“Brands need to gather customer data in several ways. In brick & mortar, ask your employees what they are hearing/seeing. Develop a comprehensive process and tools to establish the journey – key pain points and how those can be solved to enhance the customer experience,” says Stephanie Hoppe, CMO at Strategic Transaction Management, LLC, in Clootrack's 102 CX Expert report in which 102 CX experts were interviewed on their biggest CX challenges.
In today's fast-paced business world, if data is king, then customer data is the crown jewel. Understanding consumer behaviors and preferences are crucial to providing excellent customer service and staying ahead of the competition.
However, without proper organization and standardization, customer data can quickly become a confusing mess. That's where customer data normalization comes in. By streamlining and organizing customer data, you can unlock customer insights that were previously hidden and gain a competitive edge. In this day and age, businesses simply cannot afford to overlook the role of customer data normalization.
In this article, you will gain comprehensive knowledge about customer data normalization. Let's dive into it!
What is Customer Data Normalization?
Customer data normalization is the process of standardizing and organizing customer data in a consistent and structured way. This involves cleaning and formatting the data to ensure that it is accurate, complete, and error-free. By establishing a set of rules and guidelines for how customer data is entered and stored, businesses can ensure that all of their data is uniform and easily searchable.
One of the primary benefits of customer data normalization is that it enables businesses to gain a more comprehensive and accurate understanding of their customers. When customer data is consistent and organized, you can identify patterns and trends in customer behavior more easily. They can also use this data to create more targeted marketing campaigns, improve customer service, and make more informed business decisions. In short, customer data normalization is an essential tool for any business that wants to unlock the full potential of its customer data.
Why is Customer Data Normalization Important?
Normalizing customer data ensures that it is consistent, precise, and ready for analysis, which in turn enables you to provide more personalized and efficient customer experiences, comply with data privacy regulations, promote customer centricity, and stand out in the market.
Here are 4 crucial points that demonstrate why customer data normalization is important for your business:-
1) Improving Data Quality
Improving data quality is crucial for any data-driven organization. It ensures that decisions made based on the data are reliable, and one way to improve data quality is through customer data normalization. You can collect data from various sources and in various formats. By normalizing data, you can avoid errors and inconsistencies that may arise from using different data sources or data formats.
Customer data normalization also plays an important role in improving data security and compliance. When customer data is normalized, it is easier to monitor and secure. Normalized data can be more easily audited, tracked, and protected against unauthorized access. Compliance requirements demand that your organization manage customer data responsibly and protect it from misuse. Normalizing customer data helps you comply with these regulations and avoid penalties and legal issues associated with non-compliance.
2) Enhancing Customer Experience
Customer experience is a key factor in building brand loyalty and driving business growth. By normalizing customer data, you can gain a more complete and accurate view of the customers. This, in turn, can help you provide more targeted and personalized customer experiences that meet the unique needs and preferences of each customer. For example, if an organization has normalized customer data that includes purchase history, browsing behavior, and demographic information, it can use this data to recommend products or services that are most relevant to each customer. This personalized approach can improve customer satisfaction and drive repeat business.
Customer data normalization can also help you improve the efficiency and effectiveness of customer interactions. By having a centralized, standardized view of customer data, you can streamline operations and avoid duplicating efforts. For example, if a customer contacts a support representative with a question, the representative can quickly access the normalized database to understand their history with the organization and provide a more informed response. This can lead to faster issue resolution, a better end-to-end customer experience, and greater ROI.
3) Facilitating Data Analysis
Data analysis transforms raw data into meaningful insights that top executives use to inform decisions and drive business development. By standardizing the format and structure of customer data with data normalization techniques, you can get more accurate and reliable insights that can inform strategic decisions. It enables you to combine and analyze data from different sources more easily.
When customer data is normalized, it is easier to merge with other data sources, such as sales data, marketing data, or demographic data. This, in turn, can lead to deeper insights and a more comprehensive understanding of customer demands and requirements. For example, an organization that has normalized customer data can analyze the impact of marketing campaigns on customer behavior, such as purchase frequency or purchase amount. This type of analysis can help organizations optimize their marketing efforts and drive revenue growth. In summary, customer data normalization is critical for facilitating data analysis as it ensures data consistency throughout, completeness, and easy integration with other data sources.
4) Improving Decision-Making
Inaccurate or incomplete data can lead to poor decisions that can have a negative impact on business outcomes. Normalized customer data enables you to make data-driven decisions more easily. When you standardize customer data, it is easier to examine and understand. This, in turn, help your organization identify patterns, trends, and opportunities that may not be apparent with unnormalized data.
