- Customer Management and Analysis
What Is Customer Analysis? A Comprehensive Guide to 16 Frameworks and Tools That Directly Impact Sales!
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 strategic direction. To develop superior strategies, it is essential to grasp the environmental factors surrounding the company, including market conditions and competitors. Since comparing one's own company against the market and competitors is fundamental, visualizing the company's current situation through existing customer analysis serves as the axis for all business activities.
While the term "customer analysis" is broad, there are various methods available. Representative approaches include decile analysis, segmentation analysis, and behavioral analysis. Among these, we will focus on RFM analysis, which is both the most fundamental and the most important.
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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 (total purchase amount). Generally, RFM analysis is a type of customer analysis 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 solely on RFM items, we end up approaching the same people every time and cannot identify potential prospects..."
By using the attribute information held by uSonar, it becomes possible to visualize the profile of existing customers from perspectives beyond basic information and RFM items. Therefore, uSonar's customer analysis allows for One-to-One marketing based on an individual understanding of each customer, enabling approaches 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.
Essential Data Preparation for Customer and Purchase Potential Analysis
For many B2C companies, data preparation is the first challenge in conducting customer analysis. Many companies struggle with duplicate data caused by input errors or missing information, as well as customer data scattered across 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 may 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," meaning that true high-value customers may be overlooked. Therefore, building accurate customer data is indispensable for conducting precise customer analysis.
At uSonar, we also support the construction of "Unique Customer" data as a preparatory step for accurate customer analysis. We process data by utilizing various knowledge bases, including name masters, address masters, and map checks. For data organized into "Unique Customers," we append attribute information associated with the uSonar consumer database to conduct customer analysis from multifaceted perspectives beyond RFM items.
Customer and Purchase Potential Analysis to Identify Potential High-Value Customers
In uSonar's customer analysis, we append attribute information from the uSonar consumer database in addition to RFM items, allowing you to identify potential high-value customers who are likely to contribute to profits, as well as customers with a high probability of churn. As a result, it is possible to improve the LTV of existing customers.
In other words, we conduct "predictive customer analysis" to understand which customers should be followed up with in the future, rather than just RFM analysis based on current purchase history.
uSonar's customer analysis goes beyond general attribute information to delve into the inner profile of existing customers using the following attributes:
[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, as well as corporate attribute information.
By utilizing these attributes, we visualize "Potential High-Value Customers" who could become valuable customers in the future.
Use Cases for Customer 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 processes on customer data registered across multiple stores or business locations by individual or household. This allows for the construction of deduplicated 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 rank. This allows for the identification of customers to focus on (customers with low current transaction amounts but high diagnosed potential). - Attrition: Visualization of Churn-Prone Customers
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 probability of churn, enabling 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 with no current transactions but for which there is likely a need). - 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 versus those that should not be prioritized.
