How should an operator plan an inventory H5?

The day before yesterday, the WeChat report was finally released after much anticipation, but it only had a small-scale impact, and many users were confused and could not find the entrance to generate the report.

Considering that two days ago, "2018 Alipay Annual Bill" dominated the circle of friends and stayed at the top of Zhihu's hot list for several days, why is the communication effect so different for the year-end inventory of the same major companies?

2018 Alipay Annual Statement

2018 WeChat Annual Data Report

At the end of each year and the beginning of each year, all kinds of annual reviews, lists, and reports are flying all over the circle of friends.

Users are no longer surprised by this, but they are still excited and looking forward to it. It seems that they cannot get through this year until these inventory H5s come out.

Despite the presence of the "three big mountains" of WeChat, Alipay, and NetEase Cloud, more and more apps and platforms are joining the year-end review army, hoping to become famous in one battle.

I can't help but wonder, why do year-end review H5s have such strong vitality and spreadability?

As an operator, how should you plan an inventory H5? Today, let’s talk about year-end inventory planning.

01What is inventory-based H5

The essence of inventory-type H5 is still an inventory of content, but the operations staff presents the essence of the inventory in the form of H5. According to the logic of inventory, it can be divided into two categories: content-based inventory H5 VS data-based inventory H5.

In order to help you better understand, I have selected excellent cases for each type to illustrate.

1) Content type

Content-based review H5 is similar to the annual major events review article. It may start from a certain field or theme and show the major events related to it in the past year, such as entertainment industry biography, annual film list, top ten keywords of the year, etc.

NetEase News "2018 Entertainment Industry Pictorial Biography"

Zhihu "2018 Movie List"

Of course, in today's world where users' attention is at stake, simply letting users "watch" can no longer satisfy the ambitions of operators, so some content-based H5s also add interactivity:

Tencent News: "18 seconds to crack the emoji of the year"

Interface News "Which photo will you use to mark 2018? 》

In addition, there is a special category in the content type: time-based inventory. That is, according to the timeline model, the major events of a brand or product in the past period of time are displayed linearly.

Time-based inventory is no longer common due to the lack of surprise in the content itself and the low degree of connection with users.

Coca-Cola: Sharing Happiness for 128 Years

2) Data type

Data inventory is common in tool-type apps, which review user data to show the user's "activity trajectory" or "consumption trajectory" for the year.

Due to the high user value and users' willingness to voluntarily forward and share for the purpose of showing off, this type of inventory H5 is likely to produce screen-sweeping hits.

Luckin Coffee: "2018, the unforgettable blue moments I've walked with you"

Douban "2018 Douban Books, Movies and Audio Report"

Before planning a year-end review H5, operations staff can first think about which type of review logic is suitable for their products. In this process, the following factors need to be considered:

023 types of data inventory

Judging from the situation that has been flooding the WeChat Moments in recent years, data-based inventory logic is the most widely circulated, such as the "2018 Alipay Annual Bill" and NetEase Cloud Music's "2018 Annual Listening Report".

Data inventory H5 can be further divided into three types: report data, user data and product data.

① Report-type data inventory H5

It means making the industry report into H5 format, which often contains a large number of charts and analyses like the report. For example, “The Truth About Generation Z User Behavior” released by Tencent Video is a typical example.

Report data/Tencent Video "The Truth About Generation Z User Behavior"

② User data inventory H5

Integrate and display data related to the user (that is, "me"). For example, although it didn’t go viral, QQ Music’s “Your Music Memories of 2018” was also well done.

User data/QQ Music "Your 2018 Music Memories"

③ Product data inventory H5

Displaying the product's own data performance is mainly used for brand communication. For example, the "Huawei 2016 Annual Report Summary" shows the achievements made by Huawei in 2016.

Product data/ "Huawei 2016 Annual Report Summary"

These three data types can be mixed and used. For example, yesterday’s “2018 Tencent Data Report” mixed three types: user data and smart life are product data, crowd emoticon user habits are report data, and the last “N Days with WeChat” is user type.

Inventory of product data in the 2018 Tencent Data Report

Report-type data inventory on the left, user-type data inventory on the right

Among the three data types, user data inventory is the most likely to be shared by users. The reason behind this is easy to guess. Users like to share content related to themselves. User data inventory H5 can often meet users' needs to show off and allow users to actively forward it.

03 User data inventory H5 easily overlooked pitfalls </h2
Next, I will tell you about a pitfall that is easily overlooked when doing user data inventory H5, that is, user data does not come with the product itself!

At the end of 2014, the “Alipay 10-year bill” went viral on WeChat Moments. At that time, I was working in content operations at Tieba, and I also wanted to do a similar user data inventory on Tieba.

