Ansu Mathai Samuel, a Senior Business Analyst at GoDaddy, explains how clean data helped to achieve $2M in revenue.
While the latest IAB State of Data 2025 report shows nearly two-thirds of global advertising professionals citing data quality as their primary AI concern, this challenge hits particularly close to home in India. IBM's 2025 AI Outlook for India reveals that 46% of Indian enterprises identify data accessibility issues as their primary obstacle. This clearly shows, for example, in the development of healthcare AI, as its adoption is being slowed down significantly by the lack of standardisation, the absence of full-scale data-sharing frameworks, and the usage of different languages and standards in different regions, which creates compatibility problems. Both for businesses and public organisations, the fundamental challenge remains the same: The efficiency of even the most sophisticated marketing campaign can be undermined by poor data quality, yet organisations often rush into action without establishing a solid data-based foundation. This is exactly the class of problems Ansu Mathai Samuel, a Senior Business Analyst at GoDaddy and previously a Senior Business Intelligence Analyst at Infocepts, a Senior IEEE Member, a winner of the prestigious BrainTech Award 2021 as Best Data Analyst, and a jury board member for the Cases&Faces award, works with, helping companies to establish practical instruments and pipelines for efficient and reliable data-driven campaigns. His view on data quality and its strategic significance was summarised in six scientific publications on data analytics and machine learning, but what’s equally important, his strategy was proven in practice multiple times, bringing businesses tangible results in profits and operational efficiency.
Data Quality as a Strategy
In marketing, for the data to be applicable, its level of quality is measured in terms that do not only concentrate on accuracy. There is a wide range of other more pressing factors, which in the course of practice, include completeness, consistency, and timeliness in relation to the level of quality that the data holds in its mass. While some companies in marketing concentrate on the development of quality algorithms coupled with high technology, other more valuable factors abound in Samuel’s experience in the same field.
“In marketing campaigns, it is the quality of the input that is a multiplier,” adds Ansu Mathai Samuel. “The value for the marketing campaign can be limited by the nature of the data on which it is built.”
Samuel’s work at GoDaddy exemplifies this principle in action. By improving data accuracy by 30%, his team enabled executive leaders to make high-confidence decisions, reducing errors in business reporting and directly impacting the company’s strategic direction. As the inputs were cleaned and harmonized, it amplified the efficiency of subsequent marketing initiatives — proving the importance of data quality as a campaign foundation.
Through his data-driven approach, Samuel delivered $2 million in incremental annual revenue — a remarkable 15% boost to the business unit’s performance. This wasn’t achieved through a single breakthrough but through systemic improvements in data quality that enabled better customer insights and more effective campaign optimisation.
Preventing Errors from Amplifying
The recent rise in AI and machine learning in marketing has created both unprecedented opportunities and amplified risks. If the data quality is decent, the effectiveness of the campaign will be significantly enhanced. However, if data quality issues are persistent, they too become magnified — decreasing the operational efficiency of the entire system. It’s the well-known machine learning principle: garbage in, garbage out.
The methods Ansu Matthai Samuel offers to mitigate the risks of poor data quality are deeply rooted in his scientific research, which consistently bridges theoretical insights with practical application. For instance, in his article “Data Quality Issues and Their Impact on the Accuracy of Analytical Models” (International Journal of Science and Research), he outlines the problem in detail and highlights the key ways to ensure data quality in business analytics. He further develops these ideas in another article, “Leveraging Machine Learning to Optimize Marketing Campaigns” (International Journal of All Research Education and Scientific Methods), where he accounts for the data quality factor in optimising marketing strategies. These methods described in these articles later became the backbone of GoDaddy’s marketing data pipelines, cutting reporting defects and increasing revenue. In healthcare, a similar approach is used to normalise multilingual data and reconcile mismatched patient IDs, making the entire system more reliable.
His research clearly demonstrates that even seemingly minor data inconsistencies can cascade into major marketing failures. When businesses deploy AI-powered personalisation engines, recommendation systems, or predictive analytics without first addressing underlying data quality issues, they risk automating and scaling their mistakes. This is why organisations must prioritise data hygiene before implementing sophisticated ML solutions — even the most advanced algorithms won’t yield reliable results if they operate on flawed input.
How Better Data Powers Smarter Segmentation
Samuel’s research also explores other key aspects of data-driven marketing, particularly customer segmentation. High data quality enables precision in customer segmentation that brings marketing effectiveness to a new level. His work “Data Segmentation Methods in Predicting Customer Behavior”, published in the International Journal of Scientific Engineering and Science, outlines methodologies that businesses can use to identify distinct customer groups and predict their behavior. In other words, he explores practical applications of these methods, such as building personalisation strategies or improving retention rates.
In today’s competitive environment, the impact of improved segmentation extends far beyond demographic targeting. If businesses can acquire clean and comprehensive customer data, they can identify behavioural patterns, predict risks, and personalise experiences at scale.
“The benefits companies get from advanced segmentation and improved data quality in general will compound over time,” concludes Samuel. “They include improvements in customer lifetime value, retention rates, and campaign ROI.”
The Growing Importance Across Many Industries
The data-driven approach developed by Samuel is applicable across industries — from building hospital analytics dashboards to processing NASA aviation datasets — but it yields particularly impressive and tangible results in the e-commerce and retail sectors. For instance, his solutions for predictive analytics and text mining implemented for a Fortune 500 eCommerce client allowed them to optimise coupon promotions, reducing the maintenance of purchase orders by 10%. This project exemplifies how quality data enables businesses to optimise not just their customer-facing campaigns but also internal operational processes.
Looking ahead, the importance of data quality will only intensify as AI and machine learning become more prevalent in marketing. Companies that are able to establish strong data quality foundations today will be positioned to leverage emerging technologies effectively, while others will face amplified problems as they attempt to scale. Samuel’s research and practical experience provide a roadmap for businesses seeking to build this competitive advantage.
For India’s markets, the takeaway of these cases is immediate: with a projected $25–30B value pool around AI. will be adopted more efficiently where the underlying data is reliable, linkable, and timely. In healthcare and retail alike, the binding constraints are structural, so gains will come less from new model classes and more from interoperable registries, common schemas, and verifiable provenance. In other words, data quality functions as infrastructure. Viewed this way, “AI readiness” is not a technology race but a compounding property of the information environment. Jurisdictions and sectors that solve for completeness, consistency, and timeliness see model lift translate into durable operating advantages; those that do not merely automate their noise. India’s next wave of AI productivity, therefore, will be determined less by frontier algorithms than by the country’s success in building these connective data tissues — turning today’s heterogeneous records into tomorrow’s shared, trustworthy substrate for analytics.
