TLQ 1.2 | The Profile: Tapestry’s Zeynep Schoenwaelder

The Profile: Zeynep Schoenwaelder

The Sticky Business of Data Trailblazing

Data is the new oil, right? The promise is real — behavior prediction, reduced expenses, effective forecasting, insight-driven decision making. Combine these possibilities with an on-demand cloud, artificial intelligence to tackle the tedious work, and machine learning to refine specific business inadequacies and there is reason to believe.

While the retail industry has been experimenting with big data, the fashion industry is just beginning to dip its collective toe. Fashion is complicated — relatively limitless SKUs, lengthy lead timelines, analog supply chains, and a heritage based less in data and more in feel. However, there is a collective understanding now that fashion brands & retailers must bring data-driven tools to the forefront of decision making. And while this reality is welcome news, the thinking must evolve again — the data-driven decisions that are being made must go beyond the obvious use cases (digital demand, forecasting) and insights need to be cultivated across each aspect of the business. The top and the bottom lines are at stake and these organizations owe to their investors, employees, partners, customers, and stakeholders nothing short of a true investment in efficiency.

Easy, right? Luckily, leaders are emerging and planting flags. In this profile, we go deep with Tapestry’s Global Head of Strategy and Data Labs, Zeynep Schoenwaelder. For those unfamiliar, Tapestry is the New York-based holding company of Coach, Kate Spade, and Stuart Weitzman. Zeynep built and now leads the Data Labs group, which provides tools designed not only for the needs of Tapestry’s current brands but for an evolving portfolio of brands over time as well.

In the following pages, we will explore the engineering and evolution of Data Labs — its products and how they are utilized, the culture of adoption, and the tolerance for investment at the corporate level. And finally, we will look at the questions still left to be answered post-Data Labs.

We invite you to consider the Data Labs blueprint — and whether your organization can learn from it. Is your company culturally ready for data science? Are you organized to execute? If the big data foundation were in place today, how would you use the system to build your business? What products would you build? Do you have a grasp on the big data ecosystem and landscape? And finally, does it make more sense to build or buy?

Tapestry is trailblazing in the space and has made many of the hard decisions you and your team will undoubtedly face. Let’s navigate their story – starting where they started and landing where the Data Labs group is today. And perhaps this piece will bring insights you can apply to your business today.

BUILDING A FOUNDATION TO SEE DATA AS THE NEW OIL

“One of the questions for a brick and mortar first business is how much you invest in areas like this and how do you invest in those? And how do you prioritize it as well… Those are things we have to grapple with and try to solve. I think as digital grows and that becomes a focus, these two organizations should be symbiotic, and that’s the only way to empower business leaders across brands and organizations for the biggest opportunities.”

What does it take to build a foundation? Or better yet, how do we define the foundation? It means identifying the company objectives (alongside stakeholders), identifying opportunities and risks, hiring a long-term leader to write the plays and quarterback the project, having executive buy-in placed to stand behind a project that will take years to show its full value, and being prepared to make mistakes along the way. A key consideration is the actual org chart. At The Lead, we have visited with many brands and retailers, and it’s clear that no two companies are organized the same. Big Data is intended to deliver business insights for more informed decision making. So does that group belong under a Chief Digital Officer? Under a Chief Information Officer? Or is it its own business unit? At Tapestry, the decision was made early to keep it separate, operating independently at the strategic level, and reporting directly to the CEO.

“One of the first decisions we made was to say we want it to live with strategy because we want data analytics to be part of and focused on the most important strategic questions for the company and create that synergy from a content perspective to the biggest questions we work on.”

Once Data Labs was formed and built into the org chart, Zeynep hired Fabio Luzzi, VP of Data Labs to lead the team. The rest of the ‘lean and mean’ Data Labs team are a combination of data engineers, data scientists, product development, and business consultants. The next decisions to be made were — where are we building and what platform gives the ability to scale efficiently? For Tapestry, the answer was Amazon Web Services as an on-demand cloud platform that “created much more agility and the ability to do things faster and easier” – with Hadoop layered on top as the core development language. These first few layers of the stack gave Tapestry the scalability they were looking for, a key objective of Data Lab’s mission. The process took almost a year. Scalability, agility, and synergy were and continue to be the group’s Northstar.

THE EVOLUTION

Once the foundation was built, the big machine needed to be fed data. It was easy to pull CRM data right off the bat because Coach had invested heavily in this area since its founding. But more data sets were needed, and data integrity had to be confirmed. So the work of collecting the data sets from all three brands’ brick and mortar and online stores began. This included detailed customer data, multi-channel purchase history, site browse data, and so on. Finally, with the data sets and the hooks in place, the team worked to normalize the data and prepare it to move it onto the data science platform.

