Is Live E-commerce Killing Physical Stores? - Huxiu.com#
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Is Live E-commerce Killing Physical Stores?#
This article examines the relationship between e-commerce and physical stores, and explores the impact of live e-commerce on physical stores. Through data analysis, it is found that there is a statistically significant negative correlation between the increase in e-commerce users and the decrease in physical stores. At the same time, the consumption of different categories of e-commerce has different degrees of impact on different types of physical stores. However, e-commerce has not completely eliminated physical stores, but has created new impacts on physical stores through the rise of live e-commerce.
• 🛍️ The increase in e-commerce users leads to a decrease in physical stores.
• 📺 The development of live e-commerce has a new impact on physical stores.
• 🛒 The impact of e-commerce consumption on physical stores varies by category.
Since 2023, the Chinese economy has experienced ups and downs. In the consumer sector, we have experienced a festive first quarter, a contraction in the second quarter, a recovery in the third quarter due to cross-provincial travel during the summer, and stability in the fourth quarter.
From the average daily queue situation of a well-known hot pot brand's stores, we can see the specific manifestation of the above trends:
It can be seen that in November, which has just ended, the average number of queued tables for this brand's stores nationwide was about 128, the highest month since the bottoming out in April and May, but still a considerable gap from the peak of 166 tables queued daily at the beginning of the year.
Similar changes have also occurred in life service stores nationwide. If we observe the increase and closure of various types of stores at the end of October and the end of January, we can obtain the net changes in each type of store, as shown in the following figure:
Except for bars, sports and fitness, scenic spots and tourism, leisure and entertainment, and beauty-related stores, which are still growing, all other types of stores have experienced varying degrees of decline. Among them, home furnishings, hotels, parent-child, shopping, and learning and training stores have decreased the fastest, with a decrease of more than 10%.
The relationship between e-commerce and store changes
The downturn of physical stores is not only caused by the inherent fluctuations of the world and Chinese economy, but also by the rise of e-commerce, or live e-commerce, according to some opinions in the public opinion.
The impact of e-commerce on employment or the economy has been a topic of debate since the emergence of early e-commerce platforms such as Taobao. However, with the stable economic growth in China over the past decade, this debate has lost its ground for fermentation.
However, with the slowdown in economic growth, the downturn of various retail stores, and the ban on live e-commerce in countries such as India and Malaysia to "protect the real economy" to varying degrees, the discussion on the relationship between e-commerce and physical stores has resurfaced.
So, what is the relationship between e-commerce and physical stores? We can draw the following graph using the e-commerce sales data of each city and the data of physical stores in each city:
It can be seen that both the number of e-commerce users and the e-commerce sales have a statistically significant negative correlation with the changes in the number of stores in the city. The negative correlation is more significant in terms of the change in the number of users, reaching a significance level of 0.0001, with a t-value of 5.76 and a coefficient of -0.161. This means that when the number of e-commerce users increases by 1%, there is a simultaneous observation of a 0.161% decrease in the number of local offline stores.
The two graphs above show the correlation coefficients between different categories of life service stores and the development of e-commerce. The intersection of the confidence interval and the red line indicates that there is no significant correlation, while the left and right sides represent negative and positive correlations, respectively.
It can be seen that except for bars, karaoke, life service stores, and other types of stores that do not have a significant correlation with e-commerce, most other stores show a negative correlation with e-commerce growth.
Among them, hotels, sports and fitness, leisure and entertainment, beauty, and pet-related life service stores decrease with the increase in e-commerce sales; hotels, learning and training, beauty, food, and scenic spots related life service stores decrease with the increase in e-commerce users.
From the above graphs, it seems difficult to summarize the relationship between e-commerce and physical stores in a few sentences. Whether it is "I stores/E stores", essential stores, flexible stores, or holiday stores/daily stores, no dimension can effectively distinguish these physical stores with different relationships with e-commerce growth. Therefore, it is particularly important to find the mechanism of coexistence between the two.
What is the mechanism of mutual influence between e-commerce and physical stores?
There may be multiple explanations for the phenomenon of "the faster the growth of e-commerce sales/users in a city, the more obvious the decline of stores". For example, is it possible that there are some factors behind it that make people more inclined to shop online and reduce their frequency of visiting physical stores?
- Changes in population structure
Population is a possible common influencing factor. In our previous article, we studied the contribution of different genders and populations to stores, and found significant differences. In the example of e-commerce and physical stores, for example, when people who are more "homebound" transition from students to young people with purchasing power, and at the same time, the population of people who are less "homebound" and have purchasing power gradually decreases, it may lead to an increase in e-commerce purchases and a decrease in the total number of life service stores.
To verify if such a relationship exists, we conducted the following regression while controlling for the age structure of each city's population, especially the young people who have a greater impact on consumption:
After controlling for the age structure of the population between 15 and 39 years old, we obtained the following results:
It can be seen that whether it is the sales amount or the number of users, even after controlling for the proportion of the population between the ages of 15 and 39, the coefficients still remain significant, especially in the relationship between the number of users and life service stores, where both the coefficient and significance remain unchanged. This robustness test still holds after including the proportion of all age groups.
Therefore, the increase in e-commerce purchasing power and the closure of stores this year may not be caused by changes in population structure.
- Consumption crowding out within categories
Based on the impact on different types of physical stores, we also divided e-commerce product categories and created a correlation matrix between different e-commerce product categories and different physical store categories, as shown in the table below:
The table lists the relationship between the e-commerce consumption amount of different categories and the growth of different categories of physical stores. Blank spaces indicate no significant impact between the categories, orange and red indicate positive impact, while yellow and green indicate negative impact.
