Biến dữ liệu khách hàng thành vàng: Chìa khóa để tạo ra trải nghiệm xuất sắc và phát triển kinh doanh. #Dữ liệukháchhàng #Pháttriểnkinhdoanh

Dự án đặc biệt của VB: Xây dựng nền tảng chất lượng dữ liệu khách hàng. Hãy tìm hiểu thêm trong loạt bài viết tại đây: Xây dựng nền tảng chất lượng dữ liệu khách hàng. Trong bối cảnh kinh doanh nhanh chóng ngày nay, ở đó sự cạnh tranh khốc liệt và khách hàng đòi hỏi nhiều hơn bao giờ hết, dữ liệu đã trở thành yếu tố quyết định cuối cùng cho các nhà lãnh đạo doanh nghiệp. Việc thu thập dữ liệu là chìa khóa để khám phá bí mật về hành vi và sở thích của khách hàng, mở đường cho trải nghiệm khách hàng xuất sắc và thành công kinh doanh không đối thủ. Nhưng thực sự, thu thập dữ liệu là gì? Thu thập dữ liệu không chỉ là quá trình tẻ nhạt thu thập thông tin lung tung. Đó là một nghệ thuật tỉ mỉ liên quan đến việc chụp những điểm dữ liệu đúng từ các nguồn đúng, sắp xếp chúng một cách có cấu trúc và biến chúng thành thông tin hữu ích. Đó là việc nhìn nhận rằng dữ liệu có nhiều hình thái khác nhau, từ thông tin cá nhân của từng khách hàng đến các mẫu hành vi của toàn bộ thị trường. Những nhà lãnh đạo doanh nghiệp nhận thức được tiềm năng biến đổi của việc thu thập dữ liệu đặt mình là những người tiên phong trong ngành của mình. Họ nhận ra rằng mỗi mảnh dữ liệu đều chứa một kho báu ẩn chứa đang chờ được khai quật. Với các công cụ và công nghệ phù hợp, họ có thể đào sâu vào đại dương dữ liệu rộng lớn, khám phá những viên ngọc quý vô giá giúp họ ra những quyết định thông minh, tạo ra trải nghiệm khách hàng xuất sắc và cuối cùng là vượt qua đối thủ. #Transform2023
Hãy tham gia cùng chúng tôi tại San Francisco vào ngày 11-12 tháng 7, nơi các nhà lãnh đạo hàng đầu sẽ chia sẻ cách họ đã tích hợp và tối ưu hóa đầu tư trí tuệ nhân tạo để đạt được thành công và tránh các rủi ro thông thường. Đăng ký ngay! Vì sao nó quan trọng với doanh nghiệp của bạn? Đối với lãnh đạo doanh nghiệp, việc thu thập dữ liệu không chỉ là một công cụ – nó là chất xúc tác cho sự phát triển chưa từng có, tập trung vào khách hàng và quyết định chiến lược. Dưới đây là lý do vì sao: Khám phá bên trong tâm trí của khách hàng: Sâu đắm vào tâm trí và trái tim của khách hàng bằng cách khai thác sức mạnh của dữ liệu. Bằng cách thu thập và phân tích thông tin về sở thích, thói quen mua hàng và đặc điểm dân số, bạn có được một sự hiểu biết thân mật về khách hàng mục tiêu. Với kiến thức này, bạn có thể tùy chỉnh các sản phẩm, dịch vụ và chiến dịch tiếp thị của mình để vượt xa sự mong đợi của khách hàng. Tạo ra trải nghiệm độc đáo: Thu thập dữ liệu cho phép bạn tạo ra những trải nghiệm tuyệt