Product analysis of dressing assistant APP

I, Xiao Bai, am doing product analysis of app for the first time. Please give me your advice.

Product version: 1 1. 18.3

Operating system: Android 8. 1.0

Mobile phone model: glory note 10

Dressing Assistant is a mobile APP focusing on "Women Dressing with Fashion Community". It connects users and fashionistas through matching, watching and scouring, aiming to build the largest fashion media platform on the mobile side, break the closed fashion circle and give every fashion girl a chance to become an influential fashion media. And by focusing on creating personalized matching reading streams and shopping platforms for users. Since it was launched in October12,110, the number of users has exceeded 30 million. More and more fashionistas are setting up their own clothing assistants, sharing their matching experiences and discussing their shopping experiences. Dressing assistants are expected to become the most professional collocation and sharing platform on the mobile side.

Women's dress community +b2c quality e-commerce

Slogan: Meet a better self.

The young white-collar users that dressing assistants want to catch are different from the elite white-collar groups, and their spending power makes them pay more attention to the brand of clothing. Most young white-collar workers are unwilling to pay brand premium. Their bigger pain point is that the selection cost of platforms such as Taobao Tmall is too high. What the dressing assistant wants to do is to help these "new white-collar workers" improve their shopping efficiency.

Therefore, the product is positioned in the clothes collocation of high-quality products, which shortens the shopping time of users and reduces the difficulty of users' choice while providing high-quality products.

user portrait

According to the data of iResearch, 93% of female users and only 7% of male users, which is related to the female positioning of the platform. 85% of users are under the age of 35. These people are young, love beauty, busy with work and have certain economic ability.

It can be seen that the users who can get dressing assistants are mostly women under the age of 35 (after 85) who love beauty. They are usually busy at work, have little spare time, pay attention to the quality of clothes, and their spending power is medium to high.

User modeling

Name: Xiao Li

Gender and age: female, 25 years old.

Education: Bachelor's degree

Occupation: Web designer

Income: 8000

Device: iPhone 7 Plus

Features: good popularity, good looks, single, like to participate in various social activities,

Commonly used apps: QQ, WeChat, Weibo, Taobao, Alipay, Tencent Video, Meitu Xiu Xiu.

Scene: Xiaoli works as a page designer in an Internet company. She works 996 hours a day and is very tired. Today is Monday. In order to relax everyone, the boss informed everyone to set up a group this Sunday and prepare to go camping. However, Xiaoli made a mistake. The clothes she often wears have faded out of date. She is usually busy with work and has no time to buy new clothes. How can she not show her personal charm when she seizes such a good opportunity? So at lunch, she complained to her colleagues around her. Colleagues recommended her a "dressing assistant" app. On the app, she quickly customized and matched a set of favorite and suitable clothes according to the weather and body temperament she went out on Sunday, and placed an order for purchase. I bought it before I finished eating. After two days, the clothes were sent to the office, and the size and quality of the clothes were very good. Wearing the same clothes as the seller's show has aroused the envy of colleagues around. Xiaoli not only wore this suit to go out to play on Sunday, showing her personal charm, but also won the male ticket and everyone's praise, and then embarked on the peak of her life.

user demand analysis

According to the above analysis of user groups and typical users, combined with the specific functions of dressing assistants, the user needs are divided into three layers.

1. Basic needs: you can buy fashionable and high-quality clothes.

2. Expectation demand: according to the weather, scene, personal figure, personality hobbies, etc. The system can quickly match the customized collocation suitable for individuals, reduce the difficulty of selection, and at the same time, the collocation is more suitable for yourself.

3 Charm needs: discover online celebrities who wear fashion, establish social relationships, and learn wearing skills.

The seller of "Women's Community+Quality E-commerce" shows his unique style in the form of "combination and collocation" on the dressing assistant, attracting users with the same taste to become fans. On the dressing assistant platform, every matching buyer is fashionable from the media, which can directly transform the influence of the community into economic benefits.

Since the establishment of 20 12, the dressing assistant has successfully completed two gorgeous turns driven by the needs of users. From a women's fashion sharing community similar to UGC to a women's fashion community that provides professional guidance and advice (PGC) for female users' needs of drying, watching and scouring. Later, I gradually realized that it is difficult to guarantee the quality of clothes and services from dressing assistants to guide users to other e-commerce websites. In order to provide users with more secure services, after a period of testing and optimization, the dressing assistant was officially transformed into a quality e-commerce company focusing on pure women's "matching shopping" in May 201April, and a shopping system was built on the basis of the community to turn fans into purchasing power.

As for the target group, dressing assistants will shift from college students to young white-collar workers with higher spending power, so that the unit price of customers will increase to around 300~500 yuan; In terms of commodities, upgrading is reflected in the improvement of commodity quality and the integration and optimization of back-end supply chain. On the one hand, the dressing assistant expands the supply side from Taobao merchants to fast fashion brands; On the other hand, they will control the circulation link, and merchants can only enter the "dressing assistant" through the platform invitation system; The final content upgrade is to improve the richness of fashion information content. The platform will increase video and other forms of expression, broaden the content sources at the same time, and integrate PGC users, matching businesses, fashion from the media and other channels in the form of today's headlines.

Dressing assistants are similar to American Talk and Mushroom Street, all of which change e-commerce shopping from "shopping around" to "shopping". In the era of mobile shopping, which pays attention to "short frequency and fast speed", it provides users with a shopping mode that saves time and cost. The difference is that the dressing assistant pays more attention to "collocation". The characteristic of these people is that they don't want to spend a lot of time doing "amoy" behavior, but are willing to go to a platform with quality assurance (perhaps the price of this platform is not the lowest in the whole network) to buy high-quality products steadily, and the core of quality e-commerce is "good goods".

