Is the guy's ranking credible?

When shopping, we often encounter the problem of choosing among multiple commodities, especially for high-value commodities. Gai Ranking is born to solve this problem. With the improvement of shopping environment and the increasing rationality of consumers, how does Gai's ranking stand out from it? The author will combine the current actual market environment and user usage, analyze the ranking of Gaide from multiple angles, and put forward suggestions for improvement.

this article will analyze from the following aspects:

product overview

market analysis

user analysis

function analysis

summary

1. product overview 1.1 product introduction

Gaide ranking is in the form of PGC ranking to provide objective and neutral goods and services for users with shopping and consumption needs. Its ranking basis mainly comes from the major rankings, which are sorted by editors to prevent bidding ranking.

1.2 product function frame diagram

product architecture

1.3 typical use path

a. users shop on the website and want to find out what products are worth buying under a certain category.

B. the user wants to find a restaurant/other city service.

C. the user has something he wants to buy, and wants to get a shopping subsidy or find a coupon.

D. Users don't have a clear shopping goal, just look around.

second, market analysis 2.1 market boundary

Gaide ranking is an auxiliary shopping APP that mainly provides consumers with various commodity ranking and city service ranking services, and at the same time provides more favorable purchase channels.

The target group is rational consumers who have certain spending power and pay more attention to product differences and product parameters. With the coverage of its commodity ranking and urban service ranking becoming wider and more perfect, the number of target users of products is also increasing, and the market boundary is expanding.

2.2 Estimation of market space

The people covered by Gaide ranking are mainly rational consumers who shop online, have certain spending power and pay more attention to product differences and product parameters, and the user groups are mainly between 25 and 4 years old.

according to the 43rd statistical report on China's internet development released by CNNIC in 219, the number of netizens in China reached 829 million, the number of online shopping users reached 61 million, accounting for 73.6% of the total number of netizens, netizens aged 2-39 accounted for about 5.3% of the total number of netizens, netizens with high school education or above accounted for about 43.1%, and people with a monthly income of over 3, yuan accounted for about 45.1%.

It is estimated that by 22, online shopping will cover about 8% of the netizens, and the number of netizens will reach about 9 million. Internet users aged 1-19 account for 17.5%, and those aged 4-49 account for 15.6%. High school education and above basically refer to users over 18 years old. These people account for the vast majority of groups with a monthly income of more than 3, yuan, and the target population also covers a small number of students aged 19 and below and the elderly.

according to the data of previous years, the number of people with a monthly income of 3,-5, has not changed much, and the number of people with a monthly income of more than 5, has increased greatly every year, with an annual increase of about 4 million. On the one hand, this part of the population comes from the previous lower income group for promotion and salary increase, and on the other hand, it comes from the original non-income student group, which is basically the coverage group of online shopping. Based on this calculation, the ceiling of the market is about 3 million to 35 million.

The estimate of Ai Media Consulting

2.3 data

2.3.1 APP's own data

In the past year, the number of downloads on the Android side was about 1 million, and the number of downloads on the iOS side was about 42,. In April this year, the number of independent devices on iResearch dropped significantly from the previous average of 4, to 55, to 24,, and it is estimated that the actual monthly users are 15, to 25,.

Data source-iResearch data

According to Analysys data, Gaide ranked 11.6 startup times per month, .6 hours per month, 2.1 startup times per day and 6. minutes per day.

2.3.2 competing product data

1) product ranking category

There are very few APPs in the market that are similar to Gaide's ranking. At present, only one app named Top 1 ranking has been found, which is highly similar in business. The Top 1 ranking mainly provides product ranking service and also has the function of shopping rebate. However, there is too little data on the Internet, only that there is a certain amount of downloads on the Android side, but the function is not perfect, and there are too few active users in general, so that they are not entered by the main data platform.

2) e-commerce shopping guide platform

In terms of content shopping guide, we choose what's worth buying in PGC+UGC mode, and Xiaohongshu, a content community in UGC mode. In terms of price shopping guide, we choose rebate and shopping in OGC mode for comparison. (Analysys is limited by the trial account, and the data that can be viewed is old)

Comparison between Xiaohongshu and what is worth buying (data source–Analysys Qian Fan)

There is a big gap between Xiaohongshu and what is worth buying, but the user viscosity is opposite. What is worth buying is started 66.72 times per month, little red book is 23.31 times, and what is worth buying is used for 5.1 hours per month, and little red book is 2.6 hours. What is worth buying is started 5.13 times per person per day, little red book is 3.52 times, what is worth buying is used for 23.4 minutes per person per day, and little red book is 18.62 minutes.

