(2026 Latest Edition) How Can You Effectively Utilize Customer Data? Collection, Analysis, Management Methods, and Use Cases

Customer Data Utilization Is Essential for Marketing

In the modern era, rapid technological innovation is accelerating market maturation, leading to the increasing commoditization of products and services. In a market saturated with similar products, where the needs of customers and general consumers are becoming increasingly diverse and sophisticated, it is no longer easy to differentiate based solely on functional value, such as product performance or price. Furthermore, the widespread adoption of the internet has added a new step to traditional purchasing behavior: self-directed information gathering. As customer information literacy rises, a trend is emerging—even in B2B business—where value is placed not only on the product itself but also on added-value factors such as the quality of representative support and after-sales service.

Against this social backdrop, for companies to achieve sustainable growth, they must provide unique customer experience value that competitors cannot replicate. To deliver superior customer experience value, a deep understanding of the customer is the most critical challenge, requiring strategic data analysis that accurately captures the latent needs of prospects and consumer insights. Without a profound understanding of the pain points and service requirements of both prospects interested in your products and existing customers, it is impossible to create services that address essential demand.

B2B companies, which primarily engage in inter-company transactions, must nurture prospects from initial product awareness through to contract closure, and tend to face higher costs for acquiring new customers compared to B2C businesses. Because resources available for marketing and sales activities are limited, companies must establish mechanisms to efficiently approach prospects with high conversion potential and loyal customers. This makes the process of deepening customer understanding essential, and the strategic utilization of customer data is indispensable for creating unique customer experience value that sets a company apart from its competitors.

Two Types of Customer Data and Collection Methods

Customer data can be broadly categorized into two types: Quantitative Data, which is aggregated and measured numerically, and Qualitative Data, which is difficult to quantify or categorize. The role of customer data management is to utilize these quantitative and qualitative data sets according to specific analytical objectives to achieve a deeper understanding of the customer. Here, we explain these two types of customer data and their collection methods.

Quantitative Data (Information That Can Be Quantified)

Quantitative Data refers to information that can be clearly expressed numerically, such as sales revenue, company size, number of employees, number of sales meetings, number of orders, and order values. Other information classified as quantitative data includes customer attributes such as the prospect's company, industry, and job title, as well as access logs like website search traffic, page view rates, and bounce rates. Because quantitative data can be aggregated and measured as concrete figures, it is easy to process and is utilized as foundational data for research and analysis tasks, including market research, demand trend forecasting, marketing analysis, and customer analysis.

Methods for Collecting Quantitative Data

There are various methods for collecting quantitative data. Representative examples include website access analysis, customer information obtained during inquiries, visitor information gathered at events or exhibitions, interviews, surveys, and probability sampling. For instance, by collecting data on conversion rates and inflow keywords through website access analysis, companies can objectively analyze prospect interests and website issues from a bird's-eye view. Additionally, at events and exhibitions, customer attributes can be extracted from the business cards of acquired prospects, leading to the creation of customer touchpoints and sales opportunities.

Qualitative Data (Non-Quantifiable Information)

Qualitative data is a concept representing linguistic information that cannot be expressed numerically. It refers to data that is difficult to quantify and requires psychological judgment, such as satisfaction levels regarding a company's products or customer service, the purchase intent of prospects toward products or services, and impressions of the corporate brand. Additionally, information such as points to note during customer interactions, records of troubleshooting or complaints, and key decision-makers also falls under qualitative data. Qualitative data serves as foundational data for analyzing sensory and emotional factors, such as uncovering potential customer demand and consumer insights, or gauging the purchase intent of prospects.

Methods for Collecting Qualitative Data

Primary methods for collecting qualitative data include inquiries from corporate websites, social media posts, capturing VOC (Voice of Customer) from contact centers, and conducting customer surveys or interviews with members. Qualitative data collected through these channels provides linguistic and emotional insights into the sentiments of prospects and the perceptions of existing customers, serving as a vital resource for creating superior customer experiences. While it cannot be compared or verified through quantification like quantitative data, it is utilized in business areas such as sentiment analysis that cannot be measured by numbers, and text mining using natural language processing.

Three Representative Methods of Customer Data Analysis

There are various methods for customer data analysis, and it is important to adopt approaches suitable for your company's business model and analytical challenges. Here, we introduce the following four representative methods of customer data analysis.

• Segmentation Analysis
• Pipeline Analysis
• Qualitative Information Analysis

1. Segmentation Analysis

Segmentation Analysis is a component of STP Analysis, which integrates Segmentation, Targeting, and Positioning. Specifically, it segments customer information based on various criteria such as industry, company size, corporate culture, decision-making authority, and purchase history of similar products. By analyzing prioritized segments, projected revenue, response metrics, and conversion potential from the categorized customer data, businesses can execute focused approaches toward high-profit segments.

