Customer Data

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Definition of Customer Data

Customer Data is the behavioral, demographic and personal information about customers collected by businesses and marketing companies to understand, communicate and engage with customers. Customer data is defined as the information your customers provide while interacting with your business via your website, mobile applications, surveys, social media, marketing campaigns, and other online and offline avenues. Customer data is a cornerstone to a successful business strategy. Data-driven organizations realize the importance of this and take action to ensure that they collect the necessary customer data points that would enable them to improve customer experience and fine-tune business strategy over time.[1]

Businesses today are awash in more data than ever before. There’s transactional data, demographic data, and virtually infinite amounts of behavioral data. Add it all up and you’ve got data from anonymous ad impressions to known customer purchases, all the way through to product usage and customer service. Customer data is a superset of all this data together. Typically this data is stored in silos, whether organizational or technological, making it very difficult for companies to provide consistent customer experiences across various channels and consumer devices.[2]

Customer Data Architecture[3]

In today’s digital landscape, customers are interacting with brands on more devices and across more channels than ever before. This leads to disjointed customer experiences, which hinder companies’ ability to drive increased engagement through hyper-personalization. Oftentimes, these disjointed experiences collect data in silos, making it also more challenging to analyze and generate insights across marketing and business initiatives. To help brands become more relevant in the market, Accenture and Google Cloud developed the Customer Data Architecture, which enables real-time, audience-centric marketing and personalization across channels by leveraging integrated customer and enterprise data. The solution combines Google Marketing Platform data with other enterprise data sources to help companies gain a deeper understanding of their clients and translate that into real-time activation. Other challenges that marketers face, which are addressed by Customer Data Architecture include:

  • Decentralized data: Third parties often control media delivery and performance data causing downstream measurement and integration challenges
  • Fragmented ecosystems: Digital ID ecosystem with limited overlapping IDs inhibits real time matching of users across the customer journey and leads to inefficiencies in marketing activity
  • Disjointed customer experiences: Difficulties executing targeted ads, personalized experiences and seamless integrations across channels due to operational, technical, and regulatory limitations
  • Technology silos: Technology is becoming more fragmented with each marketing platform having its own set of processes, interfaces and datasets, making it more difficult to effectively analyze and utilize data sources.

Customer Data Architecture addresses these challenges by developing a 360 view of the customer. It unifies data across silos to build a complete customer profile to boost relevant messaging and profitability. It works using 4 key areas: Data Access & Management, Identity Resolution, Disjointed Customer Experiences and Integrated Data and Technology.

Customer Data Architecture
source: Accenture

This approach enables us to get a deeper understanding of the customer, connect their journey, to enable personalization at scale. In turn, this helps companies become selective with their targeting – reducing media wastage on prospects who are unlikely to convert, while focusing on high-value leads. Ultimately, this leads to better returns on marketing investment and improved customer experience.

Types of Customer Data[4]

There are four main kinds of customer data that CDPs collect and organize.

  1. Identity Data: Identity data builds the foundation of each customer profile in a CDP. This type of data allows businesses to uniquely identify each customer and prevent costly replications. Identity data includes:
    • Name information, such as first and last name
    • Demographic information, such as age and gender
    • Location information, such as address, city, and zip code
    • Contact information, such as phone number and email address
    • Social information, such as Twitter handle and LinkedIn address
    • Professional information, such as job title and company
    • Account information, such as company-specific user IDs and account numbers
  2. Descriptive Data: Descriptive data expands on identity data and gives you a fuller picture of your customer. The categories of descriptive data will vary based on the type of company. For example, a car dealership may collect lifestyle details about their customers’ cars, whereas a diaper company would collect details about the number of children in customers’ families. Descriptive data includes:
    • Career information, such as previous employers, industry, income, and job level
    • Lifestyle information, such as the type of home, vehicle, and pet
    • Family information, such as the number of children and marital status
    • Hobby information, such as magazine subscriptions and gym memberships
  3. Quantitative or Behavioral Data: Quantitative data allows businesses to understand how each customer has engaged with their organization, whether through certain actions, reactions, or transactions. Quantitative data includes:
    • Transaction information, such as the number and type of purchased or returned products, the number of abandoned carts, and order dates
      • This information also includes RFM analysis — recency (How recent did this customer make a purchase?), frequency (How often does this customer make a purchase?), and monetary value (How much does this customer spend on a purchase?)
    • Email communication information, such as email opens, email click-throughs, email responses, and dates
    • Online activity information, such as website visits, website click-throughs, product views, and social media engagement
    • Customer service information, such as communication dates, query details, and service representative details
  4. Qualitative Data: Qualitative data provides context for customer profiles; it gives customer data personality. This type of data collects any motivations, opinions, or attitudes expressed by a business’s customers — whether relevant to the company or not. Qualitative data includes:
    • Motivation information, such as How did you hear about us?, Why did you purchase this?, or What made you choose this product over others?
    • Opinion information, such as How would you rate this product?, How would you rate our customer service?, or How likely are you to recommend us?
    • Attitude information, such as favorite color, animal, textile, or food

