LINE has one of the largest number of users in Japan among SNS, and has a large number of active users who use it on a daily basis, so it is a platform . However, the number of official LINE accounts of companies that want to connect with users is increasing year by year, and users receive messages from many companies every day. Therefore, it has become even more important to deliver messages that are tailored to each user’s interests at the best possible time.
table of contents
- What is necessary to deliver messages that are suitable for users?
- Data integration is key to better understanding your users
- Four main steps to message delivery
- Actual analysis and future plans
- LINE User ID integrated analysis provided by Digital Shift
- summary
What is necessary to deliver messages that are suitable for users?
Up until now, companies’ LINE official accounts have mainly been distributed all at once, resulting in one-way communication, but now they are tailored to each user to improve the customer experience and build good relationships with users . Communication is becoming more important. Under these circumstances, we believe that the important thing in operating the LINE official account is to create a detailed message delivery design that focuses on each user, such as ” when, to whom, and what kind of message should be sent. ” You can
To achieve this, it is important to increase the resolution of user behavior and understand where users’ interests lie . Therefore, it is necessary to analyze “user behavior data within LINE” extracted using the unique user ID assigned by LINE (hereinafter referred to as LINE User ID) obtained with permission from the user. Additionally, by linking LINE User ID to the company’s own customer database (1st party data) of store visits, purchases, etc., it becomes possible to design distribution based on user preferences such as purchase channels and purchased products.
Next, we will discuss what kind of analysis and measures to take using LINE User ID and our own customer database , based on the case study of an apparel company that Digital Shift supports in operating LINE official accounts . I’ll show you what I did.
*User data stored in LINE and our own databases is obtained with permission from users and used within the scope of permission. Additionally, user data will be handled in a manner that does not identify individuals.
Data integration is key to better understanding your users
In the operation of a typical LINE official account, message distribution is designed based on “user behavior data within LINE (data that can be obtained via the Messaging API *1)” such as clicks on distributed messages and conversions via LINE . Masu.
At Digital Shift, we use customer databases owned by apparel companies, including “user behavior data inside LINE” and “user behavior data outside of LINE,” in order to design more detailed message distribution . We worked to improve the accuracy of the data.
The apparel company we introduce as an example is a brand that can be purchased not only online but also at physical stores, so it is not possible to link “user behavior data outside of LINE” such as online and store membership information and purchase history . , which was an important factor in improving the accuracy of the data . Based on this point, in this initiative we first integrated “user behavior data outside of LINE” and “user behavior data within LINE” to create a data infrastructure.
This makes it possible to analyze behavioral data targeting users who have taken a purchase action, and to incorporate data such as purchase channel, purchase time, and purchased product into distribution design.
We aim to deeply analyze not only “user behavior data within LINE” but also “user behavior data outside of LINE” to the purchase status, and aim to deliver segmented content according to the user’s interests. In addition to continuing this, it is also important in aiming to maximize revenue via LINE, which will ultimately lead to increased loyalty.
Four main steps to message delivery
There are four main steps from data integration to distribution of “user behavior data outside of LINE”.
1. Developing an analysis environment for “user behavior data”
The data will be integrated by linking “user behavior data within LINE” and “user behavior data outside of LINE” based on the LINE User ID.
2. Prioritizing issues through analysis
Understand user distribution and sales structure in order to implement measures systematically and efficiently. Determine the priority of the issues, such as what to solve will lead to building good customer relationships in the long term, or whether it will be an effective means to improve KPIs such as sales and ROI .
The following points may be considered in the analysis:
- Is the user blocked?
- Is it click active (have you clicked within the past year)?
- Do you have purchasing experience?
- When will you make your first purchase?
- How many times have you purchased in total?
- How long does it take from the first purchase to subsequent purchases?
- What is the purchase channel (web, store, etc.)
- What is the purchase category/item?
- What are the search characteristics for purchasing?
3．Planning measures based on analysis results
Based on the analysis results, consider measures to address the issues. We will formulate measures to determine which content to distribute to whom, at what timing, and what kind of content will be an effective means of addressing the issue.
It is also important to prepare an analytical environment for monitoring in order to judge the results of measures.
Four. Implement measures to address issues
Developed a PDCA schedule for implementing measures . We will implement the measures and distribution plans that we have devised.
