[Dry Goods] Common data analysis table headers and assessment indicators for operations positions (I)

Internet operations are inseparable from various performance appraisals, traffic, conversion rates and other data indicators in daily work. At the same time, data analysis has become an essential skill for Internet operations positions. Therefore, we will encounter various data tables in operations. Of course, different data headers are involved for different positions. Today, I will introduce to you the header design and analysis methodology of various operation positions.

1. Related headers for site search list operations and SEM operations

In-site search generally involves a special operation position in which a larger e-commerce company has a dedicated search R&D team and BI data support. In terms of evaluating in-site search content, the key point is to allow users to search for the products they want in the most worry-free way. Based on this purpose dimension, the user's behavioral purpose is divided into various behavioral purposes, which becomes the evaluation indicator of in-site search operations. The table header design mainly includes: keywords, number of searches, number of searchers, number of clicks/rates in the product display area, first screen click rate, number of clicks on advanced filter items, number of searches with no results, percentage of UVs reaching the details page, number of searches added to shopping carts, number of valid orders, conversion rate of valid orders, and number/rate of search word changes. These data dimensions reflect the behavioral nodes of users in the search process. Based on the data collected from these nodes, it can be reflected which link in the search process has a problem.

Here is a brief explanation of the logic. For example: for a keyword, there may be n customers searching every day. The more times the keyword is searched, the higher the popularity. In order to prevent artificial search cheating, the number of searchers (counted by UV) is added. After the search input is completed, a search result page will appear. The result page contains a product display area and an advanced filter area to help customers quickly filter the product dimensions they want. In this way, the click-through rate and number of clicks of advanced filter items can effectively measure whether the advanced filter items are helpful to the customer's choice, or whether they are the product dimensions that the customer wants. If the click volume is low, it means that the advanced filter items cannot truly reflect the consumer's demands and need to be optimized (how to optimize? This will not be expanded here and will be analyzed next time). The product display area is the product ranking calculated by the automatic sorting factor. Generally, the products on the first screen are the click-concentrated area. Therefore, the click-through rate of the first screen is added when measuring the click volume to assess the display quality of the first screen. The higher the click-through rate, the more reasonable and attractive the product ranking is (this is not always the case. Sometimes a high click-through rate may also be due to other reasons such as cheating).

After searching, users will click on the product display area to enter the product details page. In theory, users will only make purchases after entering the product details page (of course, there are also rich and stupid people who buy without even looking at it). Therefore, recording data at this point can measure how many UVs enter the details page through search, which is also a criterion for evaluating the overall quality of search. Of course, if a customer enters a keyword and a page with no results appears (indicating that there is no product description the user wants), then you need to verify the number of searches for the keyword. If the number of searches is large and the frequency is high, verify whether there are similar products on the market. If not, you can record the demand and provide it to relevant procurement and investment promotion to introduce the product. At the same time, at this vacant stage, you can recommend similar products or related pages for the keyword with no results. When users enter the product details page through search and add the product to the shopping cart and make a purchase, the number of shopping carts and the number of valid orders are used to measure the effective order conversion rate.

Thus, a complete process indicator header is completed. I believe everyone knows how to analyze it. The evaluation indicators of SEM are similar to those of on-site search. However, since SEM requires bidding, there will be a ROI (return on investment) issue. The revenue output brought by bidding keywords is the ultimate standard for measuring SEM. Compared with on-site search, the evaluation indicators of SEM extend to outside the site. The main table headers include: promotion account, promotion plan, promotion unit, promotion keyword, display volume, number of clicks, click-through rate, and number of valid orders generated; ROI = gross revenue from valid orders/bidding fee. You can search on Baidu for the detailed operating specifications and background bidding operations of SEM, which will not be elaborated here.

2. Headers related to traffic, activities and page operations

All routine operations positions are involved in this type of header. No matter how things change, the essence remains the same. Without further ado, the relevant headers for traffic conversion include: traffic source, uv pv, uv of reaching the product details page, number of added to shopping carts, number of submitted orders, number of valid orders (paid), valid order amount, (in the tourism industry, there is also the issue of signing a contract. Valid orders in the tourism field refer to orders that have not yet been paid, so please note this). This table header contains all the relevant dimensions of the traffic funnel. If there is anything else, it will be subdivided step by step. The abnormality in which link can be seen through this table. At the same time, these tables can also be used to take stock of the proportion of traffic sources and the operational quality of each channel source. The data analysis table headers for online activities and page operations include: page traffic source, page UV, bounce rate, double bounce rate, click rate, page dwell time, number of UVs entering the details page, and number of valid orders.

As far as page operation is concerned, the page's click rate and second jump are the core standards for measuring a page, and the sales generated are the ultimate evaluation indicators. Let me also talk about the analysis logic. Traffic enters the page through various channels, and clicks will be generated on the page, or the page may be directly jumped out. The longer the page stays, the more attractive it is to users. After a click occurs, the page will jump to another page (which may be a details page or a link to other channels, etc.). At this time, the data of not directly jumping to the page but clicking through the page to another page is called the second jump rate. After entering the details page, a complete behavioral logic will be generated to determine whether to purchase. Many aspects and searches follow similar rules. You see, no matter how things change, they still remain essentially the same.

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