Growth Haker is no longer a strange term. The original definition was "a group of people who use data to drive marketing, use the market to guide product direction, and implement growth goals through technological means." The core is to rely on the power of technology and data to achieve various marketing goals, rather than the traditional marketing role of spending money to acquire users.

With the development of domestic growth hacking, it also represents an operating method of "achieving user acquisition and retention in the fastest way, lowest cost, and most efficient way, and ultimately increasing revenue." But until now, what everyone is familiar with is still traffic growth, such as:

• Airbnb cleverly uses API to allow homeowners to share rental information on Craiglist, which already has a mature user base, with one click, thus gaining a large number of users at almost no cost;
• Instagram used data analysis to simplify the complex app, cutting out other features and leaving only the photo, comment, and like functions, and added filters. The number of users increased 20 times in just one day after its launch.
• Maimai (a domestic workplace social software) will obtain the user's address book and send text messages to his friends, such as "Zhang Xiaofan commented that you are very responsible, hurry up and come to Maimai to see more people's comments on you", which attracted many people to download it, including me, although this is a bit unethical.

There are countless cases like this, but most of them are applied to traffic growth, while growth hacking is an AARRR all-round growth process. As the Internet enters the second half, retention and monetization capabilities become increasingly prominent, so this article will analyze previous cases and teach you how to grow the fourth R (Revenue).

1. Draw the key conversion path

First, you need to map out the main path from when users open your product to when they finally make a purchase, and use data analysis to find one of the key paths to start improving conversions. This requires you to:

• Familiar with the business - have a clear understanding of the source channels of completed orders, such as natural conversion, operational activity conversion, sales conversion, channel business conversion, etc.;
• Be familiar with the product - no matter what channel you use, most of the final payment is done through the product. However, no matter what conversion method you use, there may be different paths, so you must be very familiar with the product.
• Familiarity with data - If there is no dedicated data analyst in the team, you also need to have a certain understanding of the location, acquisition, cleaning, and analysis of the required data.

Without further ado, let's get to the point. One of the author's company's products is online schools, and since the average customer spending is not low, they are offering a 1-yuan trial lesson, which allows customers to try before purchasing. Since the core feature of the online school is that two teams compete in groups to answer questions, you need to register for the trial class first to get enough students in the class, then take classes together at the same time, and finally complete the purchase actively or with the intervention of a CC (course consultant).

From the analysis of students who have completed purchases, we can see that paying users mainly come from natural conversions and CC conversions, and their behavioral paths mainly include direct purchases from the product detail page and purchases after a trial listening experience, so the key roadmap is drawn as follows.

2. Optimize product detail pages and shorten the path

In combination with the key path, in order to increase purchase volume, I mainly operate from three aspects: optimizing the product detail page, shortening the core path and improving the conversion rate of each step.

STEP 1: Product details page optimization

Since both paths must pass through the product details page, the product details page framework layer is reconstructed first:

• Information stratification to reduce the simple listing of horizontal information;
• Remove useless course covers and move the introduction videos with higher click rates to the top;
• Added description of core service content.

The final data was improved but not significantly. In essence, the information was just rearranged, and the information acquisition and visual experience were better, but the core needs of users were not solved .

STEP 2: Shorten the audition path

① Problems and strategies

In addition to purchasing directly on the product detail page, users are more likely to make a purchase after a trial experience. However, the trial experience requires four steps and there is constant loss of users during the process, especially after purchasing a trial lesson and then waiting a day or two before taking the class, which results in serious loss in this process.

So the problem is: if you take the trial class directly, you can only listen to it through video, and you cannot restore the real classroom experience of multiple people being divided into groups to PK and answer questions; if you take the trial class in a group, you need to wait for the class at a fixed class time, which will result in a large loss.

In order to solve this problem, I later considered a strategy that allows people to directly attend the trial class or to form groups to PK in answering questions (the specific strategy is not the focus of this article, so I will not go into details), simplifying the trial process into one step, as shown in the figure below.

