The only constant in the modern business and industry scenario is change, but when it comes to Fashion Industry the flux is mind boggling. Season, Market, Design, Color, Texture, Silhouette, the list is big. With new tools like internet, this challenge is increasing manifold. For quite some time the industry relied on the old-school fashion instinct. But now the challenges are too much for the old-school fashion instinct to handle.
Big Data analytics, which has already in use in many industry and service segments across the globe is making inroads into Fashion Industry. In fact, it is already too late, as Fashion industry has always been the most appropriate to adopt modern management analysis tools and techniques like business analytics, but probably inertia and apprehension towards this technology held up the adaptation for so long.
Fashion is a business which survives on forecasting and prediction. Forecasting information on trends, must haves, and styles follow each other against the background of market reactions and consumer behaviour. One wrong prediction and you have huge unsold inventories in your factories and stores. In this scenario, business analytics proves to be extremely valuable because new and refined tools are really great at detecting key information to forecast the fate of every stock keeping unit associated with a new item.
A good business analytics system is able to read into large amounts of data and aggregate the information shared by the consumers through social media and other websites, supporting better forecast on trends and market size.
Business intelligence acquired through latest BA tools, can help the industry achieve a deep insight in the consumers’ wishes and preferences about color, style, size, and other attributes of fashion products. This will help the business to forecast the performance of new items by simply leveraging the features they share with other products which are already on the market, drastically reducing unsold inventory.
SAP Performance Benchmarking has shown that those companies that were using business intelligence were achieving 54% higher operating margins than companies with low or no adoption of analysis strategies (source: Consumer Products Industry Insights based on SAP Benchmarking, 2012). The figures have now changed significantly in favor of BA tools.
New data-driven platforms are already helping buyers and brands make informed decisions. Combining different factors such as search queries, social media activity, e-commerce best-sellers, and consumer feedback, business analytics is already helping brands to successfully identify emerging trends, and act accordingly.
The case in point is of one of fast fashion brand Zara. Apart from having a team of great designers who design great clothes, they have a data processing center which is open every hour of the day. Most stores struggle with the problem of limited supply, which stems from the time elapsed between order and distribution. Zara solved this problem with an adaptive, data-driven supply chain management.
Zara’s process starts in a similar way to the traditional retailers – with an initial order. The difference is that instead of ordering the bulk of the quantity for the season, Zara only orders a small amount of merchandise. Once the merchandise hits the stores, Zara collects sales data and analyses each SKU’s sales against supply. Zara does even more, it analyses performance of features of different SKUs. For example, they might identify that pants with patches sell better than pants without patches, or that certain colors or fits move faster than others. Zara then uses these insights to guide their following orders.
Of late, fashion retailers are increasingly turning to data analytics to keep up with the latest trends and client demands. Apart from having to meet the demands of “fast fashion” – turnaround time from the ramp to stores – retailers must also price items correctly, know when to reduce them, stock enough of the right styles, colors, fabrics and sizes, and ensure that stores are well supplied and operate efficiently.
Another good example where Data Analytics was used to handle challenges and improve business prospects with reduced unsold inventories is British fashion brand Burberry, which is one of the most famous luxury labels in the world, offering premium quality products, recognizable designs, heritage, exclusivity, and a global reputation.
Burberry implemented data analytics with customer feedback to better understand customers and offer personalized services through the omnichannel (app, website, email, and in stores), which helps enrich customers’ experiences and builds a better connection between the company and their customers. Burberry also keeps conducting quantitative and qualitative research into luxury fashion customers to make sure their products can still excite customers and their spirit can also inspire customers.
During the 2016 London Fashion Week, Burberry created a Facebook chatbot to show off their latest collection and offer customer service at the same time. The Facebook chatbot Burberry used is an automatic communications tool that can understand human language and answer customer questions by providing options, then customers click the best option to find their answer, step by step. Chatbot offers personalized interaction, and although it is not as intelligent as a human customer service agent, it is good for finding answers to some frequently asked questions. Customers could also view the collections and shop on Burberry’s official website through the chatbot. During conversations, chatbot can monitor the interaction and record data about the customer’s preferences and choices and offer the customer relevant responses based on what it learns. And the data collected by chatbot can be processed and analyzed for different purposes. For example, chatbot analytics lets Burberry know what makes customers happy and what has disappointed them, based on customers’ responses. That, in turn, lets Burberry reach its customers more effectively. When connected with a relevant back-end system, the data collected by chatbot can be combined with data collected from other channels to help the company gain integral
and more accurate insights into customer behavior. Chatbot utilization represents a step further in Burberry’s data analytics applications. In the future, as the technology behind chatbot keeps updating, it could become a more effective tool for the company to understand its customers and have better predictive abilities when making buying decisions.
The above two examples suggest that data analytics enables fashion companies to generate valuable insights to make better fact-based decisions with the goal of taking actions to improve business performance.
Though, business analytics cannot replace creativity: data can monitor trends, but in order to launch one we still have to rely on the instinct and the trained eye of designers. But it certainly has some advantages which make it a strong case to use BA tools in Fashion industry. Some of the advantages are –
These new data analysis approaches operate in real time, which is critical in allowing fashion companies to rapidly adjust to market trends and customer demands.
Data analysis technology optimizes the fashion companies’ recommendation systems. Like in the example discussed above, Burberry encourages its customers to voluntarily share their shopping preferences, purchase history, and social media accounts through its digital loyalty program. The purpose for the recommendation system is to improve customer shopping experiences and promote the company’s products.
Data analytics improves the performance of inventory and logistics management. To avoid deeply discounting items due to overstocking. With ample relevant information available to the management, data analytics changes the decision-making process of the fashion industry, which has historically been dominated by intuition and experience.
Now data analytics and its tools are seamlessly integrated in the fashion industry and are contributing significantly to its profits and providing better products to the buyers in timely manner due to saving on resources and forecasting more accurately. This has also added up another dimension to the career opportunities for the fashion graduates, as fashion being a highly specialized field, a fashion graduates acquiring professional business analytics skill would be any time far better and much in demand than a regular business analytics professional.
Another chance to upgrade your skill. Give it a thought.
Ref. Data Analytics and Applications in the Fashion Industry: Six Innovative Cases (2019) by Yue Du