Disassembling the AARRR model: How to attract 150,000 fans in 10 days at 0 cost?

I believe that many people engaged in operations have a basic conceptual understanding of the Pirate Indicator (AARRR). However, knowing is easier said than done. Once used to solve actual problems, they know what to do, but they don’t know how to do it. They also need to go through a process of being at a loss, exploring, and stepping into pitfalls. This article will disassemble the A ARRR model by reproducing a case I have done, so as to provide some help to operations partners.

Use "5W1H" to attract new customers

Let’s first briefly explain the meaning of pirate indicators. The five letters correspond to: exposure and new customer acquisition, activation and conversion, retention and service, value mining, and communication and recommendation . Their function is to provide a funnel-shaped traffic conversion framework.

When attracting new customers, we use "5W1H" to sort out what to do, that is, to answer these 6 questions: who are the target users, where are the users, why are users willing to participate, what to use to guide users, when to attract traffic, and how to execute new customer acquisition activities.

At that time, we were making weight loss products, and our target users were white-collar workers and mothers who wanted to lose weight but did not want to go on a diet and lose weight easily. There were six exposure channels to choose from: our own Taobao store, one-to-one promotion through Qianniu, cooperation with Weibo celebrities, Baidu paid promotion, mini-program placement and WeChat Moments. However, since it was our first attempt and the effect was unknown, we did not dare to invest heavily, so we finally chose our own Taobao store, WeChat and mini-program with almost no cost.

We calculated the estimated value of attracting new customers through the Taobao channel through the number of visitors, collections and add-to-cart rates of Taobao stores, and then used software tools to count the number of active users of WeChat and Weidian. The sum of the three was used as the target value for attracting new customers.

The user demand is very clear, which is to lose weight, but the pain point reported by most users is that they lack determination to lose weight and are prone to give up. Therefore, we planned an event - lose 5 pounds in a week, aiming to create an atmosphere for joint weight loss and achieve the goal of weight loss through mutual supervision among members of the event. In order to let users know about this event, we created some event pages in the store and even made small pop-up windows to promote the event to everyone who entered the store.

At the same time, we edited the corresponding words and links in Qianniu, directed users to the event page, and posted texts and posters on WeChat Moments. This is a relatively conventional promotional method. In addition to the fixed exposure event page, considering that users have more time to pay attention to information at night, we choose to push event details at night.

During the execution stage, we mainly created an atmosphere of enthusiastic registration in the circle of friends, constantly posting pictures of weight loss results, the number of people who have registered to participate, chat records, etc., to make users who have not participated have a sense of anticipation and urgency. Through case promotion - attracting registration - creating a hot registration atmosphere - attracting registration... such a cycle allows more users to participate.

Use "B=MAT" to activate and convert users

In order to better activate, convert and attract new users, we used a formula "behavior = motivation, ability, trigger", which is the three conditions that need to be met for a behavior to occur.

Some people may wonder: "According to the model, shouldn't conversion be done after retention?"

We thought so at the time, but when we actually did it we found that the transformation could be done in the second step. We realized that although the framework is a good thing, if we are kidnapped by the framework and forget the purpose of what we are doing, the result will be counterproductive.

We understand the user's behavioral motivation as: what value this activity can bring to the user. As mentioned in the new customer acquisition stage, our activities can help solve the problem of insufficient determination for weight loss and increase users' success rate in losing weight.

In addition, our conversion model is: pay to join the group, free products (weight loss products are given as gifts), and we have set up reward rules: people who lose more than three kilograms in the end have the opportunity to purchase weight loss courses at a 50% discount, and if they lose less than two kilograms, it is a 10% discount, as a preparation for subsequent product conversions. This makes users feel that they are getting a good deal, and they have a strong motivation to combine internal and external factors.

The second condition of the behavior formula is ability. Our understanding is to minimize the user's participation path and lower the cognitive threshold, that is, to give clear participation steps: scan the payment QR code - add operators after successful payment - receive gifts - invite operators to join the group

During the event, our operations staff will also make daily reports, such as announcing yesterday’s weight loss rankings, each user’s progress toward the target value, and a list of users who have completed the task ahead of schedule, etc., implying that users are capable of completing the weight loss plan.

