How to build a 2B user operation system!

Taking Ouyeel Cloud Commerce’s B2B growth practice as an example, the author explains how to use growth thinking to reconstruct the 2B user operation system.

What kind of business model is B2B and what can we do? My sharing today will start from these two aspects.

1. Approaching the "Growth Model"

1.1 What is a “growth model”?

First of all, what is a “growth model”?

There is a saying in Operation Light that the development of any concept needs to go through three stages.

During the "chaotic period", we will have the intuition that something will be useful, but we don't know what its methodology is or what kind of system operation function it has. There is only a group of pioneers who can prioritize doing this. After tasting some success, more people will be willing to get involved and develop their own theories. We call this stage the "period of blooming a hundred flowers." Finally, after a round of ups and downs, concepts with stronger logic are settled down and enter the "mature stage".

Before 2000, the entire Internet was still in the software era of the information revolution, and there was no such position as product manager. There are business needs and development teams, with more emphasis on project management, but no product managers.

But around 2004 and 2005, many product concepts emerged, but Internet products were still limited to a company's yellow pages, QQ, or e-commerce. Operations were just emerging at that time, so what was his position?

For example, a position like editing yellow pages content already has the feeling of making the product reach users, but there is no systematic operation and it is more like a promotion function.

As the product forms become more and more diverse, such as forums, platform e-commerce, and social networking, we have more operations positions: category operation, user operation, content operation, community operation, social group operation, new media operation, etc. But the essence is the same, that is, our products should not only stay at the functional level, but also reach more users.

By 2013, we will find that a lot of operational work needs to be measured. After setting operational goals, we also need to use data to tell us the effectiveness of various operational methods. Operations are not limited to doing miscellaneous work. We need to have systematic operations to prove the value of what we do. Therefore, the data comes up.

The whole process is to make the "product". The product manager has a clear goal, which is to connect with more users. The way of connection is based on experiments and is supported by data. This is the "growth model".

1.2 To achieve growth, we need business model support

When making a product, we should not limit ourselves to creating functions, but should also consider the user experience after the functions are created and whether the user behavior path of using the product is smooth. We need to meet this deep need of product managers through experiments. The success or failure of experimental design depends on two factors:

Factor 1: Depth of understanding of the product’s user base and the entire market.

For 2C, we can gain knowledge about products and users from life experience. For example, when selling a children's insurance, the target group is mothers. We have an idea of ​​mothers' behavioral characteristics: they will browse some parenting websites or maternal and child products. After collecting all kinds of behavioral data, it is easy for us to label us as mothers.

But for B2B, this is a difficult point. B2B awareness is completely dependent on professional knowledge of the industry. For example, if a student working in 2C wants to switch to B2B in the steel industry, he needs to understand the scenarios in which steel traders consume products, etc., which is not something that can be learned from our life experience.

Factor 2: The information level of the entire industry.

For 2C, the various hacking methods and hacker skills that can be seen now have reached a stage where there are sufficient and adequate choices. The way we choose to reach out depends on whether the person is online and whether he can be reached.

For B2B, industrial Internet needs to connect various business units with different levels of informatization within the industrial unit. Our industry includes steel mills, fleets, warehouses, shipping units, traders, etc. The level of informatization of each business unit varies.

Even if the practitioner is online and can be reached via mobile phones, his occupational label is unclear. We cannot find this person through very common 2C methods such as text messages, mini-program push, etc. Therefore, our experimental methods will be very limited, and the main contradiction lies in the poor hacking methods.

After completing the experiment, we still need to use data to measure the experimental means, and how to do data analysis tests the business expertise of analysts. There are two types of growth experiments:

(1) Randomized controlled trial (RCT): widely used in the medical industry. After determining the analysis goal, determine the "control group" and "control group", and anchor the final indicator performance based on the duration of the experiment.

(2) Retrospective study: For example, we have observed that the number of active users of a steel plant is declining. This requires a retrospective study to see which variables have an impact on the results: it may be because the platform did not provide timely after-sales service, resulting in poor user experience and loss; it may also be that problems with the steel plant's production scheduling led to delayed delivery, causing the ordering users to reduce their trust in the platform.

The above understanding is entirely based on our understanding of the business, which is a huge professional challenge for those who are doing growth in the B2B industry.

