With the rapid development of industry scale, the consumer market in first-tier cities began to saturate, and foreign convenience store chains began to sink into the market. The competition of convenience stores in second-and third-tier cities will become more and more fierce. How to empower offline brand chains with data and artificial intelligence in the era of big data will be one of the difficult problems faced by physical retailers. This article will provide thinking direction for practitioners from the perspective of convenience stores and how to empower big data.
Convenience store was born in the United States, and gradually became a new retail format because of its miniaturization, high gross profit, convenience and streamlined SKU. In the mid-1990s, the concept of convenience stores began to enter China. In 20 19, the total number of convenience stores nationwide reached132,000, an increase of more than10,000 over the previous year.
Judging from the expansion performance of individual convenience store enterprises, petroleum convenience stores (Yi Jie and Kunlun Hospitality) are eye-catching in store expansion, followed by local brands Meiyijia and Tianfu, and foreign convenience stores are mainly distributed in first-and second-tier cities.
However, observing the urban layout of foreign convenience stores in China in recent years, 7-ELEVEn has opened its first stores in Fuzhou, Changsha, Xi 'an and Hefu since the end of last year. Rosen, another Japanese convenience store, has moved faster, and opened its first stores in Changsha, Shenyang and Taizhou last year.
As far as the national business pattern is concerned, the layout of foreign convenience stores is considered as another proof of the "sinking market" in recent years, which also means that the competition of chain convenience stores in the sinking market is more intense.
With the development of science and technology and cities, the consumer market in first-tier cities is gradually saturated, while in second-and third-tier cities, it is difficult for chain brand convenience stores to expand their stores and integrate into the local market.
Traditional mom-and-pop shops have small investment capital, small geographical restrictions and strong operational controllability, and the location is often near the place of residence. For chain convenience stores, in addition to the surrounding consumer market, the location of stores should also consider the purchase and purchase issues (small streets and alleys can not be uniformly distributed, increasing costs), customer portraits and so on.
At this time, the traditional site selection method is to observe and calculate multiple target locations manually offline, which is very expensive in manpower and time, and the portrait of customer groups cannot be accurate. Imagine how to determine a person's spending power through his appearance in a short time.
But in the era of big data, this information can be obtained at high speed and conveniently.
Digital is one of the earliest big data technology companies in China to set foot in the intelligent application of offline big data. Deeply ploughing offline big data for 5 years, it can gain real-time insight into the intelligent dynamic data of people and fields, and efficiently provide users with analysis, customer portraits and surrounding passenger flow. Digital to offline retail (such as chain convenience stores) has three major values:
1 Fast store location expansion: Digital has full-dimensional dynamic human field big data and its own massive data label, covering 200+ cities and 80 million POI libraries, which can provide enterprises with batch offline human field data and facilitate the scale expansion of chain brands.
When a brand enters a new city, it can quickly judge the location information of different areas of the city and help the brand to occupy the consumer market quickly and effectively according to its own positioning (such as community/business circle, etc.). ), and use artificial intelligence algorithm to analyze the surrounding customer groups and the direction of people flow, thus helping brands to be closer to consumer psychology in product positioning.
Real-time monitoring of old store data: For brand chain stores, many old stores that have been operating for many years are facing changes in the surrounding municipal or consumption environment, such as the establishment of new shopping malls and the demolition of old buildings.
When the turnover of an old shop fluctuates, the traditional way of investigation is offline investigation, but the change of passenger flow is easy to observe, but the change of customer portrait cannot be judged in a short time. Digital big data can feedback the changes in the portraits of the surrounding markets and customer groups in the first time, and make timely adjustments in business direction and commodity selection.
3 Comparison of competing shops: Before moving in, the original number of competing shops and passenger flow portraits in the same area can bring high reference value to the brand; After the opening of the store, the emergence of new competing stores in the region is also an important reason for affecting the turnover of the store. Digital offline big data can help brands observe the surrounding competitive environment in real time, analyze advantages and disadvantages, and make operational adjustments in time;
4 business model precipitation: why is the turnover of the two stores that are also opened in the city center very different? Which turnover is better between the hospital and the school? How to adjust commodity display according to the law of crowd movement? These data, which are difficult to be systematically counted by traditional manpower, can quickly help stores to precipitate a set of methodology and form their own business model, which has great reference value for further brand layout and store expansion, and effectively save the expansion cost of new stores.
Brand convenience stores "sink" into second-and third-tier cities, which is inevitable for urban development and is likely to be an opportunity to redefine local consumption trends. Under this premise, the time for brands to occupy the market is particularly precious.
The retail industry has changed from "goods-field-people" to "people-goods-field" in the era of big data. Only by gaining insight into passenger flow and customer group information in advance, adding local scene data, and finally combining the characteristics of the brand itself, can we quickly enter the local consumer market and seize the consumption share.
Chain brands enter new cities with high investment costs, and the traditional location method is not enough to support the rapid expansion of brands. Mass big data is the "secret weapon" for modern brands to quickly expand their territory. Based on five years of high-tech precipitation, Digital has the largest identification database in China, which can bring strong decision support to chain brands in brand positioning, customer insight and marketing.