② A/B testing and full release

In order to verify the feasibility, we selected three courses for transformation first to verify the audition situation and conversion status. During the verification period, data needs to be observed in real time and adjusted continuously. If there is good data performance, it can be synchronized with the project team in a timely manner to enhance team confidence. In this way, relevant personnel will be more motivated to cooperate in continuous adjustment and verification.

Fully open: After verification with controllable variables, the number of students opening the three new trial courses has increased significantly year-on-year, while other courses that have not been reformed have basically remained unchanged. So three days later, we will change all courses to the new version of trial (launched on Wednesday). After four days of operation, the growth data is as follows: the number of trial courses has increased n times, and the number of online school openings has increased by about 40%.

3. Improve conversion rate

In addition to shortening the path, the product solutions launched at the same time also applied some techniques to improve the conversion rate of each step.

① Limited audition times:

There are only three free trial opportunities per month for each lesson, which uses scarcity to increase the value of the trial and avoid useless data caused by too many children randomly trying out the lessons (the rarer the opportunity, the higher the value seems to be);

② Get a coupon by trying it out

• After you exit the trial listening session after more than 5 minutes, you will receive a coupon to promote conversions while the iron is hot.
• Valid for seven days: The coupon is valid for this course and for seven days, because experiments show that users' fear of losing something seems to be stronger than the emotion of obtaining the same item. Therefore, setting a clear and visible expiration date for the coupon can not only increase the user's sense of urgency in worrying about losing the coupon, but also provide a better reason to kindly remind the user later - "avoid the coupon expiration";
• xxx people have used: social proof principle.

4. Follow up data and continue to iterate

① Verify the stability of the process through data:

A very important follow-up of a newly launched project is to verify whether the entire process is stable and normal through various data. We followed up on the data within a week after the project went online and found two serious problems:

Problem 1: The number of pop-up boxes is much less than the number of full 5-minute auditions (normally, the two should be similar). We speculate that there may be some cases where no pop-up boxes appear. We also received feedback from colleagues that no pop-up boxes appear. So we traced the source and found that the current implementation scheme has unstable pop-up boxes . After the developers corrected it in time, the number of clicks on "Use Now" has increased significantly.

Question 2: One day, the number of clicks on the "Use Now" function suddenly dropped to single digits. After an emergency investigation, it was found that the developer had changed the code.

② Different conversion strategies for different users

After the user has auditioned the content, different conversion pop-up windows are provided according to the nature of the user . For example, member users do not need to receive coupons and are advised to directly open an online school, etc., and the conversion data is further improved.

③ Expand the number of trial users

• Enhance the audition entrance guidance: improve the audition conversion rate of the product detail page by externalizing group PK and inviting people to join the team.
• Win points by auditioning: Through operational means, you can win a certain number of points by participating in the auditioning, which will increase the conversion rate from entering the APP to auditioning;
• Targeted invitation to audition for some users: For users with certain characteristics, large pictures are pushed to invite them to audition, which improves the conversion rate from entering the APP to auditioning;

Judging from the data performance, these strategies have played a certain role in increasing the number of auditions.

Summarize

Through this project, I discovered that growth has no end. If there is time to continue iterating, there is still room for improvement in both the number of auditions and the conversion rate. If the product level is improved to the limit, it can also be improved through operations, marketing channels, etc.

In fact, "growth hacking" has always existed, but why it suddenly became popular in 2018? I think it is mainly because:

① Popularization of cognition:

With the introduction of the book Growth Hacker into China and the continuous popularization of Growth Hacker by Professor Fan Bing (just like Everyone is a Product Manager popularized product managers to a certain extent), the concept of growth hacker in China has been improved.

②Environmental requirements:

The early growth of the domestic Internet was basically brutal (burning money). Nothing is more direct and violent than money, so to a certain extent, the emphasis on "technical" growth was reduced. However, in the context of a capital winter and enhanced user "immunity", the high cost of acquiring customers and the difficulty in monetizing have created pressure. Everyone needs to calm down and grow more patiently and with greater technical skills.

In short, growth is an eternal topic for a company's survival.

Author: Salmon's Dream

Source: Salmon's Dream (hongruzd)