The fashion industry sets styles and trends in apparels and accessories. It is a huge industry that is dynamic and is intrinsically subject to quick changes. For those who work in fashion retail, optimizing the inventory for each season is a huge challenge.
In fashion, you have to take into account several factors like color, style, fabric, and so on for each type of apparel. The style that was the craze in jackets just a few months ago might have fallen out of favor by now. Fashion trend changes can be triggered by what a celebrity wore at the red carpet at a high profile function last week.
It is difficult to predict what will sell and what won’t.
Besides determining what items should be in your inventory, you also need to decide the pricing. Pricing optimization depends on so many factors like the margin you can charge on each item, which also needs to take into account the overheads of running your business.
Other factors that influence pricing include your customer base, what they would be willing to pay for an item, how much your competitors are charging for that same item, and so on.
Retail Analytics – The Magic Crystal Ball for the Fashion Industry
Retail analytics is the process of analyzing all the data available to you. Previously, this was confined to in-house data. This includes you inventory and sales records, analysis of which products did well last year/season, and the preferences of your customers.
However, you would also need to study the trends and patterns in the industry as a whole, the performance of your competitors, and much more.
With the explosive growth of social media and the ability of customers to express an opinion on everything they buy and share it with millions of others through platforms like Facebook and Twitter, you actually have access to a lot more valuable insights. However, this is unstructured data and not readily accessible through conventional methods.
Hence, Big Data.
Big Data is a combination of technologies that helps capture relevant data for each industry across competition, across different sources, across different media like text, audio, images, video etc, and across the globe. This collected data is generally of such huge volume that it requires a lot of computing power to process. Big Data uses a set of technologies to capture, store and analyze this data.
Big Data processing requires the deployment and integration of many different resources, and requires huge investments. Which is why most firms use third party data analytics providers to help them harness this huge information source and create meaningful reports they can use. As an instance, let us look at a report on a certain category of apparel created for the Fall Season 2016 by Intelligence Node.
Intelligence Node’s exclusive Outerwear Report predicted trends in Jackets and Coats for the Fall Season of 2016. This kind of report involves accessing billions of data points and using different technologies to sort through, analyze, and extract meaningful information.
For this report, Intelligence Node tracks 1 billion products across more than 130,000 brands for more than 1100 categories each day, to provide real-time information. Fashion retail needs this kind of deep and insightful information to create assortments of stocks in different categories that will sell within the short shopping window for each season.
The average price for outerwear is $598, making it one of the most expensive segments in Fashion Apparels. On average, over 55,000 unique outerwear products are stocked by fashion retailers across the US.
A fashion apparel analysis and report needs to take into account several factors like color, fabric, type, and size. The Outerwear Report Fall 2016 provides clear insights into the top trending styles, fabrics, colors, and collars for Jackets and Coats. It also reports the average pricing for each segment in the particular type of apparel – Accessible Fashion, Aspirational Fashion, and Luxury Fashion segments. Top trending fabrics, colors, collars, and closures are listed and ranked. The report also provides information on the assortments available by gender classifications – Women’s Collection, Men’s Collection, and Unisex Collection.
A timely and insightful report like this, updated in real-time, can help fashion retail outlets map a clear strategy for their stock of outerwear. Combined with in-house analytics reports that extract information from their own inventory and sales data, these clear, concise, and visual reports impart valuable information for stocking and pricing strategies each season.
Data Analytics and the Retail Industry
In this era of multi-channel, omnichannel and cross-selling strategies, it is important to collect data across all available sales channels and also data from across the industry to create a successful master plan for your own inventory and sales. You need to determine what part of the inventory you need to keep and what to discard. This cannot be based solely on sales data.
For instance, a huge and popular retail chain decided to discard a few of their stocks that were not doing so well in terms of sales percentage. However, total sales suddenly declined after this move. A large number of the chain’s best customers liked many of the discarded SKUs. When they couldn’t find them in that shop, they decided to go shop somewhere else where they could find these products.
Data Capture and Customer Service
For online sales, data is automatically recorded and stored, easily available for analysis. However, in-store sales and trends were not always easy to record and track. Now though, with the ability to capture information through wifi and technologies like Beacon, if you have the customer’s permission, you can track and record every part of their shopping habits and product preferences, even in brick-and-mortar stores. Beacon technology is predicted to influence more than $44 billion in sales in 2016 alone.
Collating and cross-matching all the cross-channel data can give a deep understanding of customer behavior and their likes and dislikes. Your inventory decisions based on data analysis, and information learned from capturing customer sentiment can help you create a winning blueprint for the coming season, helping you customize your stock according to your customer base, and providing personalized service to your customers.
According to a recent survey, 74% of retailers believe that developing a more engaging, personalized strategy is going to be critcal for in-house business.