Customer Data - Ownership[5]

Customer data is not only a valuable asset but also a responsibility. With growing concerns around online privacy, companies must now speak to the source, ownership and distribution of their customers’ data. Luckily, most modern companies can choose how and where to collect and use their data. Customer data falls into three types: first-, second- and third-party data, each having implications regarding privacy.

Customer Data Ownership
source: Signal

  • First-Party Data: First-party data is information collected and owned by the company with which a user has interacted firsthand. Companies collect their customers’ behaviors, demographics and preferences through in-house software or systems for use in marketing campaigns or product development.
  • Second-Party Data: Second-party data describes first-party data that companies share with trusted partners. This customer data, the exchange of which is limited to those partnerships, enables marketers to reach a wider audience while still delivering personalized content.
  • Third-Party Data: Third-party data gets collected by an outside source that has had no direct relationship with the user. This data gets stored and distributed to companies targeting large audiences, striving to cast wide marketing nets with little personalization.

How Best to Use Customer Data[6]

  • Deliver a More Personalized Shopping Experience: By maximizing the use of data, a business can deliver a more personalized shopping experience to its customers. It can inform its customers about the special offers and discounts that are available to them. It can maximize opportunities to increase their profit. Companies that use the Brick and Mortar system can use their data to improve their customer’s in-store experience. Using customer location data, a company can decide which area to be focused more on selling a particular product. They can also segment customers based on other factors.
  • Customize the Promotions and Special Discounts: Proper analysis of customer’s data will help the business to provide customized offers and appropriate marketing. Loyal customers are always the most profitable customers. Through customer analytics, companies can provide long-term customer loyalty and increase their profit. The analysis should be made on which channel brings more customers to the business, and steps should be taken to improve that channel. Right promotion at the right channel gives more happy customers and high profit.
  • Pick your North Metric Star and be Thoughtful: You should choose what metric you should focus on. Choosing the exact metric will let you focus on what to track and what tools to be used to measure and improve the data. Some organizations track everything and get confused finally. To start with, select a track and list the questions which are to be answered through the analysis. Name the track events and follow the consistency. Add new events when you have new questions.
  • Improve your Products or Services: Through Customer Analytics, a company can easily identify which product is most liked by the customers and what price they are willing to pay for it. You can also get feedback from the customers regarding the products or services. Using such feedback and reviews, the company can improve the experience. The company can also use data to identify the most relevant users to get feedback on their products.
  • Improve your Marketing: The data will help to segment the customers based on several factors like region, price range, and others. The companies can define marketing strategies based on this data. Such a marketing campaign will be more effective and will also reach the target audience. Thus, in turn, it increases the sales of the product or service and brings in more profit.
  • Provide Better Customer Service: Use data like purchase history or support history to provide a better customer experience. Customer call support and mail support will also help the organization to create a mass amount of data in real-time.
  • Use Customer Data to Create New Product or Services: Through customer analytics, the company can know the expectations of the customers towards their product. It will help the company to develop new products that will fulfill the expectations of the customers. This will increase sales and improve the profit of the business.
  • Reduce Risk and Fraud: Through analysis of data such as customer behavior and customer churn rates organizations can take efforts to maximize their efficiency and reduce the risk and fraud taking place in the organization.
  • Create Shareable Content: The data you have gathered from customers through surveys can be converted into an attractive and well-designed infographic. This infographic can be shared on social media sites like Twitter and others. They can also be used for marketing purposes and can be posted on blogs for driving traffic to the site.
  • Customer Experience Matrix: The customer experience matrix will help you to find out the extent of the company’s interaction with the customers and the effectiveness of the data. Both these things will help you to understand the kind of customer experience a business offers to its customers. The customer matrix, as seen in the picture below, explains four kinds of customer experience. On the left of the X-axis, you can see the companies with poor customer data. Such companies have disconnected method of storing data and so provides poor customer experience. On the right of the X-axis. you can see the companies that are using their data more effectively. They have connected methods of storing data. All the details of the customers are stored in a single queried system which is easily accessible. In such companies, if any question arises regarding the data, it brings actionable insights and answers.