Comparison between Yitao and Rebate (data source–Yiguan Qian Fan)

The user scale of Rebate and Yitao is similar, but there is a gap in user viscosity. The average startup times of Yitao Monthly is 18.55 times, and the rebate is 11.67 times. The average usage time of Yitao Monthly is 1.12 hours, and the rebate is .44 hours. The average daily startup times and rebate of Yitao are similar, both around 2.7 times. The average daily use time of Yitao is 9.78 minutes, and the rebate is 6.29 minutes.

It can be seen that the user viscosity of price shopping guide apps is much lower than that of UGC content shopping guide apps. After a well-managed community develops to a certain extent, a considerable number of users can be retained, and users participate in community discussions and content improvement, which also increases users' desire to buy, and users complete their purchase behavior in the APP, forming a complete closed loop.

Little Red Books are mainly for female users, and mainly for beauty and clothing. The user groups and contents are more single but more targeted than what is worth buying. What is worth buying is aimed at a wider range of users, and because it mainly provides preferential information, the update speed is fast and the information is timely, and users will use the APP more frequently.

However, too much information about what is worth buying also leads to problems. Too much information is messy, and at the same time, lack of in-depth articles will dissuade many users, and the remaining users actually spend most of their time sifting through a lot of information.

The overall difference between the two price shopping guide apps is that Yitao is richer in content, adding more content information on the basis of simple rebates and coupons, but the user stickiness of the two products is still far lower than that of UGC-type content shopping guide apps.

3) City Service

Here we select Public Comment and Word of Mouth, both of which are apps that provide ranking and group purchase information of local life service businesses.

There is a certain gap between the users of the two apps, and the user's viscosity is higher than word of mouth. Public comment has been launched 15.8 times per person per month, with a reputation of 1.13 times. Public comment has been used for 1.6 hours per person per month, with a reputation of .43 hours. Word-of-mouth was launched 2.55 times a day, slightly higher than the public comment of 2.36 times, and the average daily usage time of public comment was 9.89 minutes, higher than the word-of-mouth of 6.57 times.

Public comment and word-of-mouth are almost the same market positioning and functions, and they are both promotion modes of UGC+. The reason why public comment is superior in user viscosity is that word-of-mouth entered the market late, and it has been deeply cultivated in the market for many years, with richer merchant resources, more perfect and more choices than word-of-mouth.

The reasons for the difference in user scale are: first, the time to enter the market is different; second, many users use word-of-mouth in Alipay without downloading the word-of-mouth client, which has an impact on the usage of word-of-mouth APP; third, public comment has business cooperation with WeChat, which has a secondary portal under the payment function, and at the same time, there are WeChat applets, which make it easier to get traffic on WeChat. As a product of Ali, word-of-mouth will be limited in the content dissemination on WeChat, and it is not as small as WeChat.

2.3.3 Comparative analysis with competing products

Gated ranking is a PGC model, in which editors with specialized knowledge select and rank goods or services, or integrate authoritative rankings on the Internet to produce different rankings. The advantage is that the output quality can be controlled, but the disadvantage is that the user participation is low.

compared with competing products in UGC mode, the user stickiness and activity are lower, and the user stickiness data is closer to the two price shopping guide apps in OGC mode. There are few products ranked by PGC mode in the market, which can not be compared with other products, which is also the core competitiveness of Gaide ranking.

2.4 summary

The core function and the most competitive place of Gai's ranking is the product ranking in PGC mode.

The characteristics of this ranking list are: first, it advertises that it is free of charge, that is, it is objective and fair, and second, it covers a complete range of goods. In addition to the common beauty care and digital 3C in other apps, it also covers a large number of daily necessities, electrical appliances, etc., and urban services have also been ranked in PGC mode.

However, at present, the objectivity and fairness of the ranking is not enough, and it is not reliable enough. Objectivity and fairness, on the one hand, refers to not accepting money and not doing "bidding ranking", on the other hand, refers to the accuracy of ranking, which is not very satisfactory. In the evaluation of online search, a large number of users report that the ranking is not accurate and objective, and the ranking standard is not transparent, especially the evaluation of some professional users, which makes the ranking of Gaide not convincing enough.

