How Data Analytics Informs the Fashion Industry

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.

What Is Data Analytics?

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.

Access 3 Steps to Simplifying Demand Forecasting for Fashion Retailers

What Is Fashion Analysis?

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.

What Is Fashion Analytics?

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.

Key Takeaways

  • Fashion analytics helps businesses stock the right inventory at the right time to correspond to seasons and consumer trends, in order to sell it at the most profitable price points.
  • Fashion analytics helps businesses in the fashion industry get better at targeting customers, forecasting trends, managing inventory, planning and designing collections, and personalizing offerings, all based on more data-driven factors.
  • There are four principal types of fashion analytics used by businesses in the fashion industry: descriptive analytics to measure past performance, diagnostic analytics to determine the root cause of a problem, predictive analytics to project future results, and prescriptive analytics to suggest next steps.

Fashion Analytics Explained

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.

Why Is Fashion Analytics Important?

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.

Four Types of Fashion Analytics

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.

  1. Descriptive. Descriptive analytics provides the backbone of reporting; it’s impossible to have business intelligence tools and dashboards without it. It addresses fundamental questions of “how many, when, where, and what.” This type of analytics also provides the foundation for more sophisticated types of analytics that follow in this list.
  2. Diagnostic. Diagnostic analytics helps answer why something occurred. Diagnostic analytics often uses two separate techniques: “alerts” and “query and drill down.” The query and drill down technique pulls out more detail from a report. For example, a manager might want to see why a sales rep closed significantly fewer deals one month, and a drill-down could show that she worked fewer days due to a two-week vacation. Alerts notify users of a potential issue before they need to look it up. For example, the application could send a manager or analyst an alert warning that employees have fewer hours scheduled in a given period, which could result in a decrease in closed deals.
  3. Predictive. Predictive analytics helps retailers anticipate future events. This can often take the form of what-if analysis, which, for example, would let a seller see what would happen if it offered a 10% discount versus 15% or estimate when it would run out of stock based on a set of possible actions.
  4. Prescriptive. Prescriptive analytics is where AI and big data combine to take the possible outcomes from predictive analytics and identify what actions to take. This category of analytics builds on the previous three. Using advances in artificial intelligence and machine learning, prescriptive analytics provides business users with suggested actions to get a desired result. For example, if analytics forecasts that a business will be stuck with too many sweaters at the end of winter, prescriptive analytics might suggest a target discount offer to a certain type of customer, based on previous purchase history or a cross-sell.

How Can Fashion Brands Collect Data?

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:

  • Point-of-sale systems: Retailers use POS systems to take customer payments, accepting either cash or digital payments (credit/debit cards, digital wallets, and so forth). Businesses also use POS systems to gather limited amounts of customer information. Modern POS systems can connect payments to a customer using store loyalty identifiers, such as a phone number or loyalty number.
  • Customer relationship management (CRM) applications: CRM systems collect and manage pertinent information about customers and their connections with a company, including contact information, interactions with company representatives, purchases, service requests, and quotes or proposals. CRM systems help businesses develop stronger relationships with their customers. They let salespeople and their managers do tasks such as generating reports on how many customers are being contacted at any given time and estimating their likelihood to make a transaction. Fashion brands can use CRM to manage data on basic characteristics about a customer, including location, buying preferences, periodicity, and lifetime value.
  • Customer experience (CX) applications: More holistic than CRM, CX systems go beyond collecting and compiling information about customers and their interactions with a company. These applications help sales and service agents make offers and launch marketing campaigns and online ads. CX systems generally are designed to help organizations track their interactions with customers from the time they first connect, through the sale, and as customers receive services, get support, or engage in a new sales cycle.
  • Enterprise resource planning (ERP) applications: ERP systems let businesses bill customers and collect payments from them, track production and inventory, manage transactions with suppliers, perform risk management and compliance activities, and maintain their general ledger. A complete ERP suite also includes enterprise performance management, which is software that helps teams plan, budget, predict, and report on an organization’s financial results. ERP systems tie this multitude of processes together and enable the flow of data between them. By collecting an organization’s shared transactional data from multiple sources, ERP systems eliminate data duplication and provide data integrity with a single source of auditable truth.
  • Online shopping carts: In ecommerce, online shopping carts take orders and collect payments, and they can also track what customers have browsed but decided not to purchase. This creates a data source that’s particularly ripe for applying machine learning and other AI algorithms to spot trends in these abandoned digital carts and posit theories as to why an item wasn’t purchased.
  • Loyalty programs: Loyalty programs let retailers track customers across any channel (online, phone, catalog, in-person) by assigning them a loyalty number they can use every time they shop. Customers have incentives to use loyalty numbers because of the promotions, discounts, and other financial and nonfinancial rewards they receive. In exchange, businesses get a better understanding of their customers’ needs and preferences. For example, loyalty program members can receive early access to new products in exchange for filling out a survey or spending above a certain threshold. e.l.f. Cosmetics, a beauty brand popular among Generation Z consumers, for instance, rewards loyalty program members for contributing content, giving feedback, and voting on contests . Members earn points they can redeem for cash, gift cards, and other perks.
  • Customer call logs: Businesses can use AI to review customer call logs and identify specific areas of concern, such as a repeated product failure, as well as to understand customer sentiment trends through analysis of tone, word choice, and other indicators.
  • Online chat logs: Businesses can also use AI to review chat logs to understand why customers are reaching out to them and whether they’re frustrated or happy, all of which can be used to improve services in the future.