For example, an organization that has normalized customer data can analyze customer behavior across different demographics, such as age or location. This type of analysis can assist organizations in tailoring their offerings and marketing strategies to specific customer segments, leading to increased revenue and customer loyalty. Eventually, customer data normalization improves decision-making by ensuring accuracy, reliability, and easy customer data analysis.
6-Step Process to Normalize Customer Data
Here are the 6-step process general steps to normalize data:
1) Identify the Data to be Normalized:
Determine which variables need to be normalized based on the specific analysis or machine learning model.
2) Choose a Normalization Method:
Select a normalization method based on the type of data and the specific needs of the analysis or machine learning model. The most common methods are min-max normalization, z-score normalization, decimal scaling normalization, and logarithmic normalization.
3) Calculate the Normalization Parameters:
Depending on the chosen normalization method, calculate the necessary parameters such as the minimum and maximum values, mean, standard deviation, or largest value.
4) Apply the Normalization Function to the Data:
For each data point in the chosen variable, apply the normalization function to scale the data to the desired range.
5) Check the Results:
After normalization, check that the data is within the desired range and that the data input error from the normalization process has not introduced any biases or errors.
6) Use the Normalized Data:
Once the data is normalized, it can be used for analysis or training machine learning models. Remember to use the same normalization parameters when applying the model to new data to ensure consistency.
How to Implement Customer Data Normalization
Here are 5 steps you must follow to implement customer data normalization effectively:-
1) Identify Data Sources
In the first step, you must identify all the different systems, applications, and channels that collect customer data, including customer relationship management (CRM) systems, marketing automation platforms, social media platforms, and website analytics tools. By identifying data sources, businesses can ensure that they have a comprehensive view of all the data they collect on customers, and can begin to develop a plan for normalizing the data. Additionally, identifying data sources can help businesses identify any data silos or redundancies in their data collection and management processes, which can help improve the overall efficiency of their data operations.
2) Develop Data Normalization Rules
Normalization involves organizing data in a structured and consistent way to reduce redundancy and improve data quality. The normalization rules dictate how data should be stored and organized in a database, ensuring that data is easily retrievable. The normalization rules typically involve the methods you will follow to break down data into smaller atomic units and organize them into tables with unique identifiers. By developing robust data normalization rules, you can ensure that the customer data is accurate, consistent, and easy to manage.
3) Create a Data Normalization Process
Creating a data normalization process involves applying the normalization rules developed in the previous step to the customer data you possess. The process typically begins by identifying the various data elements that need to be normalized and breaking them down into smaller, atomic units. Next, the data is organized into tables with unique identifiers, ensuring that each data item is stored in only one location. The process also involves removing redundant data and establishing relationships between the different tables.
4) Implement Data Normalization
Implementing data normalization involves applying the normalization rules and data normalization process. The implementation process may include converting existing data to conform to the new data model, updating data entry forms and applications to ensure that data is entered in the correct format, and training staff on the new data model and processes. It also sets the foundation for future data management initiatives, as data is now structured in a way that is easy to maintain and expand upon.
5) Monitor and Review Normalized Data
Once the data has been normalized, it is necessary to monitor and review the data continuously. This may involve conducting periodic audits to identify any data inconsistencies or errors, reviewing data entry processes, and updating the normalization rules and procedures as needed. Regular monitoring and reviewing of normalized data help you identify and resolve any data quality issues, ensuring the data remains reliable. It also assists in managing data management practices, so the procedures remain up-to-date and effective.
Challenges of Customer Data Normalization
Here are 4 challenges that you might face during the customer data normalization process:-
1) Complexity of Data Sources
Organizations may have customer data stored in multiple databases or systems, each with its own data structure and format. This can make it difficult to identify and extract the relevant data, as well as to apply normalization rules across all data sources.
To address this challenge, you can:
- Develop a data integration strategy that includes identifying all data sources and defining a plan for how to extract and integrate the data.
- Use data profiling tools to analyze the data and identify patterns and relationships between different data sources.
- Implement a data quality management program to ensure that data is authentic, comprehensive, and uniform across all sources, reducing the risk of errors during the normalization process.
2) Variability in Data Formats
Customer data can be stored in different formats, such as text, numbers, or dates, making it difficult to apply normalization rules consistently. In addition, data from different sources may use different naming conventions or codes for the same data element.
