how to quickly judge the store's operating conditions, understand the competitive environment in the business circle, and control the change of category data? The "competition analysis" function module in the business background management staff can provide the most direct first-hand data for the business operators as a reference.
? "Competition analysis" is a functional module in the background management staff of Xinmeida merchants.
through this module, business operators can add/query/compare the operation data of their stores, business districts and categories. Its functional core is data kanban and decision assistance. By comparing the ways of operating stores and competing for some data items in stores, and taking this as a reference, the system automatically gives optimization suggestions and promotion plans. The entrance to "competitive analysis" On the PC side, under the "Business Staff" menu on the backstage homepage.
? On the mobile side, the "All" menu on the home page
Taking the screenshot of the PC interface as an example, this paper introduces the subdivision function under "Competitive Pair Analysis" respectively. In the competition analysis, the display of the data/index of the store.
yesterday, the last 7 days and the last 31 days are the time axes, which are derived from the data fitting indicators of the business circle and even the whole category, and are displayed in the form of indexes, which are not real data. Exposure index.
an index fitted according to the exposure times of the store in the merchant list page, that is, only the exposure times of the store's display list page after the user searches for a keyword or clicks on a category menu on the comments of app/h5/pc are calculated. The higher the score, the more times the user sees the store information. Popularity index.
an index fitted according to the number of times users click to view the store information on the merchant list page, that is, the data of the click times of the store after the list page is exposed by searching, and the higher the score, the more users click to view the store. Trading index.
The indicators fitted according to the purchase and consumption amount of users, that is, the overall sales data of stores in reviews and beauty groups, at least including group buying and flash discounts, etc. The higher the score, the higher the consumption amount of customers from Xinmeida. "Competitive ranking" data is the same as "store data" above.
view the data or index of related competing products by industry and region, and arrange them from high to low by default.
in addition, select stores and add competing stores, and compare the operating data in detail, and also operate on this page. Adding the button of competing stores becomes business comparison, that is, it is added successfully, and up to 5 competing stores can be added at the same time. And you can view the added competitive stores in "Competitive Attention".
although the number of competing stores is limited, in practice, the competing stores are selected and then the competing stores are cancelled, and then other stores are added. The following logic can be referenced in the selection of competitive stores. Take the natural month as the unit, and fix 3-5 competitions every month.
select a fixed number of fixed stores as reference for competition in a fixed period, and replace the stores with lower reference value in a fixed period of time based on year-on-year/month-on-month data, which is more conducive to intermittent decision-making. Data comparability.
it is not a rational decision to choose the store with the best data, but its data is much higher than your own store. And choose those data is relatively better, or in the exposure display, merchant page browsing, purchase order, etc., one or several items are slightly higher than their own stores, so the competition has greater reference value and stronger visibility. Selection of internal areas in business circle.
online consumption is relatively concentrated, and the competition is also relatively concentrated. You must keep at least one store in the same business circle when choosing the right store. Subjective judgment based on store brand.
in terms of brand, price, product and customer group positioning, brands similar to the stores they operate can also be used as a reference for competing with stores. Changes in online data can indirectly reflect the online business strategy of stores and even the overall operating direction of brands.
? Official advice.
in the list of competing stores, the comment official will mark some reference stores, such as high visitor overlap and high exposure overlap. These are the suggested results obtained through the reference of big data in the background, which has certain reference value. "Business comparison" refers to the data display and analysis results of the comprehensive comparison of competing stores.
yesterday, nearly 7 days and nearly 31 days are also used as the time axis to display data or indexes, including two modules: data comparison and detailed comparison. Data comparison.
It shows the comparison of four key data intuitively, and according to the comparison results, the government provides some optimization suggestions. However, after all, the optimization proposal is machine computing, which tends to be more functional, more presentational and of average reference value. We also need to refine the problem a little bit by ourselves through data. Detailed comparison.
The detailed comparison data of stores vs. competitors are compared in the dimensions of display exposure, merchant page browsing and purchase order, and displayed in the form of "operation point", which is also the most important reference data for operation optimization. Expose and show the competition.
exposure mainly includes natural exposure and extended exposure. Click rate, similarly including natural click and promotion click. The above two data are related to the promotion of general delivery, so external factors such as "delivery bid", "delivery target population" and "delivery materials" should also be considered.
on the whole, the factors that affect the click-through rate include all the elements displayed in the search results in the list, so the promotion is mainly considered from the perspectives of store name, main picture, star rating, reference price, group purchase items/prices, and even branding.
in addition, by fitting the index and some known data, we can also roughly calculate the real data such as the promotion budget and store page views of competing stores. Several formulas are provided for reference only:
exposure index = natural exposure index+extended exposure index;
click rate = exposure/clicks;
number of views = exposure * click rate
Presumption formula: exposure = natural exposure index x its weight value+promotion package index x weight value; This method is only for reference, and its weight proportion or other relationship factors are unknown, even the product manager who designed this function can't calculate the exact unique value. However, through some simple guesses and calculations, we can basically grasp whether the store lost to the competition in natural exposure or exposure, and even roughly match the difference data.
? Merchant page browsing competition.
that is, what the user did in the store page and a series of results, including where he clicked, where he looked, how long he looked, and so on.
The principle of matching the results of merchant page browsing is relatively simple. You can see which data is lost to the competing part and how the competing part is done, and optimize it according to this logic.
Give a few simple examples:
The technician's viewing rate is low. Is it because our technician's photos are not taken well, and the technician's information is not creative?
The viewing rate of reviews is low. Is it because the recently updated/topped reviews in the store are of low quality and have no bright spots?
The methodology and logic are mostly similar, because the favorable rate is low, whether our customer operation is not done well, and so on. Buy an order and compete.
Although this module is very simple, it has the greatest practical significance. After all, group buying is the most important way to get customers in stores, and there is not even one.
as explained earlier, the trading index is strongly related to sales, and the purchase conversion rate refers to the ratio from the number of visitors to the number of buyers. 2.7% means that if your group purchase has 111 visitors, it will generate 2.7 orders.
ps, why do group purchases that nobody buys have a trading index of 225? Presumably, 225 may be the basic value, which is a concept with 1 in mathematics.
Therefore, the reference significance of purchase conversion rate here is greater, and another formula is provided for reference:
Purchase rate = total number of group purchases/total number of group purchases;
Therefore, the purchase rate of store group purchase is the most important indicator to judge whether the store group purchase setting is good or not. With the same online traffic, the stores that buy high will get more customers.
if the conversion rate of competitive group purchase is higher than our store, we can analyze the competitive group purchase settings in detail, including title, main picture, price, details, project, copy, detailed picture design, purchase requirements and so on. The above is the functional explanation of competitive pair analysis, because competitive pair analysis actually involves the overall analysis of the whole store, including the main operation modules of the store. This paper mainly explains its operation logic, and the analysis and methodology involving specific details are not fully discussed, so it will be updated separately later.