How Is Data Analytics Used in the Fashion Industry?

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.

  • Collection planning and design: A well-planned and designed collection is crucial for success in the competitive world of fashion. Fashion analytics offers insights into the latest trends, letting fashion retail brands create collections that appeal to the right shoppers. Analytics helps guide company buying decisions by offering fashion analysts greater breadth and nuance around rising and falling trends in areas such as colors, style, fit, and accessories.
  • Inventory management: Fashion analytics enables businesses to track the performance of products, enabling them to plan their inventory accordingly. Businesses that effectively manage their stock levels to match customer demand can avoid out-of-stocks with popular items and excessive discounting of unloved goods.
  • Trend forecasting: Fashion analytics provides insights into current and forthcoming trends, which make this technique closely tied to collection planning and design, letting fashion businesses create collections that hit the mark with shoppers’ current tastes.
  • Consumer targeting: Fashion analytics helps businesses offer their products to the shoppers who are likely to be most receptive to the products and the marketing messages. Having used trend forecasting to put together a collection tuned to the latest styles, fashion businesses can use consumer targeting to reach the right people with the right appeal to drive sales.
  • Sales forecasting: By leveraging the insights provided by fashion analytics, businesses can more accurately forecast sales, helping them increase sales and reduce the need to discount or liquidate stocks. Forecasting properly is especially important since consumers have choices of where to shop and will likely abandon retailers who don’t have what they want in stock. 63% of consumers said they would switch brands rather than wait for something to be back in stock, according to research by Oracle.
  • Data-driven decision-making: Fashion analytics provides valuable insights into customer behavior and preferences, enabling businesses to make decisions that will result in higher sales and profit and that are based more on data than on instinct.
  • Personalization: Fashion analytics helps businesses personalize their collections and marketing messages to meet the needs of their customers, resulting in higher satisfaction and higher sales.

Find out how easy it is to manage purchasing, distribution, order fulfillment, and financial close.

What Is the Role of a Fashion Analyst?

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.

Six Phases of Fashion Analysis

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.

  • Data discovery involves researching and collecting data related to fashion retailing. It includes determining which data sources are available, which sources are most relevant, and how they can be used.
  • Data preparation includes cleaning, structuring, and formatting data for analysis. It involves readying the data for use in statistical models by checking for errors and outliers, filling in missing values, and transforming the data into a format that’s suitable for analysis. This process includes ensuring that data is using consistent labels across all sources so that it isn’t duplicated or otherwise misinterpreted by the analytics engine.
  • Model planning requires determining the best statistical model to be used for analysis and selecting the inputs and outputs for the model.
  • Model building involves building, testing, and running the statistical model and analyzing the results.
  • Communicating results centers around creating reports that include visualizations and summaries of the results and presenting those findings to stakeholders in the most useful format.
  • Operationalizing is perhaps the toughest phase because it requires turning the results of the analysis into actionable insights. This step focuses on developing strategies and plans for implementing findings of the analysis and putting insights into practice.

Benefits of Fashion Analytics

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.

  • Avoiding out-of-stock situations. Fashion analytics can help retailers keep more of the right products in stock and in the right channels. Shoppers will quickly switch brands and retailers if they’re unhappy with the selection offered or if what they want is out of stock, jumping to another website or physical store.
  • Extracting maximum profitability. Analytics can help fashion businesses price goods to help them maximize revenues and reduce the need to discount at the end of a season.
  • Identifying the right channels. Fashion analytics can help businesses ensure that people see appropriate marketing messages and that retailers use the most effective sales channels, whether online or in-store.

Challenges 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.

  • Bad data. Customer data that’s out of date could cause a retailer to miss an opportunity or make inopportune offers to a customer—for instance, if the customer has aged out of a particular trend or fashion category.
  • Inconsistent data management. Lack of consistency in data labeling can lead to incorrect interpretations of data. This occurs commonly when businesses use different technology vendors for POS systems, merchandising, inventory management, and other parts of their information technology landscape because different vendors have varying codes for things such as color, size, fit, and discounts.
  • Incomplete data. It’s difficult—but crucially important—to have as complete a view of a shopper’s data as possible, drawing on different systems throughout the enterprise. As people interact with a company over time, data about them gets entered into a variety of systems, including call center logs, billing records, and loyalty card applications, and all these data points can help create a rich view of the customer that informs the analytics and, ultimately, the seller. “You can't take advantage of all the analytic capabilities at your disposal unless you have a very rich dataset on which to build those analytics,” says Flinn, the former Neiman Marcus merchandise planner.

How to Get Started with Fashion Analytics

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.

  1. First, ensure you’re confident in the data you’re using and that different sources of data are using consistent naming conventions and methods of counting. So, for instance, when combining weather data around stores in the US and other countries, be sure to standardize on Celsius or Fahrenheit. Otherwise, you might end up trying to sell snow boots to Canadians in July.
  2. Once you’ve vetted your data sources, develop reports providing descriptive analytics so you have a good picture of what happened over the last reporting periods.
  3. Then you can move onto the next phase of diagnostic analytics to explain why something happened.
  4. As you move to predictive and prescriptive analytics, you might start by limiting queries to a single subject area, such as pricing strategies or inventory levels, rather than trying to tackle everything at once.

Future of 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.

Support Growth with Data-Driven Decision-Making

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.

Fashion Analytics FAQs

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.

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