Statistical data of Analysys Qianfan showed that, in the 2017Q3, active users of bookkeeping wealth management applications were decreased to a small scale. ranked first with the obvious advantage in 13.26 million active users, followed by and The gap between and in size of active users was narrowed gradually. With less active users, such applications as,,, etc. at the third tier experienced severe competition for rankings, wherein it was the first time for and came among the top 10 list. Top three applications gained a sharp growth on a moving basis, of which rose by 100%, while and embraced a growth rate of 100% due to small user base.
Compared with users of investment wealth management, the users of bookkeeping wealth management attached more importance to fund management and expenditure reduction. User selection of applications depends on the convenience and professionalization of bill management.
As a leading application of bookkeeping wealth management, developsmany innovative products specific to relevant demands around its professional services. initiatessuch bookkeeping functions as photograph, classification, item, template, etc.; regarding scenarios, it launchesthe applications for different scenarios, i.e., business, marriage, baby, decoration, and so forth; in aspect of finance, supportsthe fund management such as card/bill bookkeeping in currencies, data graphs, creditor-debtor relation tracking, etc. continues to take a leading position in the bookkeeping area for its professional and innovative applications. To strengthen competitiveness, all application vendorscontinued to launch professional products. In the 2017Q3, increased the function of regular autobids in support of statements on vehicle loans and housing loans, and revised the wealth management modules, thus enriching wealth management products continuously; with much important to customer experience, increased the function of social insurance inquiry, provided the personalized preferences for customers and optimized customer services.
Perhaps bookkeeping convenience is the next focus of competition in this regard. Users generally hope to save time and cost, and pay close attention to difficulty and complexity of bookkeeping operations. Thus, for the same bookkeeping results, less key actions will secure more competitive edges. The ideal way of bookkeeping is to achieve automatic bookkeeping and present fund flow through clear graphs. Automatic bookkeeping is feasible along with the development of third party payment. Applications within the domain are highlighting the convenience of bookkeeping; for example, supports the billing management of multiple banks, one-click import of billing information such as Ant Credit Pay, JD Baitiao, online loans, etc., as well as repayment via Alipay and WeChat. gains a sustainable growth of active users through convenient bookkeeping function.
From the aspect of length of use, ranked first with length of use of 11.245 million hours, followed by and ranking second and third, respectively. The length of use of and was also relatively high in the third quarter.
From the perspective of application, all APPs launched the comprehensive function of wealth management to cover fund operation of users based on the core of bookkeeping function. Statistical data of Analysys Wanxiang showed that female users, aged 24-35, accounted for 70% of total users of bookkeeping wealth management applications, and medium and high-end consumers accounted for 84% of the total. Users generally have strong Internet resilience and consumption capacity, as well as very high potential of consumption conversion.
Female users pay close attention esthetic appearance of operation interface and readability of contents. Compared with professional interpretation of wealth management, life stories become indispensable elements. Meanwhile, users have a certain spending power and pay much attention to the quality of life. Against user groups,, on the one hand, boosted marketing promotion, sponsored the films in line with the perspective of users, satisfied the quality pursuit of users, and released wealth management test papers to recall their feelings for examination; on the other hand, launched community application, organized community groups of bookkeeping order, fans tribe, home life, etc. released the articles around “wealth management + life”, and created a high-grade wealth management community. introduced open class in its bookkeeping wealth management community to help user groups explain all skills and achieve the most preferences. For users aged 24-35, what they concern most is housing purchase, investment, insurance and child education. Vendorscan launch the more target-oriented community applications with a wider coverage from the perspective of demands in these areas, so as to further expand the services of bookkeeping applications.
In the second half of development stage, bookkeeping wealth management applications aim to explore the residual value of existing customers, and render target-oriented wealth management services to gain higher user viscosity. In September, declared that it completed the Round C Financing of USD200 million led by the investor KKR, and would intensify R&D of innovation products. Driven by capital, all applications in this domain will increase their inputs in financial technology. Perhaps, convenient bookkeeping is a key of rapid development.
Description on Upgrading of Analysys Qianfan A3 Algorithm: Analysys Qianfan A3 Algorithm applies machine learning approach to make its data more accurately reproduce the actual behaviors of the users and enable product valuation assessment in a more objective manner. The upgrading to the entire algorithm involves the overall process of data collection, cleaning and computation:
1.    Collection Side: The SDK is upgraded to adapt to the open API of Android 7.0 and higher; machine learning algorithm is adopted to upgrade the filtration algorithm of “Non-user Subjective Behavior”, which avoids mistakenly elimination while providing more accurate recognition.
2.    Data Processing Side: By machine learning algorithm, data completion algorithm is enabled for fragmented behaviors of the users, the equipment uniqueness identification algorithm is upgraded, filtration algorithm of abnormal equipment behaviors is increased, etc.
3.    Algorithm Model: External data sources are introduced and integrated with Analysys’s owned data for mixed data sources, AI algorithm robots are trained and adjustments are made on algorithms of some indicators.
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