What is content operation?

Content operation refers to a series of marketing activities related to content based on product content planning, content creation and editing, content optimization and publishing. For different channels, content operation has new media content operation (such as WeChat’s content operation), content platform operation (for example, the content operation of Jane’s book), etc. According to different business, content operation can be divided into promotion content operation, product content operation, user content operation, etc.

The importance of content operations.

Content operation occupies a very important position in the whole operation activity. First of all, the content can establish the connection between users and products. The content can convey the brand value while also cultivating the user’s cognition of the product. Secondly, content operation is also a part of product service. Users can not only consume content directly, but also help users to consume products. In a word, good content operation is very helpful for users to pull new, retain and transform users.

The steps of content operation.

Using data-driven content to run content is an essential skill for marketers. Before creating content, we can use data analysis to identify the target users and set the right goals (for example, how many new users will we bring after our content is released?). And to a certain extent the impact that the content is about to have. To use data-driven content, we must master these three steps:
Data collection, data analysis, data feedback.

1. Data acquisition: understand target users and competitors.

The difficulty with data-driven content operations is that we must understand our target users accurately. Only by accurately understanding the target users can we output the content that is close to the user’s needs and arouse the resonance of users.
In order to understand users, we need to collect as much user information as possible, including users’ online and offline behaviors. For example, a user in the online search what questions, which topic is active in social media, like click what content and so on these online behavior are we to understand the need to collect information. User behavior data acquisition is to let us understand the user’s interest and interests, and the common behavioral characteristics of users. After that, we can group users according to their common interests and behaviors, and produce targeted content for different user groups.
In addition to collecting user behavior data, we also collect behavioral data from competitors, for example, what they developed. In what channel to promote; How many new users, how many transformations, and so on. Through understanding the performance of competitors, we can speculate about what keywords or subject to users more attention, the user has no interest in what content and to attempt to innovation, find the breakthrough point, to create some fresh content to win the user’s heart.
The purpose of data collection is to better understand target users and competitors. We can use these data to think about how to add value to our brand, how to produce differentiated content to attract users.

2. Data analysis: identify the most effective content promotion channels.

By collecting user behavior data and competitor’s behavioral data, we have a very deep understanding of when and where the target users are and what they want.
After we have produced targeted content, then it is how to promote it through the most effective and influential channels. In a blog to share is far from enough, no matter how much we output the contents of the user requirements, if the user had never seen these content, then we are purely a waste of time and energy.
There are many channels for promotion, such as email push, paid-for display advertising, big V cooperation with large impact on target users, or simple repeat marketing. Regardless of the promotion channel, we need to analyze target user behavior data and competitor behavior data to determine.
Data analysis can prevent us from promoting blindly. We can find out the best distribution channels for user feedback by analyzing the different performance of different content distribution channels.

3. Data feedback: prove the value of content.

After content output and promotion, we evaluate the content of our output and prove the value of our efforts.
How does the user interact with our content? Does our content cause a lot of discussion? What actions do users have after they have access to our content? · such analysis can give us a clear understanding of the value of content.
It is difficult for some brands to set the return on content investment, but we can describe the impact of content through proxy indicators. We can use different scoring systems to evaluate the participation of different stages and compare the effects of different content modules. For example, click browse content 1, and further participate in thumb up 2, collect content 3, share content 4, and then evaluate the purchasing habits of different scoring levels. Ultimately, we can figure out the relationship between these scoring data and sales data. The higher the sales, the greater the value of the content we produce.

Summary

The success of content marketing is no accident. The key to the success of content operations is the ability of operators to properly manage and use data output to be beautiful and to guide participation and transformation.

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So in terms of operations, what is the same cohort?

In the same period, the group belongs to a subgroup of users, which refers to the group of users who have the characteristics of common behavior within the specified time.“Common behavior” refers to the similar behavior in a certain time period, which in addition to sort them out according to different time of new users, may be classified according to different behaviors, such as “in June 2017 for the first time to buy”, “on the second week of October, 2017 products began to reduce the use of frequency”, etc.
Note that the cluster analysis focuses on the differences between groups during the same phase of the customer’s life cycle.

Why is Cohort Analysis important?

