Hundred-fold growth, data-driven - [Yunqi Record] Facing the rising mobile payment, how to do a good job in data operation

Abstract: Alibaba Cloud Data Plus provides not only BI tools, but also a complete set of Alibaba Cloud systematic services behind it. Only when you use it can you really feel the power of cloud BI.

The Shanghai Yunqi Big Data Session brought exciting content. The following is the speech record of COO Chen Dengfang (Chen et al.'s speech was also the most attended speech in this session):

Hello everyone, I'm Receipt The co-founder of Qianba, COO Chen Dengfang.

Let’s first introduce the industry we are in: offline mobile payment.



Today, mobile payment has become a necessity in daily life, but the depth and scope of the impact of the payment revolution will greatly exceed the impact of the POS industry 15 years ago, for two reasons:

1. Mobile payment replaces both card transactions and cash transactions, and then There are more than 70 million merchants, which is more than 3 times that of the stock POS.

2 Mobile payment has fundamentally changed the ability of merchants to connect with consumers. From our understanding, this provides a brand new opportunity to promote changes in the operation and management of offline merchants. In a word: big industry, big outlet.

Introduce the money: Money is a non-mobile payment company that is doing mobile payment.



Our business vision "Smart Business". We hope to use technology and data to help merchants improve business efficiency and reduce costs in today's scenario where the cost of production factors is increasing rapidly and consumption habits are changing rapidly.

Receive Money Bar entered the market in the second quarter of 2015, and today there are more than 500,000 offline stores using the Receive Money Bar service. From H&M of the four major international fast-moving consumer goods to the Lanzhou ramen shop on the street, we mainly cover national brand retail and small and medium-sized street merchants.



Using a few figures to describe our operations, we provided 100 million offline mobile payment services to merchants in May 2017, with the highest daily transaction size exceeding 4 million in a single day.

In the 18 months from January 2016 to the present, our monthly compound growth was 27%.

From December 2015 to now, the transaction size has increased by 40 times.

Receive money in our target market is the industry leader.

How to understand and locate the application of data

So as an entrepreneurial enterprise, how do we understand and locate the application of data?



Sphinxes describe our characteristics more vividly. Our lower body is a traditional ground push company. We have 500+ BDs serving a large number of small and medium-sized merchants across the country, and we have partners in third- and fourth-tier cities to help us penetrate. Our upper body is a company whose products and operations are Internet-based, and the products delivered are multi-end ToB Saas services, which involve a lot of investment in product and operation development.

Because of this characteristic, the three questions our core management team keeps asking themselves are:



Why are they asking these three questions?

            1. Breakthrough in operational efficiency: Because the payment business is a channel business, its core lies in high efficiency.

            2. Business model innovation: The biggest difference from traditional payment is that it changes the connection between merchants and consumers, thus providing new possibilities to build our services.

            3. Enhancement of customer value: The final and continuous condition of all business behaviors is what value is provided to customers and how scarce it is.

Regarding operational efficiency, let me talk about it:



First of all, from the perspective of business operation:

First, because the transaction flow is very small compared to traditional POS, the profit brought by mobile payment is very thin. So we have to answer a question: How to do business with customers who can only make 10 yuan a month? We need to have high customer acquisition efficiency and low operating costs.

Second, in the payment business up to now, the differentiation of our products is not high. Therefore, a challenge for all players in this industry is how to retain customers. And this is the key point of whether it can really be profitable from a customer.

Let's talk about the development stage:

First, we entered the business in the second quarter of 2015. From the initial team of less than 10 people to today's more than 700 people, it is in a process of rapid expansion. Rapid expansion is the most dangerous time for a company, because management can get out of control and lose what it used to be good at. Therefore, our biggest challenge is the precipitation of management methods and systems to ensure that the company does not lose its shape in the rapid development.

Second, the business operation model is changing rapidly. The mobile payment industry is changing very fast. The policies of Alipay and WeChat have been adjusted and optimized, the market structure is changing rapidly, and competitors are changing rapidly. We must constantly adjust the performance system and management mode to adapt to changes in the market environment. The impact this brings to us is that management has to keep up with changes in the market.

Our answers to the above questions are two core capabilities: data-based operation capabilities and data productization capabilities.



This is also the problem we are solving based on the data plus platform.

Next, share some practice. Let’s talk about 4 sales scenarios first.

Scenario 1: Regional strategy



How to make money from customers who only bring 10 yuan in profit every month? Strategy is very important. In the regional analysis scenario, we don't just look at all the cities covered. We will look at the various business districts subdivided under each city. For example, Shanghai is divided into more than a dozen commercial districts such as residential, commercial, tourist, and university towns, and pay attention to the comprehensive output of the merchants in each business district. In this process, we will find many places that are different from conventional perceptions:

for example, is the value of the business district in the urban area high or the value of the business district in the suburbs high? The conventional understanding is that urban areas are of high value. But not necessarily. Because the competition in the urban area is very fierce and our industry has a great relationship with the competition.

Another example is that the main users of mobile payment are young people and many college students. Therefore, everyone understands that University Town is a good business district. But not necessarily. Because first of all, there are 3 months holidays in the university year, during which there is no business for merchants. The resale rate of merchants near Second University is high. Because college students are very price-sensitive, it is difficult to operate near a university. Third, because everyone thinks that the business district around the university is a good business district, the competition in university towns is extremely fierce.

In addition, the business district is dynamic. The high input and output of this business district today does not mean that the input and output of this business district will be high tomorrow.

Therefore, we must always pay attention to the changes in the business district, study the possibilities behind it, and decide how to adjust the allocation of human resources based on these analyses.