vời cho khách hàng của mình. Bằng cách sàng lọc qua dữ liệu khách hàng, bạn khám phá ra những mô hình và xu hướng ẩn giấu giúp bạn cung cấp gợi ý cá nhân, ưu đãi độc quyền và thông điệp tiếp thị độc đáo. Mức độ cá nhân hóa này không chỉ làm hài lòng khách hàng của bạn mà còn xây dựng lòng trung thành vững chắc. Hướng dẫn đường với quyết định thông minh: Kho dữ liệu to lớn bạn tích luỹ cung cấp thông tin quý giá về động lực thị trường, hành vi của khách hàng và hiệu suất hoạt động. Có kiến thức này, bạn có thể đưa ra quyết định thông minh ảnh hưởng đến số phận của doanh nghiệp của bạn. Cho dù đó là phát triển các sản phẩm đột phá, thiết kế chiến lược giá cả hoặc khai phá thị trường chưa khai thác, việc ra quyết định dựa trên dữ liệu trở thành vũ khí bí mật của bạn. Chinh phục nghệ thuật hiệu suất: Bằng việc thúc đẩy việc thu thập dữ liệu và phân tích, bạn bắt đầu một hành trình về sự xuất sắc vận hành. Khám phá điểm nghẽn, tối ưu quy trình và tăng cường hiệu suất bằng cách theo dõi kỷ hiệu hoạt động của mình một cách tỉ mỉ. Với các chỉ số hiệu suất chính thời gian thực, bạn có thể điều chỉnh linh hoạt dẫn đến tiết kiệm chi phí và những đóng góp đáng kể về năng suất. Mở cánh cửa cho tương lai: Hãy nhận thức về sức mạnh của việc thu thập dữ liệu và thúc đẩy doanh nghiệp của bạn tiến về dự báo và phân tích tiên đoán. Sử dụng dữ liệu lịch sử, khám phá xu hướng tương lai và đoán trước nhu cầu của khách hàng như chưa từng có. Hãy trở thành một lãnh đạo tầm nhìn, dự đoán các thách thức tiềm năng và áp dụng các biện pháp chủ động để ngăn chặn sự thất thoát, cải thiện sự duy trì khách hàng và bảo đảm ưu thế cạnh tranh của bạn. Loại dữ liệu được thu thập: Bằng cách khai thác một loạt các dữ liệu khách hàng, các nhà lãnh đạo doanh nghiệp có thể khám phá các mẫu ẩn, giải mã sở thích của khách hàng và đưa ra các quyết định chiến lược có căn cứ. Thông tin cá nhân: Danh sách thông tin cá nhân bao gồm tên, địa chỉ, thông tin liên lạc và các thông tin cá nhân khác mà khách hàng đồng ý cung cấp trong quá trình tương tác với doanh nghiệp. Thông tin cá nhân giúp xác định các khách hàng cụ thể và tạo điều kiện cho việc giao tiếp hiệu quả. Dữ liệu dân số: Dữ liệu dân số bao gồm các đặc điểm như tuổi, giới tính, mức thu nhập, nghề nghiệp, giáo dục và tình trạng hôn nhân. Thu thập dữ liệu dân số cho phép doanh nghiệp phân đoạn nguồn khách hàng, hiểu đối tượng khách hàng mục tiêu và phát triển chiến lược tiếp thị nhắm mục tiêu. Lịch sử mua hàng: Thu thập dữ liệu về lịch sử mua hàng của khách hàng cung cấp thông tin về hành vi mua hàng, sở thích và quan tâm sản phẩm của họ. Điều này giúp doanh nghiệ