It can be seen that the impact of e-commerce consumption on different categories of physical stores is inconsistent. The most significant negative correlation is found in the "home appliances" category, where the growth of e-commerce consumption in this category is significantly negatively correlated with 13 categories of life service stores. The largest negative correlation coefficients are found in hotels and learning and training, which decrease by 0.37% and 0.19%, respectively, with a 1% increase in home appliance consumption.
The next most negative correlations are found in the "maternal and child", "automotive products", and "apparel" categories, which are related to the decline of 35 categories of life service stores.
Although most categories have negative correlation coefficients with physical stores, there are still positive correlations. Books, food, beauty, and daily necessities are examples of this.
Why do different e-commerce categories and physical stores have different substitution relationships? One possible explanation is consumption crowding out. When a certain category of goods and offline life service stores are more concentrated in a certain group of people, the substitution between this category of goods and offline life service stores becomes stronger.
To calculate the age and gender concentration of goods and offline life service stores, we conducted the following regression for each category of goods:
For all consumer goods/categories, for each age group of the population, we calculated the coefficient β, which represents the fastest growth of a certain category of goods or stores when the proportion of this age group and gender in the local population is higher.
For example, the following graph shows the population influence coefficient for hotel stores:
The graph shows two curves, some of which are above the 0 line, indicating that this part of the age and gender population will bring a higher growth rate for hotel stores. In other words, the higher the proportion of people under 30 years old, the faster the growth of hotel stores.
For hotel stores, the most negatively affected e-commerce category is home appliances, while the most positively affected category is beauty. We took air conditioners from home appliances and lipsticks from beauty and plotted the age and gender consumption curves for these two categories as follows:
It can be seen that the age group with the highest contribution to the consumption of air conditioners is more consistent with the age group with the highest contribution to hotel growth, but there is a significant difference compared to the age group with the highest contribution to lipstick consumption.
In the same age group and gender, if there are multiple consumption choices at the same time, and there is crowding out in terms of time, energy, and money, it will lead to an increase in one aspect and a decrease in others. This may be an important reason for the different relationships between e-commerce and physical stores in different categories.
Time and energy consumption are more significant than money consumption
However, the use of money may only play a small role in the substitution effect of e-commerce on physical stores. After all, e-commerce has been booming for more than a decade, and if we say that e-commerce has eliminated physical stores, it may seem like killing the donkey for its meat, as the growth of various offline stores in the past decade cannot support such a conclusion.
However, what is the difference between e-commerce in recent years and e-commerce in previous years?
Perhaps the biggest difference lies in "live streaming".
"Live streaming e-commerce" integrates video and consumer behavior, and it may indeed stimulate consumers to spend more money, but it also dilutes this consumption in more time and space, through consumers' increased time, energy, and attention, thus crowding out other consumption.
Imagine a user who, in a three-hour period, would originally spend 100 yuan in e-commerce in half an hour, and then spend the remaining two and a half hours going out with friends for social activities such as watching movies, dining, and entertainment, also spending 100 yuan. But when the consumption becomes live streaming e-commerce, the user chooses to stay at home for three hours and consume through live streaming, resulting in a total consumption of 150 yuan—e-commerce consumption has increased, but total consumption and offline store consumption have both decreased.
We can calculate the relationship between the number of users of various applications in each city and the number of physical stores in the city, and conduct a simple verification.
The explanation of the above graph is similar to Figures 5 and 6. The intersection of the confidence interval and the red line indicates no significant correlation. The further to the left of the red line, the greater the negative effect of the number of users on the number of physical stores in the city.
It can be seen that among various applications, whether it is WeChat, Taobao, JD.com, Pinduoduo, or the total number of users of various applications, there is no statistically significant relationship with the number of physical stores in the city. The applications that have a significant negative correlation with local physical stores include Douyin, Kuaishou, and Alipay, with a coefficient of approximately -0.03, which means that for every 1% increase in the number of users, there is a significant decrease of 0.03% in the number of physical stores. Among them, Douyin and Kuaishou rank first and second, both of which are applications that focus on short videos. Video applications consume not only users' money but also their time. After their time is occupied, the scene of offline consumption naturally disappears.
In conclusion
The existence of physical stores is one of the key factors that bring people together in cities.
When e-commerce emerged, people were worried that physical stores would decline due to the development of e-commerce. Many years later, these concerns did not become a reality because consumers still need to go offline, and retail stores have become offline display platforms for goods. Consumers experience in physical stores and place orders online; offline consumption spaces also need various types of entertainment and service stores to fill them, such as murder mystery games. We can confidently say that e-commerce has not eliminated physical stores.
However, when live streaming e-commerce emerged and developed rapidly, can this judgment still hold? This may depend on how people allocate their time, energy, and money. And the allocation of time seems to be a paradox in live streaming e-commerce. On the one hand, live streaming saves people time to buy high-quality goods through strict selection and push services, but on the other hand, it locks people in video entertainment, consuming more time, thereby reducing consumers' offline time and completely taking away some consumption scenarios of physical stores.
Of course, the coexistence and decline of live streaming e-commerce and physical stores can also be explained by another logic—when people do not have enough money to spend, they can only watch short videos for entertainment, and physical stores will decline as consumers tighten their wallets.
Which explanation is more valid? This may be another question that is difficult for us to discuss.