How do offline stores build a user growth system?

With the development of the Internet, people are becoming more and more accustomed to online consumption, which has had a great impact on the sales volume of offline stores. This article uses two core methodologies to analyze how offline stores build a user growth system. I hope it will be helpful to you.

Let me talk about what the user growth of offline stores includes:

• The first is the growth of private domain traffic fans, including social networks, corporate WeChat, public account fans, and communities;
• The second is the growth of ordinary membership;
• The third is the growth of stored-value members;

There are two core methodologies here, one is the traffic pool closed-loop theory, and the other is the user ARPU growth model.

The closed loop of traffic pool means forming a closed loop of users in private traffic pool-membership-online mini program.

First of all, the user's private traffic pool is connected to various traffic channels, including the official online website, third-party food delivery channels, B2C channels, offline self-owned stores, and cooperative stores with similar business formats but non-competitive relationships.

We need to introduce traffic into the traffic pool in a unified manner. The specific operation is that customers from the official website and stores enter the social group and corporate WeChat, and customers from third-party takeaway channels and B2C channels enter the merchant's middle platform and are merged into the membership pool through joint membership cooperation and interface docking. Customers from third-party cooperative stores enter the merchant membership pool through welfare coupons. In this way, we have initially realized the process of diversion and aggregation.

The next step is to build a closed loop for users, that is, the online-offline flow. Users in the traffic pool are imported into the online mini-program through live broadcast, community, and KOC operations. Customers place orders and choose to pick up the goods themselves or have them delivered. Self-pickup customers return to the store, and the store conducts corresponding value-added activities to eventually convert in-store customers into value-added members, achieving ultimate retention growth. The headquarters then converts loyal store members into KOCs and directs them to the community to promote the products. High-quality content is then shared back to the community to achieve a new round of fission.

The significance of a closed-loop traffic pool for merchants is to minimize customer acquisition costs and lock traffic firmly in the private traffic pool. Currently, the cost of online traffic diversion for most businesses remains high. Take Meituan as an example, the commission cost accounts for 18%. Add to that various subsidies, and there is little gross profit left from promotional activities. However, the pain point is that users who come to the store are always Meituan users. If these users cannot be retained, they will always be working for Meituan.

The core of building a closed-loop traffic pool lies in how users from different channels are deposited into our own private traffic pool. Regardless of whether the users come to the store or not, we can reach users through different channels. The importance of social groups, corporate WeChat, communities, official accounts, and live broadcast platforms is self-evident. These private domain marketing tools need to form a system to form a true closed loop.

Secondly, shopping guide acquisition + fission acquisition bring new users to the store

After the closed loop of the traffic pool is formed, a stable customer acquisition system is initially formed. On the one hand, online channel users are deposited into the membership pool as much as possible. On the other hand, the subjective initiative of offline store employees is brought into play. In addition to converting customers who have already visited the store into the community, the shopping guide customer acquisition model can be used to stimulate the enthusiasm of store employees in customer acquisition.

Take Jomoo Wang as an example. During the epidemic, store operations were hindered, offline customer traffic dropped sharply, and the backlog of goods led to a decline in profits. With the help of WeChat for Business + shopping guide digital marketing tools, Jomoo bucked the trend and achieved a month-on-month revenue surge of nearly 42.6%.

How did they achieve this amazing result?

First, we created a closed-loop traffic pool, including private traffic pools such as 1,000+ communities in the WeChat ecosystem, the Jomoo flagship store mall mini program, the popular live broadcast room mini program, and official accounts. By forwarding, pushing, subscribing, and watching, we directed global traffic to the mini program live broadcast, completing the closure of the online traffic pool.

