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What is Behavioral Analytics?
What is Diagnostic Analytics?
The Difference Between Data Analytics and Statistics
Data Analytics vs. Business Analytics
What is Data Analytics?
The Difference Between Data Analytics and Data Visualization
Data Analytics vs. Data Science
Quantitative vs. Qualitative Data
Data Analytics Processes
Data Analytics vs. Data Analysis
Data Analytics Lifecycle
Data Analytics vs Business Intelligence
What is Descriptive Analytics?
What Is Google Analytics 4 and Why Should You Migrate?
Google Analytics 4 and eCommerce Tracking
GA4 Migration Guide
Understanding Data Streams in Google Analytics 4
GA4 vs. Universal Analytics
Understanding Google Analytics 4 Organization Hierarchy
Benefits and Limitations of Google Analytics 4 (GA4)
What are the New Features of Google Analytics 4 (GA4)?
What Is Customer Data?
Collecting Customer Data
Types of Customer Data
The Importance of First-Party Customer Data After iOS Updates
CDPs vs. DMPs
What is an Identity Graph?
Customer Data Analytics
Customer Data Management
A complete guide to first-party customer data
Customer Data Protection
What is Data Hygiene?
Difference Between Big Data and Data Warehouses
Data Warehouses versus Data Lakes
A top-level guide to data lakes
Data Warehouses versus Data Marts
Best Practices for Accessing Your Data Warehouse
What are the Benefits of a Data Warehouse?
Data Warehouse Architecture
What Is a Data Warehouse?
How to Move Data in Data Warehouses
Data Warehouse Best Practices — preparing your data for peak performance
What is a Data Warehouse Layer?
Key Concepts of a Data Warehouse
Data Warehouses versus Databases: What’s the Difference?
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
How to Create and Use Business Intelligence with a Data Warehouse
How do Data Warehouses Enhance Data Mining?
Data Security Strategies
How To Handle Your Company’s Sensitive Data
How to Manage Data Retention
Data Access Control
Data Security Technologies
What is Persistent Data?
Data Sharing and Third Parties
What is Consent Management?
What is a Data Protection Officer (DPO)?
What is PII Masking and How Can You Use It?
Data Protection Security Controls
What is Data Integrity?
Data Security Best Practices For Companies
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What Is Customer Data?
Our modern digital experience involves routinely interacting with companies via social media, websites, mobile applications, experience surveys, and marketing campaigns. We often volunteer our data (for example, when we provide our email address when signing up to a website), while other times we provide it passively by consenting to cookies, location tracking, or by logging into a website with our social media profiles.
Successful customer engagement is built on personalization. Businesses want to collect trusted, relevant, and contextual data and aggregate it into a comprehensive understanding of their audience, so they can offer personalization and provide the right experience at the right time to the right customer.
Beyond capturing digital interactions, any successful understanding of the customer must also capture information from the physical world. This can include the products that customers own (captured by offering rewards for signing up for a warranty), their purchasing preferences (collected through supermarket loyalty programs), and where they travel (gathered via frequent flier programs).
A comprehensive business view of customers can drive your decision about which actions to take next: which ads to serve, which experiences to tout, what data to capture, and how to fine-tune your business strategy over time. This comprehensive, aggregated profile is often referred to as a “360-degree customer view” or “customer 360.”
Definition of customer data
Customer data is data collected by companies about their customers to help understand them better. The goal of this data collection is to improve communication and customer engagement. Useful data for this purpose includes demographic information, but also information on the customer’s behavior and opinions.
This data can be either structured (for example, clickstream web application user event data that is collected and processed in near-real time), or it can be unstructured (for example, a customer survey that gets answered with some free text).
Customer data can also be separated by how it is acquired. First-party data generally refers to customer data your organization is collecting / creating directly. This can include analytics of user behavior on your product, notes a sales or customer success representative may make in a CRM system, support tickets submitted by the customer, and more. Third-party data is data that originates outside your organization. For example, accessing the above mentioned purchasing or travel data from other providers would generally be considered third-party data. This article primarily focuses on the types, collection, and usage of first-party data.
Types of customer data
Customer data is usually broken down into four main types:
Personally identifiable information (PII) is any data that identifies the specific individual who supplied the data. It is highly valuable as it can be used to directly appeal to consenting users, and incredibly sensitive as it can be misused — to target, scam, harass, or impersonate someone. Examples of PII data include: names, email addresses, physical addresses, phone numbers
Engagement data gets created every time a customer digitally interacts with your business.This could mean tracking when they have requested a software demo, when they have viewed a marketing campaign web page, what they have shared on social media, or the contents of their email conversations with your customer support team.
