Now, analyzing data seems to be the mantra of Internet practitioners. People who make products, operate and market keep saying what the data is like, but not many people really know the true meaning of the data and understand it. I had a great chat with a colleague who is the largest digital commodity trading platform in China and gained a lot.
For data, having * * * knowledge means looking at the data and promoting the adjustment of products, operations and market strategies through reasonable and thorough analysis. But this knowledge depends on the intermediate stage of data, and the advanced stage can predict the business trend of the next quarter, half a year or even a year through huge multidimensional data analysis. Of course, there will be some deviation in the forecast. More importantly, if I want to enter the expansion of new business, I can calculate how much capital investment, personnel investment, market and operating resources investment will reach in a certain period in the future, or conversely, how much investment and how long it will take me to reach this scale. This is the highest stage. Under normal circumstances, this aspect may not be accessible at all, and basically a few people reach the limit of the intermediate stage.
The Internet has many fields, and each field has different concerns. Let me talk about two familiar areas: community and e-commerce. When it comes to data, we must first understand the dimensions of statistical data and analytical data. Personally, there are generally user dimensions, operational dimensions, content dimensions in communities, and operational dimensions in e-commerce. I take the recommendation list as a dimension.
User's dimension
From the perspective of users, website data is actually what is commonly called the level of web analysis. This dimension mainly depends on the channel through which users come to the website and what behaviors users have on the website. The main purpose is to provide the basis for marketing personnel to promote the effect, help product personnel to analyze which pages, regions and modules on each website are most attractive to users, and make timely strategic adjustments.
The first data point user source channel of Web analytics, from which users come to our website. Enter the URL directly, open the favorite link from the favorite, or search on the search engine (so what are the top 20 search keywords). Or from Weibo, various forums and other new media, click on our website link to come in. If the website is also doing marketing at this stage, it is best to have an independent statistical logo for each link published, so that you can clearly see the traffic of different advertising spots in different media. In this way, marketers can use these data to find channels that can bring stable processes to the website, and at the same time eliminate channels with poor results. The top 20 search keywords mentioned above are also important sources for SEM to determine keywords.
The second data point is the user's behavior on the web page, that is, after users come to our website in various ways, what are the common landing pages and what characteristics of these pages need to be analyzed. Pay attention to users' clicking behavior on the page. The average user will look at several screens, which buttons or links are easier to click, and how long each page stays. These data product personnel need to pay more attention. By analyzing the behavior of users on various web pages, we can provide a great basis for us to make product decisions.
The third point is on the user's access path, which pages users will go to one after another after entering the login page, which pages they will register for login and which pages they will jump out of. From these data, we can clearly outline the access path map of typical users. By analyzing the source channels of users together, we can find that users on those channels have the highest access depth and the highest conversion rate when they come to the website, so that marketers can adjust their strategies in time and increase the promotion of these channels with large traffic and good results.
The fourth point is the registration process. Generally speaking, the registration process of many websites is not very short, which requires at least two steps, and some can reach three or four steps. The key point is that the registration process is complicated, so no matter how good your promotion is, the modules of the website are easy to use, and the final conversion rate is still terrible. By monitoring this process, we can see where the users who are willing to register are lost, whether they have filled in too much information, whether they have not sent confirmation information, and so on.
Finally, to sum up, users' source channels, UV, PV, residence time, webpage click heat map, first hop rate, second hop rate, access path, conversion rate and marketing should also pay attention to your CPM, CPC and user conversion cost.
Second, the operational level.
The dimension of operation is the follow-up behavior of users on the website. In this regard, communities and e-commerce have their own points to pay attention to.
For e-commerce websites, the analysis of user dimension is to analyze the dimension of user source and operation, and then analyze the income situation. The first data point is the number of daily orders, which depends on the overall sales of e-commerce websites and is also the most important data indicator. The second is the customer unit price. The amount of each order is basically the product of the order quantity and the customer unit price, which is almost the overall sales volume of the e-commerce website, which is not very different from the actual situation. The next step is to see the success rate of order payment. Many people have this experience. On the e-commerce website, we may put a lot of goods in the shopping cart, but in the end we will delete some goods in the shopping cart, or many orders will not be paid in the end. E-commerce operators are very concerned about this data. If there are a large number of unpaid orders, it is necessary to analyze what the problem is. Is there a problem in the registration link or a problem in the payment link that prevents users from paying?
The fourth data point is the rate of return, which is very important If there are a large number of returns, the loss is very large for the website, and the reasons for the returns should also be analyzed.
The fifth is the order delivery cycle. The delivery cycle of each order is different in different regions, first-tier cities and second-tier cities, but it tests the overall logistics level of e-commerce.
Another overlooked data point is the complaint rate. The user experience of e-commerce is a whole process from online to offline, and focusing on a certain link of service is fatal. Users often complain about problems in a certain link, leaving a very poor impression on users. The complaint rate is the experience of the overall service level of e-commerce. It is difficult to build a brand, but it is very easy to destroy a brand.
For e-commerce, the last key data is the repeat purchase rate or secondary purchase rate of users. This data tests the loyalty of users. A user's first purchase experience is good and he is satisfied with the goods, so the probability of second purchase behavior is very high. The time period of multiple purchases by users is also a data point that needs attention.
For the community, there are many differences between the operational data that needs attention and e-commerce. Take the quality content sharing community as an example. The number of newly registered users, the number of registered old users and the number of PV per capita are the overall data of the community. Then, how much content does the community produce every day, specifically, how many different types of content, such as words, pictures, videos, etc. What was the growth rate the day before and what was the growth rate compared with last week or last month? At the same time, add attention, comments, forwarding, etc. These data are the overall expression of the interactive atmosphere of the whole community. Of course, we should also consider the loss situation. The ratio of two-week non-login, one-month non-login and two-month non-login to the total registered population of the community. The higher the proportion, the more dangerous it is to community products and operators, which should be paid attention to.
Of course, for the community, high-quality active users are the key to creating a community atmosphere. Then for these high-quality users, we must focus on it. Through data analysis, how many users meet the quality standards increase every week, the content released by everyone this week, the number of all kinds of content and interactions, and how many people are on the verge of losing. These data will help operators adjust their strategies. For example, seeing that many users are active but the published content is not good, how to guide users; There are still users on the verge of losing, and you need to consider what methods to save these users.
Third, the dimension of goods and content.
In fact, this dimension should also be placed in the operational dimension, but this piece has indeed been ignored by many people, so this dimension has also been singled out.
In e-commerce, we should not only pay attention to the overall users and sales data of the website, but also pay attention to the data of a single category and a single product. Sales volume, average purchase volume, amount and return rate of a certain category. Do the same data analysis for a single commodity to see the sales volume, order number, amount and return rate of this commodity within a certain period of time. Through this analysis, we can see the trend of hot categories and hot commodities, and the choice of subsequent operation, marketing or promotion is very clear.
So is the community. We need to look at the overall data of the community, but the content in the community is as important as the people. It is especially important for communities with high-quality content sharing. In addition to the different types of words, pictures and videos, there is also the classification of the content itself. Including photography, travel, food, fashion, animation, movies and other different labels. In the community, the tags of content are added by users themselves. Then the first data point to pay attention to is how many users added their own tags this week. In this way, we can see how much fresh content the community produces every week. The second is the amount of content published by users under each label, how much per day and how much per week. In this way, we can see which tags are the most active, and the subsequent related operation activities can find the direction. The third data point is the number of user interactions under each tag, including the number of different behaviors such as comment, forwarding, favorite or like. This data clearly shows the active degree of users in different tag content, which is very important for the operation and activity of community atmosphere.