Fresh e-commerce app: screening and sorting of commodity display

Editor’s Guide: almost all the commodities in the fresh e-commerce app are presented in the form of commodity cards. The main pages carrying commodity cards are search results list page and category guide list page. This paper mainly analyzes the filtering and sorting functions of these two types of pages. The analysis of this paper is mainly based on the three fresh e-commerce apps of HEMA, Qixian and xiaoxiangshengxian. < / P > < p > find out whether there are products you need on the platform, and find the products that meet your needs; find out what products are on the platform, so as to help you make clear your purchase intention. For the platform, if the user does not carry out any operation, it is difficult for the platform to accurately know which products are more valuable to present to the current user. < / P > < p > understand the user’s purchase intention, help the user clear the purchase intention, and stimulate the user’s purchase intention. When the user provides the purchase intention information to the platform through the interactive function, the platform will display the products that meet the user’s purchase intention to the user and enter the corresponding form of product list page. < / P > < p > filtering: further reduce the scale of the list of goods, only show the goods that users have strong purchase intention; sorting: give priority to the goods that users are more likely to buy, so as to reduce the cost of comparing goods. < / P > < p > all the products of the fresh e-commerce app are displayed in the form of commodity cards, and you can enter the commodity details page through the commodity cards to view the specific commodity information. < / P > < p > the user determines the commodity set through the category guide function: obtain the user’s purchase intention information through the category guide function, and display the products that the user may purchase to the user, usually in the form of commodity list on the category guide page. User search to determine the product set: through the search function to obtain the user’s purchase intention information, the user may purchase the product display to the user, usually in the form of product list in the search results page. The platform actively recommends the product set: the platform makes full use of the page resources, and displays the products that users may buy to users based on the user information that the platform has learned; such product displays mainly appear on the home page, activity page, content page, product details page, etc. In addition, in the search results page or category guide page, when the number of matching products is insufficient, the flow can also be used to display related recommended products. Among all the above page types, the search results page and category guide page are the most important ones, and the commodity set in the commodity list is obtained by the user’s active interaction. < / P > < p > generally, the number of commodities in these two forms of commodity list is relatively large, but users usually have the need to compare the alternative commodities when they purchase commodities. Excessive number of alternative commodities and irregular ordering are obviously not conducive for users to quickly find satisfactory commodities and make purchase decisions. < / P > < p > therefore, users need to further narrow the scope of the commodity set by filtering function, and use the sorting function to sort the commodities according to the tendency of personal evaluation of commodity value. < / P > < p > filtering function refers to the function of “triggered by the user actively, adding filtering conditions on the basis of the existing commodity set, so as to get the commodity set that meets the user’s intention”. < / P > < p > on the fresh e-commerce app, the screening function is mainly used to help users further reduce the displayed product set; since the screening function is triggered by users’ initiative and reflects users’ purchase intention, the screened products are more likely to be purchased by users. < / P > < p > different users have different core concerns when comparing goods. For example, some users are very sensitive to the price of goods, some users are very concerned about the brand of goods, some users are very concerned about the logistics time, and some users are very willing to refer to the sales volume and evaluation of goods. < / P > < p > in addition, the core value points of different categories of goods are also different; for example, most users may care most about the freshness of vegetables and fruits, while for beer, most users care most about the brand, and they may care most about the origin and variety of rice. < / P > < p > the core point of product screening function design is the determination of screening dimensions, which requires product managers to have a deep understanding of user trading behavior, and needs a lot of data as support. < / P > < p > from several mainstream fresh e-commerce apps, we can see that the common commodity screening dimensions include brand, price, type, promotion, etc., and different commodity display pages provide different screening dimensions. < / P > < p > below, we will only interpret these screening dimensions and their locations. How to determine the deep-seated model behind these screening dimensions will be introduced in the next article. < / P > < p > the main screen of the three mainstream fresh e-commerce apps, HEMA, Qixian and xiaoxiangshengxian, is shown in the figure below, and the specific screen functions are shown in the table below. < / P > < p > as can be seen from the above table, each fresh e-commerce app only designs the filtering function in the search results page, but not in the category guide page. < / P > < p > the internal reason is that, on the one hand, the commodity list obtained from the category guide page itself is the result of screening by category dimension; on the other hand, from the perspective of user business scenarios, users who purchase commodities through the category guide function usually have unclear purchase goals, which is exploratory. < / P > < p > therefore, most users do not have a clear price range, brand and other purchase intention, so it is not cost-effective to provide users with the screening function of these dimensions.. < / P > < p > from the comparison of the three apps, it can be seen that all three of them design the fixed dimension filtering function into the form of secondary entrance, which