What is Customer Data? What is the Role of Customer Data?

It might be an overstatement to say that today’s business environment has become super competitive, and companies that do not continuously innovate their business operations with data at the core will have to sit on the sidelines as their market is disrupted.

Data technology, science, and processes are rewriting the business rules and driving organizations toward digital transformation. Specifically, customer data plays a crucial role in businesses today. Let’s explore the concept and role of customer data through the article below.

I. What is Customer Data?

Customer data is defined as information that customers provide when interacting with your business through websites, mobile apps, surveys, social media, marketing campaigns, and other online and offline paths.

Customer data is the foundation for a successful business strategy. Data-driven organizations recognize the importance of this and take actions to ensure they gather the necessary customer data points to enhance the customer experience and adjust business strategies over time.

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II. Common Types of Customer Data

Next, let’s explore the common types of customer data: Personal Data (PII and Non-PII)

Personally Identifiable Information (PII)

Personally Identifiable Information (PII) includes any data that can reveal an individual’s identity.

• Linked Information
  Linked information is any data that can be used to identify an individual without needing additional data points. Examples of linked information include:

  • Full name

  • Address

  • Email address

  • Login information

  • Driver’s license number

  • Passport number

  • Credit/debit card details

  • Date of birth

  • Phone number

• Linkable Information
  Linkable information is data that cannot identify someone on its own, but can do so when combined with another data point. Examples of linkable information include:

  • Last name or first name

  • Location: Country, state, city, ZIP code

  • Gender

  • Race and ethnicity

  • Age group

  • Job details

Non-Personally Identifiable Information (Non-PII)

Non-PII refers to anonymous data, which, unlike PII, cannot identify a specific individual. Non-PII includes a range of elements, such as:

• IP address

• Cookies

• Device ID

a. Interaction Data

Interaction data tells you how customers engage with your brand across different marketing channels. This data includes customer behavior on your website, interactions on social media, and through customer service, etc. Here are the details for each channel:

• Website and mobile app interaction: Website visits, app engagement, most viewed pages, user flow, traffic sources, etc.

• Social media interaction: Likes, shares, comments, video views, etc.

• Email interaction: Open rates, click rates, bounce rates, email forwarding, etc.

• Customer service data: Number of requests, complaint/inquiry details, feedback, etc.

• Paid advertising interaction: Impressions, click rates, cost per click, conversions, etc.

b. Behavioral Data

Behavioral data helps you uncover hidden patterns that your customers exhibit throughout their buying journey. Interaction data may or may not be part of behavioral data. Here’s how you can gather this data:

• Transaction data: Registration details, purchase details, past purchases, average order value, cart abandonment data, customer lifetime value, loyalty program details, etc.

• Product usage: Repeat actions, feature usage, feature duration, task completion, devices used, etc.

• Qualitative data: User attention, heat maps (data on clicks, scrolls, mouse movements), etc.

c. Attitude Data

Attitude data is driven by customers’ emotions and feelings. It reflects how they perceive your brand and services. Attitude data is often gathered through surveys, interviews, focus groups, feedback, customer complaints, reviews, etc. Examples of attitude data include:

• Customer satisfaction

• Sentiment

• Product desire

• Preferences

• Motivations and challenges

• Purchase criteria

III. The Role of Customer Data

Efficient and Scientific Management of Customer Data

Managing customer data is crucial for business operations. By developing a strong database and regularly cleaning, categorizing, and avoiding data duplication cycles, companies can ensure they are using data effectively and cost-effectively:

• • Reducing administrative costs: Integrating a consolidated customer database, analyzing, and providing data quickly. With this setup, customer records are automatically uploaded without errors or data loss, minimizing work time and optimizing personnel management.

  • Reducing data duplication: Avoid software data duplication. A reliable database system can effectively handle redundant and missing data. You can also unlock data as needed.

  • Saving time, easy access to information: Using databases can improve business process efficiency and save valuable time. This is the most important factor in creating a customer database as it allows convenient access to information and the ability to prepare reports when needed.

