Etam Turns Data Into Value, Providing Insights at Speed and Scale With Snowflake

Since 1916 Etam has operated with a single goal in mind: to make women feel free to be themselves. From its origins, selling synthetic silk stockings to women entering the workforce, to the present day, bringing to market a range of lingerie, swimwear and clothing products, Etam has long placed craftsmanship, beautiful materials and innovation at its heart. The fashion brand has always catered to the modern woman, today offering everything from runway wear and sustainable lace to a line dedicated to post-mastectomy patients. 

To be a company focused on serving an evolving world, Etam has also needed to evolve with it — whether that be in product design or business strategy. It has realized, as all retailers have, that data is integral to its success, but the company lacked a central strategy that historically made it difficult to turn data into value. Sophie Buresi, Data Director at Etam, is on a mission to change that. 

“Data is really important to all our business teams,” she says. “Everyone from style, to purchasing, to retail and logistics uses it — but we’ve never had a real, unified strategy.”

With more data sources than ever at Etam’s disposal, Buresi set out to oversee the company’s data transformation, and in 2023 she placed Snowflake’s AI Data Cloud at its heart.

Etam’s data transformation had two main goals: to regain control and governance of its disparate data, and to enable a range of new use cases so it could create value from data at scale. Buresi knew that achieving these objectives would require her to free data analysts from the complexity and maintenance responsibilities of an on-premises data infrastructure.

“We knew we needed a central place to store our data,” she says. “And because we’re a company that doesn’t have lots of engineers, it was important for us to simplify our data processes as much as possible – Snowflake was the obvious choice.”

Today, Etam is in the process of replacing its entire legacy data stack with Snowflake. The company has currently moved around 70% of its data into the AI Data Cloud, and even what remains on-premises is readily accessible through Snowflake’s platform, whether it be data related to transactions, clients, products or warehousing information. Engineering times for data users has been reduced by between 50-100% depending on the use case, and Etam is able to easily share data from Snowflake with various tools, like Salesforce, for use in specific applications, such as personalization and logistics optimization.

“When you know that a tool is integrated with all the important actors in your ecosystem, it’s a great reassurance," Buresi says. “With a team of our size, I don’t have enough people to build connectors for each new tool, so it’s great to know that those are built in and upgraded when they need to be.”

With Snowflake acting as a central data store, Etam has begun exploring a variety of new use cases to realize even more value from its data. The first is a project that uses an app built on Streamlit to collect data that doesn’t exist in a structured way within any of the company’s systems. 

“There’s lots of business data that only exists in emails or spreadsheets,” Buresi says. “We wanted to collect this and apply it to other use cases, like connecting business objectives and monitoring marketing spend.”

The company is also exploring various business intelligence use cases, one of which includes rebuilding and centralizing its core enterprise dashboards on Tableau — a visualization platform which integrates seamlessly with the Snowflake AI Data Cloud — to provide timelier, more detailed insights.

“We are currently talking with all of the business teams to understand what they need, and are producing about 20 bespoke dashboards,” Buresi says. “We also plan to train people in Tableau, so if there are specific dashboards they need, they will be able to build their own and pull data directly from Snowflake.”

Having a centralized store of data also allows Etam to experiment with a wide variety of both predictive and generative AI use cases — some of which are implementing Snowflake’s own Cortex AI, while others are built with external partners using data directly from Snowflake’s AI Data Cloud. 

The six use cases launched in the first year of using Snowflake include marketing applications, in-store recommendations, customer personalization capabilities built using Dynamic Yield, and various solutions to optimize supply chain and inventory. 

“It’s really important for us to be able to reduce the number of products that we have to manage,” Buresi says, specifically in reference to the latter. “With Snowflake we are forecasting demand up to 18 months ahead to help purchasing teams buy the right volumes.” 

An initial proof of concept for this use case saw an 8%-15% improvement in forecasting accuracy. Etam is also using AI to identify which products have the highest markup and optimize their promotion to gain a better return on investment from its recommendation strategy, which had previously been manually done by staff.

Although Etam has only been using Snowflake for a short time, the benefits of having data in the AI Data Cloud are already apparent — like the ability to analyze massive volumes of data quickly. 

“Before, we could capture data that came from customers browsing our websites, but we couldn’t use it — there was just too much of it,” Buresi says. “That’s the type of data we can now use, and it’s super insightful. We can now design, develop and scale a data project in two-to-three months, whereas before it would take us six months, minimum.”

As a result, the company’s data ambitions have increased exponentially, in line with its enhanced capabilities. Etam’s 2024 data roadmap was double that of 2023, and 2025’s is set to be even more impressive.

“A lot of that is because we’ve been able to decrease the number of problems that we had on-premises,” Buresi says. “When you have an automated, fully monitored environment in the cloud, you can more or less eradicate these problems and spend that time on more rewarding work.”

As an added bonus, these rewards are now clearer to see, with Snowflake’s as-a-service model enabling Etam to calculate the individual costs and returns of each project. The payback of two use cases alone is forecast to deliver a €5-7 million ROI.

Going forward, Etam plans to continue to use Snowflake for more BI, data science and data collection use cases. The company’s experiments in generative AI will also help business leaders gain access to critical data more quickly, replacing huge reports with an AI chatbot that will allow users to query data in natural language.

Having seen the potential value in the data it has, Etam will now place a data analyst trained on Snowflake within each business team, ushering in a new age of data democratization and ensuring every business unit can feel the benefits.

“Snowflake is an ultra-flexible, high-performance data platform that will power our data use cases today and tomorrow,” Buresi says. “It’s given us the opportunity to unleash data value creation at scale, and there’s plenty more to come.”