Therefore, based on the above issues, from the aspects that we can describe, such as the platform stage, business awareness, market environment capabilities, etc., if we want to achieve good growth, we must have the support of the business model. If there is no such business model, we must spend time to find, cultivate and catalyze it.

1.3 Five stages of growth and development

Phase 1: Hacker, Skill Phase

In 2015, many concepts of "growth hacking" were proposed, and all companies were curious about how to use the Internet, using very hacker, information, and high-tech means to acquire customers?

Stage 2: “Data Measurement” Awareness Stage

Traditional Internet growth methods include Airbnb, which used a presidential election to bring a wave of growth; and Facebook, which was able to open up the market in Africa with the help of a round of code optimization.

At this stage, we will become more aware of "data measurement". We need to see the data performance of each means and have a "data measurement" business system. We need to be able to break down the means according to the goals, measure the target effects achieved through the means, and use data to monitor the performance differences of indicators. We will also do a lot of "point" optimization. For example, for operations, we need to optimize the registration conversion rate, order placement rate, and so on.

The third stage: the "Tao, Shu, Qi" stage

Suppose we finally improve the order conversion rate by reducing pop-up boxes, optimizing copywriting, and strengthening visuals, and have passed the first two stages. However, due to business value orientation, the product manager added some designs that reduced the conversion rate.

For example, a steel mill stated that a batch of goods they sold were quality surplus materials and they needed to make a mandatory reminder on the platform - no after-sales service would be accepted. Such mandatory reminders will be presented in the form of pop-up boxes or very obvious risk warnings during the pre-sales stage. This will definitely reduce our optimized conversion rate. In this case, we have to think about:

• What value does the platform have?
• As a middleman, is it enough for us to just act as a matchmaker? Do we still need to provide regulatory obligations for the platform?
• What role can we play in the entire platform and product? Why do they need such a platform to meet their procurement needs?
• Why did the platform model emerge in the entire steel industry at a time point like 2013?
• What is the relationship between the short-term benefits of growth and the long-term core value of the product? Who exactly brought about the growth of the product?

In this case, we should not only look at the transaction conversion rate, and not only look for tools like GrowingIO to monitor the transaction conversion rate data; we should also understand what the entire growth methodology is and what the product life cycle is, and rise to the "Tao, Shu, Qi" stage.

Stage 4: Business Model Stage

To connect products and users, we need to understand the business model of the products so that we can design means that can bring about conversions. For example: Why is this the business model? After the 2008 crisis, who are our users? Are they the terminals? Are you a trader? What stage are they currently in? What are the pain points of the industry? In which direction should the platform focus its efforts to develop smoothly? This is our thinking on business models.

The fifth stage: data center stage

The contradiction arises when we try to answer deeper business questions. The data we need to use is not just behavioral data, because the behavior on the platform is very limited. Unlike 2C products, all actions can be completed on the App.

Users may browse resources and place orders on the platform, but freight, material use and other links are all offline, and we have a lot of business teams doing ground promotion, and these data are in the CRM.

As the demand for data analysis deepens, those who are engaged in growth must push companies to build a data middle platform so that the data that needs to be analyzed and the monitoring of business indicators can become tools that everyone can use through the construction of a big data platform.

To sum up the above five stages, in fact, the essence of growth is a guiding ideology and way of thinking, and the real summary comes from practice.

The connotation of growth is to measure the changes in detailed indicators through data and drive the growth of the indicators, while the extension of growth is to take into account the business model in which the indicators are located, make the results of refined work more lasting, and make optimization suggestions for the way of business development.

1. How does B2B achieve growth?

Given the existing business environment in the industry, how should B2B achieve growth?

2.1 Learn the classic growth system of 2C

Knowing ourselves and knowing our enemies, we need to know how 2C works. The classic 2C customer acquisition model is: first reach the users you want to market to, and there are many ways to do that; after reaching out, you need to arouse the user’s interest, and only when the experience is good will there be conversion, that is, an order.

Look at the "dopamine" picture on the right, which can bring people pleasure. The key to user experience lies in the generation of pleasure, and the generation of pleasure lies in the secretion of "dopamine".

We can make full use of human nature (such as emotions, red envelopes, anxiety, rankings, vanity, achievements, etc.) to design hacking methods and convert such people into users.