Customer Experience Matrix

  • Define Interests: One great idea to discover about your customers is to define groups or categories of interests that are relevant to your brand or business. After defining, you should create a relationship like which customer will fit in which group. If a particular pattern is seen to be recurring, then you can focus on that particular interest or category to maximize your business or marketing campaign.
  • Look for Gaps between Interests and Products: Cross-check the interest data with how a customer has engaged with your product. This will help you to know the gap. You can have different categories of interest for a particular range of products. If there is no conversion made or no purchase or subscription from that particular category of interest, then the organization can take a step to up-sell or re-engage the products.
  • Target People who are More Likely to Convert: Behavioral tracking and social login tracking can help you to target the relevant people for your product or service. Relevant interests and patterns within a particular group can also increase the conversion rate.

Customer Data Benefits[7]

  • Customer Data = Superior Customer Experience: It’s not rocket science — in order to provide customers with a great experience, you need to know their preferences and interests. When you know your customers, you can make every interaction meaningful. That means you need to serve them what they really want. This goes for every product or service you offer — and includes the kind of messaging you use to communicate with them. In a CMO Survey, 80% of consumers say that customer experience is just as important as the products or services a company offers. As such, customers expect businesses to care about them as individuals. 76% of people would like companies to be aware of their expectations and needs, and that is only possible through customer data. In a market where 54% of people believe that businesses don’t have their best interests in mind, you need to offer something really special — a personalized experience that “wows” them. Most consumers today would prefer an “Amazon-like” buying experience — one that clearly shows the customer that the company cares about them, by providing the following:
    • A Truly Personal Experience: Based on the items you have browsed or previously purchased, Amazon offers personalized recommendations of products you might like. Not only do they display these products every time you visit their website, but they’re also highlighted in emails that offer discounts on previously viewed or similar products. And some of these emails just want to make sure you’re taking full advantage of your existing subscriptions. 59% of shoppers agree that personalization has the power to influence purchase decisions. No wonder Amazon is the biggest ecommerce company in the US, with a net revenue of over $177 billion.
    • Excellent Customer Service: In 2017, 54% of consumers stopped making purchases from businesses that provided poor customer service. If you don’t want to lose half your customers to the competition, you might need to step up your CS. Digging into your customer data allows you to do that. With customer data, you’ll be able to understand their likes, dislikes, and interests. Additionally, if you’ve got a record of a customer’s past complaints and order information in your customer service software solution, you’ll be able to serve them better and quicker. Amazon leverages their customer data to provide fast and effective resolutions to customer queries. They save your phone number and provide a callback option so that you don’t need to spend hours on hold. Their customer service team has all your order-related and account information at their fingertips, which minimizes any hassles.
    • Social Proof: Social proof matters in consumer purchase decisions. Almost 60% of consumers have reported that they check out social media and blog reviews before buying a product. Using customer data, Amazon displays social proof in the form of product ratings and reviews, not only to encourage people to buy, but as a way of showing they care that customers have access to the objective feedback of their peers.
    • Easy Checkout: Almost 70% of online shoppers abandon their carts just before checking out. 23% abandon carts because they can’t see the total costs up front, and 28% of consumers do so because the checkout process is too long or complicated. Amazon combats both these issues head-on. They allow you to resume your shopping where you left off, so any products that were in your cart are still there. Plus, they notify you of any price changes to those items — and credit card information only needs to be entered once.
      Make customer data the foundation of your customer experience. If you can show how you’ll use their information to provide a better experience, your customers will be more than willing to share it.
  • Customer Data = Better Business Decisions: Data is at the heart of every impactful strategic decision. Without implementing data-driven policies, you’ll be playing a guessing game and you’ll lose more than you gain. Relevant customer data can help answer questions like:
    • Which segment of customers brings in the most revenue?
    • What are their common characteristics? Do they belong to the same geographical region, income group, educational background, or age group?
    • Which segment of customers buys a particular product the most?
    • Which products are particular segments more inclined to buy next?
    • Which of your customers churned and why?
    • How should you price an upcoming product and which segment should you focus your marketing on?
      The answers to these questions can help you make key strategic decisions that will push your brand forward. Whether you want to study the feasibility of launching a new product, enter a new market, or build better relationships with your customers — you need customer data.
  • Customer Data = More Effective Marketing: It is five times more expensive to acquire new customers than to retain your existing ones?

