16 Customer Analysis Methods
Below, we introduce 16 primary methods used for customer analysis.
Since the most suitable analysis method varies depending on the products or services handled, please utilize these methods strategically as you proceed with your analysis.
1. Decile Analysis
Decile analysis is a method that categorizes customers into ten equal groups based on their purchase history data, ranked by total purchase amount. The term 'decile' is derived from the Latin word for 'one-tenth'.
By calculating the composition ratio based on sales for each customer segment, you can identify which groups are high-value customers contributing to revenue, enabling effective sales promotion tailored to each segment.
2. RFM Analysis
RFM Analysis is a method that classifies customers into three categories based on their purchasing behavior: Recency (date of the most recent purchase), Frequency (purchase frequency), and Monetary (purchase amount).
Because it identifies customers based on more than just purchase amount, it requires more consideration than Decile Analysis; however, by accounting for the most recent purchase date and purchase frequency, it allows for more detailed analysis.
It can also be used as a guideline for considering re-engagement strategies for customers with high purchase amounts whose purchase frequency has begun to decline.
3. Segmentation Analysis
Segmentation Analysis is a method of grouping customers based on characteristics such as attributes and purchase history. By clearly grouping and analyzing data by gender, age, and location, you can provide services that differentiate your company from competitors.
Furthermore, by grouping customers based on psychological factors and behavioral characteristics, you can develop and implement strategies specialized for common segments.
4. Behavioral Trend Analysis
Behavioral Trend Analysis involves grouping customer segments that intend to make purchases during specific seasons. By utilizing Behavioral Trend Analysis to understand purchasing attitudes and motivations during specific seasons, you can provide products and services that align with the needs of your high-value customers.
Furthermore, it is possible to uncover other potential customer needs and resolve their challenges.
5. Cohort Analysis
Cohort analysis is a method for analyzing customer retention rates by indexing the behavior of specific user groups, such as the "Millennial" or "Gen Z" generations. For example, by analyzing and grouping customers who have used distributed coupons, you can redistribute coupons to customers identified as having not used the service recently, thereby maintaining retention rates. Consequently, cohort analysis is an essential metric for building long-term customer relationships.
6. LTV Analysis
LTV analysis stands for Life Time Value. It refers to the total profit a customer brings to a company over a specific period, starting from the beginning of the business relationship.
LTV can be calculated as: Average Customer Unit Price × Profit Margin × Purchase Frequency × Duration of Relationship.
The reason LTV analysis is important is that business models focused solely on one-time product sales result in sporadic profitability. Customer analysis is intended to cultivate fans of your services, and allocating advertising costs to customers who do not continue their engagement is inefficient. In short, a higher LTV indicates a stronger relationship between sales and the customer, which is more likely to generate consistent revenue.
7. CTB Analysis
CTB analysis stands for "Category," "Taste," and "Brand." While Decile analysis and RFM analysis focus on purchase amounts, CTB analysis allows for a deeper understanding of customer utility (attributes and types). It is frequently used in e-commerce sites and the retail industry.
By conducting CTB analysis and segmenting customers, it becomes possible to propose products and services that align with their specific utility (attributes and types). A key feature of CTB analysis is the ability to propose new product plans or implement marketing initiatives before customer needs become explicitly apparent.
Other Analytical Methods
8. Pipeline Analysis
Pipeline analysis is a method of managing and analyzing the sales process that prospective customers follow, likening it to a pipeline. It is characterized by the ability to streamline sales activities by visualizing both qualitative information, such as sales representative actions and marketing initiatives, and quantitative information, such as elapsed time, volume, and response rates at each stage of the sales process.
While pipelines vary by company, typical processes include the following:
• Events, Exhibitions, and Seminars
• Inquiries and Content Downloads
• Follow-up by the Inside Sales Department
• Initial Meetings
• Demonstrations and Quotation Presentations
• Nurturing
• Closing
• Customer Success
It is important to design the analysis with the understanding that a sales pipeline is not a simple straight line, but rather involves branches, stalls, and lost deals (reversals).