So what role does data play in this process? In fact, what we have always wanted to measure is the secretion of "dopamine". However, our ability to acquire data is limited. We currently do not have an instrument that can measure through the screen how much dopamine is produced when users are reached. We need to use some explicit variables to describe such implicit variables.

For example, we believe that after a person secretes "dopamine", he will definitely click. The number of clicks is the explicit variable we can obtain, as well as conversion rate, activity, new user bounce rate, length of stay, etc. This is the classic structural equation model in statistics.

What this entire growth system wants to prove is that the first half of growth focuses on the number of users, while the second half focuses on user experience. User experience needs to be characterized by indicators through variables and measured in multiple dimensions. Simply put, it means whether people are moved by the means of contact and feel pleasure.

2.2 The essential difference between B2B and 2C marketing targets from Ouyeel Cloud Business

Let me explain to you that our business model is a steel e-commerce company and an industrial Internet platform.

A steel coil is produced by a blast furnace steel plant. After receiving an order, it will be transported from the on-site warehouse to a designated social warehouse, converted into spot resources, and then reach the hands of traders through the circulation link. This is our business model.

Ouye Cloud Commerce acts as a service provider and middleman in the entire process. We connect two ends. On one end, we provide traders with invoices, contracts, money, after-sales services, etc.; on the other end, our marketing team will attract blast furnace steel mills to settle in the mall. At the same time, we also provide the following two services:

• Service 1: A SaaS service system. We provide settlement and data reporting services to the blast furnace steel mills on the platform to attract them to stay.
• Service 2: A technology marketing platform. As a professional technology marketing platform, we can provide financing, pickup, invoices, brand number services and other services to attract traders to purchase on the platform.

Below is a simplified diagram of our platform’s business model.

Next, let’s talk about the object of our operation. It is not a single person, but an organizational structure, for example:

• General Manager: is the key decision maker in this organizational structure. But this role is not easy to reach, because there is no mobile phone number obtained through any channel that can tell you the professional role.
• Marketing Department: We need to determine the sales system for the entire resources with the Marketing Department.
• Pricing Department: For the steel industry, the traditional pricing model is to set a price per day or a price per week, which is very non-market-oriented. If we want to adopt the Internet sales method, there must be a transformation process to new retail. The pricing department plays a very important role in this process. We need to use data such as marketing popularity, user behavior, sales speed, sales rate, etc. on the platform to help us make pricing decisions.
• Technical Department: Simply putting resources on the shelves will lead to difficulties in resource management of steel mills. Therefore, we need to go to the technical department of the marketing steel mills, let them recognize this model and promote the transformation of the steel mills' sales system.
• Branches and subsidiaries: In addition to the general manager and three departments, the branch company controls the resources and channels.

The above is the organizational structure we want to market, which is fundamentally different from the 2C marketing targets.

2.3 How to reach B2B marketing targets?

Grasp the connection between the two touches

For the B-side, we reach someone through various means, so what should be the way to impress him? We need to connect two points: the key factor that impresses an individual, and the factor that this person has to market the entire organizational structure in the company.

If he sees a beautiful woman, he may click on it, and the click-through rate will be very good. But will he change the entire organizational structure and sales system just for a beautiful woman? Will you introduce such a sales channel into the company? Basically impossible.

He has a professional label in the organizational structure. If our sales system is a good solution for him to reduce costs and increase efficiency, because by working with us, his career path has become wider and the performance of the company organization has become better, then this person's professional attributes have come into play. He can then help us build connections and market his entire organizational structure with solid evidence.

Therefore, the top priority of this activity is the connection between the two touch points. If ignored, even if the data performs very well, it will be useless for the final supplier to settle in.

Understanding the Differences Between 2C and B2B from Metrics to Growth

When doing 2C, in order to improve a certain indicator, we will rely on our understanding of sociology, human nature, and psychology to design growth methods. But when doing B2B, these alone are useless. We need to understand the macro-economy, the entire trade ecosystem, the entire corporate organization, and even China's revolutionary history; we need to understand the participants in this ecosystem and the current social stage, so that we can propose favorable means to reduce costs and increase efficiency. This is the difference between the theoretical basis requirements of 2B and 2C.

The core point I want to express is that B2B wants to stimulate changes in corporate production relations.

