What Is Customer Data Analysis?
Customer data analysis is the process of analyzing various information regarding customers to gain a deeper understanding of them. Through customer data analysis, organizations can clarify aspects such as the following:
- Identify target customers
- Understand customer needs
- Verify customer satisfaction with company products and services
Without a clear grasp of who your customers are and what they desire, it is impossible to encourage them to choose your products or services or to build long-term, positive customer relationships. Therefore, it is essential to collect and analyze diverse data related to customers to derive insights that support business strategies in 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 name, age, gender, address, and contact details, as well as behavioral history, such as past purchases and responses to advertisements.
In B2B contexts, customer data becomes even more complex. It is necessary to collect, organize, and manage a vast amount of data, including information about the entire organization, the specific departments and individuals negotiating with your company, and the ultimate decision-makers.
Customer data can be collected through various media and touchpoints, such as websites, POS systems, 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 to support marketing and sales is becoming increasingly important in today's environment, where customer needs and behaviors are prone to change. While collecting customer data, ensure you establish an appropriate management system using IT tools, as described below.
Types of Customer Data
Customer data is diverse but can be broadly categorized into two types: "quantitative data" and "qualitative data."
Quantitative data is "information that can be quantified." It is used to perform 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 as more data becomes available for reference, the results are less likely to 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 generally expressed in words. In terms of customer data, "customer feedback" received through surveys or customer support is a prime example. Such qualitative data is effective for understanding customer thinking that cannot be seen through numbers. Additionally, company names, locations, industries, and representative names are also utilized as qualitative information.
It is important to use quantitative and qualitative data appropriately according to the purpose. For example, "which product has the highest sales" can be clarified through quantitative analysis, but when clarifying "why customers prefer that product," qualitative data analysis, such as survey results or trends by location, is more suitable.
Tools for Customer Data Management and Analysis
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 vast number of companies and can also be used for managing customer data. Many people are accustomed to using Excel, and if it is already implemented, the lack of additional introduction costs is an attractive point. However, since it is not a solution specialized for customer data management or analysis, there are many inconveniences. 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 integrated with various tools, including SFA and MA mentioned later, making it a strong choice 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 known as a "Sales Force Automation" system. It is designed primarily for the automation and streamlining of sales activities and 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 features. However, it is a recommended choice when you want to realize the streamlining of overall sales activities, such as the automation of quotation and daily report creation, and case management for each sales representative, in addition to customer data analysis.
MA
MA is a solution aimed at the automation and streamlining of marketing activities. With MA, you can automatically send information via email newsletters, SNS, etc., according to the results of customer data analysis.
MA is basically a tool used for lead nurturing (cultivating prospective customers). By passing those results to a CRM or SFA, the sales department can efficiently approach promising customers. If you appropriately integrate MA with each system, you can establish an environment where you can track the process of each lead and customer reaching a contract as data.
All 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 separately or integrating them as needed.