9. CPM Analysis
CPM analysis stands for Customer Portfolio Management and is a method of analyzing existing customers based on three axes: Purchase Frequency, Purchase Amount, and Days Since Last Purchase. It is well-suited for identifying high-value customers from current transaction data. However, it is important to note that it cannot be used to analyze customers with whom there is no existing transaction history. For analyzing untapped markets, remember that in addition to transaction status, separate analysis based on attribute information such as location and interests is required.
10. Basket Analysis
Also known as Market Basket Analysis, Basket Analysis is an analytical method used to identify correlations between purchased products. As the name suggests, it visualizes the correlation between products by analyzing items placed in the same basket (shopping cart) simultaneously. The correlation is visualized by calculating the following four indicators.
• Support Level
Support Level = Number of Customers Who Purchased Product ① and Product ② Simultaneously ÷ Total Number of Customers
• Confidence Level
Confidence Level = Number of Customers Who Purchased Product ① and Product ② Simultaneously ÷ Number of Customers Who Purchased Product ①
• Expected Confidence Level
Expected Confidence Level = Number of Customers Who Purchased Product ② ÷ Total Number of Customers
• Lift Value
Lift Value = Confidence Level ÷ Expected Confidence Level
11. 3C Analysis
3C Analysis is a method for conducting an environmental analysis that includes both your company and its customers by examining 'Customer (Market)', 'Company', and 'Competitor'. In 3C Analysis, it is important to be conscious of gathering objective facts and primary information. Caution is required, as the validity of the collected information directly impacts the validity of your strategy.
For B2B companies, performing a 6C analysis—which combines your company's 3C with the customer company's 3C—allows for the accurate visualization of the environment surrounding your sales and marketing activities.
12. Customer Journey Map
This involves comprehensively grasping and mapping the touchpoints (Customer Journey) that a prospective customer has with your company from initial awareness through consideration to the final purchase. A key feature is that by comprehensively managing all touchpoints where your company and customers interact, you can optimize marketing and sales initiatives across multiple departments.
13. SWOT Analysis
This is a method for organizing the environment surrounding your company into four aspects: Strengths (Internal Environment × Benefit), Weaknesses (Internal Environment × Detriment), Opportunities (External Environment × Benefit), and Threats (External Environment × Detriment). By shifting the perspective to your company, you can visualize the status of the services that customers are receiving (and that your company is providing). By developing marketing initiatives and management strategies based on the organized situation, you can optimize your business operations.
14. Persona Analysis
Persona Analysis is a method used for product development and marketing strategy by defining a target customer as a specific fictional character (Persona) and conducting analysis from that perspective. This analysis includes steps such as detailed collection of customer data, creation of the persona, understanding their needs and goals, and the formulation of product development and marketing strategies, as well as regular updates to the persona.
Collecting customer attribute information, behavioral data, and opinions or feedback in as much detail as possible is a crucial step that is often overlooked. This information serves as the foundation for establishing specific personas.
15.AIDMA
AIDMA represents the psychological process in customer purchasing behavior. Each letter represents the following phases:
A (Attention): First, the product or service must attract the consumer's attention. This involves making consumers aware of the existence of a product or service through advertising and promotion.
I (Interest): The consumer develops an interest in the product or service. By providing information such as features, benefits, and usage, you stimulate the consumer's interest and curiosity.
D (Desire): The consumer develops a desire for the product or service. As interest deepens, the consumer begins to feel a want to acquire the product or service.
M (Memory): The consumer retains the product or service in their memory. It is important that the consumer remembers the product or service both before deciding whether to purchase it and after the purchase.
A (Action): The consumer finally takes action to purchase the product or service. Having passed through the previous phases, the consumer moves to specific purchasing behavior.
By applying an approach suitable for each phase, you can promote consumer purchasing behavior.
16. Cluster Analysis
This is an analytical method used to identify similarities and correlations within data and group data with the same characteristics into a single category (cluster). In marketing, it is utilized for customer segmentation and the development of target marketing strategies.
Representative methods for creating clusters (clustering techniques) include K-means clustering and hierarchical clustering.
