There are 4 core points in the competitive store data analysis, namely, competitive store crawling, competitive store traffic structure data analysis, competitive store category structure data analysis and competitive store traffic data operation analysis. Merchants can understand the operation mode of competitive stores based on the data of competitive stores, and then effectively adjust the operation mode of their own stores.
1. Competitive store crawling
By crawling store data, merchants can understand from which dimensions to find their own competitive stores. There are many ways to crawl competitive stores, and competitive stores can be found from the dimensions of keywords, target groups, products, prices, locations, marketing activities, visual shooting, etc.
By analyzing the visual shooting, store classification, store marketing plan and other contents of competitive stores, merchants can understand the basic data of competitive stores, mainly including the shooting method of competitive stores, the design and production method of detail pages, and the classification composition of store categories
Store marketing plan, single product marketing plan setting, coupons, full discount settings, etc. By crawling the store brand, merchants can understand whether the competing store is an original brand, whether it sells multiple brands, as well as the style, crowd positioning (crowd label), attribute data (product applicable season, applicable scene, basic style), etc. of the competing store. By obtaining the price, sales volume, and ranking of the competing store, merchants can understand the overall sales volume of the competing store’s products, and thus crawl the core products for data comparison and analysis.
2. Analysis of traffic structure data of competing stores
Using the market situation function of Business Advisor to analyze the data of competing stores (“monitoring stores” – “competing store identification” – “competing store analysis”) means that merchants monitor the sales ranking data of competing stores, analyze the category structure data, and analyze the sales data of core products, find the data differences, and then improve and optimize the data of their own stores’ weak points. By monitoring the data of competing stores, merchants can understand the real-time, 7-day, 30-day and periodic data of competing stores, and understand the traffic index, search popularity, transaction index, customer group index and industry ranking of competing stores. By comparing with competing stores, merchants can understand the differences in their own store data and rankings, and can timely understand why the data of competing stores suddenly rises or falls according to the changes in the data in the competition list, and whether the decline is an overall decline or a decline in individual stores, so as to help stores better understand the data status of competing stores and reflect the problems of their own stores.
Merchants can use the market situation function of Business Advisor to analyze competing stores (“monitoring stores”-“competing store identification”-“competing store analysis”), match the data of competing stores, and identify high-quality competing stores by identifying lost competing stores and high-potential competing stores.
Merchants can use the Business Advisor tool to perform trend analysis on store traffic index, payment conversion index, transaction index and other data, understand the growth of competing store data, understand the differences between their own store data and their data, and thus optimize weak data. Through the market situation function of Business Advisor to analyze competing stores, merchants can view lost stores and lost products, understand the loss direction of stores according to the system loss of competing stores and high-potential competing stores, find similar stores and conduct data collection and analysis, so as to understand the improvement direction of their own store data.
Merchants can use the business advisor tool to analyze data and find potential high-quality competing stores. Through store monitoring, merchants can compare and analyze their own store data with that of competing stores, thereby finding differences and optimizing and improving their own store data.
Merchants use the market situation function of the business advisor to conduct comparative analysis of competing stores, conduct comparative analysis of store data based on time periods, and understand the data changes of competing stores throughout the year, so as to better understand the growth process of the store, find the advantages and highlights of the store, and then optimize their own store data.
Merchants use the market information function of Business Advisor to compare the differences in time and growth points (“Competing Stores” – “Competing Store Comparison” – “Key Indicators”). At the same time, merchants can understand the differences in transaction index, traffic index, search popularity, collection popularity, and purchase index within a certain period of time, so as to optimize and improve the data of their own stores.
3. Analysis of Category Structure Data of Competing Stores
Merchants use the market information function of Business Advisor to analyze (“Competing Stores” – “Competing Store Analysis” – “Category Sales”), and understand the category transaction composition data, category payment amount share data, and category payment amount share ranking of competing stores by year and month according to the time period, and understand the differences between their own stores and competing stores in The gap between category layout and category sales can be used to optimize and improve the category layout.
Based on the transaction composition data of competing stores, merchants can understand the proportion of payment amounts in the core categories of their own stores and the proportion of amounts in the core categories of competing stores, so as to compare the two stores’ advantageous categories, transaction categories, and visitor-concentrated categories. Merchants can use the category structure data analysis of competing stores to, based on the sales of competing stores’ categories and based on their own supply chain and profit situation, appropriately update their stores to increase store traffic and sales.
Merchants can refer to stores that are better than their own to optimize the category structure to increase store sales. At the same time, merchants should also consider whether there are any missing categories in their own stores and whether the store categories are rich. , helping stores to better optimize their category structure. Merchants use the market situation function of Business Advisor to analyze (“Competing Stores” – “Competing Store Analysis” – “Competing Store Price Range”), compare price groups, and thus determine advertising strategies. At the same time, they can adjust the average order value of their own store products based on the distribution of average order value of competing stores. It should be emphasized that the price range of competitors can be used as a reference, but the quality and cost of goods from different sources are different, and the specific profit margins are also different. They cannot be completely copied from competing stores. In fact, it doesn’t matter whether the goods are expensive or cheap. Goods in each price range have corresponding consumers. Expensive goods are not necessarily good. The important thing is to make the target customers think that the price and value of the goods match.
4. Competitive store flow Quantity data operation analysis
Merchants use the market function of Business Advisor to analyze (“Competing Stores” – “Competing Store Analysis” – “Competing Stores”). Compare the traffic structure distribution of competing stores. Merchants can view the source of entry into the competing stores, and can also collect traffic data of competing stores from different dimensions such as traffic index, customer group index, payment conversion index, transaction index, etc., to understand the traffic structure of competing stores, find the lack of traffic in their own stores, and then optimize the traffic layout.
Competing store traffic data analysis refers to the comparison of traffic structure and traffic data for competing stores. Merchants can find the data gap with competing stores and the direction of improvement of their own stores through traffic gameplay analysis, thereby helping to improve the traffic data of their own stores. Merchants can refer to and learn about the traffic data operation of competing stores through comparative analysis of segmented traffic data. By analyzing the traffic structure composition of competing stores, merchants can understand the proportion of search traffic visitors and the proportion of through-train visitors of competing stores, so as to help stores increase traffic in a targeted manner. By analyzing the traffic data of competing stores, merchants can find the optimization direction and new traffic data operation methods for their own stores based on the category structure, traffic structure, and visitor ratio of competitors, so that the store can understand its own problems and find solutions. By comparing the traffic structure of competing stores, merchants can understand the data of competing stores. Merchants can analyze the traffic structure and data of competing stores and think about whether such operations are suitable for their own stores, thereby increasing the data traffic of the store.