The last one is the trigger. We chose a poster with strong contrast: we found the before and after comparison pictures of a seller who lost 20 pounds in the store, and then threw out a copy: In ten days, you can wear the tight jeans you want most.

Service + connection makes users stay

When we successfully get users to sign up for an event through the behavioral formula, this is just the beginning. It is also possible that users will cancel their payments, resulting in a wasted effort. Therefore, user retention is a long-term battle that requires continuous exploration of user needs. After hitting several pitfalls, we summarized two points: service + connection.

We use WeChat groups as a traffic carrier. The advantages are instant messaging and high opening frequency, but the disadvantages are also obvious: files cannot be retained for a long time, and the information flow is arranged in chronological order, which can cause group messages to be pushed down by other information and ignored. Therefore, the best way to stay in a WeChat group is to make the topic active.

We require participating users to check in in the group every day to record the completion of daily tasks, weight status and weight loss slogans. This is an extensible topic. Others can extend more topics based on someone's check-in content, trigger discussions in the group, and attract more users' attention. At the same time, it enhances the social relationships between group members and allows users to stay in the group with social costs.

Secondly, we will invite some users who have achieved significant weight loss results to hold a small sharing session, which will not only increase the users’ sense of honor, but also cultivate community KOLs, laying the foundation for reducing operating costs in the future.

In terms of service, we will remind users before the daily check-in deadline and make corresponding data records, such as the rankings mentioned above, answer users' daily questions and other routine services. We also organize offline meetings to enhance connections between users.

Mining user utility with value as the center

After the first three steps, users have a sense of belonging to the community and trust in the product. We should now consider how to bring more business value through user word of mouth.

In the early stages of operations, one of the most difficult aspects of our experience was user research: we spent a huge amount of effort, but it was still difficult to truly understand what users were thinking. After the initial operations, we have basically understood the user's tone, and the activity process can be made more skillful and reasonable. There have been great improvements in the guidance and message delivery of key links. These are materials that can be reused in the life cycle of new users.

In addition, in some new activities in the future, the designed posters may be modified by testing seed users. At the same time, the value of users can be fully explored and service details can be continuously polished.

Identify the motivations of users to recommend and guide their promotion and growth

After fully exploring the user value, we believed that the word of mouth was good enough and users would actively recommend their friends to buy the product, but we found from the data that the growth did not meet our expectations. Our final analysis concluded that we need to identify the motivations behind user recommendations. Although we have user word-of-mouth, it is only a prerequisite for users to make recommendations.

We borrowed the Octagon behavior analysis method and combined it with the characteristics of our own users to come up with three motivations for user recommendations:

What are the actual benefits of recommending

1. What internal incentives can recommenders get?

What benefits can the referral get?

Based on the first point, we have set up a distribution system, that is, a commission mechanism. You can get a cash reward for recommending a user. In addition, when new users join the group, we set up coupon rewards. This not only increases the repurchase rate, but also achieves the purpose of fission.

Regarding the second point, in addition to obtaining substantial benefits, users will also receive social incentives when their recommended friends successfully lose weight. In addition, when friends lose weight together, they will compare with each other and gain a sense of honor.

Last but not least, we have set up a newbie gift package in which the referrals can also receive discount rewards, and through a large number of cases, we can also arouse the referrals' expectations: completing the weight loss plan.

The above is our more effective recommendation mechanism based on actual feedback. Prior to this, we had set up multiple sets of alternative plans and conducted small-batch tests. Therefore, we recommend that when making recommendations, you should make several sets of plans and test them before formally implementing them to avoid pitfalls.

The above is an activity case I did using the AARRR model. The summary is: Don’t be kidnapped by the framework, always remember your purpose, and find a method that suits you based on the purpose to deal with problems flexibly.

Source: Deng Juan