Once a channel or tool is introduced into a company, their entire production relations will undergo changes. Under the original production relationship, an Excel spreadsheet could be used to manage the sales system, and the sales of a contract could be completed through phone calls and offline paper documents.

However, to work on the Internet, more people will have to switch to technical marketing, more repetitive labor will be replaced by platforms, and the production relations have undergone a radical change. The core is commercial value.

2.4 Three major misunderstandings in the B2B growth process

In actual growth work, practitioners are prone to several misunderstandings:

Myth 1: There is no strong causal relationship between measured indicators and growth measures.

For example, we designed some tasks for novices because novices can bring very good "retention", but some people think that novices have no impact on "transaction volume".

After a novice has registered, he needs to take the first step before trading - uploading the business license and authenticating the company information. This step is the main task for novices, and we need to describe more detailed indicators for this function. For example, whether the "certification conversion rate" has increased, and whether the "conversion cycle" has been shortened, both need to be measured more reasonably.

Myth #2: Failing to respect the experimental nature of the growth process.

We need to persist in taking some measures. For example, in order to improve the timeliness of users submitting invoices to us, we need to display the "invoice arrival rate" and publicize both good and bad behaviors so that the platform can play the role of inspection and supervision.

In this process, we need to keep experimenting. There is no conclusion that "displaying the vote rate" is a perfect solution. The "vote rate" may not increase, and the transaction volume may not increase. At this time, you need to find cases in 2C to help you understand the essence of the method. For example, can posting a high score list lead to an increase in test scores?

Misconception 3: There is no gradient in analysis and it is superficial.

The data team itself has a very strong binding relationship with the business, and this binding relationship depends on human operations and human initiative. The initial business requirement is to help count user and report-level data and monitor whether the number of active users on the platform has increased or decreased. If it ends here, it will not be beneficial to the development of the work.

It is not enough for the data team to achieve excellence when taking on business, but we must also explore in depth. This requires us to find business sensitive points through the accumulation of "points" and verify hypotheses. For example, the business model changes: Is it good or bad if the number of bidding sessions on the platform increases? Will the number of users decrease? Will some end users be lost because they are unwilling to spend too much time on purchasing? These are the business sensitivities that the data team needs to cultivate in order to provide a complete analysis framework and understand a business event from multiple angles.

After active thinking, you can feed the results back to the business unit and do data analysis for the purpose of growth. These are the levels of data analysis (statistics – verifying hypotheses – data analysis for the purpose of growth).

3. Ouyeel Cloud Commerce’s Growth Practice

Back to the B2B business, let’s look at Ouyeel Cloud Commerce’s growth practices.

3.1 Three Growth Agenda of the Steel Mill End

Ouye Cloud Commerce acts as an intermediary and connects with steel mills. Steel mills are very important value unit producers on the platform and the main tenants of the platform, and can directly bring traffic to the platform.

When facing them, what we need to market is the organizational structure. How do we do it?

(1) Field promotion. Although this is very low in 2C, it is essential for B2B. Because we cannot reach the key decision makers for marketing through SMS or apps, and the marketing cycle may be as long as 6 months or even 1 year.

(2) Products. What we want to produce is not just a platform, but an idea. We hope that steel mills are willing to break the currently established sales system and give the platform a chance. The platform must also develop a lot of transaction tools: bidding, discounts, user management tools, etc. These are the values ​​of the platform and even the management methods.

(3) Values. The timing of B2B marketing depends on the alignment of the steel mill’s and platform’s concepts, as well as the motivation to make systematic changes.

In fact, we seldom measure the data from steel mills because it is extremely difficult. Suppose we want to measure a variable, “Steel Plant Cooperation Depth”, its influencing factors may be:

