The Center for Mathematical Modeling (CMM) has been key in the development of the venture of two Chilean engineers who now plan to make the leap to the U.S. and Asia. Combining advanced mathematics with software technology, the startup models how consumers react to different marketing activities, running multiple hypothetical scenarios and predicting results.
Is it possible to know through mathematics how effective a commercial appearing on TV, on a banner or in an influencer’s video is? This was the question that Chilean entrepreneur Andrés Groisman asked himself and which led him to develop one of the most effective mathematical modeling-based ventures of recent times.
Groisman and his partner Andrés Gottlieb created the startup NoiseGrasp in 2014, with the purpose of helping companies define how much and where to invest in marketing in the most efficient way possible. “It’s the way for them to have much higher sales, with the same investment in advertising,” says Groisman. All this through strategies implemented with machine learning and other data science tools.
The venture was born when Groisman was working in the United States, where he noticed that these types of services are usually very expensive, even for large companies that operate globally. “The challenge was to see how to do something similar, but more affordable for smaller companies. And for that we had to develop a much more scalable process, where we could automate steps that conventionally take months and cost a lot of money,” he explains.
To start the business, the industrial engineer from the U. de Chile joined his knowledge with that of his partner, a civil engineer in computer science, to strengthen the strategy and polish the services of the venture. But they needed to find a way to optimize the process, reduce analysis time and the cost involved in performing a marketing analysis, so they turned to the Center for Mathematical Modeling (CMM) at the University of Chile.
Machine learning was the answer to make this process more scalable, automated and efficient. With this, NoiseGrasp was able to establish itself as a market analysis service, through the generation of mathematical models with artificial intelligence. “Although it is not 100% automated, all these data science techniques have been fundamental to achieve what we want to do. If it were not for these techniques, we would be the same as anyone else, with the much more traditional techniques, which are slower and more expensive,” Groisman details.
Accuracy and speed
By using predictive mathematical models, based on artificial intelligence, NoiseGrasp has achieved over 90% accuracy in the advice it provides to its clients, even when there is a limited or deficient amount of information. In this way, it can reveal and predict how different marketing initiatives impact a brand’s sales. In order to incorporate these data science techniques, Groisman turned to CMM researcher Daniel Remenik: “It was a relatively new thing for CMM to have this kind of relationship with a startup that was starting from scratch. It was a completely new model, which we were creating during the journey of this venture,” recalls NoiseGrasp’s CEO.
Groisman looks forward to the future of his venture, with which he has effectively managed to optimize the advertising investment of companies in less time and at a lower cost. “On average, we improve the effectiveness of the ads that brands place by around 15%. That can be a very significant difference if we think that many millions are spent on advertising per campaign,” he concludes. They are currently working with different brands seeking to optimize their marketing investment in Latin America, with a view to soon expanding to the United States and Asia.
By Francisco Corvalán, Fundación Encuentros del Futuro,
for the Center for Mathematical Modeling (CMM)
Previously published in Beauchef Magazine