3 cognitive models of growth operations: Growth does not equal fission

In 2018, there is one word that no one working in operations can avoid: “fission”. The background of growth hacking and the success of fission marketing of NetEase, Xinshixiang and others have given these two words a kind of magic, but fission cannot represent growth thinking, it is at most a means.

The depletion of Internet traffic has made the heat of growth continue to rise. More and more companies have established growth positions or even departments. For a time, the supply and demand of growth talents were unbalanced. Most bosses offered high salaries, hoping to recruit elites to alleviate their anxiety.

So what is growth? This article will share some ideas on growth operations in the data statistics dimension through a scenario.

"Boss, the Growth Group is bullying us again!"

At yesterday's morning meeting, I had just sat down in the conference room, the sweat on my head wasn't dry yet, when I heard the technical leader complaining to the boss. His eyes were so sharp, and the pen in his hand was pointed at me, as if he was holding an M249 that only appeared in airdrops.

As for the reason, the day before the meeting, I raised a requirement to recalculate the dimensions of operational data with my boss. My boss asked me to communicate with the technical staff, and I made a data statistics requirement overnight and sent it to the technical boss on WeChat . It is the picture below:

It doesn’t matter if you can’t see it clearly, there will be detailed instructions below. It is placed here to let everyone see how many points need to be counted, but quantity is not scary, quality is more scary.

Facing the complaints from the technical boss, the boss calmly acted as a peacemaker: "We should try our best to meet the requirements they put forward. According to the schedule of your current project, we will implement it as soon as possible. If there are many requirements, we can divide them into several versions for iteration."

Ouch, are you going to delay this? That won't work. "Boss, these needs must be prioritized at the top. Without a data analysis platform, it will be difficult for us to achieve growth. Besides, these needs of mine are just basic statistics, which are not too difficult."

“Is this still basic? We have already done the basic statistics, PV/UV/ retention /active… These are all available on the current platforms, aren’t they enough for you to just look at these? To achieve growth, we need to organize activities, do fission, and build communities !” The technical boss roared again.

This is where we encounter the first misunderstanding of growth operations: growth is by no means equal to fission, nor is it equal to community. Growth is the use of data-driven user growth behavior, which requires the use of data to establish cognition in three dimensions: product, user, and market.

1. The difference between growth data statistics and regular operation data statistics

At present, most Internet teams will count PV/UV/downloads/registrations/APP launch times and retention analysis on the next day, 3 days, 7 days, 15 days, and 30 days when doing statistical analysis. Paid products will also count the number of paying users and sales to have a clear understanding of the product operation status, and most of the operating indicators are also centered around these assessment data.

However, for growth operations, the dimensions of data statistics need to be more refined. When doing data statistics dimensions, key concepts such as "user story map", "funnel analysis", and "user data portrait" are often introduced.

"Wait, explain to me the reason for these statistics on user portraits . What is this called user portrait?" Before I could finish, the technical boss roared again.

Taking the data statistics of user portraits as an example, conventional user portraits are an overall summary of users based on age, gender, region, occupation, consumption capacity, etc., but user portraits in growth operations are based on statistical user feature data to conduct multi-dimensional screening of users. For example, my statistical dimensions include user-related information such as region and device, as well as product usage data such as visit duration, number of reposts, and keywords of browsed articles. Targeted operations are performed on the users who result in the screening.

For example, e-commerce merchants can push coupons for a product to users who have viewed the product within 24 hours but did not purchase it and whose stay time is more than 30 seconds. For the operation of Internet products, you can filter the keywords of the articles users browse, the length of time they read the articles, and other dimensions to push long or short articles on related topics to users in a targeted manner.

2. Growth operations need to establish three kinds of cognition through data

"Very good, this requirement must be met." The boss looked at the technical boss, who twitched his mouth... "Go on, tell me the overall idea."

After getting the approval from my boss, I couldn't help but feel emboldened. I planned to go back and make another iteration of the data statistics version 2.0. He glanced at the technical boss with a frozen expression, suppressed the carnival in his heart, and continued to enjoy the loneliness of a growth operation.

Growth operations require the establishment of product awareness, market awareness, and user awareness through data statistical analysis.

1. Product cognition - using data to provide feedback on the user experience of products and content

Product awareness refers to the use of data statistics on user behavior. Taking information APP products as an example, users use the product mainly to read information, so user behavior includes user usage time, the number of articles read within a period of time, article reading progress, reading speed and the reading time of a single article.

It should be noted here that product awareness should count the main functional points of the product. For example, user forwarding and comments are also user behaviors , but they are not the main functional points of information products, but belong to the category of user awareness.