THE GTM PRODUCTS

With a foundation in and the concrete poured, Zeynep’s team was ready to start parsing the data. It was time to build the applications that create value from this lake of data.

“There are two angles to it. One is the deeper level insights. And the other is the productization of the data. You need both for an effective Data Labs platform, and both require hard work to make it sticky in the organization.”

So the question became– what are our key business issues that can be most effectively solved with this Big Data platform? What are the business challenges and questions we want a deeper insight into so we can make better decisions for the business? Of course, each company will have a different set of answers to this question.

We want to walk you through how Tapestry prioritized the products they built and the internal process for making those decisions. But before talking about how they decided what to build, we’ll go ahead and tell you what they did build.

One application, utilized most by the merchandising and marketing teams to inform product launches, leverages and cross-references product data with their knowledge about consumer segments allowing teams to customize offers (merchandising, marketing, and promotions) to best meet customers’ needs.

Another, a real estate development tool, can be used for mapping Tapestry stores in relation to their competitors and multi-brand retailers. That data can also be intersected with customer data (digital and in-store), helping teams determine where to open (or close) stores and effectively gauge the impacts on digital sales in those areas.

THE CULTURE OF ADOPTION

These two aforementioned ‘apps’ were not built in a vacuum. The Data Labs team invested heavily in corporate conversations and user feedback before writing a single line of code.

“The users help at the inception in terms of ‘what are the things we want to have’ but they also help when we have beta versions. We will do testing with them to get their feedback. They end up being the advisors slash Super Users that become stakeholders from the beginning. Because if you do anything in a vacuum, no one is going to use it.”

Office hours and roadshows were accompanied by deep dives with brand presidents and extensive work with the Super Users (the key clients for the Data Labs products). Conversations had to begin with the business questions and a focus on the must-haves and transitioned into discussions on where else Data Labs could make an impact.

“Sitting down with the brand presidents and saying here is what we are planning and here are some of the key things you can expect from us, but also making that a conversation to say what else would be good. This is an important area of focus between the brand and the holding company because we want to balance making an immediate impact and investing for the future. This is more about products and platforms you are building, which should create the ability for them to scale. But that’s perhaps not as interesting or immediately critical for them — so part of it is finding that balance.”

Few can appreciate the monumental task of changing culture and user behavior – whether in the B2B or B2C world. This was not lost on the Data Lab’s team and they actively tried to over-communicate and over-engage – through user testing, beta launches, communications to stakeholders, and ongoing engagement with leadership. The reality was (and is), the team could build this phenomenal tool, but if no one uses it, then it is of no use. It absolutely has to be sticky.

“I think it’s a key challenge to keep it sticky. Really the only way to overcome it is if you start with key questions that the business needs solved, the things they would love to have. If we design things that meet their needs and help their life, then they are going to be quicker to adopt.”

THERE ARE ALWAYS QUESTIONS

Trailblazing is a tough business. Each new step is delicate and there are myriad big decisions to be made. Resources are scarce and the winds always shifting. With a focus on scale, engagement and the ever-important stickiness, Data Labs has mitigated some of those challenges – but the reality remains: this is an ongoing investment at a publicly-traded company. And with the mission being driven by scale — while entering a more mature phase of its development, key decisions around build vs. buy require attention. These are the questions (and tradeoffs) in speed and control.

“Culturally we are a very analytical company. It’s something that struck me when I first came here as a management consultant. The company works on data and analytics. It’s not just about gut and instinct and ‘I think this product is going to do well’. There has always been in the DNA this drive for data and always the combination of fashion and science that allows us to focus on this and makes it a priority.”

WHAT COMES NEXT?

As of writing this article, Zeynep’s team continues to strengthen and expand on the current applications with new momentum and energy. Since Jide Zeitlin took on the role of CEO of Tapestry in September, he has continued to focus on value creation through Tapestry’s Data Labs team, balancing both the short term immediate impact from the insights and applications while building for longer-term scale and efficiency. They are also investigating how to utilize AI and predictive analytics to address challenges with assortment planning, product allocation, retail pricing, and forecasting. Zeynep continues to be focused on scale and building a more data-driven culture. Of course, tomorrow continues to be unknown in many ways.

“Coming back to your controversial question, I think what keeps me up at night, is how do we create more impact and value? That’s sort of the other big question. We have invested in a good foundation, but how do we make a bigger impact? And that is what I am tasked with figuring out.”

We want to thank Zeynep for spending time with us to go deep into Tapestry’s next-generation approach to running an enterprise fashion business and sharing these priceless learnings, experiences, and insights with our community.