Insights

In today’s world, data analytics can help fashion players to better manage inventory, profitability, consumer targeting, and more, making collection planning more streamlined and precise than in the past.

Artificial Intelligence-based trend forecasting is one form of predictive analytics which provides future trend behavior, from geography to market potential, in order to guide fashion brands through the collection planning process.

In a time of uncertainty, fashion and luxury companies are struggling to monetize their data. Leading firms are moving swiftly, gaining market share, and creating lasting value. The use cases for data and analytics are varied, numerous, and fairly well known, but where to focus along the value chain isn’t always intuitive.

The challenge often lies in pinpointing where and how to integrate data into the business in a cross-functional way, and building the appropriate operating model to do so. Fashion and luxury companies in particular, that have integrated data into their planning, merchandising, and supply-chain processes have seen tangible results. 

Data-driven decisions around stock and store optimization have increased sales by ten percent. Furthermore enhancing visibility throughout the supply chain has streamlined inventory management, improved returns forecasting, and optimized transport networks reducing inventory costs by up to fifteen percent.

Define the data strategy and prioritize the business domains

The data journey starts with setting a vision for how data will support business goals over the next two to four years. A shorter horizon may be too shortsighted and not ambitious enough for fashion and luxury businesses. Any longer, and time to impact makes the upfront investment untenable. This vision-setting process is best led by a chief data officer (CDO), someone senior in the  organization who can champion the change through the many competing business priorities.

The data journey is a collaborative process including most executives, since use cases hit so many parts of the value chain. The CDO translates that vision into a set of core priority business domains and defines specific use cases for each priority domain.

Invest in data architecture and platforms aligned with business domains

Modern fashion data architectures handle core retail day-to-day data sets that are large and unstructured, such as SKUs, sales, point-of-sale (POS) transactions, stock transactions, e-commerce touchpoints, customer 360 information, and radiofrequency identification (RFID). The truth is, most fashion and luxury companies have expensive legacy systems built on inflexible, non-scalable, and limited data warehouses that cannot integrate new data sources.

Most turn to data lakes as a solution, which serves as the organization’s single source of truth and features several layers for data consumption. However, modern data architectures must evolve across all layers, drawing on new architectural paradigms including cloud-based data platforms, serverless and containerized data platforms and applications, no-SQL databases, flexible data schemas, and solutions that provide real-time data-processing capabilities.

Define a high-performing data and analytics operating model

Data management is often the Achilles’ heel of many fashion and luxury companies. The absence of high quality data and clean taxonomies, and the general lack of common language and understanding around data across the organization, wreak havoc when starting on an analytics journey. This could not be more true for core data sets; data from POS transactions, for example, are a mix of structured and unstructured data and include sensitive personal information such as credit card numbers. And SKU–product data, which is key to managing integrated omnichannel stock, typically comes with unstandardized formats from suppliers, generating a need for tight master-data management and integration of several merchandising and vendor-management systems.

Fashion companies have tackled the problem by setting up a value-backed data-operating-model framework across 20 to 30 data domains -such as sales, stock, and store transactions, among others - that have a clear owner in each business unit. These owners are best placed to define what kind of information is needed from various business functions and understand what such data can measure. They can also work collaboratively to ensure that the organization has a uniform definition of data and put processes in place to monitor data quality. Ownership is important, as it builds the mindset that the process of getting data right is not just an IT issue, but it is critical for decision making across the organization.

Develop talent and build a data and analytics culture

Many fashion and luxury companies have taken the leap of upskilling their workforces and reinventing talent and culture practices. We see fashion companies grabbing talent from academia, digital natives, and start-ups; few build their bench purely in-house. However, talent is often hidden in plain sight. Some leading businesses have found success with data academies to train new data professionals - such as data architects, data scientists, and data stewards -and ensure that core decision makers, such as designers, merchandising teams, and e-commerce teams, can translate data and analytics to fit business needs. A data culture that not only accepts data-driven insights and modeling, but also is hungry for it, is critical to get value from the data investment. Too many fashion companies make the leap only to find the business is stuck in old ways of working, and these firms tend to view data and analytics with an unfair amount of skepticism.

How to get started and shape a winning data road map

Many fashion and luxury companies have harnessed the power of data to build stronger relationships with their customers and drive sales. They have also achieved operational efficiencies that have increased margins. But building data capability is only half of the equation. A data-transformation strategy and road map can put it into practice and set companies on a path to unlocking value.

References

Imran Amed, Anita Balchandani, Achim Berg, Jakob Ekeløf Jensen, Saskia Hedrich, and Felix Rӧlkens, “The state of fashion 2021: In search of promise in perilous times,” December 1, 2020, McKinsey.com

Antonio Castro, Jorge Machado, Matthias Roggendorf, Henning Soller, “How to build a data architecture to drive innovation—today and tomorrow,” McKinsey Quarterly, June 3, 2020, McKinsey.com

Bryan Petzold, Matthias Roggendorf, Kayvaun Rowshankish, and Christoph Sporleder, “Designing data governance that delivers value,” McKinsey Quarterly, June 26, 2020, McKinsey.com

Alejandro Díaz, Kayvaun Rowshankish, and Tamim Saleh, “Why data culture matters,” McKinsey Quarterly, September 6, 2018, McKinsey.com