Nguồn: https://venturebeat.com/data-infrastructure/unleashing-power-data-transforming-customer-experience-business-success/

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This article is part of a VB special issue. Read the full series here: Building the foundation for customer data quality.

In today’s fast-paced business landscape, where competition is fierce and customers are more demanding than ever, data has become the ultimate game-changer for enterprise leaders. Data collection holds the key to unlocking the secrets of customer behavior and preferences, paving the way for exceptional customer experiences and unparalleled business success.

But what exactly is data collection?

Collecting data is more than just a mundane process of gathering scattered bits of information. It’s a meticulous art form that involves capturing the right data points from the right sources, organizing them in a structured manner, and transforming them into actionable insights. It’s about understanding that data comes in various forms, from individual customers’ personal details to the behavioral patterns of entire markets.

Enterprise leaders who embrace the transformative potential of data collection position themselves as pioneers in their industries. They recognize that every piece of data holds a hidden treasure waiting to be unearthed. With the right tools and technologies at their disposal, they can delve deep into the vast ocean of data, uncovering invaluable gems that empower them to make informed decisions, shape exceptional customer experiences and ultimately stay ahead of the competition.

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Why is it important for your business?

For enterprise leadership, data collection isn’t just a tool — it’s the catalyst for unprecedented growth, customer-centricity and strategic decision-making. Here’s why:

Unveiling the customer’s mind: Delve deep into the hearts and minds of your customers by harnessing the power of data. By capturing and analyzing insights on their preferences, purchasing habits and demographics, you gain an intimate understanding of your target audience. Armed with this knowledge, you can tailor your products, services and marketing campaigns to exceed their expectations.

Crafting unique experiences: Data collection empowers you to create extraordinary experiences for your customers. By sifting through customer data, you uncover hidden patterns and trends that enable you to offer personalized recommendations, exclusive deals and bespoke marketing messages. This level of personalization not only delights your customers but builds unwavering loyalty.

Guiding the way with intelligent decisions: The treasure trove of data you accumulate provides invaluable insights into market dynamics, customer behavior and operational performance. Armed with this knowledge, you can make informed decisions that shape the destiny of your enterprise. Whether it’s developing groundbreaking products, devising pricing strategies or unearthing untapped markets, data-driven decision-making becomes your secret weapon.

Mastering the art of efficiency: In embracing data collection and analysis, you embark on a journey toward operational excellence. Discover bottlenecks, streamline processes and boost efficiency by meticulously monitoring your operational data. Armed with real-time key performance indicators (KPIs), you can make agile adjustments that lead to cost savings and remarkable gains in productivity.

Unlocking the future: Embrace the power of data collection and propel your enterprise toward predictive analytics and forecasting. Using historical data, unveil future trends and anticipate customer needs like never before. Be a visionary leader, foresee potential challenges, and take proactive measures to prevent churn, improve customer retention and secure your competitive edge.

Types of data collected

By harnessing a diverse range of customer data, enterprise leaders can uncover hidden patterns, decode customer preferences and make informed strategic choices. Such data intelligence empowers enterprises to customize their offerings, provide personalized experiences and design effective marketing strategies, resulting in heightened customer engagement and loyalty.

Personal data

Personal information: This category encompasses data such as names, addresses, contact details and other personally identifiable information (PII) that customers willingly provide during their interactions with a business. Personal information enables the identification of individual customers and facilitates effective communication.
Demographic data: Demographic data includes characteristics such as age, gender, income level, occupation, education and marital status. Collecting demographic data allows businesses to segment their customer base, understand their target audience and develop targeted marketing strategies.
Purchase history: Gathering data on customers’ past purchases provides insights into their buying behavior, preferences and product interests. This enables businesses to personalize product recommendations, offer relevant promotions and enhance the overall customer experience.
Customer preferences: Obtaining data on customer preferences, such as preferred communication channels, product features or delivery options, helps businesses tailor their offerings to individual preferences. This data facilitates the provision of personalized experiences and the cultivation of long-term customer loyalty.

According to Soumendra Mohanty, chief strategy officer at data science company Tredence, personal data plays a crucial role in helping businesses understand customer behavior, preferences and needs. This understanding enables businesses to personalize their services and products, leading to improved customer experiences. Additionally, personal data allows for targeted marketing campaigns, increasing conversion rates and sales.

Analyzing personal data also provides insights into customer churn, allowing businesses to take proactive measures to retain customers.

It is important, however, to use personal data in compliance with privacy regulations to maintain trust.

In summary, when used responsibly and effectively, personal data can significantly drive business growth and return on investment (ROI).

Profiling customers across various digital platforms, physical stores and other touchpoints can be challenging because of the scattered nature of first-party customer data. Moreover, the utility of third-party data is decreasing thanks to stricter privacy regulations. Therefore, businesses need to develop intelligent strategies to consolidate and interpret customer data while respecting privacy norms. By using first-party customer data, third-party data partnerships, and machine learning algorithms, businesses can accelerate their journey towards customer personalization.

Implementing personalization strategies tailored to an enterprise’s maturity level is key, ranging from channel-specific recommendations to cutting-edge next best actions.

By structuring and streamlining customer data, businesses can integrate valuable customer insights into all their operations and applications. This approach empowers businesses to achieve a high level of customer-centricity, resulting in increased engagement and customer satisfaction.

