Parfois

Guilherme Efe Macário is Business Controlling & Distribution Director at Parfois, where he leads Data Science. With expertise in business intelligence, AI, and omnichannel strategy, he turns complex datasets into actionable insights that drive growth and efficiency.

In an exclusive interview with Retail Tech Insights, Macário shared his key observation on the future of data and analytics in today’s dynamic business environment. 

• Can you share your career journey and the key experiences that led you to your role at Parfois?

I joined Parfois in 2015, starting in the Finance department as part of the Value Protection team. Although under Finance, the team worked closely with Systems. My main responsibility was ensuring the quality of all transactional stock data exchanged with our global franchise network. This gave me a deep understanding of the systems’ complexity and the importance of a shared language when working with data.

A major milestone during this period was the development of a global stock management strategy tailored to different store and partner models.

Later, I moved into an Analyst role within the Buying department, gaining broader exposure to business and product dynamics. I saw the importance of scalable processes and data clarity for effective decision-making.

In 2020, after a Covid-driven restructuring, I was asked to lead the Business Controlling and Distribution team. At the same time, with two senior analysts, we began building what would become the Data Intelligence department now a transversal unit that includes BI, Data Analytics, and Data Science.

Balancing operational leadership with data development accelerated our transformation. It allowed us to create impactful tools, streamline decision-making, and move quickly without unnecessary bureaucracy.

• How do you align financial performance metrics with day-to-day operational decision-making across retail and e-commerce channels?

Offline and Online must be viewed as an integrated ecosystem. Treating them in isolation leads to misaligned strategies. While some cannibalization is inevitable, these channels should complement each other.

“True retail transformation does not start with data science it starts with clean data, clear goals, and tools that solve real business problems”

We observe shifting dynamics throughout the year for instance, sales peaks or promotions often boost Online performance. Our stock allocation must reflect this seasonality and the characteristics of each channel.

At Parfois, our strategy is clear, Online is the full brand catalogue. This supports our physical stores, which can fulfill online orders, and helps mitigate stock risk across our 1,000+ store network.

• What specific predictive analytics models or tools have you found most effective in optimizing inventory distribution across Parfois’s international footprint?

Technology opens vast possibilities for building predictive and sophisticated models. But these must be seen as tools to enhance decision-making not as the goal itself.

Many problems can be solved with simpler, more intuitive solutions. As managers, our job is to provide practical, efficient answers that serve the business without assuming stakeholders are data experts.

The models we use are often blended approaches, evolving alongside our data and the industry. It is this agility paired with continuous optimization that drives performance.

Maintaining these models and monitoring their outcomes is essential. But so is staying aware of new tools and trends that may deliver even better results.

• In what ways do you foster a culture of continuous improvement and data-driven decision-making within your teams?

I believe in this approach deeply, and that belief helps shape the team environment. We aim for constant improvement in our processes and in ourselves. Experience is our advantage if we use it to do things better.

With younger teams, context is crucial. It is important to explain where we came from, where we are, and why we are going in a certain direction. The “why” behind initiatives drives alignment and motivation.

We always keep in mind that we are here to serve the business. If the aim is to sell more, better, and faster, then the team must see how their work directly contributes to that. When they do, execution becomes purposeful and timely.

• With data science becoming increasingly embedded in retail operations, what retail analytics trends do you believe will have the greatest impact over the next 3 to5 years?

Many companies rush into data science without the necessary foundations. A clean, structured data environment is critical before introducing complex models.

Strong data engineers are essential to ensure quality and structure. Skilled analysts are equally vital for interpreting data and providing daily business insights. Only then should companies move into data science. Launching data science without this groundwork risks discrediting the entire function.

The future lies in a holistic data approach: integrating Data Engineering, Analytics, and Science with Infrastructure and Integration. These functions are interdependent like the roles in building a house. None can be skipped.

• What advice would you give to aspiring leaders aiming to leverage digital tools and analytics for transformative impact?

Start at the beginning. Be honest with yourselves and transparent with the organization about the complexity, resources, and time required. Be perfectionists deliver robust solutions that would not be questioned. Focus on solving the real, daily challenges of the business with practical tools. Do not overcomplicate. Remember: you are here to serve the business. Most of the time, they do not care how you solved it just that the results are there, better and faster.

Build trust through outcomes. When the business sees value, that is when real transformation begins.