To address this challenge, you can:
- Develop a data dictionary that includes standardized definitions and naming conventions for all data elements.
- Use data transformation tools to convert data into a standardized format, ensuring consistency across all data sources.
- Implement a customer data governance program to ensure that data is easy to use across all business units and applications.
3) Inconsistent Data Quality
Customer data may be insufficient, incorrect, or outdated, making it difficult to apply normalization rules effectively. Inconsistent data quality can also result in data redundancies and conflicts, which can impact the precision and reliability of the data.
To address this challenge, you can:
- Implement a data quality management program that includes data profiling, cleansing, and enrichment.
- Use data validation rules to identify and remove duplicate or inconsistent data, reducing the risk of mistakes during normalization.
- Develop policies and procedures for data maintenance and management, ensuring that data quality is monitored and improved over time.
4) Resource Constraints
Implementing a data normalization process can require significant resources, including time, money, and personnel. Organizations may struggle to allocate the necessary resources to effectively implement and maintain a normalization process, especially if they have limited IT staff or budget constraints.
To address this challenge, you can:
- Develop a data normalization project plan that includes a timeline, budget, and resource allocation plan, ensuring that resources are effectively allocated to the project.
- Leverage automation tools and technologies to streamline the data normalization process, reducing the need for manual intervention and minimizing the workload on the IT department.
- Consider outsourcing some aspects of the data normalization process, such as data cleansing or transformation, to third-party vendors who specialize in data management, freeing up internal resources for other business initiatives.
Best Practices for Customer Data Normalization
Achieving effective data normalization requires a deliberate and systematic approach. And adopting best practices for customer data normalization can help you avoid common pitfalls and ensure that the customer data is accurate for critical business operations.
Here are 5 best practices you must follow for customer data normalization:-
1) Standardizing Data Formats
Standardizing data formats involves ensuring that all data fields have a consistent format, such as using the same date format or phone numbers having a uniform structure. By standardizing data formats through database normalization, you can avoid mistakes that may occur when data is entered manually or imported from different sources. This not only improves the correctness of customer data but also saves time and resources by reducing the need for manual data cleaning.
2) Applying Consistent Naming Conventions
Consistent naming conventions ensure that data fields have clear, concise names that accurately reflect their purpose. For example, a field that contains a customer's first name should be named "First Name" rather than "FName" or "F_N". By applying consistent naming conventions, businesses can avoid confusion and errors that may arise when data is shared or analyzed by data analysts or different teams. This also guarantees that data remains easily understandable and accessible, even as it is updated or modified over time.
3) Eliminating Duplicate Data
Duplicate data can cause inconsistencies and inaccuracies in customer profiles, resulting in wasted resources and missed opportunities. By identifying and removing duplicate data, you can ensure that they have a complete and accurate view of each customer, and avoid sending multiple messages or offers to the same person. There are several ways to eliminate duplicate entries, including using data cleansing tools, implementing strict data entry protocols, and regularly auditing customer data.
4) Identifying and Resolving Inconsistent Data
Inconsistent data can occur when data is entered or imported from different sources, leading to discrepancies in customer profiles. For example, a customer's address may be entered differently in different systems, leading to confusion and inaccuracies in customer communications. By identifying and resolving inconsistent data, you can ensure that the customer data is accurate and up-to-date.
5) Mapping Data to Common Structures
Mapping data to common structures make it easier to analyze and report on customer data, as data can be more easily compared and contrasted. For example, data fields that contain a customer's address information can be mapped to a common address structure, such as Address Line 1, Address Line 2, City, State, and ZIP Code. By mapping data to common structures, you can confirm that customer data is uniform and standardized across systems and applications.
Customer Data Normalization Practices are Crucial for Better Outputs
Customer data normalization is an essential process that businesses must undertake to ensure consistency and accuracy in their customer data. By standardizing the format, structure, and values of customer data, you can improve your decision-making processes and increase operational efficiency. Normalized customer data also makes it easier to integrate with other systems and applications, facilitating data sharing and collaboration.
Furthermore, with normalized data, you can easily identify and redact sensitive information such as social security numbers and credit card details, ensuring that they are not stored or shared without proper consent. Overall, investing in customer data normalization can bring significant benefits to businesses, including improved customer experiences, increased revenue, and reduced risks of data breaches and regulatory violations. So, it is a must to follow customer data normalization practices.
Read More: From Data to Action: Data Visualization for Smarter Customer Experience Decisions.