Behavioral data

Website analytics: Website analytics tools track and collect data on user interactions with a business’s website. This includes information on page views, click-through rates, bounce rates and conversion rates. Analyzing website analytics data helps optimize website design, navigation and user experience.
Clickstream data: Clickstream data refers to the sequence of web pages a user visits and the actions they take on a website. It encompasses data on the duration of visits, specific links clicked and interactions within the website. Analyzing clickstream data provides insights into user behavior and interests as well as areas for potential website improvement.
Social media interactions: Data collected from social media platforms includes user engagement metrics such as likes, comments, shares and followers. It offers businesses insights into customer sentiment, brand perception and trends. Social media data assists in social media marketing, content creation and reputation management.
App usage patterns: For businesses with mobile applications, collecting app usage data is crucial. It involves tracking user activities within the app, including time spent, features used and user flows. App usage data assists businesses in improving app functionality, optimizing user experience and identifying opportunities for engagement.

Operational data

Sales and transaction data: This category encompasses information on purchases, order history, payment methods and revenue generated. Analyzing sales data helps identify popular products, revenue trends and customer buying patterns, aiding in inventory management and forecasting.
Inventory management data: Data related to inventory management includes stock levels, replenishment rates and supply chain information. By collecting and analyzing this data, businesses can optimize inventory levels, avoid stockouts and improve supply chain efficiency.
Supply chain data: Supply chain data includes information on suppliers, logistics, transportation and production processes. Collecting and analyzing supply chain data enables businesses to streamline operations, identify inefficiencies and optimize the supply chain for cost savings and improved customer satisfaction.
Customer support interactions: Data collected from customer support interactions, such as emails, live chats and phone calls, provides insights into customer issues, inquiries and feedback. Analyzing customer support data helps identify recurring problems, improve support processes and enhance overall customer satisfaction.

Data collection methods

Surveys and questionnaires

Surveys and questionnaires are widely used for collecting customer data. Businesses can design and distribute surveys to gather specific information from customers, such as their preferences, satisfaction levels and feedback on products and services. Surveys can be conducted through various channels, including online platforms, email and in-person interactions. The collected data can be analyzed to gain insights into customer opinions and preferences.

Online tracking tools

Online tracking tools, such as web analytics software, are used to collect data on customer behavior and interactions with a business’s online platforms. These tools capture data on website visits, page views, click-through rates, conversion rates and other relevant metrics. By implementing tracking codes or cookies, businesses can track user activities and analyze the collected data to understand user behavior, optimize website design and improve the user experience.

Loyalty programs and customer accounts

Loyalty programs and customer accounts provide businesses with valuable data about their customers. By incentivizing customers to create accounts or enroll in loyalty programs, businesses can gather information such as customer demographics, purchase history, preferences and contact details. This data helps businesses personalize offerings, track customer loyalty and develop targeted marketing strategies. Additionally, customer accounts enable ongoing data collection and allow customers to manage their preferences and interactions with the business.

Social media monitoring involves tracking and analyzing customer interactions and mentions across social media platforms. By monitoring social media conversations, businesses can gather data on customer sentiment, brand perception and trends related to their products or services. Social media monitoring tools enable businesses to collect data on likes, shares, comments and other engagement metrics, providing insights into customer preferences and enabling proactive engagement and reputation management.

Data partnerships and third-party sources

Businesses can also collect data through partnerships with external data providers or third-party sources. These sources may include market research firms, data aggregators and industry-specific databases. Through data partnerships, businesses can access additional demographic data, market research insights and industry trends that complement their existing data. These partnerships allow businesses to expand their data collection capabilities and gain a more comprehensive view of their target audience or market.

It is important for businesses to ensure compliance with data protection regulations and ethical guidelines when collecting data through these methods.

Using various data collection methods, businesses can gather valuable information to better understand their customers, improve their offerings and make data-driven decisions to enhance customer experiences and overall business performance.