I listed some basic user data, including "when joined, how many bars followed, how many posts made, how many likes received", etc., and excitedly ran to the data group to submit data requirements. It turned out that the data was incomplete and I could only obtain the user's data for the past 4 years. The plan failed.

So here is a suggestion for those who are working on new projects. When deploying data statistics for the product, remember to collect basic user portraits. This way, when you want to use the portraits for publicity every year, you won’t be in the same situation as me.

Some friends may want to ask, my user data is not so complete, but I also want to do a user data inventory, do I have to give up?

In fact, if H5 itself does not have precise requirements for data and only needs rough data, it can also generate exclusive data reports for users through the data submitted by users and some product-specific algorithms, thereby achieving better communication effects.

For example, Dongqiudi's "Comparing Salaries with Top Football Stars" is a typical "inaccurate" user-based inventory H5. By entering your personal monthly salary, you can compare the time spent on buying a house or a car with that of the football stars.

Dongqiudi "Comparing Salaries with Top Football Stars"

The communication effect of report-type H5 is also good, ranking after user-type data. It is also more diversified in data acquisition. In addition to product data, it can also obtain data through user surveys, reference to other literature, etc.

Finally, product data basically belongs to the brand-level communication, and the probability of it going viral in the circle of friends is very small, unless it is a brand like WeChat, Taobao, Alipay, QQ, Tieba, Baidu, and the product report has become an IP. There is hope of dissemination, because except for internal industry practitioners, users who have not used your product basically will not care about your product data.

According to the communication effect, the ranking of these three types of H5 is: user-type data inventory > report-type data inventory > product-type data inventory

04How to do a good job in data inventory H5

After understanding the types of data inventory H5, the next step is how to make an excellent data inventory H5. From these inventory-type H5s that have gone viral on WeChat Moments, I have analyzed some experiences that can be reused when doing year-end inventory.

1) Content mining

The content mining here focuses on data mining, which is the original reference data of the data inventory type H5. In this stage, we need to explore what types of data are needed to make operational decisions. Specifically, the following types of data may be included.

① Basic user data

Basic user data with product characteristics, such as zodiac sign, age, gender, registration time, location, relationship status, income, industry, number of times the product is used, peak product usage, etc.

② Interaction data

Interactive data with product characteristics such as posts, comments, replies, likes, searches, followers, geographic location check-ins, clock-ins, purchases, etc.

③ Product data

Male-female ratio, total number of users and growth, geographical distribution, function usage time, DAU/MAU, keyword frequency, transaction volume and growth, comparison of mobile/PC terminals in terms of users and revenue, and other interactive data with product characteristics.

2) Content Integration

The data mined in the first phase were scattered and had no specific themes.

Therefore, operations need to discover patterns from these data, plan data themes that can arouse users' interest, and then determine some interesting dimensions to support it based on the themes, while allocating the mined data to these dimensions as much as possible.

Here we take the "2018 Alipay Annual Bill" and NetEase Cloud Music's "2018 Annual Listening Report" as examples.

The data dimensions of Alipay's annual bill include overall consumption data, Ele.me data, offline payment data, travel data, environmental protection data, public welfare data, etc., including the following core data:

NetEase Cloud Music's annual listening report includes basic data, mood data, hobby data, consumption data, etc., including the following core data:

3) Content packaging

Content packaging mainly refers to the packaging of data. Currently common packaging includes highest, proportion, accumulation, average, trend, distribution, comparison, and simulation.

Of course, there is also data that is very meaningful in itself and does not need to be packaged, such as who is the first user you added as a friend. I won’t go into details here, but in general there is one principle: present the most interesting data to users.

4) Content dissemination

Now that data has been mined, integrated and packaged, can we start disseminating it? That is not the case.

It is also necessary to check whether the key path data statistics of each page have been completed, monitor the traffic of each module of the page, and make real-time adjustments to the topic content based on data analysis.

In terms of content reaching users, operations are mainly responsible for in-site promotion (home page, push, EDM, SMS, pop-up windows). Basically, interesting user data inventories will cause spontaneous participation and dissemination by users in the site.

05 Conclusion

The biggest advantage of Internet companies and traditional companies is that user behavior can be accurately recorded and has massive amounts of data.

It is precisely based on this advantage that Internet products can publish user behavior data inventories at the end of each year for dissemination, which can often achieve the effect of flooding the circle of friends.

I hope that all of you operations partners can make a great year-end inventory case.

Author: Operation Research Society, authorized to publish by Qinggua Media.

Source: Operation Research Society (U_quan)