A set of live broadcast review methodology!

1. Why do we need to do live broadcast review ?

Although the "live broadcast +" model has become an industry trend, its significance and value are often challenged when the payment benefits generated by the product's access to the live broadcast module are not obvious. Therefore, the first priority is to verify its value.

At the same time, new fields lack reusable methodologies, and only continuous exploration, optimization, and sedimentation can promote their healthy growth.

Finally, in the live broadcast field under the product scope, the targeted attraction is still mainly product users. Continuously exploring new tracks that live broadcast can open up for products and bringing new customers to the products is the direct value reflection of the "live broadcast +" model for products.

2. How to review the live broadcast?

Live streaming is fast-paced and highly competitive. While taking into account rapid review, it is also necessary to consider the periodic and holistic monitoring of live streaming data. Therefore, live streaming review can be divided into two major directions: rapid review and periodic review .

1. Quick review

On the day of the live broadcast or the next day, quickly review yesterday's data, focusing on the intuitive data performance and user feedback of a single live broadcast. The purpose is to gain experience from the day's live broadcast and quickly apply it to subsequent live broadcasts. It is a continuous PDCA process.

In terms of review, it can be divided into the data side and the user side :

1) On the data side , we focus on the data performance of this period, and at the same time compare it horizontally with other live broadcast periods to clarify the data performance of this period.

① When making a horizontal comparison, it is necessary to compare the four core data of the live broadcast (total number of viewers, maximum number of online viewers, number of comments, and average online time) with other periods to clarify the "pros and cons" of the live broadcast.

(Core data of current live broadcast)

② When focusing on this issue, we focus on the overall trend of changes in the number of online users in the live broadcast data, locate the highest online point, and fit it with the live broadcast content, which can help us locate the "highlight points" of the live broadcast.

2) The user side focuses more on the process of the host broadcasting and the user listening and answering, including:

① During the live broadcast, identify nodes with abnormal user emotions and feedback (such as negative comments) to locate possible pain points of users.

② When fixed students serve as “live audience”, any abnormal problems they notice (such as unnatural information connection, etc.) should be reviewed in time.

③ In the interactive comments in the live broadcast room, the most popular content can be used as the content that can be "returned" in subsequent live broadcasts and included in the live broadcast content demand pool.

2. Stage review

After accumulating multiple live broadcasts, what we need most at this time is a periodic review . Convergent analysis of multiple live broadcasts from a macro perspective can help us clarify the core value of live broadcast for the product, and identify the core user circle, attraction points and the "rhythm" that live broadcast should have.

In the stage review, in addition to the data side and the user side , we also need to pay attention to the market side :

1) Data side - Different from quick review, on the data side we focus on the value of live streaming to the product from an overall macro perspective, as well as the overall data performance of live streaming, hot spot distribution, etc.

① From the perspective of product value positioning through live streaming, without exploring payment methods, what live streaming can do for the product is to attract new users, increase user activity, and extend the time users stay in the product.

(Data range to focus on when positioning value)

After clarifying the value, we should then clarify how to make the live broadcast better in the future. The data here can be divided into two major directions: one is hot spot analysis , and the other is live broadcast rhythm analysis .

① In hot spot analysis , it is necessary to break down from the perspective of single period, classification, and word item .

First, single-period live broadcast is simply to sort the data of a single period of live broadcast, find the "good" live broadcast and extract its characteristics; secondly, the classification analysis focuses on the horizontal comparison between various types of live broadcast data. From the comparison of classification data, it can be seen which type of live broadcast is more attractive to users.

Finally, by breaking down the title of each live broadcast into terms , filtering out meaningless terms, and then multiplying it by the number of people watching the corresponding live broadcast, and taking the corresponding average number of people watching the live broadcast for each term that appears 2 times or more, we can locate the core keywords that users are most concerned about and that can attract the most users.

(Hotspot Analysis)

② In terms of live broadcast rhythm analysis , thanks to the huge amount of data from periodic reviews, we can cluster the live broadcast trend lines under multiple live broadcast clusters. On the trend lines, we also focus on the three core points of "attracting new users, growth, and loss."

(1) The peak period when users enter the live broadcast (i.e., the new peak) can be used to locate the highlights of the live broadcast and make corresponding arrangements to retain users.

(User enters live broadcast rhythm analysis)

(2) By comparing the data trends of the live broadcasts that are above the median and below the median after the live broadcast data are sorted, we can see what the data trends of a good live broadcast should be like.

Taking the live broadcast I analyzed this time as an example, it can be seen intuitively that the "bad" live broadcasts "climb" very slowly during the golden period of growth, and after the golden period, the live broadcast data gradually flattens, and "bad" live broadcast data becomes a foregone conclusion. Therefore, what we need to do is to promote growth as much as possible during the golden period of growth .

(Analysis of user inflow and outflow relationship)

(3) So during the golden period of growth, from the perspective of product strategy , what should we do is to retain users? Or try to attract new members? From the data, we have two ways to judge, namely correlation analysis and hypothesis analysis:

1. In the correlation analysis , the number of new users/churn rate is compared with the maximum number of online users, and the correlation is analyzed using a data model. The correlation coefficient shows which indicator has the highest correlation with the maximum number of online users, that is, the indicator with the greatest impact.
2. In the hypothetical analysis , we can compare the new users/churn rate in the live broadcast data "below the median" with the data of the live broadcast "above the median", and keep other factors unchanged. We can see in which case the maximum number of online users is higher and the data is better, so we can determine where we should focus more efforts.

2) User side - We need to identify the live broadcast audience and user circles, and conduct a service experience review based on the viewing experience of this audience. This will help us better define the focus, scope and promotion model of subsequent business.

In terms of user circles , we clean the audience data and locate the audience's characteristics from various dimensions such as age, gender, region, and channel . At the same time, considering that live streaming itself is based on products, its user circle basically overlaps with the product itself, but there will be certain differences.

Therefore, TGI analysis (Target Group Index) can be combined here to locate the differences between the live broadcast user circle and the product, so as to adjust the live broadcast content more targeted at the audience.

(User circle analysis)

At the same time, due to its special nature, live streaming itself can also be compared to the thinking of service experience design . Therefore, when analyzing on the user side, we analyze user behavior, needs, pain points, and quick points from the three major links of before, during, and after the live streaming, so as to locate the opportunities for live streaming optimization in each link and promote optimization.

(Live-Experience Map)

3) Market side - When analyzing the market, we not only need to pay attention to similar competitors that are doing well in the live broadcast field , but also analyze the latest hot topics among the audience. This will help to introduce the core topics of live broadcast in subsequent publicity and promotion, and can also lead us to think about new tracks for live broadcast to help the product expand its user circle.

3. Summary

As a new track of the Internet, the live broadcast + model can be used in various aspects such as product promotion, customer acquisition, and activation. Challenges and opportunities coexist. But precisely because it is a new track, there is a relative lack of systematic methodology.

This article is mainly to share the live broadcast review methodology that I have accumulated in my work. You are also welcome to share any insights or ideas with me.

Author: Tomato

Source: Tomato