At present, the Industrial Internet promotes the continuous acceleration of technological innovation in various traditional industries and their digital transformation. As a model of the integration of the service industry and the Internet, the real estate service industry has ushered in new growth opportunities in recent years. The rapid growth of online users has put more demands on the digital transformation of the real estate service industry.
Huawei Analytics Service 6.2.0 version adds new housing rental and housing industry reports and burying point templates, with users as the core, focusing on users’ key behaviors of online viewing, helping real estate service platforms increase users’ interest and sensitivity in listings, and effectively shortening housing and housing purchases Trading cycle.
1. Data overview to understand overall growth
The data overview dashboard presents the overall user trend of the application that operators in the Internet real estate service industry are most concerned about, such as the number of new users yesterday, the number of active users yesterday, and the average use time yesterday, so that the overall operating status of current applications is clear at a glance.
*Overview of the industry data for renting and buying houses
The data overview dashboard also supports the addition of filters to further segment user growth. For example, for the real estate service industry, the purchase policy of each region will directly affect the user's demand for buying a house. Through the screening of filters, you can compare and understand the user growth trends of different cities, and scientifically evaluate the direct impact of policy, economic and other macro factors on incremental users.
Second, user analysis to see specific performance
The user analysis board starts from the perspectives of active users, active user login time distribution, per capita login time, retention performance, etc., presents the current application usage in detail and comprehensively, helps developers to fully grasp user dynamics, and rationally optimizes on this basis Allocate operational resources.
*Analysis of users in the renting and buying industry
Three, housing data optimization resource arrangement
High-quality housing information is one of the driving forces for the growth of users in the Internet real estate service industry. As a platform party, enabling users to easily and quickly find housing that appeals to them is the key to whether to rent a house or buy a house.
The housing data kanban focuses on key data such as new houses, second-hand houses, rented houses, pageviews, house types, and amount preferences. Such users' actual access preferences can help the real estate service platform to plan housing listings more scientifically and rationally on the one hand. On the other hand, by comparing and analyzing the housing sensitivity and attention of users in different regions, regionally differentiated sales strategies can be formulated to enable offline store brokers to more accurately capture user housing intentions, improve the accuracy of viewing, and shorten Trading cycle.
*Indicating housing data in the renting and buying industry
*Indicating housing data in the renting and buying industry
provides supporting embedding template, ready to use out of the box
To help developers improve the efficiency of burying points, Huawei Analysis Service also launched the burying point template for the rental and purchase industry. Corresponding to the above three data boards, there are three modules: data overview, user analysis, and house data. Right out of the box, after burying points according to the events and parameters provided by the template, you can view the above-mentioned housing and house purchase industry report data.
Support code burying point and visual burying point, developers can flexibly choose the way of burying point. Full-link tracking from application integration to embedded point development, embedded point verification and management greatly improves the efficiency and accuracy of embedded point.
* Schematic diagram of burying point template for renting and buying a house
In addition, other multi-dimensional analysis models of Huawei analysis services can also be used to implement refined operations for users. Renting and buying a house are low-frequency consumption behaviors, and the lasting activity of users is conducive to the final transaction of online orders.
For example, according to the trend of users' access to listings, with the help of Huawei's analysis service audience analysis model, they can be hierarchically targeted and promoted according to their listing preferences. To "family who cares about small two-bedroom families", push new news about cost-effective and convenient listings; for "family people who focus on quality improvement of three rooms or more", you can attract them through news such as technology decoration of famous enterprise houses and school district housing recommendations. .
The above is a brief introduction to this new report on the rental and purchase industry of Huawei Analytics Service 6.2.0. For more details, please visit Huawei Analytics Service official website and experience the Demo for free: Huawei Analytics Service | One-stop user behavior Analysis platform | Demo experience .
Obtain development guidance documents: Android , iOS , Web , fast application
For more details, please refer to:
Huawei Analytics official website
Obtain development guidance documents:
Android SDK integration document
Quick Application SDK Integration Document
and learn about the latest technical information of HMS Core for the first time~
java
**粗体** _斜体_ [链接](http://example.com) `代码` - 列表 > 引用。你还可以使用@来通知其他用户。