An example of a business case of creating consumer demand is Qingdao Vientiane City:

Each shopping center faces multiple positioning focuses at various stages of its lifecycle, where it needs to continually test whether the services and products it offers are in line with the characteristics, needs and preferences of local consumers.

The first positioning focus is in the planning stage before the project starts construction. The second focus is six months to a year after the project opens, so don't look for solutions after problems arise. For commercial real estate operators in the planning stage and those who have just opened, the case of Qingdao Vientiane City brings a lot of thoughts.

Before the opening, the official microblog of Qingdao Wisky City already had more than 10,000 followers, and the number of followers of the public number was the first in Qingdao's shopping centers, but the number of visitors to the site was lower than expected. Some marketing activities can attract a large number of customers, but the conversion rate of consumption in the field is obviously insufficient. In response to all these phenomena, China Business Data's initial prediction is that the services provided by Vientiane did not match the characteristics, needs and preferences of local consumers.

CBD firstly captured people's behavioral trajectory data through cell phones, and then inversely deduced the distribution of consumers' residence and workplaces, as well as the distribution of consumption destinations in the residential districts that were targeted to the visitors, thus accurately describing the characteristics of the clientele and redefining the competition in the Qingdao city's commercial relations. Further labeling of visitor segments with high and low frequencies provides sufficient data support for strongly connected loyal and low-frequency segments, respectively.

By integrating various dimensions of data related to Qingdao residents and consumption, as well as in-field data monitoring and crawling, China Business Data dismantled the problem from both internal and external levels, and systematically analyzed Vientiane City's big data.

For the F&B sector, CIMB Data retrieved online data from more than 11,000 F&B outlets, and labeled the data by dimensions such as time period, taste, category, work meals and weekend meals, to comprehensively analyze Qingdao residents' consumption preferences and category distribution.

Further comparative analysis of the on-site and competitors' food and beverage formats reveals that, in terms of the fast food categories favored by Qingdao residents, there are significantly fewer on-site stores in Wanxiang City than in the competitors. In terms of seafood and local specialty snacks, which are also favored by Qingdao citizens, there is a lack of categories in Vientiane. In view of the above problems, China Business Data suggests increasing the number of seafood restaurants and fast food snacks in small areas.

In terms of fast fashion, although the total number of branded stores in Vientiane City ranks first in Qingdao, the stores are spread over four floors. 450,000 square meters of large volume leads to the dispersion of similar brands, which results in the ineffective wandering of consumers who have the will to buy.

By analyzing the big data of the venue, we found that there is a lack of interaction between the fast fashion brands, and the conversion efficiency of the users who come to the venue due to the consumption demand of fast fashion goods is low.

Benefo Plaza's fast-fashion category has many advantages, and Zara's sales are almost double that of Vientiane. In terms of industry concentration, Belle Plaza's industry is relatively centralized. Brand pricing, Belle Plaza brand grade is relatively low, the price range is more reasonable. The style of goods, Belle Plaza is mainly less lady style, while Vientiane City is not obvious style characteristics.