How has the Measurement of Advertising Effectiveness Redefined Using New Econometric Approaches?

Shruti Dash from India, as a Consultant and Data Scientist at Amazon Ads, connects corporate standards and new frameworks through the science of measuring the impact of advertising, allowing brands to save money on useless advertising.

In 2025, the global advertising industry is undergoing a quiet but profound transformation: from New York to Bangalore, marketing analytics is moving away from intuition and creativity to the rigorous science of econometrics and causal inference, the analysis of causal relationships. The recent $250 million investment by Databricks to expand its research and development center in India, as well as the launch of the new econometric suite of Amazon Ads tools in the United States, demonstrates how two economies – mature and fast-growing – converge on the same vector: accuracy in measuring the impact of advertising.

According to Statista, U.S. brands will spend nearly $94 billion on AI and data-driven advertising this year, while India’s investments in the same field are projected to surpass $4.5 billion, marking a 40 percent annual growth. Together, these markets are defining how digital advertising is valued, optimized, and justified.

Such changes are monitored and implemented by an experienced specialist from India, Shruti Dash, an analytics consultant at Amazon Ads in New York. The Ad-Promotion Synergy Framework she developed helps the world’s largest brands, from fashion to sports, understand how advertising and promotions interact with each other, enhancing or, conversely, replacing each other’s effect.

How econometrics changed the marketing language

When advertising shifted online two decades ago, performance metrics were simple-clicks, impressions, conversions. Today, those numbers are no longer enough. Marketers want to know why a campaign works, not just whether it works. In response, global technology firms are investing heavily in frameworks that combine causal inference and econometric modelling, two fields once confined to academic economics.

In the United States, companies like Amazon Ads and Google Ads Data Hub are implementing these methods into everyday marketing tools, allowing brands to accurately determine the incremental effect of each unit of the invested budget. Shruti Dash’s career is symbolically linked to these implementations. At Amazon Ads, she leads the development of measurement frameworks for global clients. Her framework combines econometrics and elasticity modelling, allowing us to identify when advertising and discounts mutually enhance or, conversely, neutralise each other’s influence. She has led the design and execution of measurement frameworks for major fashion and sportswear advertisers, helping them quantify the true impact of their media investments across channels.

“Our work combines advanced econometric methods and methods of causal inference with practical business decision-making that affects multi-million dollar budget allocations. The goal is to identify how these two levers interact, where one amplifies or substitutes the other, and how brands can optimise spend across them. And all this is usually done in a short time with full focus on the client,” Shruti Dash commented.

Advertising under the microscope inside the framework

One of the core challenges for advertisers today is not only isolating the effect of each individual lever, but also understanding how these levers interact with one another over time. Shruti Dash’s work at Amazon Ads operates within this scientific and research-driven paradigm. Rather than relying on simplified attribution logic, her analytical approach draws on econometric modeling and elasticity estimation to examine how changes in advertising intensity and promotional activity affect demand.

Shruti addresses this challenge in her scientific article “Challenges and Prospects of Using Synthetic Control Groups in Measuring Digital Advertising Effectiveness: A Data Quality Perspective.” In the paper, she examines why traditional approaches to measuring digital advertising effectiveness often fail in real-world environments, where clean control groups are difficult to construct due to overlapping campaigns, fragmented user journeys, and data limitations.

These research insights are closely reflected in Dash’s applied analytical work. Her Ad-Promotion Synergy framework uses an approach that measures how demand responds to changes in advertising intensity and promotional activity. Using time-series and panel data, the framework estimates the incremental effect of each campaign while adjusting for overlapping promotions, seasonality, and external market factors.

“Frameworks help brands quantify the impact of media and make investment decisions. We are building systems that give brands an honest understanding of how advertising contributes to sales growth, and how promotions contribute to it. This helps you make decisions based on data rather than intuition. It is important for me to implement strict testing standards and support science-based decision-making in the team,” Shruti Dash shared.

In 2025, her team applied these models in Amazon Ads projects for major home goods brands. Econometric analysis allowed them to reduce inefficient costs by up to 18%, while maintaining a long-term recognition effect. This data has become an argument of trust between analytics and business and has strengthened Amazon Ads’ position as a strategic partner for customers.

Going beyond the numbers

In an age where data is doubling every few months, the capability to not capture data, but derive insight from that information, becomes a competitive advantage. This is why leading organizations are working to create analytics as a common communications language between business, science, and technology. Shruti Dash, however, belongs to a new generation of practitioners who consider meaning in the data. Her philosophy on Amazon Ads is that advertising is more than a stream of metrics, and is storytelling in the language of likelihood and patterns. Under her guidance, the Amazon Ads team has built a process-based system in which every experiment is seen as a type of learning and not just as a reportable experiment in their outcomes.

“It is now important to expand the use of advanced econometric and causal relationship methods to evaluate advertising. I intend to improve the advertising promotion optimization system that I am developing and extend it to additional categories in Amazon Ads. In the long term, my goal is to help establish industry standards for measuring true media effectiveness and mentor the next generation of data scientists in this field,” Shruti Dash shared.

She is going to build on the framework by expanding to additional business categories and presenting her research at industry conferences where media measurement and ad optimization standards are discussed. Shruti Dash is already doing this by sharing her expertise at various professional contests: she became a winner at the American Business Expo  Award in the category “Advertising Tech”, which is a prestigious international competition that honors breakthrough ideas, leadership, and impact in business and technology. This indicates her desire to share her accumulated experience.

Shruti Dash also is a member of international community Hackathon Raptors, which is a forum for data science professionals to collaborate on solving real-world problems.  This type of leadership is important not just for Amazon, but also for the field in general. It shows that success in analytics is not measured by the number of graphs one creates, but by how deeply it allows one to understand the person behind the graph.