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#All #Apparel

Nike / China
City Ranking, Trade Zone Analytics, Kiwi


Main Solutions  ⇂

City RankingTrade Zone Analytics
Kiwi, a GIS platform




Background: Nike, the largest supplier of athletic shoes and apparel and a major manufacturer of sports equipment in the world.

Nike, Inc., found in 1972, is an American multinational corporation that is engaged in the design, development, manufacturing, and worldwide marketing and sales of footwear, apparel, equipment, accessories, and services. The consumer presence of Nike in China started in 1981, and now Nike has over thousands of stores in China.

Challenges:
  1. New department, new area. No one know how to use data effectively.
  2. Hard to find out new trade zone in China
  3. Need a platform not only to study market competition and trend but also to track consumer trends and compare market data with channel performance easily, even you are not a technical business operation or strategy manager.

There are thousands of Nike stores in China, so if Nike used a traditional way to manage all stores, they must spend a big sum money in personnel expenses, transportations, etc. The method was too slow, and the cost was too high. Therefore, they decided to establish a new department to solve this problem. But, how to do? Nike have a lot of useful data, but, in fact, they did not know how to use it in a right way or use the data effectively. Besides, this department also wants all the data result can be known by other departments. But how to let non-technical colleagues know the data and GIS? Besides, Nike definitely wants to open more stores in China, but there are already having thousands of stores. Where was the new trade zone area? How to find it?




Main Solution Methods we used in this case:
  1. Kiwi, a location intelligence platform
  2. Trade Zone Analytics
  3. City Ranking

There were three major methods that we choose to solve problems: store segmentation, trade zone management, and software development of marketing planning. This new department did not familiar with managing stores, and also did not know how to use their useful data effectively. For helping Nike can manage stores easily in the future, getchee collected all the data that Nike had, and depended on different key factors such as distribution of total population, working population, household number, and age to make a customized a systematical store segmentation. According to this model, Nike can control and manage everything around and within their store – nothing more, nothing less.

Collect data → sort out by different factors → construct a model

Second, trade zone management. Nike has already having over thousands of stores in China, so finding a new trade zone area was a challenge. But we accepted this challenge and overcame it. For finding new trade zone, we depended relative data such as the point of interest, heat map, working population, key competitors, and target consumers to re-examine and redefine each area. We compared multiple sites attributes against local potential and competition, and found out the new trade zone for Nike.

Re-examine all data → compare multiple sites → find out new trade zone

And for the last one, non-technical colleagues can also easily track consumer trends and compare market data, we applied all models and data to our Kiwi, a location intelligence platform, and made a customized dashboard with important data results that Nike needs for colleagues who are not technical people. In this dashboard, they can easily know such as the market growth, age distribution, etc. They can also overview the market channels and business development easily. Furthermore, they can efficiently report business performance to managers. This customized platform really helps relative colleagues save time and money on data collection and reporting, and also convenience for them to manage trade zone and store segmentation visually.

Apply data to Kiwi  manage trade zone, store segmentation etc. online   Data visualization (dashboard) 

Nike in Greater China satisfied with the model of stores segmentation because the models that we made have above 90% accuracy. Furthermore, we found out 1,600 new trade zone for Nike by covering over 170 cities in China. Besides, Nike love our platform and dashboard. They also consider to replace Nike Arcgis with Kiwi, a location intelligence platform, in the future.

 Above 90% accuracy 
√  found out 1,600 new trade zone 
√  replace Nike arcgis with Kiwi in the future 
√  reduce the stress for managing store and planning stores

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