16 Customer Analysis Methods
Below, we introduce 16 primary methods used for customer analysis.
Since the most suitable method varies depending on the products or services you handle, please utilize these techniques strategically as you proceed with your analysis.
1. Decile Analysis
Decile analysis is a method of analyzing customer purchase history data by dividing it into ten equal groups based on the total purchase amount. The term "decile" is derived from the Latin word for "one-tenth."
By calculating the composition ratio of sales for each customer segment, businesses 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 of classifying customers into three categories based on their purchasing behavior: Recency (the date of the most recent purchase), Frequency (purchase frequency), and Monetary (total purchase amount).
While this method requires more consideration than decile analysis because it identifies customers based on more than just purchase amount, it allows for more detailed analysis by accounting for the most recent purchase date and purchase frequency.
It can also be used as a guideline for considering re-engagement strategies for customers who have high purchase amounts but 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, companies can provide services that are differentiated from those of their competitors.
Furthermore, by grouping customers based on psychological factors and behavioral characteristics, companies can develop and implement strategies tailored to specific common segments.
4. Behavioral Trend Analysis
Behavioral Trend Analysis is the process of segmenting customer groups based on their purchasing intent during specific seasons. By leveraging Behavioral Trend Analysis to understand purchasing attitudes and motivations during these periods, you can provide products and services that align with the needs of your high-value customers.
Furthermore, it allows you to uncover other potential customer needs and identify opportunities to resolve their challenges.
5. Cohort Analysis
Cohort Analysis is a method for analyzing customer retention rates by indexing the behaviors 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 those who have 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 Spend × Profit Margin × Purchase Frequency × Duration of Relationship.
The importance of LTV Analysis lies in the fact that business models focused solely on one-time sales result in sporadic profitability. Customer analysis is intended to cultivate fans of your services, and it is inefficient to spend advertising costs on customers who do not continue to engage. In short, a higher LTV indicates a stronger relationship between sales and the customer, which is more likely to generate sustainable revenue.
7. CTB Analysis
CTB Analysis stands for 'Category,' 'Taste,' and 'Brand.' While Decile Analysis and RFM Analysis focus on purchase amounts, CTB Analysis enables a deeper understanding of customer utility, attributes, and types. It is particularly 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 with similar attributes and types. A key feature of CTB analysis is the ability to plan new product proposals or launch marketing initiatives before customer needs become 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. A key feature is 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 Material Downloads
• Follow-up by the Inside Sales Department
• Initial Meeting
• 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, pauses, 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: Number of Purchases, 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 prior transaction history. Keep in mind that for analyzing untapped markets, analysis based on attribute information such as location and interests is required in addition to transaction status.
10. Market Basket Analysis
Basket Analysis, sometimes referred to as Market 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 a basket (shopping cart) simultaneously. The correlation is visualized by calculating the following four metrics.
• Support
Support = Number of customers who purchased Product ① and Product ② simultaneously ÷ Total number of customers
• Confidence
Confidence = Number of customers who purchased Product ① and Product ② simultaneously ÷ Number of customers who purchased Product ①
• Expected Confidence
Expected Confidence = Number of customers who purchased Product ② ÷ Total number of customers
• Lift Value
Lift Value = Confidence ÷ Expected Confidence
11. 3C Analysis
3C Analysis is a method for conducting an environmental analysis that includes both the company and its customers by analyzing 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 the strategy.
For B2B companies, performing a 6C analysis—which combines the 3C analysis for one's own company with the 3C analysis for the client company—allows for an accurate visualization of the environment surrounding the company's sales and marketing activities.
12. Customer Journey Map
This involves comprehensively understanding and mapping the touchpoints (customer journey) that a prospective customer has with a company from initial awareness through consideration to the actual purchase. By comprehensively managing all touchpoints where the company and the customer interact, it becomes possible to optimize marketing and sales initiatives across multiple departments.
13. SWOT Analysis
This is a method for organizing the environment surrounding a company into four aspects: Strengths (internal environment × advantages), Weaknesses (internal environment × disadvantages), Opportunities (external environment × advantages), and Threats (external environment × disadvantages). By shifting the perspective to the company, one can visualize the status of the services that customers are receiving (which the company is providing). By developing marketing initiatives and management strategies based on the organized situation, the company can optimize its business operations.
14. Persona Analysis
Persona analysis is a methodology used to inform product development and marketing strategies by defining target customers as specific, fictional characters (personas) and conducting analysis from their perspectives. This process includes steps such as collecting detailed customer data, creating personas, understanding their needs and goals, and formulating product development and marketing strategies, followed by regular updates to the personas.
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 concrete personas.
15.AIDMA
AIDMA represents the psychological process involved in customer purchasing behavior. Each letter corresponds to the following phases:
A (Attention): First, the product or service must capture the consumer's attention. This involves making the existence of the product or service known to consumers through advertising and promotion.
I (Interest): The consumer develops an interest in the product or service. Providing information such as features, benefits, and usage methods helps stimulate the consumer's interest and curiosity.
D (Desire): The consumer develops a desire for the product or service. As interest and curiosity deepen, 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 transitions into specific purchasing behavior.
By implementing an approach tailored to each phase, you can effectively promote consumer purchasing behavior.
16. Cluster Analysis
This is an analytical method used to identify similarities and correlations within data and group data with shared characteristics into a single category (cluster). In marketing, it is utilized for customer segmentation and the development of targeted marketing strategies.
Representative methods for creating clusters (clustering techniques) include K-means clustering and hierarchical clustering.
