Data quality often dictates the success of analytical outcomes more decisively than the complexity of the statistical models applied to them. Nina Schertel, a partner at Liga Pesquisa, developed this perspective during an eight-year tenure at Itaú Bank, where she managed vast, fragmented databases that required meticulous preparation to yield actionable intelligence.
At Itaú Bank, Schertel navigated complex systems where customer information resided in disparate, disconnected sources. The primary hurdle involved identifying which specific variables accurately represented human behavior, a process that underscored the critical necessity of data hygiene over mere algorithmic sophistication.
This background in quantitative modeling and credit policy fostered an investigative mindset that prioritizes questioning initial assumptions. Schertel emphasizes that the most valuable insights frequently remain hidden beneath the initial layer of information, requiring both technical rigor and persistent curiosity to extract.
According to a recent report by Roastbrief US, the methodology at Liga Pesquisa integrates qualitative depth, quantitative intelligence, and neuromarketing to construct a comprehensive view of human decision-making. While neuromarketing remains the least familiar approach for many clients, Schertel maintains that it provides essential context for understanding the subconscious drivers behind reported consumer actions.
The firm utilizes a specialized approach known as LIGA – Analytics as a Service to extract value from long-dormant CRM databases. By applying a strategic segmentation lens, the team has successfully identified distinct customer groups that clients previously overlooked despite possessing the underlying data for years, ultimately leading to higher conversion rates in targeted marketing campaigns.
A notable example of this integrative power involves the synthesis of employee and customer satisfaction studies. By connecting these previously siloed data sources, the firm demonstrated a measurable relationship between internal staff sentiment and external customer experience, validating intuitive beliefs with empirical evidence that allowed the client to pivot their human resources strategy.
Schertel has implemented AI agents to handle labor-intensive operational tasks such as cleaning panel data and identifying unusual respondent patterns. These automated processes utilize advanced pattern recognition to filter out suspicious entries, allowing researchers to bypass hours of manual labor while maintaining a strict boundary regarding the final interpretation of findings.
The distinction between data processing and strategic synthesis remains the core of her professional philosophy. AI excels at pattern recognition and summarization, but it lacks the capacity to comprehend the nuanced business objectives or the complex psychological context that defines human behavior.
Human judgment serves as the final arbiter in determining which findings hold genuine strategic weight for a client. Without the ability to build a coherent narrative from raw data, technical analysis risks becoming an exercise in abstraction rather than a tool for actionable decision-making.
The future of research lies in this hybrid model where technology augments human capability rather than replacing the analyst. As organizations continue to accumulate vast quantities of information, the ability to connect disparate data points will directly correlate with improved customer retention rates and more precise market positioning for the enterprise.
Schertel’s approach highlights a shift in the industry toward data-centricity. By focusing on the structural integrity of inputs, firms can avoid the common trap of generating sophisticated models that rely on flawed or incomplete information, thereby ensuring that final recommendations are grounded in reality.
The integration of qualitative and quantitative data streams allows for a more thorough validation of consumer trends. This multi-layered approach ensures that when a client receives a recommendation, it is supported by both the statistical weight of large datasets and the nuanced understanding of human motivations provided by qualitative research.
By automating the preliminary review of open-ended survey responses, the firm has reduced the time required for initial data preparation by nearly forty percent. This efficiency gain allows the team to dedicate more time to the high-level synthesis of findings, ensuring that the final output is not just a collection of statistics but a strategic roadmap for the client.
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