As of March 5, 2023, KFC has 9399 stores in Chinese Mainland, which is one of the largest fast food chains in China. The distribution map of KFC stores is similar to the population density map of China. The number of shops in a city seems to be proportional to its population. The number of KFC stores has a stronger correlation with urban GDP, with a determination coefficient of 0.90. A GDP of around 7.6 billion yuan can support a KFC store, and KFC has 34% room for store growth. How can KFC break this “mathematical logic”, innovate continuously, and discover future expansion models.
Yum! China is definitely a giant of Chinese catering, and KFC’s store location is definitely the best in the industry. But giants also have troubles. Even if the scale of Wandian is too large, the manager of Yum! China cannot tolerate any slack. How to grow and continue to grow is a difficult problem. Before answering this question, we need to have a more “statistical science” understanding of KFC’s expansion potential.
How many KFCs can a city open? Intuitively, the distribution map of KFC’s stores is somewhat similar to the population density map of China. If we assume that the larger the population, the more KFC stores there are, then the number of KFC stores in a city will be proportional to the population. If we put all cities on a scatter diagram of the “urban resident population KFC store model”, the probability of these cities will be linearly distributed.
Admittedly, most cities are located on both sides of this line. But we also want to explore whether there are other relevant indicators, such as total social consumption? For example, urban economic output value? For example, how many fast food restaurants are there in this city?
Statistically, the “decision coefficient” R is commonly used ² We do not need to study how to calculate the correlation between two sets of data. This is a commonly used indicator in statistics. The minimum value is 0, which means that there is no correlation between the two factors. A maximum value of 1 indicates an absolute linear relationship between the two factors. Therefore, the closer the coefficient is to 1, the stronger the correlation between the number of KFC stores in a city and it is. Generally, a correlation greater than 0.6 indicates a strong correlation, and a correlation greater than 0.8 indicates a strong correlation.
After calculation, the ratio between the urban resident population and the number of KFC stores ² It is 0.80, but the urban GDP can reach 0.90. It can also be seen intuitively from the figure below that the relationship between urban GDP and the number of KFC stores is closer, because these cities are more closely distributed around the line.
If we can reach a general agreement on this matter, then the next step in predicting the scale is to find out how much GDP of a city can support a store?
The current situation is that a GDP of around 8.4 billion can support a KFC. But this is only the current situation. Since we need to predict, we cannot take the current situation as a result. We must assume that the market in some cities is not yet saturated and there is still room for growth. As for how large the growth space is, the key is to find those cities with high saturation, and see if we can summarize some rules from these cities and apply them to all cities. Calculate how many more KFC stores can open if each city can reach saturation?
What are the characteristics of saturated cities?
First of all, it must be the city that the brand focuses on, at least above its due average level. The “should” here is the average value calculated based on urban GDP, that is, the point represented by the city. Above the fitting line, there are 185 cities in total.
Secondly, its growth must have slowed or even stopped. Here, we will find 112 cities with stagnant store growth (store growth rate ≤ 0%) in the past year, forming a sample of saturated KFC cities.
If the correlation between saturated cities and GDP is stronger after calculation, we can further confirm that the number of KFC stores has a high correlation with GDP, which can be used as a basis for prediction. On the contrary, it shows that the law is not significant, and we need to go back to find a more suitable indicator.
Here we can clearly see that the correlation between 112 saturated cities and their urban GDP has reached an astonishing 0.99, which is very close to 1. The law is very significant, and can be further used for the prediction of the overall store size.
Now that we have confirmed that KFC’s store size in saturated cities is highly correlated with the city’s GDP, we can establish a linear regression equation to predict how far those cities that are not yet saturated are from saturation. That is, we can pull all cities below the red city saturation fitting line in the above figure to this line, that is, all cities have reached a basic saturation state, and then calculate the total number of stores. It is estimated that KFC has approximately 12600 stores (an average GDP of 7.6 billion can support one KFC store).
As of the end of February this year, KFC had a total of 9315 stores, with a current visible space potential of 34%. The expansion potential is mainly in the sinking market of the third tier and below.
So how do we view this result? Or how to view the guiding significance of prediction?
The prediction method introduced in this article is completely from the external perspective, that is, from the external data discovery rules to conduct the gap, using public information, data processing and logic are also very simple. Interested friends can download the store data of each brand from the Polar Ocean brand monitoring official website, and try to repeat the logic practice in the article.
Of course, any prediction is based on historical data. Just like ChatGPT was based on corpus training before 2021, Ta was wrong to tell ChatGPT what happened after 2021. If the expansion of chain stores still follows the historical trajectory, even simple regression predictions are very reliable The goal of the enterprise is to exceed the predicted value. For example, if it wants to expand its stores to 15000 or even 20000, it must be prepared. The original product structure, existing store models, and previous layout paths are not sufficient to support this scale. To achieve the expansion goal, we cannot rely solely on old experience. As the growth direction of our stores further sinks, we need to refine our store models that are more suitable for various types of markets, such as building small stores in first-tier cities,
Since the first store opened in Qianmen, Beijing in November 1987, KFC’s territory in Chinese Mainland has gone through 36 years, setting an example for Chinese chain restaurants. KFC has left an excellent sample for the location of Chinese restaurants with its own feet. This example enables us to mathematically predict the upper limit of a store. However, as a manager of an enterprise, he is not a statistician, nor can he stay in the state of forecasting for the sake of forecasting, because forecasting is not the entire purpose of business analysis. In essence, forecasting is about breaking old patterns and creating an opportunity to discover and even create new business strategies.

By orchioo

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