• Number of platform users. For example, if the steel plant produces rebar and the platform produces automotive plates, the number of users does not match at all. Can the marketing be successful? The number of users of this platform is in our business database.
• Platform value fit. Our platform is taking the new retail route, but if the steel mill is unwilling to take the new retail route and just wants to do direct sales, that is, hopes to sell rebar directly to the construction site without relying on the platform, how can we impress him in this case?
• The driving force behind the transformation of steel mills’ sales systems. Different steel mills have different market competition environments and histories, and their driving forces for change are completely different. For example, Baosteel has been a modern steel company since its establishment, and it has invested a lot of resources in information construction. Therefore, in the entire industry, it can lead the transformation of enterprise informatization.
• Concept marketing effect. Many steel mills were established early and they are the dominant players within a geographical area. They believe that their distribution systems are already very mature and stable and do not need to be changed, nor are they willing to change.
• Key person visits. Whether the key figures were visited, the number of visits, the time of each visit, the content of each conversation, etc., can all become input variables for our model of the depth of cooperation with steel plants.
• External environmental stimuli. External data changes may be a random error term in our measurement system.
• Number of steel mill resources. The steel mills have very good control over production and sales, and there are not many spot resources that can be put on the platform for new retail.

These are our influencing variables. As the demand for data analysis deepens, our demand for a data middle platform has arisen. The main contradiction has become the contradiction between "recognizing the value of data analysis" and "backward data productivity", which is a turning point.

With this turning point, people working with data can spare no effort to promote the company's digital transformation.

3.2 User Operation Framework of the Trader Side

For traders on the other side, we want to reach a staff member in the purchasing department. Often, the people in the purchasing department have full say in what channels to purchase, unless it is a large terminal that needs to conduct procurement bidding.

99% of the users of our platform are probably small terminals or small traders. They do not have long-term contracts with steel mills and do not have good purchasing channels. They have very strong autonomy, and their purchasing channels depend entirely on their own choices.

For them, we can use such a customer acquisition model to reach and convert them. In designing such a process to generate interest and secrete dopamine, you need to look at his psychology as a purchaser:

(1) You can buy good products at low prices. As long as he can buy good goods at a low price and reduce purchasing costs, he will be moved.

(2) Easy to use. Purchasing personnel will make purchases on the platform, so the platform must be easy to operate.

These are some of the means we can reach, the design differences that can produce conversion means. Therefore, for such traders, we have refined a large framework for user operations:

1. Actively promote – gain customers

The marketing department brought the main tenant (steel mill) into the mall and the goods were put on the shelves. In addition, we have to find ways to retain these anchor tenants to prove that the goods can be sold in our mall. However, many traders may not know about this platform, so we need to take the initiative to promote it, for example:

(1) Offline promotion meeting. By using a topic that a certain trader is interested in, the target objects are gathered in a physical location, and then some interest motivations are designed, such as industry contribution awards, photo walls with KOLs, good luck draws, etc., to stimulate the willingness of participating users to share and carry out a wave of publicity based on acquaintances.

(2) Field promotion based on the geographical distribution of traders. For steel markets or real estate projects where traders are concentrated, we can hold some small recommendation meetings locally to let traders know about the existence of a platform like Ouyeel.

(3) Build private domain traffic through social groups and gather traders. We are now mobilizing the strong social chains among traders through the community, making them active within the group, and thus triggering interest in resources within the group. This is what we are doing now.

Therefore, interest motivation and communication experience are the two key links in social marketing.

2. Optimize the path – conversion

After the promotion, users know about the Ouye platform, but how can they be activated by us and become our active users? We used GrowingIO to do some user behavior data analysis:

First: Registration Conversion

What kind of users can become long-retained users? We found that users who have made their first transaction have very good retention. However, due to the complicated trading rules of the platform, the conversion time for users to make their first transaction is very long. Without guidance, the conversion rate of the first transaction will be very low.

Based on this analysis, we focus our user operations work on activation. Through novice tasks, help centers, novice guides, etc., we set up a consultant-style marketing staff to help users convert into platform novices.

Second: User Activity

After users make a purchase, how can we get them into the habit of looking at platform resources? How can we make him think of the platform as soon as he purchases steel coils? We can do some personalized information push, reach out to good products multiple times, guide users to pay attention to some of our stores, and then reach out to them in new ways with store resources. 2C routines can be used directly.

3. Precision Marketing – Data-driven

How can we know what users want? How can we prioritize what users want and display it on their shelves? This requires us to label users and products so as to achieve precision marketing through data-driven means.

First: User Tags

The following two cases will share with you the importance of B-side user tags for data-driven precision marketing:

Case 1: The new user guidance function is launched. Is it useful for new users?

For us, the conversion effects of new users acquired through different channels are also different.