Product awareness statistics often introduce the concept of a “user story map,” which is a way of thinking from agile working methods and is more commonly referred to as “user usage path analysis” in growth operations. By restoring the steps that users take when using the product, we can count the number of users who stay and leave at each step. This is often called “user funnel analysis.”

Through product awareness based on data feedback, if the bounce rate of a certain page is too low, you can think about product directions such as optimizing the experience and finding bugs; if the average browsing time of a certain article is too short or too many users exit the APP, then there may be serious problems with the quality of the article.

Product awareness is an effective way to collect user feedback. In the operations team 5 years ago, we often played the role of customer service and infiltrated the user group to collect user feedback. However, there is a saying among product managers : "What the user tells you is often not his real needs", because the user only knows that he is unhappy, but does not know how to be happy. The data is real, and product awareness is a way to establish effective communication among the product, operation team and users.

2. Market awareness - using data to optimize promotion strategies and budgets

Market awareness mainly collects data on product promotions , which is used to optimize promotion channels and calculate promotion costs. Many promotion platforms now have their own data statistics background, but on the one hand, the data may not be accurate enough, and on the other hand, the data after multi- channel promotion cannot be unified and integrated.

Compared with basic promotion data statistics, the market awareness of growth operations focuses more on full-process monitoring and optimization, which is divided into five links: access channels , user conversion , role analysis, landing experience, key guidance, etc. The idea of ​​"user story map" is introduced in the promotion link. In addition to analyzing the channel promotion effect, it is also responsible for the entire promotion process.

Most basic promotion data counts the number of customers acquired and the conversion effect from different channels, but often ignores the user attributes of the source channels, which is the third link in growth operations: role analysis.

Role analysis needs to be used in conjunction with the user portrait screening mentioned above. With the source channel as the first dimension, the user role characteristics of different channels are counted, such as low usage times and no visits after registration, to analyze the common low-quality users such as dead fans and freeloaders .

The landing experience link is used to optimize the user churn rate. It mainly collects statistics on the user experience data of the promotion landing page and analyzes the user experience in the landing page link. Landing pages are mostly used to guide users to download, register or purchase products, with the main purpose of leaving sales leads. Through "funnel analysis" and "behavior analysis", you can view the user loss link and perform targeted optimization.

3. User cognition - the basis for stratification of refined operation users

In fact, whether it is growth hacking or traffic pools and other recently popular operational thinking , they have put forward the idea of ​​refined operation, just like the user screening function provided in the promotion channels and the intelligent distribution of information platforms such as Toutiao , which are all practices and interpretations of refined user operations.

Mobile Internet operators are all trying to seize users' fragmented time. However, fragmented time is limited, and users are more concerned with content or products that are useful, relevant, interesting, timely and novel. This is what is often said in the marketing industry: "You just need it, and I just happen to be an expert."

User awareness is more about improving the activity and stickiness of existing users, that is, meeting the pain point needs of existing users. Compared with the conventional operation mode, a PUSH is often sent to all users, causing user disturbance and waste of resources. Now more and more large companies, such as Tencent, Alibaba , Meizu and other brands are doing user segmentation and providing targeted push notifications.

The data statistics of growth operations are divided into several directions, including overall data statistics, behavioral feature statistics, user portrait statistics, etc. The overall data is an analysis of the average value of user behavior. As a reference indicator for user stratification, the behavioral feature statistics should be designed according to the characteristics of the product.

For example, information products focus on user stickiness and can analyze data such as user dwell time, visit depth (number of articles viewed) and interaction index (collections, shares, comments, UGC , etc.). E-commerce products use user payment behavior as a key indicator and can be stratified based on data such as user browsed product features, number of transaction products, average single transaction price, and discount attractiveness.

After stratifying users through data screening, there needs to be an effective way to reach them and guide them in key behaviors such as encouraging, awakening, stimulating conversions, and triggering sales.

After explaining the whole idea, the boss still had a calm face, but the corners of his mouth slightly raised. The technical boss remained silent, with the corners of his mouth trembling violently. After half a minute of silence, the technical boss said: "I need to recruit 4 people, algorithms, data mining, front-end and UI. The construction period is at least half a year, and a server needs to be added. It will cost about 500,000. Do you agree?"

The boss was a little uneasy, "Tell me your reasons!"

"This is not data statistics at all. It is not as simple as embedding points. It requires opening up interfaces of various channels, including promotion and reach, and also data cleaning, calculation analysis, and visual presentation. This is a conservative estimate. According to this approach, it will be good if it can be achieved in half a year, and there is no guarantee that he will have other tricks later!"

The boss regained his composure and asked, "Is there any other solution?"

For growth operations, they are currently facing such an embarrassment: companies want to grow, but lack a data analysis platform, and independent development will indeed face high costs. For small teams, third-party data statistics and analysis platforms can be used to guide operational behavior.

Source: Zhang Xiaohuai