BrandMuscle: Marketing and Unleashing Hyper-Local Activation

BrandMuscle is a leading local, channel, and partner marketing solutions provider for over 175 of the world's largest brands. The firm has a team of 750 full-time employees who offer the industry's best client service, product development, and marketing execution support. The business simplifies channel marketing by enabling brands to activate and amplify local markets with award-winning services and SaaS. The company's flexible ecosystem of solutions helps brands increase revenue through local affiliates and protects businesses from legal, fiduciary, and other compliance risks. The organization's platform combines advanced technology with proven marketing services to serve highly regulated verticals. Over 1.5M local businesses use BrandMuscle's tools to scale corporate branding, messaging, and demand generation, seamlessly engaging customers and boosting ROI. The company's robust ecosystem covers every aspect of local channel marketing and unleashes hyper-local activation for affiliates.

An Astute Leader with a Vision for Innovation

Ashutosh Bagga began his data science career at Mu Sigma 2016 as a trainee decision scientist. There, he had the opportunity to solve problems using data for multiple Fortune 500 firms across the automotive, retail, and investment banking sectors in US, UK, and Australia. At BrandMuscle, he leads the data science team and liaises between the clients and tech. In the last 2 years, he and his team have worked to define and build towards their vision for enabling marketing for small and medium-sized businesses using state-of-the-art recommendation systems for media placement and optimization. They have also developed Generative AI capabilities to create content at scale for local businesses BrandMuscle.ai while ensuring compliance with brand and marketing guidelines.

Symbolizing Resolute Leadership

Ashutosh has always loved solving problems. His first experience solving problems using data came from a failed startup during college. Without getting into too much detail, he learned the most valuable lesson: to build good and sustainable solutions, one needs to understand the value of being objective with data. One of his mentors at Mu Sigma taught him the most valuable lesson he learned as an early data scientist: the math won't matter if we get the business wrong. While working on a customer purchase propensity problem for one of the largest US retail firms, he realized he was applying every model he could find as a naïve data scientist. He learned the value of framing good hypotheses and developing better input features using business and domain knowledge. This can yield significantly better results than blindly applying the largest model we can find.

Conquering the Hurdles with Zeal

Ashutosh encountered two important hurdles early in his career as a data scientist. The first challenge was defining a problem: business problems are complex and interconnected by nature. For example, a customer lifetime value problem can be viewed from different perspectives, such as financial forecasting, marketing strategy, and inventory planning. As a new data scientist, he found this complexity overwhelming and needed the guidance of mentors and seniors to help him break down complex problems into solvable chunks. The second challenge was the pace of learning: data science is a field that requires constant and rapid learning. He had to keep up with AI's latest research and innovative applications in the real world, as the space is booming right now. To add value to the organization where he worked, he had to consume much knowledge, both in math and technology.

Developing the Vital Attributes

Being objective is essential for any leadership role, in Ashutosh's opinion. Given the nature of a data scientist's job, where they deal with uncertainties or probabilities, he understands the value of objectivity in his work. He tries to avoid being too attached or completely detached from his work. Ashutosh also believes that as a leader in a fast-paced AI space, he needs to experiment fast and at scale. This applies to choosing an algorithm, an input feature, or a completely new technology. He ensures that he and his team are comfortable with and embrace the "extreme experimentation" mindset.

Creating Products Lasting Impact with AI

Ashutosh believes the 10x rule of impact differentiation is essential for any good product or solution. He thinks that designing products or solutions that will create a lasting impact with or without AI must be at least 10x better than anything currently available to the target audience. He believes that with BrandMuscle.ai, he and his team have met this standard.

Convergence of Disruptive Technologies in the AI Era:

According to Ashutosh, two significant changes have resulted from the availability of technologies such as big data infrastructures and cloud computing. The first change is that data science and AI have become more accessible and widespread, as organizations that could not afford to set up AI systems before can now join the artificial intelligence era. The second change is that the boundary between a machine learning practitioner and a software engineer is becoming blurred, as both roles require more integration and collaboration. Data scientists now have to think of their algorithms from the perspective of augmenting or building software products, and software engineers now have to understand the use of more probabilistic systems instead of entirely deterministic systems while designing software. He thinks this trend will continue growing with a higher adoption rate for AI-enabled systems.

Envisioning the Company as Game Changer

Ashutosh observes that the marketing industry (especially digital/performance marketing) has been an early adopter of data science. He anticipates accelerating even further in this era of ChatGPT and generative AI. He contends that generative AI can completely upend the paradigm of slow content modification in the market by delivering higher efficiency and speed to market for content while ensuring brand compliance, unlike the previous case where a marketer could analyze content placed on their brand's behalf.