Real case｜Actual case of product data-based operation analysis!

This analysis report is a data analysis for a vertical clothing e-commerce (platform). The analysis goal is to find the operational focus for the next month and increase sales.

1. Analysis conclusion:

Conclusion 1: At the strategic level, increase the weight of men's clothing and make it a focus of future development.

Conclusion 2: On a tactical level, rapidly increasing market investment in mobile devices and improving market awareness and branding can quickly increase order volume.

Conclusion 3: In terms of operational strategy, take men's clothing as a breakthrough point, quickly gain market share through product category expansion, cooperation with well-known brands, and advantages in channels.

Conclusion 4: In terms of market strategy, advertising investment is concentrated in North China, followed by the East China market.

Conclusion 5: Increasing investment in categories such as polo shirts and jackets, and positioning the price between 100-300 yuan, can quickly increase the transaction volume of men's clothing.

Conclusion 6: Selecting "Knitwear and Chiffon" as the two categories for related recommendations can increase the average order value.

Conclusion 7: From the market perspective, the core of the current operation of the men's clothing platform is "brand awareness". After having a certain level of brand awareness, we need to build brand preference and finally user loyalty.

Conclusion 8: Stores should be operated and managed in a tiered manner. VIP stores should continuously expand their SKUs, high-potential stores should create popular products, and unstable stores should deal with unsalable products. The first task to comprehensively improve a store is to adjust the product structure.

2. Analysis ideas:

1. This is a vertical e-commerce company, and its core is clothing category. Its operating model is very similar to that of Jiuxian.com. Therefore, when we analyze this kind of enterprise, we must first look at how much value this category has in the Internet? "Online shopping penetration rate" is a very important measurement indicator. The online shopping penetration rate is only 23.6%, which means there is still a lot of room for development, at least to more than 50%.

• In 2013, domestic clothing retail sales will reach 1.9 trillion, an increase of 15.3%, and online shopping transactions will reach about 400 billion, an increase of 25.5%.
• In 2013, the penetration rate of online clothing shopping will reach 21%, accounting for 27% of the domestic online shopping market transaction volume. Clothing is the largest category of online shopping.

2. We determined that there is still a lot of room for development in clothing e-commerce, so we then made the decision whether to develop men's clothing or women's clothing. The penetration rate of men's clothing online shopping is low, but growing rapidly, so it is the focus of future development.

• In the domestic clothing market, men's clothing accounts for 38% of the market capacity, higher than women's clothing at 32%;
• For online clothing shopping, men’s clothing accounted for 17% of online shopping and had a penetration rate of 8.9%, which was lower than women’s clothing’s 41% share and 25% penetration rate.
• However, in 2012, the online shopping share of men's clothing increased by 17.3%, far higher than the 1.5% of women's clothing.

3. In the previous analysis of clothing transactions in the entire clothing market, from the analysis of the platform itself, men's clothing transactions accounted for 28% of the total clothing accessories transactions, which is higher than the industry average. This shows that the platform has a strong degree of trust in the male user market. In the early stage, from the perspective of operational strategy, we can use men's clothing as a breakthrough point, and quickly gain market share through the expansion of product categories, cooperation with well-known brands, and advantages in channels.

4. To increase the transaction volume of e-commerce, we must start from three aspects: traffic, conversion rate , and average order value. Let's first look at whether we have maximized the use of traffic from the perspective of traffic value, so here we use an indicator "average customer contribution to transaction volume" (average customer contribution to clothing transaction volume = average daily independent visitors/transaction volume, that is, the transaction volume brought to the website by a single visitor;), and we found that our traffic value is only half of Tmall 's and one twentieth of Vancl's, so there is still a lot of room for growth.

5. To increase the value of traffic, we must start with precision marketing , starting with the geographical area. We carry out targeted regional marketing based on the distribution of the platform’s own user base. The main customer base is concentrated in North China, but the penetration rate is only 7.2%, which means that there is still a lot of room for development in this region. The transaction volume increased by only 40% month-on-month, while the rest of the regions reached 50%+. Therefore, North China is confirmed to be our key development area.

6. After confirming to focus on the development of men's clothing, we also need to do category segmentation. We have two standards for which categories to develop: first, the transaction volume of this category must account for a large enough proportion of the transaction volume of the clothing category, and second, the growth must be fast enough. Therefore, we have selected polo shirts and jackets as the categories to focus on through the Boston Matrix.

7. We have chosen polo shirts and jackets, and we need to further segment and develop them into specific price ranges. This is the charm of data analysis. We need to dig deeper and deeper until we find the triggering point. Therefore, the polo shirts between 100-300 yuan are the range we need to focus on developing, and the jackets between 100-300 yuan are also the range we need to develop.

8. Another consideration for price band positioning is sales turnover rate (sales turnover rate = number of products sold during the statistical period / number of products on the counter). Categories with higher sales turnover rates are more popular with users.

9. Association analysis: Association analysis solves the problem of "average order value". What other categories of products do users who buy "men's clothing" generally buy? Usually when we are making this kind of application, we will also access another type of data, which is "homogeneous data", such as "region, age, color preference, occupation, average order value, etc." What other users with the same factors as me generally buy? This kind of recommendation generally has a high conversion rate.

10. If you operate a men's clothing brand, it will involve the issue of product selection. Which brands are our focus? For example, we will choose to compare the top 10 sales on our platform with Tmall during the promotional activities of the previous week. We will find that "Jack Jones and GXG are worth paying attention to in the future. These two brands have strong Internet purchasing power.

11. From the perspective of the operation of the entire platform, sales of 11% of our stores account for more than 80% of the total sales, which means that 90% of the stores are in a state of no sales, so the structured management of stores is the focus of our next platform operation.

12. We classify high-efficiency stores based on "sales rate" and "conversion rate" into four types: "VIP, high potential, unstable and comprehensive improvement". For stores with good sales and conversion, we need to continuously expand the number of skus of this store. For stores that have sales of various skus but not very good sales, we need to help the store find "hot items" and improve conversion; for stores with conversion but no sales, there must be a large number of unsalable products, which need to be replaced quickly.