Interview: Paulo Sampaio, Data Scientist at EDITED
I find an interesting interview with Paulo Sampaio, Data Scientist at EDITED. Paulo and his team are applying neural networks, machine learning and other models to analyze over 520 million products in real-time across 42 countries to make gradual distinctions in clothing styles, sizes, and categories. In this interview, he was asked several questions about applying machine learning, neural networks, natural language processing, and big data analytics to the retail industry. Here some of these questions with his answers:
- How is the EDITED team applying neural networks, machine learning and other statistical models to analyze products in real-time across a wide geographical area?
Paulo Sampaio: EDITED gives apparel retailers around the world the real-time data they need to always have the right product at the right price, at the right time. We apply machine learning, AI, neural networks and other statistical models to help brands understand competitor’s pricing, merchandise the best product assortments, and spot key trends early to gain a competitive edge.
- How does EDITED work with customers including some large fashion brands?
Paulo Sampaio: At EDITED, we have a data manager with extensive experience in the retail industry. She acts as the bridge between knowing our customer’s challenges and requirements, to then conveying these issues to the data scientist team to work towards the best solution.
It’s very important to have a business-oriented person working together with the data scientists, so that the company doesn’t lose sight of the customer’s needs and has the experience and insights to back this up. As a data scientist, it can be easy to get distracted with the cool research, so working together with a business partner helps everyone focus.
- How does the EDITED technology help to boost sales using AI?
Paulo Sampaio: EDITED provides the largest and most accurate source of real-time apparel retail data in the world, delivering all the insights a retailer can ask for – such as historical product information, pricing averages, replenishment data, and so on.
Using AI, this gives our customers the possibility to evaluate how their assortment is performing compared to their competitors. They can then plan their buying decisions, pricing strategies, markdowns and so on. As an example, let’s imagine that you are assessing denim jeans at a given retailer. You can create a dashboard showing black, blue and khaki styles from your competitors and you see that the black jeans have been priced up over the last couple of weeks, and are selling out across the board. This is probably the right time to replenish your stock and evaluate the best price.