Dressing assistants mainly choose buyers' shops when choosing businesses. Buyers' shops must "match and choose goods" to form an obvious style orientation, and do it around "match" from the beginning of product selection, not just a simple marketing plan. At the same time, "matching purchase" is a consumption pattern that caters to users' intuition. Users can choose clothes according to specific life scenes, which not only saves time, but also meets the needs of users. Moreover, because you can enjoy a 10% discount on the complete set purchase on the dressing assistant, you can also save some clothes purchase costs.

The average joint purchase rate of clothing e-commerce shopping guides is about 1. 1 (an average order contains 1. 1 product, or selling a product can directly drive an additional 0. 1 product), and it is about/kloc-before clothing assistants make "matching purchases".

product mix

Home page: the aggregation of products recommended to users according to various classification methods.

Discovery: Provide multiple modules and channels to enhance the interaction between users and platforms and increase the daily activity rate.

Show: Show photos of network celebrities and stars, and attach product links.

Mine: personal information management

Product function analysis

home page

Function Description: The homepage mainly displays the products in the platform by categories, and users can find the products they need through search, item classification and style classification.

Analysis: app is mainly white background, clean and fresh, which meets the aesthetic requirements of young women. The home market classifies products according to product categories, and explains the wearing effect of this category of products, such as "Young people will fight, and splicing elements will help you spell out your personality". The products with different price points are listed below, which allows users to quickly find products that suit their needs.

Reference: When displaying commodities on the home page, you can use commodity classification and explain the effect. First, users can quickly find the goods they need. Second, it shows that the effect can directly reach the user's psychology, meet the most fundamental psychological needs and improve the purchase rate.

There are several modules such as today's wear, custom wear, wearing tutorial, beauty optimization and so on.

Function description: I found that the top of the page is today's wear. I recommend it according to the weather and personal preferences today and tomorrow, and click to enter the whole body purchase page. If you don't like it, you can switch to the next group or swipe left to re-select your style and hobbies. You can also consult a collocation teacher (robot) or learn to wear it. Guide users to learn to wear knowledge in an all-round and humanized way.

Analysis: Directly hit the pain points of users and worry about dressing every day. There are different wearing instructions according to the weather, which improves the daily activity rate and retention rate of users. Through a variety of ways and channels to meet the psychological needs of users to learn collocation, enhance users' satisfaction and pleasure.

Function description: Through a few simple test questions, you can evaluate your dressing direction and give relevant suggestions and product recommendations.

Analysis: According to the test, personal suggestions are automatically generated, which are targeted and personalized. It is better to teach people to fish than to teach them to fish. This suggestion can make users clearly understand the type of clothes that suits them, and it is more instructive for users to buy.

Reference: Provide targeted solutions for individual tests, meet the individual needs of young users, and recommend related products and brands to users.

?

Function description: This paper introduces several girls who don't usually match, and through matching transformation, they become fashionable and meet the needs of the scene at that time.

Analysis: Through the intuitive changes of several girls before and after dressing and matching, people realize the importance and skills of matching, thus attracting and persuading more people to understand the importance of matching.

Function description: Show pictures and description labels of the goods actually worn, and display relevant price information on the pictures. Here are some other recommended products, which can be purchased with * * * *.

Analysis: display the price and description label of clothing products on the picture, which is convenient for users to check and consider; Matching purchases and other suitable products are pushed to users. First, it is convenient for users to find related matching products, and second, it improves the sales rate of the platform.

Dressing tutorial

Function description: classify into different wearing tutorials according to single items and styles, and recommend related products.

Analysis: Recommend different items according to different styles, and simply summarize the dressing effect created by this item, which is well connected with the customized wearing board and more effectively promotes consumers' purchase.

Reference: The plates in the platform should play a cohesive role, which is conducive to promoting consumers to achieve a certain goal. While selling goods, it also briefly summarizes the characters, scenes and effects that this single product can create. ?

Function description: Select some street photos of stars and network celebrities, code an item on your body, let consumers guess, or share it with friends.

Analysis: This section can form a good and interesting interaction with users, that is, playing some simple and interesting games around the theme of fashion wear, and can also be shared with friends for interaction, thus achieving a self-communication effect.

Reference: You can play some interesting games (not necessarily guessing questions) around the theme in the platform, with the purpose of forming a good interactive effect with users, increasing the attraction of the platform and the stickiness of users, and also playing a self-propagating effect.

Function description: Show the seller's show and star wear in the circle of friends, with product links and prices attached. You can choose relevant people according to your own positioning and characteristics.

Analysis: This is also a channel to enter the purchase page, making it easier for users to find a seller show that suits them. Height and weight help users to make better reference and promote consumption.

Reference: Almost all pages and sections have a purpose of promoting the consumer psychology. Displays the height and weight of the model, which is convenient for users to learn. This little point is worth learning from.

Function description: mainly the status information and common functions of the order.

Reference: The interface is clean and tidy, users can find the required functions at a glance, and common functions are placed in a prominent position.

Although app is also covered with information e-commerce software, its target group is more focused. No matter from the classification or personalized recommendation, we can feel the subtle differences of users' psychology, and improve the user conversion rate from multiple directions and multiple entrances. The purpose is to facilitate users to find clothes that suit them in the fastest way. The advantage of concentration is that it can firmly grasp the target group, but it is not attractive to other groups, and it can only increase the unit price of customers to maintain normal operation. However, the business model and operation ideas are worth reference and can be copied to other user groups.

The above is my analysis of this product, I hope you can give me more advice.