Enhancing Business Plans, Strategies, and Activities

Companies with access to a comprehensive database can maximize the use of customer data and improve data efficiency. This provides opportunities to improve operations and develop appropriate marketing and business strategies that meet the needs of potential customers.

Potential benefits:

• Make quick business decisions

• Generate accurate and automated reports

• Create effective marketing and sales programs

Identifying and Segmenting Target Customers

With a comprehensive customer database, companies can identify ideal customer profiles and classify the right customer groups to target desired product markets.

• • Analyze customer profiles to find your ideal customers

  • Create targeted customer files when launching new products

Effectively Managing Customer Relationships

The foundation of a growing business is the ability to build strong customer relationships. Prioritize special care for your customer base.

• • In addition to attracting enthusiastic supporters, you will also promote your business to a broader audience. This helps maintain a stable customer base and gradually increases referrals.

  • Companies with existing databases can leverage customer data better and more effectively. It helps companies optimize their operations and provide guidance for prospective customers in preparing appropriate marketing and business strategies.

IV. How to Collect Customer Data

You can collect data from any channel where customers interact with your brand. While there are hundreds of ways to gather customer data, in this section, I will provide the essential methods you can use to gain deeper insights into your customers.

1. Building a Website

Typically, a website will be the main channel through which your customers interact, and from there, you can collect customer data such as demographics, geography, as well as behavioral and interaction data.
Tools like Google Analytics, Mixpanel, Piwik PRO, etc., play a major role in helping you understand preferences, referral sources, conversion details, and their real-time behavior on your website.
While these tools have limitations, such as not collecting qualitative data, you can compensate by using additional testing/visualization tools like Crazy Egg, Optimizely, VWO, and Hotjar. These tools help you understand user behavior through heat maps, session recordings, and visualizing conversion funnels. However, use these selectively, as some tools can be quite expensive.

2. Using Social Media

You can learn a lot about your customers based on how they interact with your personal page on social media. In addition to using basic engagement metrics (such as likes, comments, shares…), you can learn more about your customers by analyzing detailed information from each social media platform.

In particular, with a little effort on online reputation management (ORM), you can not only gather customer data but also gain an understanding of the general sentiment surrounding your brand and services through the content of posts.

Furthermore, you can enhance your customer data collection activities by investing in social media advertising. Through the targeting capabilities of social media platforms, you can continuously experiment until you understand the preferences and other characteristics of your customers.

Especially, by uploading your email list to social media platforms through custom audience features, you can explore their behavior on a specific social media channel to learn more about them and push content to similar customer segments.

3. Using Tracking Pixels

A tracking pixel is a piece of HTML or JavaScript embedded into a website or email that records every visitor to the website or email open. A tracking pixel can record the IP address, operating system, browser, etc., which helps advertisers run appropriate remarketing campaigns.

Through tracking pixels, you can also track customer conversion activities.

4. Contact Information

Contact information is initial data that allows you to maintain a relationship with customers, such as email addresses. You can send emails on topics that customers are interested in, build relationships with customers, and reduce their resistance until they are willing to share more important details like phone numbers.

Most of your customers will not share all their information right away. Choose the longer, more certain approach instead of rushing the process or giving up. Additionally, you can consider offering rewards like gifts or discount vouchers when customers provide their data.

5. Partnering with Customer Data Service Companies

Customer data service companies are a valuable resource for finding customer data. Businesses can partner with these companies to gather customer information and improve the efficiency of reaching out to customers. However, it can be difficult to control the quality of the data you purchase, so additional data filtering steps are necessary before utilizing it.

6. Customer Surveys and Feedback

Customer feedback and surveys are very important in collecting customer preferences, tastes, and opinions. By asking the right questions, surveys can help you gather qualitative data on attitudes.

You can receive feedback on your products, services, sales, and marketing activities through surveys. Net Promoter Score (NPS) is a popular software used for surveys, which helps you understand customer satisfaction with your products.

When designing a survey, consider the following questions:

• What different data points should you collect?

• How should you organize the data? What tools will you need to store it?

• What measures will you take to protect customer data? And are you transparent with customers about how you collect their information?