For example, some new users are directly taken care of by the agreement maintenance platform after the steel plant goes online. Since there are steel plant salesmen to guide them offline, they will definitely become trading users of the platform within a week. There are also some new users who register through offline promotion. They do not have access to good resources and have not formed the habit of looking at products on the platform, so their conversion rate may be low. The activation methods for these two groups of new users must be different.

The importance of customer acquisition channels, whether the industry attributes itself are terminal, and the degree of dependence on steel mills will all affect the activation methods and cause differences in data performance. Therefore, when doing data analysis, we must fully consider the business and establish corresponding variables for analysis.

Case 2: Will the launch of the ticket arrival rate display function affect the timeliness of supplier ticket arrival?

Similarly, when measuring the effectiveness of the ticket arrival rate display function, the classification of suppliers should also be taken into consideration.

There is a type of steel mill supplier that has an agreement with the platform. Whether the invoice arrives or not has no impact on the conversion of the transaction number. There is another type of mobile suppliers whose sales amount and sales frequency are very high, and the ticket arrival rate requirement is 80%.

To sum up, different user groups will have different operating methods. In general, this is the user tag management done by the entire enterprise. The bigger one is "precision marketing based on user portraits", and the smaller one is "accumulating tags for every user". People who work with data should take the initiative to accumulate data and find ways to establish a user labeling system on the middle platform.

Second: Product label

For our industry, users' purchasing needs remain at the first level. They are very sensitive to prices but insensitive to service needs. We should naturally tell users what kind of resources are hot-selling resources, price-reduction promotion resources, new resources, etc.

Third: Field

With user tags and product tags, we have people and goods, and what remains is the "place". Product design should be done on the premise that user tags and product tags are already very mature, and user and accumulated resource tags should be connected through reasonable paths.

4. Membership system – rating and rights setting

The establishment of a membership system depends on business value. What is the value proposition of members and what kind of members are valuable to the platform cannot be determined solely by models.

For the suppliers on our platform, the "steel plant" attribute is a very important label that can bring traffic to the platform; and for traders purchasing, relying on the platform, having active transaction frequency and sinking to the terminal has more value, which is our value proposition. Based on this value proposition, we will pick out 20-30 variables, and then create a membership model based on the performance of the variables. After that, we will measure the value. The next most important thing is the setting of membership rights and interests, which will eventually form a complete membership system.

After the membership system is established, it can affect the supplier's search ranking, credit limit, which zone they should enter, etc. The setting of these rights and interests will affect the degree to which suppliers value the membership value you advocate and the membership system carrier you rely on.

With the membership system, we will understand the connotation of user value, which is not only about the current performance value, but also about the expected sales, which is very interesting.

When we work with the data, we include the indicator of “number of suppliers purchased by traders” as an input variable into the model. This indicator is definitely inconsistent with the business understanding. The business will say that buying one or two suppliers has no impact on GMV. Although users who buy from a supplier are often long-term users who follow the resources and have no value to the platform, as long as the supplier's resources change on the platform, the risk of losing this user is very high.

It is equivalent to a person suffering from cancer, whose life expectancy is short, so overall, his membership value is low. Therefore, the value of our membership should take into account daily cash flow and also the length of time over which cash flow can be generated. This is the connotation of our user value.

1. My understanding of the data

Here I would like to share with you my understanding of the data:

• All data can do is measure;
• The insights and means of measuring results rely on business understanding;
• Data can only solve problems at the level of "I know what I don't know". The gold in the entrepreneurial stage is in the minds of entrepreneurs, which are perception and inspiration. Over-reliance on data will lead to local optimality.
• If you don’t respect the growth of commercial value, it’s easy to go astray;
• Growth is very light, focusing on short-term conversions; product core value is very important, requiring a long-term perspective; the two complement each other;
• Data should work with the business to set growth targets, participate in weekly business meetings, strengthen time management, and accumulate capabilities from point to surface.
• Finally, let me share with you my understanding of how growth should explode?

The first stage of growth depends on the platform, which can be promoted by people, and it also has its own development cycle; the second stage depends on the recognition of the commercial value of the product; the third stage depends on the mechanism of the entire company; in the fourth stage, it depends on the capabilities of our team. If each of us improves our own value capabilities, we will be able to make growth explode!

Today’s sharing ends here, thank you everyone.

Author: Cheng Xiaying

Source: GrowingIO