7 examples of retail information for big data

Big Data is one of the main words of fashion. But, unlike malicious words like "marketing Omnichannel" or "growth piracy," the big date is greatly undervalued. According to IBM, 62% of retailers report that using big data is giving them a serious competitive advantage. Knowing what your client wants and when you want, can be available at your fingertips with big data; All you need are the right tools and processes to use it. Let's explore 7 innovative examples of big data customization in the retail industry to inspire us.

Macy's: the traditional department store is ahead of its time

This luxury department store has a long history of providing excellent customer service and has become a household name. Despite the inheritance established since the opening of the first store in 1858, the brand passed into the digital age as a fish in the water.

Macy's uses big data to deliver a smarter customer experience. The brand analyzes various data points, such as inventory levels and price promotions, and combines these findings with the inventory unit data of a product in a given location, as well as customer data, to determine which products are on sale at each store. This ensures that the chosen products conform to customers' shopping habits at each location.

In addition, Macy's collects customer data that range from the frequency of visits to style preferences. This data is used to customize the customer experience, offering point-of-sale incentives with loyalty prizes and promotions. This data also allows you to send direct mail to your customers to generate conversions.

The Amazon shopping recommendation mechanism.

Amazon, the heavyweight e-commerce, has dominated its recommendation mechanism, but its functionality is quite simple. The algorithm is based on a user's purchase history, the items they already have in their cart, the items they rated or liked in the past, and what other customers saw or purchased recently. In fact, it has been reported that over 35% of all Amazon sales are generated by the recommendation mechanism, a testament to the importance of product recommendations.

The main reason for the mechanism of recommendations is to address the "long tail problem": the fact that rare or obscure elements are generally not sought after and therefore do not generate revenue. By recommending long tail items to buyers, you can seriously increase the return on investment potential of slower e-commerce lists.

Kohl's

Kohl's is a brand with large data plans. This brand has recently seen a 2.4% drop in sales, along with a drop in buyer traffic, and the brand's CEO has seen close to 1,100 stores. However, in a change of heart, the brand decided to implement new technologies to optimize its shopping experience and make the stores smaller. To achieve this, he invested more than $ 2 billion in technology initiatives and big data. Leaving aside product recommendations, the brand has the mission to use Big Data primarily for the benefit of its customers, in addition to making stores more profitable.

The entire online and physical shopping experience is personalized because a visitor accesses the home page and faces deals and products on each page for customized offers that counteract the abandonment of the purchase. Kohl also uses its big data to create customized marketing campaigns that were produced with customer data in mind. The brand now plans that data science will help with marketing allocation, including external data such as macroeconomic conditions and social data, which will determine which products will be stored. This will ensure that the products leave shelves more quickly.

Mall of America navigator chatbots

IBM provided the Mall of America with a chatbot called E.L.F to help shoppers navigate the vast complex. The Mall of America is located in Bloomington, Minnesota, and is the largest commercial complex in the northern states. It is home to 520 shops, 50 restaurants, 14 cinemas, 2 hotels, an indoor theme park and a museum.

ELF. You can create personalized shopping itineraries for each customer, finding the right experience for them (depending on your needs). A chatbot is operated by a simple interface similar to a text messaging platform. ELF. It is available through the Facebook Messenger application, the browser page, or the Mall of America kiosks.

Nordstrom: merging the online and offline shopping experience

This luxury retailer has mastered the use of big data to merge shopping experiences online and offline. Nordstrom's marketing team tracks Pinterest pins to identify which products are trends and then uses those data to promote the right products in their physical stores.

More than 30% of Nordstrom's budget is spent on technology, having established the Seattle-based "Nordstrom Innovation Lab" for product development and testing. In addition, Nordstrom hosts interactive touch screens in locker rooms to allow customers to order products and view inventory online.

TopShop

TopShop has been experimenting with new technologies to implement augmented reality in their shopping experience since 2010. Flagship stores have virtual assembly rooms where customers can select clothing to see how they would look on a screen. This saves the customer time and effort to try on their own clothes.

In 2015, TopShop partnered with Twitter to analyze real-time data on the social network and identified the trends as they occurred during the London Fashion Week event, which lasted five days.

These trends were grouped into posters with Twitter hashtags, so customers who pass by can send a hashtag to their TopShop account indicating their favourite products. The fashion retailer responded with a curated collection of the best selections.

This novel use of big data ensured that TopShop knew exactly what its customers wanted to buy after the London Fashion Week.

IKEA

The Swedish interior giant IKEA presented the recognition of images and augmented reality for the first time when it showed its catalog 2013. Customers can scan the catalog with their mobile devices to highlight the products that interest them and, from this, the brand offers personalized digital content and comments to inform your purchase. The brand also used image recognition technology, with which customers can scan items from the catalog and place them virtually in their own homes to see what they would look like. Then, they can select the colours and sizes that work best in space, without having to go to the store and buy the product. This allowed catalog readers to make informed purchases, which resulted in greater customer satisfaction and fewer returned items.

These innovative uses of big data really improve the customer experience and have the potential to increase sales. You do not have to be a big player in retail to use big data. You can use it yourself to get ahead of your competitors, especially if you use a Shopify storefront. This platform is integrated with Blendo, a big data analysis plugin. Add-ons and applications can be very useful ways for you to collect and extract data from multiple sources to inform your business decisions.

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