• Do you ensure that your data collection methods comply with the laws and regulations of your country?

• How will you use the data for the benefit of the organization?

V. Analyzing and Processing Customer Data

Customer Data Analytics Tools

To effectively utilize the customer data you’ve collected, you need to use efficient analytics tools such as:

• Google Analytics: This free tool provides insights into customer behavior on websites and mobile apps.

• Tableau: A powerful data visualization software that helps create reports and interactive dashboards.

• IBM SPSS: An advanced statistical analysis tool, suitable for complex analysis and trend prediction.

• Salesforce Analytics: A solution integrated with CRM systems to analyze customer data within the business context.

The choice of tool depends on the scale of the business, the type of data being analyzed, and the specific goals of the analysis.

Customer Data Analysis Techniques

There are many customer data analysis techniques, ranging from basic methods to complex techniques using machine learning. Some common techniques include:

• Descriptive analysis: Summarizes and describes the main characteristics of the data set.

• Segmentation analysis: Divides customers into groups based on shared characteristics.

• Behavioral analysis: Studies how customers interact with products or services.

• Predictive analysis: Uses statistical models to predict future customer behavior or trends.

Each technique has its own advantages and is suitable for different analysis objectives.

Data Processing and Cleaning

Before analyzing data, processing and cleaning are crucial steps to ensure accurate and reliable results. This process includes:

• Removing duplicates: Eliminating duplicate records to avoid bias in analysis.

• Handling missing values: Deciding how to deal with missing data, such as removing, filling with average values, or using interpolation.

• Data normalization: Ensuring consistency in data representation, such as date formats and measurement units.

• Validation: Identifying and addressing outliers or invalid values.

Data cleaning can be time-consuming, but it is an essential step to ensure the quality of the analysis.

By applying appropriate tools and analysis techniques, along with ensuring data quality through processing and cleaning, businesses can gain valuable insights from customer data and make informed, effective business decisions.

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VI. Applications of Customer Data in Business

Identifying Effective Customer Engagement Methods

Based on the analysis of customer data, businesses can identify effective customer engagement methods. By segmenting customers, evaluating the effectiveness of marketing campaigns, or optimizing sales strategies, businesses can improve interaction levels and business performance.

Improving Customer Experience

Customer data provides valuable information about customer needs, preferences, and behaviors. Based on this information, businesses can personalize the customer experience, offer more suitable products and services, and create greater satisfaction and loyalty from customers.

Predicting Trends and Making Business Decisions

By analyzing customer data, businesses can predict consumer trends, market behaviors, or future customer actions. These insights help businesses make strategic and effective business decisions, from developing new products to optimizing pricing strategies.

VII. Challenges in Managing Customer Data

Data Security and Privacy

One of the biggest challenges in managing customer data is ensuring security and privacy. Safely storing and processing customer data in compliance with personal data protection regulations is essential to prevent data breaches and protect the company’s reputation.

Difficulty in Integrating and Cleaning Data

Aggregating and cleaning data from various sources can be challenging. Inconsistencies in data representation, data entry errors, or missing data can affect the quality of analysis and the application of customer data.

Managing and Maintaining Databases

Maintaining and managing customer data databases requires regular care and updates. Monitoring and maintaining data quality ensures that customer information is always up-to-date and accurate, making analysis and data usage more effective.

Analyzing and Applying Insights

Managing customer data also presents challenges in analyzing data and applying insights to business effectively. Understanding and interpreting insights from data to make business decisions require industry knowledge, technology expertise, and the ability to align the insights with the business strategy.

VIII. Conclusion

Aggregating and analyzing customer data is a crucial and inevitable task in business. Without understanding customers, businesses cannot succeed. The customer information system invested in by the company helps increase sales, ensures sustainable development, and develops products according to customer needs.

This article provides all the information about customer data that we wish to share with our partners and business friends. Customer data always plays a vital role in defining the direction of a company. A quality customer data set will help the company navigate better.

Thank you to our partners and business friends for reading this article. We hope it has been helpful to you.

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