Uses of data in customer service

Personalization and Customization

Tailoring recommendations and offers: By analyzing customer data, businesses can personalize product recommendations and offers based on individual preferences and purchasing history. This level of personalization enhances the customer’s shopping experience, increases the relevance of recommendations and improves the likelihood of conversion and customer satisfaction.
Enhancing customer experience: Customer data allows businesses to understand customer preferences, behaviors and interaction patterns. With this information, businesses can tailor the customer experience across various touchpoints, such as websites, mobile apps and customer service interactions, providing a personalized and seamless experience that meets individual needs and preferences.
Improving customer satisfaction: Using customer data, businesses can identify pain points, address customer concerns and provide proactive solutions. Understanding customer preferences and past interactions helps businesses deliver more personalized and responsive customer service, resulting in higher levels of customer satisfaction and loyalty.

Predictive Analytics

Forecasting customer behavior: Using historical customer data, businesses can use predictive analytics models to forecast customer behavior. This enables businesses to anticipate customer needs, predict future purchasing patterns and adjust their strategies accordingly. Forecasting customer behavior allows businesses to proactively meet customer demands and enhance their overall experience.
Anticipating customer needs: Customer data provides insights into customer preferences, purchasing habits and product usage patterns. With this information, businesses can identify emerging trends and anticipate customer needs. By understanding what customers may require in the future, businesses can develop new products, services and features that meet those needs, staying ahead of the competition.
Preventing churn: Churn refers to the loss of customers. By analyzing customer data, businesses can identify early warning signs of potential churn, such as reduced engagement or declining satisfaction levels. Predictive analytics models can help identify at-risk customers and allow businesses to implement targeted retention strategies, such as personalized offers, proactive customer support or loyalty programs, to prevent customer churn.

Customer Segmentation

Identifying target customer groups: Customer data enables businesses to segment their customer base into distinct groups based on demographics, behaviors, preferences or purchase history. Once they understand different customer segments, businesses can tailor their marketing efforts, product development and customer service strategies to effectively target specific groups and maximize customer engagement and satisfaction.
Developing targeted marketing strategies: With customer segmentation, businesses can create more targeted and relevant marketing campaigns. By understanding the unique needs, preferences and motivations of different customer segments, businesses can craft personalized messages, choose appropriate marketing channels and deliver content that resonates with each group. This leads to improved campaign effectiveness and higher conversion rates.
Optimizing product development and pricing: Customer segmentation helps businesses identify specific customer groups that may have distinct product preferences or price sensitivities. By analyzing customer data, businesses can gain insights into what features or pricing models are most appealing to different segments. This information guides product development decisions, allows for targeted product enhancements and enables optimized pricing strategies that meet the needs of each customer segment.

Uses of data in business improvement

Performance monitoring and KPIs

Tracking sales and revenue: Data collection allows businesses to monitor sales and revenue trends, identify top-performing products or services and evaluate the effectiveness of marketing campaigns. By analyzing sales data, businesses can make data-driven decisions to optimize sales strategies, improve pricing and identify areas for revenue growth.
Analyzing operational efficiency: Businesses can use data to monitor and analyze operational metrics such as production output, resource utilization and cycle times. By tracking these key performance indicators (KPIs), businesses can identify areas of improvement, streamline processes, reduce costs and enhance operational efficiency.
Monitoring customer satisfaction metrics: Customer feedback and satisfaction metrics, such as net promoter score (NPS) and customer satisfaction surveys, provide valuable insights into the customer experience. Analyzing this data helps businesses identify areas for improvement, address customer concerns and enhance overall customer satisfaction, leading to increased loyalty and repeat business.

Process optimization

Streamlining operations: Data collection and analysis allows businesses to identify and eliminate inefficiencies in their processes. By examining operational data, businesses can streamline workflows, automate tasks and reduce manual errors, resulting in improved productivity and cost savings.
Identifying bottlenecks and inefficiencies: Data helps identify bottlenecks or areas of low efficiency in a business’s operations. By analyzing data on process flow, resource allocation and cycle times, businesses can pinpoint areas that require improvement, optimize resource allocation and enhance overall operational performance.
Improving supply chain management: Data collection and analysis play a crucial role in supply chain management. By analyzing data related to inventory levels, lead times, supplier performance and demand patterns, businesses can optimize their supply chain processes, minimize stockouts, reduce costs and improve delivery timelines.