Scenario 2: Industry Strategies Anyone who



does mobile payment knows the so-called three major industries: catering, fruit, and retail. But these perceptions are not enough for us. We have divided 110 sub-sectors, and focused on the valuable long-tail industries in addition to the industries that everyone is optimistic about. The merchant output of a long-tail industry may be 2-3 times higher than that of a regular good industry. The performance of industries in different cities is different, and the long-tail industries are also different. For example, the Internet cafes in Wuhan are particularly good because it is one of the largest gathering places for universities in China. Through the analysis of such refined data, we can identify areas of differentiation that have been neglected in the market from the headquarters and branch levels to improve sales efficiency and the output of the sales team.

Scenario 3: Management Precipitation



We have established a set of data products covering all levels of the company: from headquarters, to regions, to city managers, teams and BDs. Everyone's performance and behavior are seen through data. When the company is expanding rapidly, it is impossible to manage without a set of data management system to precipitate management methods.

I will start to talk about the level of city managers.



We require every city manager to master three things: people, laws, and strategies. That is to control people, to master the method, to know how to fight in the market of this city. Our BI tool for city managers, in addition to the traditional performance analysis and comparison, pays more attention to the support of city managers at the strategic level: understand the advantages and disadvantages of competition in each business district, the choice of industry, quickly adjust the orientation and view it on BI in a timely manner to the result after adjusting the guide. At the same time, there are real-time trajectories of all BDs on DingTalk. When the city manager issues a new order, you can see the implementation of BDs in real time. This set of things is an important tool for us to implement refined management.

The power behind:



We tried Quick BI in December last year, and decided to migrate reports to Quick BI in February this year. And we also changed the organizational structure for this purpose. Before using Quick BI, technical personnel accounted for a large proportion of report development. Doing data reports is repetitive and boring for technical personnel, but there is no way due to management needs. With Quick BI, our technical staff can focus on the design of the underlying architecture and the middle data layer. Correspondingly, we established the core BI team of the business department, and found people involved in BI production in each business line. All data presentations are done by business departments themselves, and business personnel are more deeply involved in data analysis. In 3 months we implemented and migrated more than 300 reports.

As a company manager, not seeing data is painful. The greatest value brought by Quick BI is the greatly improved agility in data analysis and management decision-making. Previously, it took at least 1-2 weeks to respond to the report request, but now it basically comes out at night on the same day. I mentioned just now that we have a big pain point, that is, the management model has been changing. In times of change, the ability to use data to reflect problems in a timely manner is more important. I myself have 15 years of experience in the financial industry, and I have done large-scale BI implementations. I clearly know that traditional BI tools are very expensive, at least consuming millions, and are basically dedicated to large companies. The affordable price of Quick BI makes it possible for startups to introduce and use BI from day one. This is a very good thing.

Our data operations don't stop at the sales part. There are also various aspects such as user operation, taking the management and real-time monitoring of user churn as an example.

User churn management:



How to reduce user churn rate? In addition to good products, good online operations, and good offline services, another important point is the monitoring of user behavior. We originally used a rules engine. For example, a merchant used 15 transactions yesterday, but not today, let's see what happened. However, there is a problem with the rule engine, that is, there are many variables, and it is difficult for the rule engine to be very accurate. For example, the influence of weather and holidays, and the transaction characteristics of different industries and different merchants are very different. Therefore, we are cooperating with the machine learning platform of Shujia, transferring our monitoring to the machine learning platform, and learning based on the behavior data of merchants to help us judge the loss of merchants more quickly and accurately.

Real-time monitoring:



Our business is 24 hours a day, and we want to ensure the reliability of transaction quality through real-time observation. Important KA merchants have separate real-time monitoring, and each city has its own real-time monitoring.

The above is the content of internal operations. Let's talk about the business innovation part.



Receive Money Bar connects merchants and consumers by providing mobile payment services. It has accumulated tens of millions of fans now. What to do with these tens of millions of fans, we have done two things: first, content e-commerce. One is advertising. The income brought by this part today accounts for more than 40%-50%, which is about to exceed the fee income.

Behind this is the data.



After this business model went through, we established a DMP system, an advertising engine and a third-party data exchange mechanism. These have helped us increase our output by at least 100%. This aspect is still in its infancy, and there is still a lot of room for imagination.

Outlook:

The next step is to build data-based productization capabilities, that is, how to turn data into products to serve customers.



We hope to help small merchants penetrate from payment data to business data through membership, marketing tools, etc. Payment data is important, but not enough. We want to understand consumer data and business data. This is the foundation that helps us make further derivations. Here are two derived examples:

The first is scenario-based financial services. Many small and medium-sized stores served by the cash bar, such as Lanzhou ramen shops, are a group that has been ignored by traditional financial services and may not even be able to handle credit cards. What can we do for them? I can know how this store compares with other stores in the same area; when there is a marketing campaign, is there any cash-out and swiping behavior. Using these data, we can help the boss to prove his credit and business ability, and obtain financial support at a lower cost in various scenarios such as purchasing equipment and paying rent.

The first is intelligent business services. Because we operate in a business district, we will get the trend data of the business situation in this area and the transaction behavior characteristics of users in this area. We hope to commercialize these data to help merchants make more scientific decisions in site selection, pricing, and marketing in this area.

This is the next step we hope to further value the data with the help of the data plus platform.

Just like thousands of startups, it is a node and a connector. Alibaba Cloud has made technology with a high threshold into a low-threshold, low-cost, and easy-to-obtain technology. Our role is to find various scenarios, combine these scenarios with technical capabilities, and help all aspects of society to improve efficiency. That's what we're doing.



Our vision for smart business, we believe in the power of data. thank you all!

Alibaba Cloud Data Plus provides not only BI tools, but also a complete set of Alibaba Cloud systematic services behind it. Only when you use it can you really feel the power of cloud BI.