue?
- What was the average revenue per active user?
- What bonuses were distributed relative to deposits received?
By breaking down recent performance metrics, teams can cross-check datasets against each other, uncovering hidden flaws previously overlooked. Gali highlighted the importance of recognising inconsistent patterns in your data, suggesting that fluctuations often signal problematic practices or unreliable data.
Steps to Validating Your Data Insights
But how can businesses be confident in these insights before making wholesale strategic changes? Gali advocates for a controlled experimental method akin to marketing’s A/B testing.
he advises isolating metrics in controlled segments to observe if results align with broader predictions. In VIP segments, controlled experiments can safely test new approaches because relationships and trust are already establihed.
Gali described a case study where inaccurate reporting significantly disrupted forecasts:
“A company relied on a forecasting report, which failed to accurately factor volatility and seasonality, leading them to pursue unrealistic targets.”
Addressing these issues required restructuring how volatility and outliers were factored in, ultimately achieving healthier, more realistic KPIs.
Building a Culture of Data Integrity Within Your Organisation
Creating a robust culture of data integrity demands proactive leadership. Gali argues that businesses must distribute the responsibility of data management across several individuals instead of relying on single decision-makers or subject matter experts.
“I’ve always been an advocate for challenging the subject matter expert,” he noted, adding:
“Businesses should encourage open collaboration and allow for constructive challenges to existing assumptions.”
Gali praised the practice of recruiting talent from outside industries to gain fresh perspectives and innovative approaches, something increasingly seen within high-performing iGaming organisations.
Effective data integrity also requires formal oversight with clearly defined purposes for each dataset or report. Before relying on reports or insights in strategic decision-making, ensure the teams using this information clearly understand its intention, scope, and implications.
Moving Forward with Clearer Insights
False positives in performance data are not merely technical errors. They are strategic threats distorting business decisions, inflating expenses, and obscuring true growth opportunities. Tackling these requires awareness, inquisitiveness, and robust processes designed to verify data accuracy rigorously.
By genuinely challenging historical assumptions, conducting controlled tests with defined parameters, and fostering collaborative environments, businesses create robust guardrails against costly data misinterpretations. Leaders who embrace these strategies position their organisations to act on accurate, actionable insights, driving genuine and sustainable business growth.
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