In the process of product development, we usually measure product revenue and product user volume as the ultimate measure of success or failure of this product.There is no denying that these indicators are important, but they could not be used to measure the product’s success recently, and most likely will hide some issues which need our attention, such as user engagement has been falling, added in gradually slow, etc.On the analysis of user behavior in the process, we need more detailed measures, so that is better for us to accurately predict product development and by version iteration in a timely manner to product optimization and improvement.

Cohort Analysis is the key to improving the retention of APP users

The success of a product, it says, is not how many downloads it is, but how to keep losing users and how to recall them.
We can’t through downloads to determine the specific situation of the development of the APP, because beautiful download data will mislead us thought APP development is very healthy, but in fact, many users download is lost in a few days.Group analysis is the key to improve user retention.
case
For the first time, users of the APP were analyzed in the same time group and observed their retention for the next seven days.

17461 new users start the APP, for the first time in October 30, with 30.6% of people in these users on the first day to start again, the fourth day 12.2%, 7.9% on the seventh day, and this shows that at the time of the seventh day approximately once every 12 users only one active users.It also means we lose 92% of our users
We need to know which groups have better retention and analyze why.Have we launched a new marketing campaign on that day?Or offers promotions or discounts?Or new features that add video to the product?We can apply these successful strategies to other users to improve user activity and retention.We can also compare the retention of different time periods:

? The preservation of the new lads:

By comparing the different time groups, we can see four days, seven days and so on.These data can give us the key information about user login experience, product quality, user experience, and market demand for products.

? Long-term retention:

By looking at the number of days a group of users are returning to use the APP again, we can see the long-term retention of each cohort, rather than the remaining days of the new APP.
We can know where the user is out, and you can know what’s the feature of active users, what they are doing and so on the one hand, will help us in new quickly find target users, on the other hand we can also affect the new users, let them follow the same route, eventually become loyal users.

Cohort Analysis can help us monitor real user behavior, measure user value and develop targeted marketing programs in real time

For example, our operations team launched a 60-day welcome campaign in September to promote user growth through a series of discounts and offers.Through advertising and social media, we have thousands of users growing every day.Five months later, our user growth was very large, and the leader was very satisfied with the result of our activity.
On the face of it, we have achieved the goal of user growth.However, when we study cohort data, starting from the lifetime value of customers, we will find that new users welcome activities in activity 2 months after purchase rate continue to reduce, by contrast, activity before the new users such as users in August, in the activities of this five months purchase rate has been stable.

If we only measure our monthly gross income as a measure, we expect revenue growth to come only from the new influx of users.However, user group data following the launch of the activity shows that once the discount activity is over, revenue will decline.The decline in revenue proves that we have not expanded our loyal user base.
As shown above, through the cohort analysis we can real-time monitoring the real user behavior trend, otherwise, we will get wrong judgment because only analyze the overall data and make the wrong decision.By analyzing the behavioral differences of each cohort, we can develop targeted marketing programs.In this case, the operator needs to develop a new strategy to improve user engagement after two months of activity.

How to implement Cohort Analysis?

Start by defining business questions

Defining a business query is a prerequisite for the study of effective results.Business questions define business goals and research attempts to solve them.
Does the customer purchase conversion rate increase after we optimize the product?Is the user turnover rate reduced after the product improvement?We need to iterate and refine these questions to ensure that it is aligned with business objectives.

Define metrics based on business queries

If the purchase conversion rate and user retention rate are the key indicators to answer business questions, we want to understand the user turnover rate and the final purchase conversion rate from registration to completion of purchase.

Definition cohort

In the previous cases, the group was based on the user who purchased the account within a week of creating an account.In other cases, we can define a group in a different way, for example, a content APP that we might post within 24 hours of creating an account.

Analyze group data over the same period

We also in a cohort of typical form, for example, rows horizontally for natural number, longitudinal for every new clients, forms the internal is to calculate retention rate every day, usually the retention rate of lateral will stay some days later in a relatively stable state, we can see from the picture, keep stable in 5 days.This means that the users are stable.Otherwise, if the retention rate keeps going down, it’s going to go to zero someday.
Let’s look at the longitudinal retention data, and if a product is in healthy development, this data should be getting better and better.Obviously this product is not, the PM should constantly improve the product according to the historical data, improve the user experience, thus improve the retention rate!

Conclusion

Cohort Analysis in the same period will help us to analyze user behavior more deeply and reveal the problems covered by the overall measure.In the current situation where the marketing mode and the activity effect are changing, learning to use the same period group analysis can help us to predict the future income and product development trends.

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