Forecast: How Retailers Will Live In Big Data Circles In The Future

　　 Related promotion

Since Dunnhumby, a subsidiary of Tesco, helped consumers set up a loyalty card project in 1994, American consumers have been collecting their consumption from retailers. data Be accustomed to.

Apart from the big data generated by consumer behavior, supermarkets also add historical seasonal sales and climate data to their database, and provide reference values for how many barbecues, beer or umbrellas to store.

In addition to the actual transaction data generated by users in the store, the supermarket also catches useful information from social media, including location and location of users' location, and so on.

The ability to analyze these data in real time gives retailers an unprecedented opportunity to develop tailor-made services for consumers online and offline.

Data analysis and control have a significant influence on store sales. Besides consumer power, weather factors can not be overlooked. For example, the weekend weather forecast for the outing season will rain. Retailers can put beer and rain gear near the store at the top. Relevance Promotion.

"If you know what your customers want to buy, and what stores do you have in your store, you can make the most suitable recommendation for them, which, of course, requires businessmen to be" timely ", said Klaus Boeckle from SAP big data analysis." companies that have already done so include B&Q and Amason. "

　　 Further customization for customers

Store clerks can query such consumer big data on portable devices. They can easily retrieve consumers' personal files and learn about their customers from their recent social media information. For example, he is prepared to have a good holiday or worry about finding a suitable evening dress for her.

Then, the clerk can recommend the customer to buy the product they need accordingly, because as a retailer, we have already understood his needs and his purchase records.

Apple's corresponding iBeacon technology -- store Bluetooth location tracking is designed to interact with smart phones. Retailers and application developers can instantly confirm their identity when consumers step into the department store. Then, those products that are specially recommended will be pushed to the customer's smartphone. As to which items to push, it will depend on the specific floor and location of the department store where the customer is located.

Based on real-time sales, Lush shop assistants can change the layout of their stores at any time.

To make the above "personalized" customization premise, first of all, we must obtain the permission of consumers, and agree that businesses and application developers can get their privacy data. In fact, businesses only want to translate these data into better services.

Cosmetics retailer Lush owns big data Analysis The equipment is used by shop assistants in stores and warehouses so that they can control sales in real time.

This practice can stimulate competition among sales staff at the level of sales performance, so as to achieve the best working condition and bring different shopping experiences to consumers.

For example, when a shop assistant finds that the bath ball and other shampoo in the store are more frequently purchased by consumers, they can independently change the display positions of these two products and put them near.

　　 The more data, the better.

This kind of recommended product based on data to consumers is quite common in online retailers, and its growth momentum is growing.

Amazon has reached about 2.4 users worldwide, with an annual revenue of nearly $75 billion. They have tracked and captured users' information and adjusted their services according to different data analysis results. In fact, Amazon's data collection and analysis capabilities exceeded the current majority of retailers in the early 2004.

At present, most online retailers can push corresponding products to their reserved email according to users' search and browsing records.

Amazon's chief technology officer, Werner Vogels, told BBC: "data will never be too much, and the smaller the better, only a certain amount of data can be obtained to delineate the analysis results carefully."

With cloud computing and real-time data processing The rise of retailers allows retailers to target customers more accurately and recommend products that are more suitable for their needs.

The "recommended purchase" in Amazon website is based on customer's previous purchase behavior and rating, because machine operation can not be perfect, but the result of its operation is constantly innovating with the upgrading of technology.

For example, consumers may want to buy a kettle. Amazon will recommend a water bottle that best suits its intentions according to the kitchenware information that it has purchased on the website.

　　 Counterattack of traditional retailers

Traditional retailers are holding big data "weapons" ready to launch a fierce counterattack against Amazon.

Martha stores, Boots, John Lewis, Argos, Dixons and Ann Summers are RichRelevance customers. RichRelevance uses the large amount of data collected from retailers to provide personalized shopping experience for physical retailers.

When retailers know which brands their customers prefer, they will push different products and promotional contents to their customers. Apache Hadoop uses 125 different operations based on customers' past and current shopping habits. Forecast When will customers buy products, and the computation time is only 20 milliseconds.

By helping consumers find the products that are most relevant to them, retailers' sales increase by an average of 3% to 10%.

The end result is that personalized retailing has become an irresistible trend, whether consumers like it or not.