Next, we will introduce the specific analysis conducted by the apparel company and the measures planned to be implemented based on the analysis results.
Actual analysis and future plans
(1) Sales composition ratio of new users and repeat users
[Actual analysis]
The first thing we started was to calculate the ratio of new users to repeat users in sales. Since the impact on sales is large, we will prioritize measures with a higher ratio.
As a result of classifying purchasing users and sales composition ratio into new users and repeat users*2 and calculating the respective ratios, new users were found to have a higher number of users and sales composition ratio than repeat users.
※2 新規ユーザーは2021年に初めて購入したユーザー、リピートユーザーは2020年に購入したことがあるユーザーと定義。
[Measures planned for the future]
Due to the high proportion of new users in sales, and based on Digital Shift’s knowledge to date, measures to encourage first-time purchases have a higher immediate effect on sales than measures to encourage repeat purchases. Therefore, in the short term, we will implement measures with priority given to new users.
On the other hand, in order to increase loyalty in the medium to long term, it is necessary to consider measures for repeat users.
(2) Trends in the number of new user purchases
[Actual analysis]
Among the users who purchased in the past year of 2021, the trends in first-time purchases are summarized on a monthly basis.
The number of first-time purchases was high during sale periods, such as discounts and PR, when users’ interest and interest is high.
[Measures planned for the future]
We hypothesize that users who have added friends to their official LINE accounts since the beginning of this year will also have a similar tendency when it comes to making their first purchases. Therefore, in months when the number of new purchases is high (sale periods when users have discounts), we systematically increase the frequency of sending messages such as sale and coupon information to “users who have added friends since the beginning of this year.” We plan to encourage first-time purchases. With this measure, we aim to attract customers to the first EC site and promote purchases in a short period of time.
(3) Period until repeat purchase by existing users
[Actual analysis]
We analyzed the number of days that have passed from the purchase date to the next purchase date for users who have made two or more purchases in 2021.
As a result of analyzing purchase frequency, we found regularity in the number of days elapsed until the next purchase, such as 5 months.
[Measures planned for the future]
The products handled by apparel companies tend to be replaced or repurchased on a regular basis, and the typical replacement period is said to be three to six months after purchase. We plan to distribute coupons that can be used on your next purchase within a period determined by analysis so that you can choose the apparel company’s brand again when it’s time to replace your purchase, giving you the opportunity to recall the brand and purchase at a discount. .
LINE User ID integrated analysis provided by Digital Shift
It is extremely difficult for companies to integrate their own member IDs into LINE User IDs. “TSUNAGARU”, a marketing
tool provided by Digital Shift that supports LINE operations that connects companies and customers, is a “ LINE User ID” that integrates various data held by companies and “user behavior data within LINE”. We provide integrated analysis services.
The apparel company introduced as an example this time has three types of membership cards and IDs: “store (offline) membership card,” “WEB ( online ) membership card,” and “LINE membership card.” Users can link each of the three types of member IDs on the website. However, there are multiple patterns that can be linked with LINE User ID, and there are also only a limited number of patterns that can be linked to “user behavior data held in the company’s database. “ It was a complicated situation.
In order to comprehensively link these three types of member IDs to LINE User IDs, the LINE User ID integrated analysis team at Digital Shift, an engineering organization with expertise in LINE operations, worked together to utilize its past LINE operation know-how. We were able to achieve this by designing the data linkage based on the original design.
In this way, LINE User ID integrated analysis integrates various data around LINE User ID, and uses that data to analyze user behavior and formulate strategies. The following two things can be achieved with LINE User ID integrated analysis.
①Data collection/integration
By linking with a company’s own database , user behavior data can be integrated and managed with LINE User ID.
②Data analysis/strategy planning
Analyze the integrated data, plan operation strategy for LINE official account , and create monthly distribution list.
We will then implement the measures and distribution plans we have created at TSUNAGARU.
At Digital Shift, this was possible because we have separate organizations that specialize in consulting and analysis.
summary
By analyzing user behavior data and utilizing it in message delivery design, LINE can aim to realize communication tailored to each user. Through measures to improve customer experience, it is possible for companies to maintain and improve sales. The reason why we are able to analyze users’ interests so deeply is because we are a platform that allows us to link “user behavior data outside of LINE” to LINE User IDs, allowing us to form deep connections with users.
