With the explosive growth of global data volume, big data technology has developed rapidly, and it has also provided strong support for the retail industry to extract useful information from huge business data. Today, Walmart's "beer ten diapers" story has become a traditional classic of data analysis, replaced by Macy's "real-time pricing mechanism", Walmart's " Polaris search engine", and Target's "pregnancy prediction index". ” and a series of retail marketing innovations realized by the use of big data analysis methods.
In an interview, FineBI product manager Julie once said: The emergence of big data is not to overthrow traditional industries, but to install a " plug-in " for it to run better in decision-making. Therefore, the foundation of the retail industry is still a strong guarantee, but it is different from the traditional sales model. The retail industry uses big data to quantify the information of commodities, so as to achieve refined marketing of commodities and precise positioning of customers. Data comes from users, and they are verified from users, and the cycle goes back and forth, and the marketing model is scientifically determined.
The changes brought about by big data have brought unprecedented innovation to the retail model, mainly from five aspects:
1. In-depth exploration of customer needs
In the past, retail business analysis was based on the transaction details generated daily. Some companies will go deeper in this area, and will consider various factors and make adjustments. It is greatly discounted, especially in the era of rapid information change, and this analysis is often based on data that has occurred internally, which deviates from the real needs of users. Nowadays, both physical sales and online sales have achieved informatization, which is conditional to track the consumption behavior of users. For example, the "pregnancy prediction index" model established by the customer data analysis department of Target supermarket in the United States can identify pregnant women in their second pregnancy. The company is doing this : to stay one step ahead of other companies with personalized marketing to mothers and babies. By getting ahead of the customer's situation, taking the lead, providing targeted health care products and daily necessities in different pregnancy cycles, and implementing personalized marketing to customers as soon as possible.
2. Improve category management
Through big data analysis, customer needs can be learned, but in the end, it is still goods and services that actually transition to the bottom layer of retail. In the implementation of category management, many retail enterprises often arrange stores according to the plan of the purchasing department, but also ensure the introduction of new products and the elimination of old products. After several months, it is difficult to combine static planning and dynamic adjustment organically, and the actual display situation and planning are quite different. In the process of implementing category management, a retail company once put the fresh goods department under the management of the dry goods department, but the result of the adjustment is that the sales actually declined. After the ownership of the goods changed, the layout of the goods stores also changed, and the goods were also moved from the fresh area to the dry goods . As a result, the consumers were not adapted to it, but the sales decreased.
3. Rebuild customer relationships
my country's traditional retail industry still follows an undifferentiated marketing model, treating all consumers as its own customers. However, a supermarket found in its user analysis that loyal users accounted for 50% , with a contribution rate of 90% , while the remaining 50% of customers accounted for only 10% of the contribution rate . For customers, if they still cast a wide net and treat them equally, the loss of loyal customers will be much greater than the cost. Therefore, it is imperative to treat customers differently . For example, the customer membership card can be used to connect with the store's POS system to develop a computer automatic promotion system. The system can use the computer system to provide personalized services to member customers, and increase customer loyalty by doing what they like.
4. Personalized and precise recommendation
Thinking from the customer's point of view, what are they most concerned about? 1. What do I need, what can you provide me; 2. Please inform me of useful information in the most concise and efficient way; 3. I hope my consumption can allow me to enjoy due respect and service. The most famous example is certainly an e-commerce company recommending books based on user browsing and purchases. This method is also applicable in the retail industry. Based on the analysis of the consumption habits of member users, preferential information can be pushed through online platforms, emails, etc., and the physical store can achieve the purpose of accurate recommendation through product related sales and member product promotion.
5. Micro store platform
At present, my country's retail industry is still dominated by large-scale, and even the Internet platform is also large-scale. For small retail stores, the cost of opening a store is high, and the impact of user traffic transfer is not small. The way that stores are integrated into Alipay, WeChat, Weibo platforms has developed rapidly and is accepted by users. Through real-time online interaction and online and offline connection, it can truly track consumers in real time and provide better services. Many stores, such as some convenience stores, are not just stores, but have become a site and terminal for online sales. People flow between multiple terminals, objects are displayed between multiple terminals, and the flow of people online and offline is one.
No matter how it develops, the core of big data is always " people " , data is only the language of user behavior, its value lies in the analysis and guidance of user behavior, and all of this will eventually return to consumption and service, so the best decision It is to use big data to improve a series of decisions from enterprises to users to form a highly effective ecological chain.