Decision-making and strategy development

Data-driven decision-making: By leveraging data, businesses can make informed decisions based on insights and trends rather than relying on intuition alone. Data analysis provides businesses with a factual basis for decision-making, enabling them to mitigate risks, seize opportunities and make strategic choices that align with customer needs and market trends.
Identifying new market opportunities: Data collection allows businesses to identify emerging market trends, customer preferences and unmet needs. By analyzing market data and consumer behavior, businesses can uncover new market opportunities, develop innovative products or services, and stay ahead of the competition.
Assessing the competitive landscape: Data analysis helps businesses understand their competitive landscape by examining market share, pricing strategies, customer reviews and other relevant data. By analyzing competitive data, businesses can identify areas where they can differentiate themselves, refine their marketing strategies and gain a competitive edge in the market.

Using data for performance monitoring, process optimization and decision-making, businesses can drive continuous improvement, increase operational efficiency and develop effective strategies to stay competitive in the market. Data-driven insights enable businesses to make more informed decisions and maximize their potential for growth and success.

Need of the hour: Data privacy

According to a Gartner survey, 60% of marketing leaders anticipate difficulties in collecting customer data while maintaining a delicate balance between privacy and value in 2023. Despite 85% of respondents having implemented formal data management policies, privacy remains a persistent challenge. However, there is a notable increase in the adoption of personalized messaging, with 42% of marketers embracing this approach.

The survey also highlights that 78% of marketing leaders empower customers to control their own data, with 82% prioritizing the use of first-party data to deliver immediate value. Significantly, concerns about trust and privacy have led almost one-third of respondents to sever partnerships with agencies or channel partners.

Furthermore, proactive marketers who prioritize first-party data consistently exceed customer retention expectations. Interestingly, organizations managing 11 or more marketing channels have shown more significant growth in their first-party customer data collection compared to those managing fewer channels.

Data privacy remains a critical and ongoing concern, requiring digital marketing leaders to develop strategies that effectively secure the data they need while placing customer needs at the forefront.

The road ahead

According to Tredence’s Mohanty, the future of data collection holds immense potential for personalized experiences and real-time decision-making in business. Advancements in AI and machine learning will enable companies to customize products and services based on individual customer preferences, leading to increased satisfaction and loyalty. Moreover, businesses will be able to respond quickly to market changes and customer needs. As concerns around data privacy grow, evolving technologies will ensure that data collection maintains user anonymity, strengthening customer loyalty and retention.

AI and ML technologies are expected to automate data collection and analysis, providing predictive insights for proactive business strategies. The Internet of Things (IoT) will further expand data collection opportunities, granting access to real-time, detailed data that can enhance operational efficiency and customer understanding.

However, evolving data privacy regulations are reshaping how enterprises handle customer data, from capture and security to distribution and analysis. Businesses must prepare for increased legal responsibilities regarding consumer data protection, driven by growing demands for privacy rights.

Despite regulatory changes, data collection by private corporations will remain a fundamental practice, albeit with changes in its nature. Companies need to adapt their data management strategies to meet the requirements of new legislative landscapes, all while upholding their commitment to customer privacy.

Data collection is moving toward a cookie-less model due to mounting privacy concerns. This shift emphasizes the importance of developing strategies that respect user privacy while still delivering personalized customer experiences. Although this change presents certain challenges, it also offers businesses an opportunity to innovate and cultivate deeper, more meaningful relationships with customers.

While businesses have extensively utilized “data at rest,” the future lies in harnessing “data in motion.” Real-time data can unlock new opportunities for customer engagement, quick responses to market changes and faster and more precise decision-making, providing a competitive edge.

One significant challenge in effective data utilization has been the fragmentation of enterprise data into isolated data “islands.” Technologies like data mesh and zero-ETL data integration are emerging as breakthrough solutions to overcome these barriers, empowering businesses to fully leverage data for tangible results. These innovations make data more accessible and impactful for organizations.

Last, with the increasing prominence of large language models (LLMs), businesses can explore innovative ways to use unstructured data. Concepts like multi-mode data management techniques will further revolutionize how businesses can tap into structured and unstructured data.

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Tin tức

Biến dữ liệu khách hàng thành vàng: Chìa khóa cho trải nghiệm và tăng trưởng kinh doanh đột phá