Michael Hickins | Content Strategist | February 2023
The fashion industry has often been driven by outsize personalities and stars—creative geniuses who drive trends based on instinct and force of will. The accepted wisdom held that the industry must rely more on art than science. Fashion analytics offers the industry a new look—letting fashion businesses supplement and support the artistic side of the trade with the right amount of data-driven science.
Fashion analytics involves using applications that draw data from a variety of sources about fashion sales, styles, and trends. Fashion analytics helps businesses assess past performance and predict future outcomes so they can make better decisions about what collections to build, what inventory levels to hold, which distribution channels to use, and what promotions can best generate more revenue.
Data analytics is the process of examining datasets to draw conclusions about the information they contain. Analytics increasingly incorporates artificial intelligence, which uses algorithmic models to search data for insights without a person telling the system where to look and what to look for. Businesses use data analytics to help them make more-informed decisions by combing through large datasets to find hidden patterns and correlations. Beyond business, data analytics is widely used by scientists and researchers to verify or disprove scientific models, theories, and hypotheses.
In fashion, data analytics can help retailers better understand shoppers’ behavior; optimize customer experience across digital and physical channels; personalize promotions based on previous behaviors or interests, such as clothing styles or fit and trends; and forecast future demand more accurately so that they can maintain appropriate inventory levels.
Fashion analysis is the process used by people in the fashion industry—most notably buyers and merchandisers at retail companies—to harness data to determine what trends are selling, what type of customers are buying them, how much inventory to order, and what sales might look like in the future.
Fashion analytics incorporates all the systems and processes needed to do fashion analysis, including the strategies and tactics as well as the technology involved. Increasingly, fashion analytics involves the use of artificial intelligence and machine learning (ML) to go beyond simply reporting on the past. With retail AI and machine learning, businesses can gain a more actionable understanding of trends in fast-moving fashion markets.
In addition to understanding what items are selling, fashion analytics provides visibility and guidance on customer behaviors that are shaping buying decisions, which can help retailers make essential decisions, such as setting the right price, offering related goods that customers might like, and stocking racks and shelves with the right products each season. Fashion analytics lets retailers use empirical data when making decisions about styles, colors, sizing, and how much inventory to purchase for their stores, rather than simply relying on gut instinct. It’s a way of “putting some science behind the art,” says Greg Flinn, a former Neiman Marcus merchandise planning executive.
Fashion analytics combines data from sources such as online shopping carts, loyalty programs, point-of-sale (POS) software systems, inventory and supply chain applications, marketing campaigns, third-party consumer data sources, and store surveys to guide business decisions. Fashion retailers and manufacturers use analytics to evaluate business performance, learn customer preferences, identify trends, and generate suggested next steps. These businesses apply fashion analytics to future-looking, forecast-based decisions, such as how much inventory to reorder, and to real-time, made-in-the-moment calls, such as what offer might entice an online shopper to complete a purchase.
Fashion analytics provides important tools for retail businesses that hope to increase sales and profit by gaining better insights into fickle consumers’ desires and behaviors. Fashion is a fast-changing industry—hot trends can fall out of favor quickly, leaving retailers and manufacturers stuck with stale merchandise they need to deeply discount. Retail analysts need clear insights into data so they can monitor business performance, quickly spot shifting customer preferences, and identify trends that can inform their choices on what items to stock and what prices to set. They do this by constantly collecting data from both customer-facing sources and production systems. Customer-facing data sources can include online shopping carts, loyalty programs, point-of-sale systems, marketing campaigns, and in-store surveys. Crucial production systems include those for manufacturing, inventory, warehouse, shipping, and finances.
In addition to providing tools to accurately assess and explain past performance, fashion analytics gives retailers a better way of predicting future consumer behaviors and suggesting future courses of action across a wide variety of high-stakes activities. For retailers, decisions on what inventory to stock in physical and virtual stores for a vital shopping season, such as the holidays or back to school, or what discount or other offer to make to online customers in real time play a major factor in their success and survival. Analytics is particularly important to the fashion industry because trends are extremely short-lived and consumer tastes so fluid. Fashion analytics doesn’t guarantee a retailer nails the trend every time, but the power of analytics can help fashion retailers improve their odds and let them spot and correct mistakes more quickly than if they didn’t have these tools.
There are four main types of fashion data analytics: descriptive analytics to report past performance, diagnostic analytics to determine the root cause of a problem, predictive analytics to project future results, and prescriptive analytics to recommend next steps. Using a mix of these analytical approaches enables retailers to better understand their customers’ behavior, optimize the customer experience across digital and physical channels, keep the right goods in stock, and personalize promotions based on customer behaviors or interests, such as clothing style, fit, and trends. Below are more details on each of the four analytical approaches.
