Big data

How Big Data Analytics enables Merchandising Optimization in Retail Stores

How Big Data Analytics enables Merchandising Optimization in retail stores

Have you ever looked at something longingly and then decided to not buy it. And then a few hours later you get a notification that the dress you loved is now on sale and suddenly you find yourself in the app and paying for it?

Well, you have been nudged!

Today’s industry is seeing a growth in big data and merchandising based on ‘data-driven-nudges’. These are one of the things you would want to exploit to the fullest if you want a hike in your profits. These nudges are so deeply ingrained into the consumer electronics world that they affect the things that a person does on daily basis. These nudges especially have been one of the cleverest weapons in any retailer’s arsenal after the advent of smart devices like phones and watches.

But these aren’t just for the consumer. These machines learned nudges also help retailers by giving them a notification about the products that might be going out of stock soon, add a product that’s selling and modify prices for a sharper competitive advantage.

Zara and Big Data

How Big Data Analytics enables Merchandising Optimization in Retail Stores

Most of the brands or fashion stores run on a bi-annual or seasonal basis and get their products exported from countries like China. That’s why for a consumer to get a product of his choice, he/she may need to wait for a very long time. That is why a store always ends up with a bunch of merchandises that aren’t sold by the end of the season.

Zara, on the other hand, sells clothes that it knows are in trend and the consumer will surely buy. Inditex is Zara’s parent company. It makes more than 84 crore garments in a year, most of which is sold by Zara. Each garment has a unique RFID tag attached to it which makes it possible to track the product from the warehouse to stores. Inditex has a central data unit which monitors the movement of all the products. Inventory management, design, distribution is achieved by the various teams of Inditex by monitoring over 6,000 of its outlets. Simple big data analytics help make all this possible.

Zara has an elite team of designers who constantly receive feedback from the sales team about product movement and consumption. This not only helps them design things that the consumer wants but also be constantly be updated with the trends.

How can Big Data help you in Merchandising Optimization?

  1.  Assortment– Merchandizers can simulate data outside and within retail info systems and provide very specific nudges. Like keep product X or add product y.
  2. Regional– Regional retailers can learn about what’s trending locally by analyzing marketplace seller data.
  3. Online marketingAlgorithms process and synthesize themes from consumer product reviews and marketing teas are then marketing teams are advised accordingly.
  4. Pricing– Changing prices for products are based on a whole lot of variables like stock quantities, competitor’s prices, demand etc.
  5. Product content– Product descriptions can be modified to add the details that consumers are looking for to give them a holistic picture and encourage them to buy the product.
  6. Promotion– Changes in the timing of promotions are suggestive by nudges and this is an opportunity to create personalized promotions.
  7. Seller– Retailers are able to make better decision to maximize sales.

Wondering how to bump up your business with big data insights? Contact us at Datahut, your big data experts.  

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