BUSINESS
Data Analytics and Fashion
By: Kelly Lanoue
Many look to the fashion industry as a more imaginative and creative space compared to the monotonous world of data analytics. Unfortunately, this expectation is not the case and never will be.
Numerous fashion brands, if not all, have adopted some form of data analytics system to deepen their understanding of consumer behavior and gain insight into competitors. The rise of the e-commerce industry creates the perfect opportunity for fashion brands to dig deeper into analytics and data collection to optimize performance. Data analytics is the future of fashion and industry professionals must understand the value of real-time data collection to drive strategy and growth.
Descriptive analytics is the simplest form of analytics and answers the question, “what happened?” In the fashion industry, this often appears when looking at historical sales data to determine trends or patterns in consumer behavior. The best way to understand descriptive analytics is to create visuals with charts, graphs, or tables to illustrate changes in data over time.
While descriptive analytics is simply the process of recognizing patterns in data, the question of “why did this happen?” is answered in diagnostic analytics. In diagnostic analytics, the data is dug into much deeper. During this stage, the root cause of trends is determined. Analysts look for explanations behind patterns and trends to better understand the variables impacting their business. They may look for connections between outside factors, such as weather and time of the year, to fluctuations in sales.
The next type of analytics, predictive, seeks to answer the question “what might happen in the future?” This involves making informed predictions based on determined patterns. For instance, a fashion retailer may have noticed that merchandise sells quickest during the holiday season, and it is reasonable to predict that this trend will continue.
The fourth type of analytics, prescriptive, answers the question “what should we do next?” This step determines the plan of action to get ahead of predictions made about the company’s future. For example, after a fashion retailer predicts that sales will be higher during the holiday season, they may opt to order more stock for November, December, and January. Through utilizing each element of data analytics, businesses can gain a bigger picture of their operating environment and make strategic decisions regarding company positioning.
Given the colossal size of the fashion industry, it can be timely and strenuous to collect data on competitors and consumer behavior. Rather than attempting to manually collect data, many companies, including Balmain, have begun using Retviews. Retviews is an automated competitive analysis platform that provides retailers with insight into competitors and the market. The platform utilizes artificial intelligence to give retailers an in-depth view of competitors’ assortment, discounts, and pricing strategies. On the dashboard, retailers can select specific products or competitors they would like to analyze and receive real-time data on the companies.
This access to information enables retailers to make confident data-based decisions. Retviews allow retailers to identify missing product categories in the market, giving a 360° view of competitor pricing strategy, and look at stock history to analyze product performance. With access to this data, retailers can be proactive in their strategy by staying aware of competitor behavior and providing consumers with the right merchandise at the right price and time.
Although gaining access to this data is essential, it is only one part of the puzzle. Retailers must ensure that employees are equipped with the skills to interpret this data and enact an appropriate strategy. Many retailers that adopt AI systems find that employees are stuck in their old ways and are very skeptical about using data analytics to make decisions. Retailers should provide employees with success stories driven by the use of data analytics and gradually introduce the idea to minimize resistance. Data analytics can be daunting and difficult to understand, however, it is the future and employees must get on board.
Speaking of success stories, there have been several major retailers who have adopted data analytic systems to drive decision-making. Brilliant Earth is a diamond retailer that was founded in 2005 with the mission of raising ethical standards in the fine jewelry industry. The three criteria the company focuses on are responsible sourcing, social impact, and climate actions. They are a certified Carbon-free company and use post-consumer recycled content for all shipping and packaging.
Brilliant Earth saw double digit-growth in the last quarter and has opened six new showrooms with a total of 21 across the country. The company is attributing this growth to its use of data to drive customer acquisition and increase sales.
Retailers can use data to analyze competitors, but they can also use it for internal collection. Brilliant Earth utilizes data that its customers provide them on their website to create personalized messages that appeal to the unique needs of consumers. One feature the company utilizes is called “live-clicker,” where customers can vote on their favorite products and express their interest. Not only is this engaging, but it provides Brilliant Earth with unique insight into its customers. Essentially, Brilliant Earth has used data analytics to capitalize on relationship marketing and personalization.
Brilliant Earth is one of many retailers that is leveraging the use of data to enhance performance and target consumers. Retailers must follow their lead and collect a plethora of data to gain a better understanding of their operating environment. The choice to embrace this future in data analytics can result in increased profits and opportunities.