For the ranking, objectivity and fairness are life. For the Gai ranking, the criticism of online professional users, especially influential professional users, will seriously hinder the promotion of products.

In this regard, on the one hand, commodities can be divided into different dimensions, and each dimension can be scored differently, which makes the evaluation criteria transparent and standardized. On the other hand, we can find influential, objective and independent evaluation agencies and media to assist in the ranking work, which is more labor-saving and convincing.

In fact, users don't particularly care whether your ranking comes from professional institutions at home and abroad, and the ranking of these institutions may be unfair. Users think that the ranking is reliable.

on the one hand, I have an evaluation on whether the ranking is reliable according to my own knowledge of my familiar fields; on the other hand, I have an evaluation on the accuracy of the ranking according to other media, professionals and other users. Therefore, it is necessary to standardize, professionalize and be transparent in ranking standards. For the existing users, it is also an important job to improve the stickiness of products to them.

III. User analysis 3.1 Age analysis

Data source–iResearch data

According to the data of iResearch in May 219, the main users of Gaide ranking are between 25 and 4 years old, especially between 31 and 35 years old.

This group of people generally have certain spending power and have formed the habit of online shopping. Users under the age of 24 account for 1.3%, accounting for a relatively small proportion. The main reasons are that most of these groups are students, have not graduated, have no stable source of income, and have limited spending power. Second, funds mainly come from parents, and consumption pays more attention to personal hobbies or personalities, and cost performance is not the most important concern.

users aged 25-3 account for 22.33%, ranking second among the five age groups. Most of these users have just entered the workplace, have some money, but their spending power is not very strong, and pay attention to cost performance. At the same time, online shopping has become an important shopping method for them. Compared with users under the age of 24, the shopping concept of these users is more mature and rational.

users aged 31-35 account for 45.15%, which is the absolute main force of Gaide ranking. Most of these users have been stable in their careers, and their income level is generally in the middle or upper middle, and they can basically achieve economic independence. Besides daily expenses, they have certain other consumption budgets. Most people have married, and they are under great economic pressure due to family, children and other reasons. They need to buy more kinds of goods than before, so they need to buy high-quality or cost-effective products through clear shopping guides.

users aged 36-4 account for 18.12%, ranking third among the five age groups. These users have basically formed stable consumption habits, have relatively fixed preference brands and purchase channels, and have strong consumption power. The main purpose of using cover ranking is to find a small number of products with high cost performance or high quality in unfamiliar fields.

users aged p>41 and above accounted for 4.37%, accounting for the least. The main reason for the small proportion is that many users in this age group are not good at online shopping, or are used to shopping directly on e-commerce platforms, not good at using third-party tools, and rarely have the idea of comparing prices or actively looking for third-party goods.

3.2 Gender analysis

Data source–iResearch data

According to the data of iResearch in May 219, there are slightly more female users than male users, and the gender ratio is generally balanced.

The reason for the overall average gender is that the ranking basically covers most commodity categories and urban services, not limited to one or two categories. Compared with apps such as Xiaohongshu and Mushroom Street, the coverage is wider, so the gender is more even, and it can provide consumption guidance for almost all users. As for the reason why there are slightly more female users than male users, the author thinks this is related to the functional change of Gaide ranking.

Source–iResearch Data

According to the data of iResearch in February 219, there were slightly more male users than female users. In the past few months, the Gaide ranking has strengthened the functions of subsidies and search coupons.

data source-iresearch data

the above picture shows the gender data of rebate APP users in may 219, and it can be seen that there are obviously more female users than male users. Gaide Ranking attracts more female users by increasing subsidies to users and improving shopping-related functions.

3.3 Analysis of user distribution

Data source-iResearch data

According to the data of iResearch in May 219, the top users are mainly distributed in the economically developed coastal areas, followed by the economically active provinces in the central and western regions.

This is mainly related to the level of regional economic development. In areas with high economic level, people's consumption ability and willingness are stronger. The city service guide of Gai Te Ranking is also to improve the major cities first, which will attract more users in these big cities, especially Guangdong, as the location of Gai Te Ranking, is also the first place to improve.

3.4 User usage scenario

User A:

A 22-year-old college student, male, who is about to graduate, wants to buy a laptop computer. He looked up the evaluation videos of Zhihu and Billie on the Internet, but he still felt unable to start because he couldn't understand them.

a friend gave him advice.