How to operate content for non-content products: The core lies in content presentation and recommendation

Content presentation and content recommendation are two common content operation methods for non-content products. Regardless of which one it is, the ultimate goal is nothing more than: reducing the user's decision-making cost, reducing the user's decision-making time, and increasing the user's conversion rate .

As we all know, content operation is an important part of Internet operation . However, in most people's perception, content operation is habitually understood as PGC , UGC or new media operation . The carriers of the first two are content-based products, while the latter is a content distribution channel , which can actually be regarded as a disguised content-based product.

So, are only these content-focused products that require content operations?

Before answering this question, let’s look at another question. What is content? Content can be text, picture (set), or (short) video. It can be said that everything that contains information is content. All products will have some or all of these three elements, so since there is content, content operation is naturally needed.

Based on the different content operation methods, the author is accustomed to generally dividing Internet products into content-based products and non-content-based products. Today, let’s talk about how to operate non-content products.

1. The necessity of content operation for non-content products

Some readers may wonder why the author did not choose content-based products to discuss content operations, but instead chose non-content-based products that are not so related to content operations for analysis. This is because, looking at the products currently on the market, non-content products account for a large proportion of them. This means that the demand for this type of content operation talent is increasing day by day, so it is necessary for us to carefully explore their conventional content operation ideas.

1.1 Market size of non-content products

Let’s first look at the current user scale of content-based products and non-content-based products. After all, only with comparison can we have the final say.

Monthly activity of Zhihu

The above picture shows the monthly active data of Zhihu, which is currently doing relatively well in the UGC field. Its number of active users in February was over 10 million, and its market penetration rate in the secondary field of "knowledge community" reached 10%.

Top 5 information apps with the highest usage in February

Information products can basically be regarded as typical PGC. In the above figure, Tencent News, which had the highest usage in February, had 150 million usage, and Tiantian Kuaibao , which ranked third, had 60 million usage. In fact, this also shows the typical head effect of Internet products, where the winner takes all. As for Toutiao , I feel that its OGC (Occupationally-generated Content) attributes are far stronger than those of several other information products, and it can hardly be considered PGC.

After looking at the usage scale of content-based products, let’s take a look at non-content-based products.

Top 5 mobile shopping apps by users in February

Top 5 comprehensive travel booking apps used in February

[PS: The above data are all from "iResearch Qianfan"]

The author selected apps in the fields of mobile shopping and comprehensive travel booking as representatives of non-content products. The reason is that these products need to stimulate users to convert and pay through reasonable and appropriate information display, and they have higher requirements for content operation. After all, if such products cannot make users pay and make profits, they lose their value of existence.

It is not difficult to see from the above two pictures: the user scale of the top products in these two fields alone is already at the same level as the top products of PGC and UGC. Among them, Taobao had nearly 300 million active users in February, and Qunar had just over 200 million active users in February. This also means that the user scale of non-content products is considerable. Of course, some readers may say that a large user scale is one thing, and a large demand for operations is another, but this does not necessarily mean that such products require professional content operations.

So, let’s talk about the necessity of content operation of this type of product.

1.2 Two “Musts” for Non-content Products to Operate Content

In today's Internet age, not only is it no longer like in the past where you could just go on a roadshow and get investment with just a PPT, it is also no longer like in the past where you can just put a product online and users will flock to it. It is easy to create a large number of individual sellers with monthly incomes of tens or even millions.

1. The increasing homogeneity of products has made it easier and easier to copy a product, and the functional experience of similar apps at the product level is almost the same. At this time, you have to rely on content operations to get the points that users care about, reduce users' decision-making costs, and increase users' conversion probability;
2. With the explosion of information and increasingly fragmented user time, we have to rely on content, the most immersive means for users, to occupy more of their time. After all, not all products have the courage to let users use them right away like WeChat mini-programs .

It can be said that these two "have tos" determine the company's demand for this type of content operation talent, while the user scale determines the market demand. These two factors together make content operations for non-content products necessary.

2. Two conventional content operation methods for non-content products

So how should we conduct content operations for such products? In fact, I have discussed this issue more than once in my previous sharing. Today I will systematically organize the previously scattered knowledge points.

The content operation methods of non-content products can be simply summarized as content presentation and content recommendation. For platform-based products, these two are effective means of promoting user conversion, second only to event triggering; and they also have the advantage of being more lasting and stable than event triggering.

2.1 Content Presentation

The so-called content presentation is actually to convey the information that users care about most to users in a special way, simply and clearly, so that they can understand it at a glance.

During this process, operators need to pay attention to the following points:

1. Based on product positioning
2. Identify user pain points
3. Information labeling or dimensionalization

It can be said that product positioning itself also determines to a certain extent what the user's pain points are, and labeling (dimensionalization) is the simplest and most clear means.