Secondly, offline stores use shopping guides’ verbal broadcasts and shopping guides’ incentives to attract new customers, allowing shopping guides to convert customers who enter or pass by the store into corporate WeChat fans. The shopping guide shares products with fans to generate purchases and gets a certain commission. The sales performance is locked to the shopping guide's personal initiative. The more fans the shopping guide attracts and the more products he shares, the greater the final profit. The shopping guide completes offline customer acquisition, directs the flow to the online mini-program for transactions, and then closes the flow of in-store pickup.

Another effective way is to acquire customers through fission.

The creation of a private traffic pool provides a good fan base for fission. As long as an attractive fission driving force is designed, the fission can continue.

Taking baking private domain traffic as an example, when operating fission, in addition to the common poster fission gameplay, using some h5 mini games can achieve very good fission effects. We have added buttons and instruction texts to guide fans into the group in various aspects of the h5 game. Fans can get some free coupons and discount coupons when playing the h5 game. There is only one chance to participate every day, and you can get another chance after sharing it to your Moments. Generally, the probability of winning increases after sharing. After playing a few times, fans in the group will summarize this rule and have a great enthusiasm for sharing. After their friends enter this h5 activity in the circle of friends or other communities, they can see the entrance to the community on the winning page, non-winning page, homepage, etc., and there is a high probability of introducing new people into the community.

After new members join the group, through a series of conversions such as community new member gifts, they will eventually bring new conversion traffic to the store and achieve growth.

Again, precise marketing empowers and retains users for stores.

In addition to attracting traffic, the most important step to growth is to retain users. Retention of new customers and retention of old customers are the most important indicators of store user growth.

The most important model we use is the ARPU growth model:

If stores want to increase the value contributed by individual users, we need to do a complete disassembly.

First of all, after gathering customers in the traffic pool, we need to retain new customers. At the user operation level, we need to model the users, identify the growth paths of new customers, and label them as new customers, growth customers, and mature customers respectively.

For operations with new customers, stores need to focus on growth within a retention period. Once the new customer does not convert and place an order within the retention period, it often means that it will be difficult to reactivate the customer after loss.

So what is the retention period?

The retention period can be understood as the average length of time it takes for users to repurchase. For example, we analyzed the customers who came to our store and found that the average repurchase cycle of customers is 10 days. Then the operation plan for new customers is to convert the second order within 10 days from the date of the first order.

Based on my experience in baking user operations, I have obtained a set of data: the retention rate of new customers who repurchase during the retention period is 40% higher than that of new customers who do not repurchase. The risk probability of new customers becoming one-time users who do not repurchase within two retention periods is as high as 60%.

When labeling, you can label it with new customer's first order, new customer's second order, etc., and set up smart marketing. 7 days after the new customer's first order, a repeat purchase coupon will be automatically pushed to the user's membership center. After 7 days, users who have repeated purchases will be labeled with new customer's second order, and the first order label will be removed to enter the next round of repeat purchase marketing process. Users who have not repeated purchases will automatically be pushed a more powerful repeat purchase coupon for a new round of repeat purchase activation.

When it comes to retaining old customers, stores need to focus on the loss of high-value users. Once high-value users flow to competing stores, it often means the loss of an important asset.

For users who are in the growth stage in the store, we need to model them, identify users who are prone to churn in advance, and conduct timely retention marketing before they churn.

When we are doing retention marketing, we often cluster users. We can stratify the value of store users based on the RFM model or directly use the clustering model, dividing them into high, medium and low value users, and focus on prediction, monitoring and early warning for high value users.

Regarding the frequency of store visits, stores need to focus on frequency-increasing marketing for users, focus on studying the consumption preferences of user groups with different consumption frequencies, and make recommendations based on their preferences.

For customer orders, the association analysis model is used to analyze which products are highly correlated for the store to guide the store in display, package activity design, etc.

In short, the growth of store users is a process of gathering customers - closing the traffic pool - fission and diversion - retention. Only by linking each step closely and operating in a systematic way can we ensure that offline stores remain invincible in today's epidemic situation.

Author: Zhao Wenbiao

Source: User Operation Observation (ID: yunyingguancha)