A Brief Analysis of the Commercial Value of User Behavior Source

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I. The Commercial Value of User Behavior

User behavior encompasses a range of actions exhibited by users on internet products, which includes page browsing, clicking, inputting, and other similar behaviors. The complete user behavior data includes the information derived from these behavior trajectories, such as time, frequency, and specific content of the behavior.

By analyzing user behavior data, we can obtain various user-related information, including the user's effective source, their product awareness, and their product preferences. This information can help us gain a better understanding of the user. Moreover, user behavior holds immense commercial value. This value lies in the fact that by analyzing user behavior data, we can identify the transaction possibility contained within it and directly convert the data into clues.

These clues contain user intention data and basic information, such as their occupation, which can be analyzed to obtain essential information, including the user's intention and psychology. This information can be utilized to conduct further marketing to users, conduct sales follow-up, or carry out marketing activities, such as free trials, to promote user transactions and increase revenue. Therefore, it is essential to study this information to grasp the psychology of users and achieve our revenue goals.

II. Reasons for Generating Leads through User Behavior

To start with, it is essential to define what leads are. Leads represent the contact details of potential customers who have shown some degree of interest or intention to purchase a product.

Two key conditions are necessary to generate leads. Firstly, the ability to establish communication with users in order to exert an influence. This can be accomplished through various channels, such as phone calls, text messages, emails, app notifications, WeChat messages, or other direct communication means. Secondly, the target user group must have a demand for the product, and there must be a potential for transactions.

User behavior generates leads because data analysis and screening can meet both of these conditions.

Concerning the first condition, a user's interaction with a product indicates that the user has engaged with it to some degree, making it easier to re-establish communication. For instance, after a user follows an official account, content can be pushed to the user, and contact details can be left through actions such as logging in and leaving funds. Therefore, the first condition of being able to communicate with users is satisfied.

The second condition is to determine if the user belongs to a potential user group for the product. Does the user exhibit behavior that indicates a likelihood of making a purchase? This is where the primary commercial value of user behavior lies. By analyzing user interactions, such as searches, browsing, and comments, we can identify the content that interests them, their level of engagement, and the likelihood of them making a purchase.

User behavior data can provide us with essential customer contact details. Armed with this information, it is possible to proactively reach out to customers and influence them.

At the same time, we can infer user preferences and buying tendencies from their behavior data, allowing us to identify potential customers and design schemes that cater to their interests. This enables us to achieve precise marketing and direct our limited marketing resources towards areas with the highest potential value, thus generating relatively high business value.

III. The Latent Value of User Behavior

User behavior value can be categorized into three levels: identifying high-quality users, gauging user awareness of the product, and understanding user intent towards the product. By screening high-intent users, we can locate and determine the scope of our key users, and focus our resources on them. Through an analysis of their cognition, we conduct targeted marketing to achieve maximum revenue, cost reduction, and efficiency increase.

1. Finding High-Quality Users

The success of any product depends on acquiring the right users. Without users, there can be no exchange of value, and the product will fail. Therefore, it is crucial to know where to find the target user group.

Resources such as funds and personnel are always limited, so it's important to acquire users at the lowest possible cost. Accurate acquisition of the target audience will help enterprises minimize the cost of customer acquisition and maximize the input-output ratio. This balance is essential for the survival of any enterprise.

For instance, to promote a product manager course, we need to locate the product manager population. But where can we find the highest density of product managers? Product manager communities are a good option, as they attract people who are interested in becoming or are already product managers. Community sites such as Everyone is a Product Manager and PMcaff are suitable places to target.

Knowing where the target audience is and where the product best matches their needs is critical. We can use channel data to compare user behavior data under different channels, including user activity, transaction conversion rate, and more. This data helps us identify which channels have the most active users and the highest transaction conversion rates. Based on this information, we can allocate our resources to high-quality channels to maximize revenue.

In summary, user behavior data is the key to finding high-quality users. By using this data, we can make informed decisions about where to focus our limited resources, in terms of both money and manpower, to improve the input-output ratio and generate more revenue.

2. The User's Awareness of the Product

The level of a user's awareness and understanding of a product is reflected in their behavior data. If a user has limited knowledge about a product, their awareness is low, and they are unlikely to use or invest time in understanding it. However, if a user's awareness is high, they have a good understanding of the product and are more likely to consider it when they have a need that the product can solve. This means that users with high awareness are more likely to purchase the product, whereas those with low awareness are less likely.

To determine a user's awareness of a product, their click behavior can be analyzed. The number and frequency of clicks can indicate whether a user has paid attention to the product, while browsing time after a click can reflect whether they have spent time learning about it. For instance, for a course-type product, if a user clicks on the course entry and spends time browsing the details page, it suggests they are interested in the course and have a general understanding of it.

If a user repeatedly clicks on the course entry or spends a significant amount of time on the details page, it indicates they have a relatively high level of awareness and can be considered a potential customer. However, further follow-up is required to confirm their preferences and intentions before making any judgments about their transaction potential.

Awareness is a prerequisite for using a product; if a user does not recognize its value or features, they are unlikely to use it, which can impact the conversion rate. Analyzing user behavior data can provide insight into the information they have browsed, their actions, and their cognitive degree of the product. Cognitive awareness is essential to initiate the marketing process.

3. The User's Intention Degree to the Product

The degree of a user's intention for a product represents their preference for it and often indicates the likelihood of them making a purchase. User behaviors that occur independently or those that they spend a long time on are typically indicative of their preferences.

Autonomous behaviors such as search queries, adding items to a shopping cart, or adding items to a collection directly reflect a user's interests and can help identify their preferences. Behaviors that involve a user spending a long time on a particular item or content also indicate their level of immersion and, therefore, their level of preference. This is evident in activities such as watching videos or engaging with user-generated content like comments and bullet screen comments, as they directly reflect users' preferences.

For instance, in the case of educational course products, analyzing a user's viewing time, comments, and interactions with free and low-priced courses can help determine their level of interest in the product. If a user watches multiple courses in their entirety or at least 80% of the content, and leaves comments or interacts with other users, this indicates a relatively high level of intention for the product. Such users can be targeted for sales follow-ups and recommended paid courses or other services.

4. User Behavior Turned into Clues

Once we have extracted the commercial value hidden within user behavior, we can identify a group of users with high transaction potential. After processing this data, we can filter out users who are not reachable due to a lack of contact information, such as phone numbers or email addresses, or because they cannot receive messages on their device. This filtered user data can then be converted into clues that are passed on to the relevant business team for further training and follow-up.

By analyzing the feedback obtained during the training and follow-up process, we can fine-tune our strategy to maximize the potential of user behavior.

To determine which types of user behavior can be transformed into clues and how to develop effective strategies, we will discuss these topics in detail in the next article.