Behavioral data is similar to engagement data, but the intent differs: instead of the focus being on improving marketing, it focuses on the customer experience of your product through their raw interactions with it. This can be quantitative data (such as purchase history or measuring how many times your customers abandon a full shopping cart) or qualitative data (such as usage heatmaps that visually show which parts of your website a particular customer is interacting with the most). Behavioral data can be really useful for finding out if your customers are struggling to use a particular feature of your website.
A rule of thumb to separate engagement from behavioral data is that engagement data measures if/how users react to something you do, whereas behavioral data measures actions users take by themselves.
Attitudinal data captures the opinions and feelings of a customer via surveys, online reviews or social media analysis. It can include their opinion on a new product, how an advertisement makes them feel, or whether they are satisfied with your product.
How is customer data collected?
Customer data comes from a variety of sources, and is collected in different ways, including:
Customer feedback: you can directly ask your customers for information by conducting surveys or interviews.
Registration: when your customers first engage with your company, they often provide some mandatory data, such as their name and email address. If they have signed up to your website, they may provide this data by filling in an online form. If they engage in person or over the telephone, it is good practice to enter any offline data into a customer relationship management system (CRM). This ensures you will have digital access to all your customer data when you need it.
Tracking: you can collect data on your customers in the background (depending on your or your users local legislation this may require users consenting to data collection) using cookies, email tracking (you can use this to find out information like what time they read your email, or what device they are on), or location tracking. If they are logged into your app via their social media account, you may have access to all their public social media, such as their likes and dislikes.
Website interactions: this is event-based data that records actions that a customer has performed on your website. Every time a customer performs an action (such as adding an item to a basket, making a purchase, commenting on an article, downloading something, or clicking a button), this can be recorded as an event with a timestamp. This kind of information can be aggregated together to give a comprehensive understanding of your customers over time. You can use an event streaming platform like Apache Kafka to stream all your events to your data warehouse where you can store these large quantities of data.
Third parties: integrating data from third parties can help provide a clearer picture of your customers. This could include information like online reviews or sentiment analysis.
Is collecting customer data legal?
It is legal to collect customer data, but it must be done in a responsible way, and must be compliant with local data laws such as the EU’s General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). The general principles behind these laws are:
- Don’t keep data for longer than is necessary.
- Ensure any data you keep is secure, especially if the data is PII.
- Respond in a timely manner to data subject access requests: a customer has the right to request to see any data you hold on them, and to correct any errors in this data.
You should consult the relevant legislation that covers both you and your users, and ensure that you are compliant. This is especially important since your users may come from a wide variety of jurisdictions and not necessarily just your own. Different countries and even different states in the same countries have differing privacy legislations. For example, some European countries have specific GDPR exemptions for data collection for analytics and performance measurement purposes, while others require consent for more or less any data collection.
Why is customer data important?
Performing data analysis on your customer data gives you an improved understanding of your market. This allows you to make savvy changes to your business to better suit your customers’ needs, as well as to improve the quality of your marketing lead generation. If you collect good quality data on your customers, you will also be able to use your analysis to personalize their experience, something many users now expect and want.
40% of U.S-based customers stopped doing business with a company in 2020 due to poor customer service. Using sentiment analysis on the customer data gleaned from your support tickets could allow you to improve your customer service, allowing you to retain customers who might otherwise have left you.
Using identity resolution allows you to get the most out of your data by building richer customer profiles. Identity resolution involves finding a link between two separate customer profiles to prove that they are the same customer, and consolidating all the information from the two profiles into one. You can enrich your data by combining it with third-party data; if you can find a matching email address or social media profile, this makes it easy to integrate that data into your own. Companies like Clearbit and ZoomInfo for example provide these third-party data lookups, as services to help your organization enrich your customer data.
A customer data platform helps provide a 360-degree view of your customers
A customer data platform (CDP) contains a database for storing all your customer data, integrated from a variety of sources, in one place. It is also responsible for cleaning your data (for example, removing nulls, defective values, or duplicates) and transforming it into a consistent format. A CDP will also often perform identity resolution for you, linking together different user profiles that it has identified as being the same person, allowing you to build a more comprehensive profile on your customers.
Aggregating your data in a CDP helps provide a more complete view of each customer, which is sometimes known as the 360-degree customer view. Your improved data can now be queried, or have machine learning models built from it, which means improved customer insights and decision making for your business. Any agile business will want to avail itself of this competitive advantage.
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