2026 Edition: How to Effectively Utilize Customer Data? Collection, Analysis, Management Methods, and Use Cases

Leveraging Customer Data Is Essential for Marketing

In the modern era, technological innovation is accelerating market maturity, 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 the traditional purchasing journey: self-directed information gathering. As customer information literacy rises, we are seeing a trend in B2B business where value is placed not only on the product itself but also on added-value services, such as the quality of 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 prospects and existing customers who are interested in your products, it is impossible to create services that address essential market demands.

B2B companies, which primarily focus on inter-company transactions, require the nurturing of prospects from initial product awareness to final contract, often resulting in higher acquisition costs compared to B2C businesses. Since resources for marketing and sales activities are limited, it is essential to establish a system that efficiently targets high-probability prospects and loyal customers. To achieve this, the process of deepening customer understanding is mandatory, 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 can be 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 gain a deeper understanding of customers. Here, we explain these two types of customer data and their respective collection methods.

Quantitative Data (Information That Can Be Quantified)

Quantitative Data refers to information that can be clearly expressed in numbers, such as sales revenue, company size, number of employees, number of business meetings, number of orders, and order values. Other information classified as quantitative data includes customer attributes such as the company or industry to which a prospect belongs, their job title, as well as access logs such as search traffic, page navigation rates, and bounce rates on a website. 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, trend forecasting, marketing analysis, and customer analysis.

Methods for Collecting Quantitative Data

There are various methods for collecting quantitative data, with representative examples including website traffic analysis, customer information acquired during inquiries, visitor information obtained at events or exhibitions, interviews, surveys, and probability sampling. For instance, if conversion rates and inbound keyword data can be collected from website traffic analysis, it is possible to objectively analyze the interests and concerns of prospective customers, as well as issues with your own website, from a bird's-eye perspective. Furthermore, at events or exhibitions, customer attributes can be extracted from the business cards of acquired prospective customers, which can then be leveraged to create customer touchpoints and sales opportunities.

Qualitative Data (Non-Quantifiable Information)

Qualitative data is a concept that represents 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 your company's products or customer service, the purchasing intent prospective customers hold toward products or services, and impressions of your brand. Additionally, points of caution during customer interactions, records of troubleshooting or complaint handling, and key decision-makers also fall under the category of qualitative data. Qualitative data serves as foundational data for analyzing sensory and emotional factors, such as uncovering the latent needs of customers or consumer insights, and gauging the purchasing intent of prospective customers.

Methods for Collecting Qualitative Data

Primary methods for collecting qualitative data include inquiries via corporate websites, social media posts, the acquisition of Voice of Customer (VOC) data submitted to contact centers, and surveys or interviews with customers and members. Qualitative data collected through these channels consists of linguistic and emotional information regarding the sentiments of prospective customers and the perceptions of existing customers, serving as an essential resource for creating superior customer experiences. While it cannot be compared or verified through numerical quantification like quantitative data, it is utilized in business domains such as sentiment analysis, which 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 the prioritized segments, projected revenue, response metrics, and conversion potential, businesses can focus their efforts on the most profitable 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 or meetings to closing a deal. Pipeline Analysis involves breaking down the sales process—including initial meetings, needs assessment, proposals, price quotations, closing, contract finalization, and post-sales support—to collect data and visualize bottlenecks. By providing a comprehensive overview of each sales stage, this method contributes to the optimization of KPIs and KGIs, leading to the establishment of data-driven, logical sales strategies.

3. Qualitative Data Analysis

Qualitative Data Analysis is a method based on qualitative data that is difficult to quantify or categorize. Customer data often includes information that cannot be expressed through simple numbers, such as user psychology and survey responses. Since quantitative data cannot capture the voice of the customer or satisfaction levels, it is necessary to delve into non-numerical, psychological, and emotional information to identify latent demand. Qualitative Data Analysis is highly effective for uncovering underlying sentiments 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 and sales activities, robust digital solutions are indispensable. 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 processing via functions, and database operations like 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, quoted amounts, status, and last visit dates. By utilizing the table function 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 sheet.

CRM (Customer Relationship Management System)

CRM is an abbreviation for Customer Relationship Management. In a broad sense, it refers to a management approach that optimizes relationships with customers, but in recent years, it has come to refer to IT systems that centrally manage customer data. CRM is a solution that allows for the integrated management of data related to customers, such as sales negotiation status, action history, purchase ratios, sales composition ratios, and contact center interaction records. Unlike Excel, it enables simultaneous editing by multiple users and integration with other tools. Furthermore, because it allows for management based on customer stages, it offers the advantage of being easily reflected in marketing strategies and sales activities.

SFA (Sales Force Automation)

SFA is an abbreviation for Sales Force Automation, which refers to a system that systematizes sales activities. It is a solution that comprehensively supports the business workflow of sales activities, including centralized management of sales status and revenue, management of sales personnel activities, progress tracking of sales negotiations, and email distribution to customers. While SFA and CRM are similar solutions, their scope of management differs. CRM is a system used not only by the sales department but also by departments such as IT and marketing to share and link customer information to facilitate business strategy development. On the other hand, SFA is a system aimed at managing detailed negotiation information with customers involved in deals and sales opportunities, and consolidating sales information that tends to be siloed. By integrating SFA and CRM, companies can aim for further improvements in sales and marketing performance.

MA (Marketing Automation)

MA is an abbreviation for Marketing Automation, which refers to a solution specialized in the automation and streamlining of marketing activities. It is a system designed to streamline the phases from customer information acquisition to sales 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 generation, lead nurturing, and lead qualification in B2B marketing, and leads to significant productivity improvements by automating tasks that were previously performed manually.

Customer Data Utilization Case Studies

As mentioned at the beginning, achieving DX is an urgent priority 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 not just IT implementation, but organizational reform through the use of digital technology. 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 systems integrator 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 significant 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 existing solutions such as SFA. Promoting the use of these systems led to seamless information sharing and improved operational efficiency. By identifying white spaces where there was no prior contact, the company was able to develop effective strategies. For more details on customer data utilization case studies, please see the link below.

• Hitachi Solutions, Ltd. User Interview: Success in Streamlining Sales and Implementing Heat Map Strategies

In today's rapidly maturing market, providing a unique customer experience is essential to differentiating your business from competitors. To create superior customer experiences, it is necessary to analyze the needs of prospects and existing customers, and translate those insights into product development that captures latent demand. This requires the optimization of customer data management, as well as the utilization of various analytical methods and solutions. To drive digital transformation and establish a management structure suited for the new era, we encourage you to engage in the strategic utilization of customer data.