- Customer Management & Analysis
What Is Customer Analysis? A Thorough Guide to 16 Frameworks and Tools That Directly Impact Revenue!
Deriving Strategy from Existing Customer Analysis
It is no exaggeration to say that analyzing existing customers is the most critical task when determining a company's direction. To develop superior strategies, it is essential to understand the environmental factors surrounding the company, including the market and competitors. Since comparing one's own company to the market and competitors is fundamental, visualizing the company's situation through existing customer analysis serves as the axis for all business activities.
While there are various methods for customer analysis, we will focus on RFM analysis, which is the most fundamental and important, among representative methods such as Decile Analysis, Segmentation Analysis, and Behavioral Analysis.
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What Is Customer Analysis? A Thorough Guide to 16 Frameworks and Tools That Directly Impact Sales!
Are You Feeling the Limitations of RFM Analysis?
RFM analysis is a method of classifying customers based on three metrics: Recency (date of last purchase), Frequency (purchase frequency), and Monetary (purchase amount). Generally, RFM analysis is a type of customer analysis method that covers the necessary elements to improve LTV (Life Time Value).
However, we often hear the following concerns from many companies:
"With customer analysis based only on RFM items, we end up approaching the same people every time and cannot find potential prospects..."
By using the attribute information held by uSonar, it becomes possible to visualize the profile of existing customers from perspectives other than basic information and RFM items. Therefore, uSonar's customer analysis allows for One-to-One marketing based on an individual understanding of each customer, realizing an approach to potential high-value customers that could not be captured by RFM analysis alone.
We can help resolve the following concerns. Please feel free to consult with us.
- Data preparation for customer analysis is difficult, and data is scattered across multiple systems.
- To increase the LTV of existing customers, we want to derive the necessary elements to nurture high-value customers.
- We want to accurately grasp the current situation, such as customer characteristics and purchasing patterns.
Data Preparation Essential for Customer Analysis and Purchase Potential Analysis
For many B2C companies, data preparation is the first challenge in conducting customer analysis. Many companies possess duplicate data due to input errors or missing information, as well as customer data tied separately to various channels such as physical stores and e-commerce sites. Even if customers can be managed by a "Customer ID," significant man-hours are required to perform data matching beyond the "Customer ID" to achieve a unique state ("Unique Customers").
However, by organizing customer data into a unique state, cases can arise where a customer previously recognized as Rank A is actually Rank S. The distribution of customer ranks differs between "Customer ID" and "Unique Customers," and companies may be overlooking their true high-value customers. Therefore, building accurate customer data is essential for conducting accurate customer analysis.
At uSonar, we also support the construction of "Unique Customer" data as a preparatory step for accurate customer analysis. We perform data processing by utilizing various knowledge bases, such as name masters, address masters, and map checks. For data organized into "Unique Customers," we append attribute information associated with uSonar's consumer database to conduct customer analysis from multifaceted perspectives beyond RFM items.
Customer Analysis and Purchase Potential Analysis to Identify Potential High-Value Customers
In uSonar's customer analysis, we append attribute information associated with uSonar's consumer database in addition to RFM items, allowing us to identify potential high-value customers who can contribute to profits and customers with a high probability of churn. As a result, it is possible to realize an improvement in the LTV of existing customers.
In other words, we conduct "predictive customer analysis" to understand whether a customer should be followed up in the future, rather than RFM analysis based on current purchase history.
In uSonar's customer analysis, we utilize not only general attribute information but also the following attribute information to delve into the inner aspects of existing customers:
[General Attribute Information] Demographic and geographic information such as gender, age, and residential area, as well as RFM-related information.
[uSonar Attribute Information] Attribute information such as wealth level and occupation, and corporate attribute information.
By utilizing this attribute information, we visualize "Potential High-Value Customers" who could become high-value customers in the future.
Use Cases for Customer Analysis and Purchase Potential Analysis
It can be used to solve the following challenges:
- Cleansing: Customer Data Matching and Construction of Unique Customers
We perform matching processing on customer data registered across multiple stores or business offices by individual or household. This allows for the construction of duplicate-free data, enabling accurate customer analysis. - Up Sell: Visualization of Potential High-Value Customers
We diagnose the potential of existing customers based on customer analysis by customer rank. This allows for the extraction of customers to focus on (customers whose current transaction amount is low but are diagnosed as having high potential). - Attrition: Visualization of Churn-Prone Groups
We diagnose the churn risk of existing customers based on the analysis of customers who have churned in the past. This allows for the identification of customers with a high possibility of churn and the implementation of early churn prevention measures. - Cross Sell: Visualization of Co-purchase Trends
We grasp the purchasing trends of existing customers based on the analysis of transaction details. This enables recommendations for products that should be introduced (products that are not currently traded but are believed to have needs). - Acquisition: Visualization of New Targets
We diagnose the order probability of targets based on customer analysis utilizing existing customer/prospect data. This allows for the screening of prospects to focus on and those that should not be focused on.