is mainly because the page space required to complete the label of each dimension filtering function determines that it cannot be presented in the form of primary entrance. < / P > < p > but in the actual business scenario, for some specific commodity sets, especially the commodity sets obtained from the search results, the filtering function is very important, even essential. < / P > < p > therefore, there is a strong and valuable demand to support the product manager to design the first level screening entrance. At present, the common form of first level screening entrance mostly adopts the form of the top area of the list as shown in the figure below, and its main advantage is flexibility. < / P > < p > on the one hand, the platform can decide whether the first level entry filter label appears according to the commodity set or search keywords in the list; when the user searches for a large number of keywords corresponding to a large number of commodities and categories, the first level filter note will automatically appear; when the user searches for key words that are accurate enough or the number of search results is relatively small, the first level filter label is not needed Sign. < / P > < p > on the other hand, the first level filter tag can span multiple filter dimensions, and the displayed filter tag can be the filter tag under each dimension; even, the display of the first level filter tag can be personalized recommendation, which can be generated based on the user’s portrait to display the characteristic products that the user is most interested in. < / P > < p > by comparing the filtering functions of three mainstream fresh e-commerce apps, HEMA, Qixian and xiaoxiangshengxian, it can be seen that the three apps only set the function of filtering by price on the search results page. < / P > < p > among them, HEMA and Qixian only have the function of user-defined interval screening; Xiaoxiang fresh food also provides the function of user-defined interval screening and recommended interval screening. < p > < p > the most effective way to “buy” products is to provide users with the most effective “target” function. < / P > < p > for most fresh product app users, price is always an important and clear point in their purchase intention. Therefore, for e-commerce platforms, screening by price is also one of the most common dimensions. < / P > < p > in addition, different from shoes and clothing, 3C, white power and other categories whose brand system has been very perfect, the brand effect of fresh goods at this stage is still relatively weak, so it is not enough to support the brand and become the main basis for users to evaluate the quality of goods. < / P > < p > at this stage, most users of fresh e-commerce app can only evaluate the quality of goods more based on the trust in the platform and the commodity quality evaluation system of “one cent, one cent”. < / P > < p > however, for the categories dominated by standardized commodities such as drinks, frozen food, Cereals, Oils and snacks on the fresh e-commerce platform, brand is still one of the most core dimensions influencing users’ purchase intention. < / P > < p > as can be seen from the above figure, the search result page of “beer” directly and explicitly provides the function of screening by brand. This function is reflected in a number of fresh e-commerce apps. < / P > < p > for the problem of whether the brand dimension screening supports multiple choices, I think that the function of supporting multiple choices should be supported on the basis of comprehensive cross ranking of many brands, otherwise it is easy to fail to achieve practical effect. < / P > < p > therefore, category is also one of the fixed filtering dimensions in most apps. In addition, as shown in the above figure, for goods like “pork” and “steak”, users are likely to need to further clarify the classification after getting the search results. Therefore, category is the most needed filtering dimension for users. < / P > < p > for apps such as HEMA, Qixian and Xiaoxiang, which focus on high-quality fresh food, based on their commodity characteristics, they usually take the origin as a common screening dimension. < / P > < p > from the overall comparison screenshot of the screening function above, it can be seen that in the three main apps compared, only Xiaoxiang takes the place of origin as a fixed screening dimension. < / P > < p > HEMA and Qixian are relatively more flexible. According to the products searched, it is decided whether the origin will be provided to users in the form of a first-class screening label, together with comprehensive dimensions such as brand, category and characteristic label; for example, for the search results of “steak”, there will be a first-class screening across multiple dimensions such as “Xiling”, “Feili”, “import” and “cereal feed” label. < / P > < p > screening by promotion activities can be regarded as the precise entry of promotion information on the search results page, and the commodity feature label can flexibly provide users with important screening dimensions for the category according to the commodity category, such as the delivery time of fresh e-commerce, etc. < / P > < p > because the sorting function is triggered by users, which can fully reflect the purchase intention of users, it is more likely that the products ranked in the front will be purchased by users; the core point of the design of commodity sorting function is also the determination of sorting dimension. From several mainstream fresh food e-commerce apps, we can see that the common commodity sorting dimensions include synthesis, price, sales volume, promotion, etc 。 < / P > < p > it can be seen that the three apps only support the sorting function in the search result page for the location of the sorting function, and the three-level category system is adopted in the category guide page, so there is no sorting function. < / P > < p > and seven fresh uses a two-level category system, so the sorting function is set in the category guide page; in the sorting dimension, there is little difference among the three, only seven fresh uses more promotion dimension in the category guide page. < / P > < p > after entering the search page, you can see that no sorting method has been selected, which is the comprehensive sorting method; users can click the price for the first time, ascending by price; click again, descending by price; when they click the price for the third time, the comprehensive sorting will be restored. < / P > < p > and Qixian and Xiaoxiang are both