You only live once but if you do it right, once is enough

The fast-moving consumer goods (FMCG) industry is undergoing a significant transformation, driven by digital advancements and the need for agility. Having spent five years in a leading FMCG company, I had the privilege to witness and contribute to this evolution during my tenure at ABinBev, the largest brewer in the world. My journey began as a Business Transformation Leader, focused on enhancing digital experiences for our commercial business. In 2019, I transitioned to the Global Analytics Organization, where I led strategy, planning, performance, and value creation for the growth analytics center.

Putting Consumers and Customers First

The cornerstone of any successful FMCG company is its consumers and customers. A core principle we live by is that the consumer is the boss. By following where consumers go and staying ahead of trends, we can ensure that we meet their needs better and faster than our competitors. The global pandemic in 2020 highlighted the necessity of accelerating business transformation through technology to serve both consumers and customers effectively. Strong partnerships are essential, as they provide insights and access to our end consumers.

Building the Enterprise Analytics Ecosystem

Over the years, the organization built a robust Enterprise Analytics Ecosystem, comprising over 300 data scientists, engineers, developers, and translators. The journey began in 2013 with a pivotal project in Bolivia, to address a business problem of declining volume and market share for a traditional product. A conjoint study revealed that increasing the price and introducing a new pack size could significantly boost volume and EBITDA. This success laid the foundation for the analytics initiatives.

In 2016, the Growth Analytics Center in India was established, and by 2019, expanded to analytics teams within regions to be co-located with the business teams. Today, they support various functions across the organization, driving growth, value, cost efficiency, and enhancing customer experience.

Capabilities that drive growth: Use Cases Across Various Domains & Functions

In Sales, tools like Assortment recommendation systems process complex algorithms to provide valuable insights to customers. Other capabilities include Customer Segmentation, analyzing customer data to group customers based on their purchasing behavior, preferences, and demographics. This helps in targeting the right customers with personalized marketing and sales strategies. Advanced analytics tools help in scoring and prioritizing leads based on their likelihood to convert, ensuring that sales efforts are focused on high-potential opportunities. Optimized routing algorithms help sales representatives plan their visits more efficiently, reducing travel time and increasing the number of customer interactions per day. The need is to continuously develop and test these tools to improve their effectiveness and learn quickly.

In Revenue Management, using advanced pricing models, analyzing market data, customer sensitivity, and competitive pricing to set optimal prices that maximize revenue and profitability. Analytics help in determining the most effective pack sizes for different market segments, balancing customer preferences and profitability. Advanced analytics provide insights into brand performance, helping in the development of strategies to strengthen brand equity and market share. Measure the impact of promotions, identifying which campaigns are most effective and optimizing future promotional efforts.

Effective Marketing is about reaching the right consumer at the right time with the right product. Advanced Marketing Mix Modelling and attribution models deliver insights and recommendations to drive impactful marketing decisions. Value is measured over the years by calculating return on investment for every dollar spent, combining short-term impact with long-term media effects for a comprehensive view.

Analytics capabilities extend to the Supply Chain, where predictive maintenance algorithms optimize brewery operations and route planning. These tools ensure efficiency, reduce downtime, and improve overall operational performance. By leveraging real-time data, businesses can anticipate and mitigate supply chain disruptions, ensuring timely delivery of products to customers.

Procurement analytics focus on supplier performance, cost optimization, and risk management. By analyzing procurement data, identify opportunities for cost savings, improve supplier negotiations, and ensure a reliable supply chain. Predictive analytics also help in anticipating market trends and adjusting procurement strategies accordingly.

In Finance, forecasting models provide robust P&L recommendations, helping to optimize revenue management and cost control. Utilizing advanced analytics to enhance budgeting, financial planning, and risk management, ensuring that financial decisions are data-driven and aligned with business objectives.

In Human Resources, predictive hiring and performance analytics help in talent acquisition and management. By analyzing employee data, identify the best candidates, forecast workforce needs, and improve employee retention. These tools also aid in performance management, ensuring that the right talent in the right roles drive business success.

Conclusion

Transforming analytics for impact at scale is a continuous journey. There needs to be a commitment to embedding algorithms and AI into critical business decisions to create significant value, yet there is always much more to achieve. By staying agile and focused on consumers and customers, strive to lead the industry in digital transformation, setting new standards for innovation and growth. Expand and evaluate new capabilities across domains, to drive future success and stay ahead in a rapidly evolving market.

Article written during my tenure as Director Analytics at ABInBev, Growth Analytics Center.