Fashion brands collect data (PDF) from internal systems, which is known as first-party data, as well as from data aggregators, known as third-party data. Among the sources of first-party data are:
Fashion analytics is an essential tool for fashion companies, helping them understand the behavior of their consumers, plan and design collections, manage inventory, forecast trends, target shoppers, and make decisions based on data rather than just gut instinct. By leveraging fashion analytics, fashion retailers and designers can gain a competitive edge, boost their sales, improve profit margins, and maximize customer satisfaction through personalization. Below are some of the areas where fashion analytics is used.
Fashion analysts ensure that merchandisers and planners have access to accurate data about fashion trends as well as provide counsel on how much to buy of a given style, where to sell the goods, and how to price them. They examine a business’s recent results and experience and the results of competitors. Analysts also closely track social media data to understand what people are watching and sharing. Fashion analysts need to combine and clean data from all these sources and translate it into a story or trend that’s valuable and relevant to people making product, purchase, and promotion decisions.
Fashion analysis is the process of collecting and analyzing data related to clothing styles, trends, and consumer behavior to give fashion retail businesses insights they can use to build their collections more successfully and maximize their profits. The process of fashion analysis is made up of six distinct phases: data discovery, data preparation, model planning, model building, communicating results, and operationalizing the results.
Fashion analytics can help businesses make better decisions on crucial issues, such as the mix of styles and colors they should carry in their stores, inventory levels to avoid out-of-stocks or overstocks, and promotional activities likely to generate revenue at higher margins. Fashion analytics can also help drive customer engagement by suggesting targeted promotions.
Below are a few examples of the benefits of fashion analytics.
While good data can help business leaders achieve better business results, incorrect data can lead to making bad decisions faster as analytics models point to the wrong conclusions. Analytics tools are only as good as the data they’re using—an issue neatly summarized by the expression “garbage in, garbage out,” or GIGO. Below are a few examples of potential problems.
Fashion analytics are iterative, with each step building on the previous step. For example, starting with descriptive analytics to get a sense of how the business has performed will help analysts determine what to ask of predictive and prescriptive analytics, once a company’s effort reaches that maturity level. Below is one possible progression for getting started with fashion analytics.
As the future of fashion analytics unfolds, we’ll see successful retailers moving quickly beyond descriptive analytics and using prescriptive analytics to automate many rote tasks that today are handled by people. This will let employees react more quickly to changes in demand because more alerts and even response decisions will be automated. Businesses will also be able to measure the impact of their decisions more quickly, which will further improve decision-making. Retailers will also be able to use more varied types of data, such as localized weather, to better understand cause and effect and more effectively forecast demand (PDF). And more retailers will bring machine learning and other AI techniques into their analysis, letting them consider more factors and options than they ever have.
Cloud platforms such as Oracle’s are making it possible for more businesses to access sophisticated business applications, including analytics tools that make use of AI and machine learning. Fashion analytics from Oracle, with built-in AI and machine learning capabilities, can help businesses keep customers happy and loyal by offering the right products while pricing products in a way that maximizes sales volume and margins.
Global fashion retail conglomerates use Oracle Retail technology to streamline processes across departments, launch new brands, and expand into additional geographies. Retailers use Oracle Retail Demand Forecasting Cloud Service to maximize forecast accuracy and automate rote tasks so their employees can dedicate more time to serving customers. Retailers also use Oracle Retail planning and optimization solutions to ensure that stores carry the latest popular fashions and make the best pricing decisions to reduce margin-busting markdowns.
The fashion industry has become more competitive than ever, with the growth of social media influencers making it harder than ever for brands to predict trends, stand out from the competition, and hold onto the loyalties of their customers. As fashion analytics tools become more powerful in their ability to spot emerging winning and losing trends and guide retailers to picking and stocking the right mix of goods, the gap between those who do and don’t use these tools well will become increasingly clear to both fashion shoppers and fashion company investors.
What does a fashion data scientist do?
Fashion data scientists help businesses in the fashion industry collect relevant data from a variety of sources and ensure that the data is accurate and labeled consistently so algorithms can be applied against data that accurately reflects reality. Fashion data scientists then help businesses understand the results of queries they’ve made to the data by preparing reports and disseminating them to stakeholders.
Is fashion forecasting a job?
Yes. Fashion forecasters work for retailers, and they use fashion data analytics to help make decisions about assembling seasonal collections and inventory management.
What role does artificial intelligence and machine learning have in fashion analytics?
Modern, cloud-based analytics engines use AI and ML to help merchandisers and other retail executives sort through huge amounts of data to spot trends and make recommendations. They can help businesses make decisions about what fashions to carry and how to price them, applying data science to what traditionally has been more of a “gut-level” decision.
How do retailers use fashion analytics?
Fashion analytics can help retailers make better decisions on what mix of styles and colors to carry, what inventory levels to hold, and which promotional activities are most likely to generate revenue at higher margins.