Let’s take the food delivery product Ele.me as an example.

Image from the Hungry app

First of all, as a takeaway ordering platform like Ele.me, its product positioning can be summarized as: providing users with convenient, diverse, delicious and safe food. Therefore, the pain points that its user group cares about are nothing more than: convenience, taste, affordability, and security.

So how does Ele.me present this information in a labeled form?

• Convenience: Displays the distance between merchants and users and average delivery time
• Delicious: Displays pictures, monthly sales, and user ratings
• Affordable: Display discount labels such as "reduced" and "new" and average price per person
• Guaranteed: time-honored brands in the category are labeled as "brand"

Ele.me presents the information that users are most concerned about in a labeled form on the list page, allowing users to have a general understanding of the potential merchants without having to enter the detail page.

In addition to this form of labeling, there is another form of content presentation, which is the dimensionalization of information. Dimensionality, in layman's terms, is actually the "screening" function of the product. Each filter item is a dimension of product information.

Image from Ctrip.com

Take Ctrip’s hotel reservation as an example. The information that users who book hotels are most concerned about are: location, price, hotel hardware, and grade. Based on these user pain points, Ctrip has set the screening dimensions to include: location (business district, transportation hub, administrative district, etc.), price, star rating, features, etc.

There is another kind of dimensionalization of information, which is the design of classification modules, such as the classification modules of JD.com in the figure below.

Image from JD app

The design logic of the classification module is also based on product positioning and user needs, presenting product information to users in a dimensional manner. I won’t go into details here.

2.2 Content Recommendation

If content presentation simply conveys content to users and reduces their decision-making costs to a certain extent, then content recommendation can be said to "directly" help users make decisions.

Compared with the "static" content presentation, content recommendation appears to be "dynamic". What we often call "personalized recommendation" is a typical "content recommendation".

Generally speaking, content recommendations can be divided into two main types. One is content-based recommendation similar to Toutiao (although Toutiao is a content-based product, its content-based recommendation model is more familiar to everyone, so we will take this as an example).

For example, if I am a new user of Toutiao, it will push some news to me. When I first clicked on entertainment news. The backend algorithm will think that I prefer this type of news, and will increase the weight of entertainment news in the content recommended to me later. The more it pushed, the more I looked; the more I looked, the more it pushed. If this continues, it will become a vicious cycle. In the end, I may not be able to read other economic news or sports news because the screen will be filled with entertainment news.

Although this may sound a bit exaggerated, it is indeed a major drawback of content-based recommendations that the content recommended to users becomes increasingly narrow.

The second is based on user recommendations , and Amazon is the undisputed number one here.

The algorithm logic is: for example, I am user A, you are user B, we both purchased maternal and child products on Amazon; and in addition to maternal and child products, you also purchased children's books. Since we are both buyers of maternal and infant products, they will determine whether I am also interested in children's books like you. As a test, it will recommend children's books to me if I happen to buy what it recommends. Then there is reason to believe that there is a certain correlation between users of maternal and infant products and users of children's books. This way, children's books can also be recommended to other users who purchase maternal and child products, thereby increasing conversion rates through accurate recommendations. Of course, in an actual algorithm it is impossible to make such a hasty judgment based on only the data samples of you and me, but this is the logic.

Obviously, the algorithm difficulty of user-based recommendation logic is much higher than that of content-based recommendation. After all, the latter only needs to make judgments based on the past habits of a single user, while the former requires information linkage among multiple users. This is why most products nowadays are based on content-based recommendation models.

Excellent operations not only need to know what users want, but also make decisions for them. Just like product managers treat users as idiots, the same goes for operations. In most cases, users are lazy and don’t like to use their brains.

Imagine that one time you want to buy an electronic product to play with, but you are a layman in this type of product. When faced with a ton of confusing product parameters and it’s hard to make a decision, there’s a high chance you’ll just not buy it, or ask a friend for a recommendation. Your friends may introduce you to other channels, and then you, the user, will be lost to the platform.

But what would happen if the platform had a reasonable content presentation and recommendation mechanism, telling you clearly that this electronic product is what you want?

What’s worse: next to this recommended product, it also comes with a matching product that seems to have a significantly lower price-performance ratio than the recommended product. In this case, it is more likely that you will make the decision you expect quickly.

This actually leads to another point about content operation - comparison is a powerful tool to accelerate user decision-making.

To sum up, content presentation and content recommendation are the two common content operation methods for non-content products. Regardless of which one it is, the ultimate goal is nothing more than: reducing the user's decision-making cost, reducing the user's decision-making time, and increasing the user's conversion rate.

Mobile application product promotion service: APP promotion service Qinggua Media advertising

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