2. Pipeline Analysis

Pipeline Analysis is an analytical method used to organize, evaluate, and improve a company's sales process chronologically. A pipeline represents the sequence of sales activities, encompassing the entire workflow from initial inquiries and meetings to final conversion. Pipeline Analysis involves breaking down the sales process—including initial meetings, needs assessment, proposals, quotation delivery, closing, conversion, and post-sales follow-up—to collect data and visualize bottlenecks. By providing a comprehensive overview of each sales stage, this method contributes to the optimization of KPIs and KGI, leading to the establishment of data-driven, logical sales strategies.

3. Qualitative Data Analysis

Qualitative Data Analysis is an analytical method based on qualitative data that is difficult to quantify or categorize. Customer data often includes information that cannot be expressed through simple metrics, such as user psychology and survey responses. Since quantitative data cannot capture customer sentiment or satisfaction levels, it is necessary to delve into non-quantifiable psychological and emotional information to identify latent demand. Qualitative Data Analysis is highly effective for uncovering genuine customer insights that cannot be read from numbers alone, helping to identify prospect challenges and existing customer needs. However, because it is prone to subjective evaluation, maintaining an objective perspective during analysis is essential.

Customer Data Management Methods and Systems

To effectively leverage customer data for marketing strategies and sales activities, robust digital solutions are essential. The following four systems are representative tools suitable for the management and operation of customer data.

• Excel
• CRM
• SFA
• MA

Excel

Excel is an excellent software for customer data management, equipped with various features such as table and chart creation, calculation via functions, and database processing through sorting and filtering. As a basic management method, users can set management items based on customer types—such as corporations or individuals—and input data including project names, company names, contact persons, contact information, quotation amounts, status, and last visit dates. By utilizing table functions to process this information, data can be converted into an optimized format for extraction and aggregation, enabling the creation of a fundamental customer data management ledger.

CRM (Customer Relationship Management System)

CRM stands for Customer Relationship Management. While in a broad sense it refers to a management strategy for optimizing customer relationships, in recent years, it has become the standard term for IT systems that centrally manage customer data. CRM is a solution that allows for the integrated management of data related to customers, including sales meeting status, action history, purchase ratios, sales composition, and contact center interaction logs. Unlike Excel, CRM supports simultaneous editing by multiple users and integration with other tools. Furthermore, it offers the advantage of managing customers based on their specific lifecycle stage, making it easier to reflect insights directly into marketing strategies and sales activities.

SFA (Sales Force Automation)

SFA stands for Sales Force Automation and refers to a system designed to systematize sales activities. It is a solution that comprehensively supports sales workflows, including centralized management of sales status and revenue, tracking of sales personnel activities, progress management of business negotiations, and email distribution to customers. While SFA and CRM are solutions with similar functions, their scope of management differs. CRM is a system for formulating business strategies by sharing and linking customer information across multiple departments, such as the IT and marketing departments, rather than just the sales department. On the other hand, SFA is a system aimed at managing detailed negotiation information with customers involved in deals and business opportunities, and consolidating sales information that tends to become siloed. By integrating SFA and CRM, companies can aim for further improvements in sales and marketing performance.

MA (Marketing Automation)

MA stands for Marketing Automation and refers to a solution specialized in the automation and efficiency of marketing activities. It is a system designed to streamline the phases from customer information acquisition to business negotiation, with the goal of maximizing lead generation and sales opportunities. MA has the characteristic of enabling effectiveness measurement based on both quantitative and qualitative data, allowing for optimized approaches to each individual prospect and existing customer. This streamlines the processes of lead acquisition, lead nurturing, and lead qualification in B2B marketing, leading to significant productivity improvements through the automation of tasks that were previously performed manually.

Customer Data Utilization Case Studies

As mentioned at the beginning, the realization of DX is an urgent task in various fields today, and the strategic utilization of customer data has become a critical management issue for many companies. DX is a concept that means organizational reform through the use of digital technology, not just simple IT implementation. To achieve this, the company-wide sharing and integration of customer data through the use of CRM, SFA, and MA are essential. For example, one SIer company faced a management challenge where each department was highly independent, leading to inefficient information sharing within the company.

Furthermore, the company could not fully grasp the areas that its own services had not yet penetrated, and the analysis of such areas required a significant amount of man-hours. Additionally, because analysis tasks were performed by individuals, similar analysis data was scattered throughout the company. The solution that broke through this situation was the customer data integration tool uSonar and the SFA solutions already in use. Promoting the use of these systems led to seamless information sharing and improved operational efficiency, and by identifying white spaces with no prior contact, the company was able to deploy effective strategies. For those who would like to know more about customer data utilization case studies, please see the link below.

• Hitachi Solutions, Ltd. User Interview: Success in Sales Efficiency and Realization of Heat Map Strategy

In today's world, where market maturation is accelerating, providing unique customer experience value is essential to differentiate from competitors. To create superior customer experience value, it is necessary to analyze what prospects and existing customers are looking for and connect those insights to the development of products that capture latent demand. To achieve this, the optimization of customer data management is mandatory, and the use of various analytical methods and solutions is indispensable. To promote the realization of DX and establish a management structure suited to the new era, please engage in the strategic utilization of customer data.