What Is Customer Data Analysis?
Customer data analysis is the initiative of analyzing and gaining a deep understanding of various information regarding customers. In customer data analysis, for example, the following aspects are clarified:
- Identifying target customers
- Understanding customer needs
- Confirming whether customers are satisfied with the company's products and services
Without grasping "who the company's customers are" or "what customers desire," it is impossible to have them choose your products and services or build long-term, positive customer relationships. Therefore, it is essential to collect and analyze a wide variety of data regarding customers to obtain insights that contribute to business strategies such as sales, marketing, and product development.
What Is Customer Data?
Customer data is a collective term for all information regarding customers. It includes basic information such as names, ages, genders, addresses, and contact details, as well as behavioral history such as "what has been purchased in the past" and "what kind of advertisements have been responded to."
In B2B, customer data becomes even more complex. It is necessary to collect, organize, and manage a large amount of data, including information on the entire organization, the departments and individuals negotiating with your company, and the ultimate decision-makers.
Customer data collection can be conducted through various media and opportunities, such as websites, POS registers, mobile apps, social media, marketing campaigns, and surveys. For example, having users register as members on an e-commerce site is a representative collection method. Various information entered during registration can be utilized as customer data.
Maintaining accurate customer data at all times to leverage it for marketing and sales is becoming increasingly important today, as customer needs and behaviors are prone to change. While collecting customer data, ensure you establish an appropriate management system using IT tools, which will be described later.
Types of Customer Data
Although customer data is diverse, it can be broadly categorized into two types: "quantitative data" and "qualitative data."
Quantitative data is "information that can be quantified." It is used to execute statistical analysis or conduct detailed research backed by numerical values. The advantage of quantitative data is that it is easy to add objectivity to analysis results, and the more data that can be referenced, the less likely the results will be biased. Representative examples of quantitative customer data include purchase amounts, purchase quantities, and purchase frequency.
Qualitative data is "information that cannot be quantified" and is basically expressed in words. In terms of customer data, "customer feedback" received through surveys or customer support is a representative example. Such qualitative data is effective for grasping customer thoughts that cannot be seen from numerical values. In addition, company names, locations, industries, and representative names are also utilized as qualitative information.
It is important to use quantitative and qualitative data differently depending on the purpose. For example, "which product has high sales" can be clarified through quantitative analysis, but when clarifying "why customers prefer that product," qualitative analysis such as survey results or trends by location is more suitable.
Methods of Customer Data Analysis
There are various methods for customer data analysis. Here, we will introduce two representative methods: "Market Basket Analysis" and "Segment Analysis."
Market Basket Analysis
Market Basket Analysis is an analytical method that identifies groups of products that are likely to be purchased together based on customer purchase history. The word "basket" originates from "shopping baskets" in places like supermarkets.
By conducting Market Basket Analysis and identifying, for example, that "Product A is likely to be bought with Product B," you can promote sales more effectively by placing product shelves closer together or conducting campaigns such as set sales. In addition, if the correlation between Product A and Product B can be specifically clarified, it can also be applied to sales promotion measures for Product C and Product D, which have the same relationship.
When displaying messages such as "Customers who purchased this product also purchased these products" to users who have added a specific product to their shopping cart on an e-commerce site, the results of such Market Basket Analysis are utilized.
When using the results of Market Basket Analysis, identify hidden purchase patterns and trends of customers, and plan and execute measures focused on them. By uncovering needs that customers themselves may not be fully aware of through combinations of products, you can aim to increase sales and improve customer satisfaction.
Segmentation Analysis
Segmentation is a method of grouping each customer according to specific commonalities or similarities, and it is used by many companies because it is relatively easy to execute. In B2B, the following elements, for example, can be cited as criteria for segmenting customers:
- Industry
- Public/Private Status
- Number of Employees
- Capital
- Sales Revenue
- Business Growth Potential
By classifying customer segments based on such information, you can systematically understand the status of your company's customer base, making it easier to prioritize which customers to approach and plan measures for each customer segment. Since you will be able to practice optimized sales and marketing for each customer segment, it also leads to improvements in sales and conversion rates.
Tools for Managing and Analyzing Customer Data
To manage, analyze, and utilize customer data, it is recommended to use IT tools. Representative tools include Excel, CRM, SFA, and MA. The characteristics of each tool are as follows:
Excel
Excel is a tool used by a very large number of companies and can also be used for managing customer data. Many people are accustomed to using Excel, and if it has already been introduced, the fact that there are no introduction costs is an attractive point. However, since it is not a solution originally specialized for the management and analysis of customer data, there are many inconvenient points. If you want to conduct full-scale customer data analysis, it is better to adopt a CRM or SFA.
CRM
CRM is a solution specialized for customer data management. It can often be linked with various tools, including SFA and MA, which will be described later, making it a strong option when you want to utilize customer data company-wide across systems and departments. It is especially recommended when you want to strengthen relationships with existing customers.
SFA
SFA is a solution called "Sales Force Automation." It is designed with the main purpose of automating and streamlining sales activities, and it is generally introduced when you want to strengthen the sales department rather than marketing.
As one of its sales support functions, it is also equipped with customer data management and analysis functions. However, it is a recommended option when you want to realize not only customer data analysis but also the automation of tasks such as creating estimates and daily reports, and the efficiency of overall sales activities such as case management by each sales representative.
MA
MA is a solution aimed at the automation and streamlining of marketing activities. With MA, you can automatically send information such as email newsletters and SNS according to the analysis results of customer data.
MA is basically a tool used for lead nurturing (cultivating prospective customers). By passing the results to a CRM or SFA, the sales department can efficiently approach promising customers. If you appropriately link MA with each system, an environment where you can track the process of each lead and customer reaching a contract as data will be prepared.
All of the tools introduced above have functions to manage and analyze customer data, but each focuses on different areas. It is important to strive for customer understanding while using them differently or linking them as necessary.