The simplest and easiest way to keep your customers happy is through knowing who they are and where they can be found. Which social media channels, for example, do they frequent? What kind of content do they prefer to consume, and in which formats? Knowledge of such information will help you focus your marketing efforts. It will help you decide what kind of content to produce and which marketing channels you should use. If your customers are mainly baby boomers, should you focus on Facebook, or Instagram? Or are emails a more effective communication channel? The better you know your customers, the easier it is to retain them. A study found that consumers are 1.3 times more likely to find value in personalized communication forms such as emails or newsletters. And they’re 1.1 times more likely to find retargeted offers important.

Customer Data Challenges[8]

Customer data is all the rage because there are so many things you can do with it and so many actionable insights you can gain like understanding your customers' journey and engaging and retaining them (if you know how to use it). Over the last few years, we’ve seen a proliferation of tools designed to help you do one of these jobs (and others) extremely well. However, with easier access to customer information and corresponding tools, you are likely also run into some challenges wrangling them.

  • Too many tools to choose from: With so many helpful services on the market, it can be difficult to figure out which one is best for your particular needs or even what tools you should start with! With a Customer relationship platform (CRM) and a Customer Data Platform (CDP) you can access and organize huge amounts of data, but how do you use it? Accel made an entire website to map this space, and Stacklist has emerged just to help startups find the right tools.
  • Data inconsistencies: When companies start using lots of tools at different times and drawing from different data sources, often they become cluttered with too much data and duplicate events with different names. Commonly people track too many events, name them all differently, and aren’t strict about where they fire off events. This makes using the data in end tools difficult.
  • Knowing when to level up: Most out-of-the-box analytics tools will help you answer important questions about your product and marketing performance. At the early stages, setting up a relational database might be overkill. However, it’s tough to know when do you need to have a more flexible, custom setup and how to set yourself up for future growth. Often when you need to answer those tough questions, you don’t have the data.

See Also

Customer Data encompasses a wide range of information collected by businesses about their customers, used to understand customer behaviors, preferences, and trends to enhance decision-making, marketing strategies, product development, and customer service. This data can include basic contact information, transaction histories, online interactions, feedback, and more sophisticated data like behavioral analytics and predictive modeling insights. Managing and analyzing customer data responsibly and ethically is crucial for building trust and delivering personalized customer experiences.

  • Data Privacy: Discussing the importance of protecting customer information and complying with data protection regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act). Data privacy is a critical consideration in the handling of customer data.
  • Customer Relationship Management (CRM): Covering the strategies, practices, and technologies that companies use to manage and analyze customer interactions and data throughout the customer lifecycle. CRM systems are key tools for leveraging customer data to improve business relationships and drive growth.
  • Big Data Analytics: Discussing the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, customer preferences, and other useful information. Big data analytics enables businesses to make informed decisions based on customer data insights.
  • Customer Segmentation: Explaining the process of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests, spending habits, and so on. Customer segmentation relies on detailed customer data to tailor marketing strategies effectively.
  • Personalization: Covering the practice of tailoring products, services, and marketing communications to the individual needs and preferences of customers based on the data collected about them. Personalization aims to enhance the customer experience and engagement.
  • Predictive Analytics: Discussing the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Predictive analytics can forecast customer behavior and preferences using customer data.
  • Customer Experience (CX): Explaining the total journey of a customer's interactions with a brand. Customer data is used to optimize the CX at every touchpoint, enhancing satisfaction and loyalty.
  • Loyalty Programs: Covering programs designed to encourage repeat business by offering rewards and incentives to loyal customers. Customer data is instrumental in designing and managing effective loyalty programs.
  • Data Management Platform (DMP): Discussing the platforms that collect, organize, and activate first-, second-, and third-party audience data from any source, including online, offline, mobile, and beyond. DMPs help in managing customer data for marketing purposes.
  • Customer Feedback and Voice of the Customer (VOC): Explaining the processes for collecting and analyzing customer feedback to gain insights into customer satisfaction, preferences, and expectations. Customer feedback is a valuable source of customer data for continuous improvement.
  • Data Mining: Covering the process of discovering patterns and knowledge from large amounts of data. Data mining techniques are used to extract valuable information from customer data sets.
  • Ethical Considerations in Data Usage: Discussing the ethical implications of collecting, storing, and using customer data. Businesses must navigate the balance between leveraging customer data for competitive advantage and respecting customer privacy and consent.


  1. Definition - What Does Customer Data Mean? Toolbox
  2. What is Customer Data Tealium
  3. Accenture
  4. Types of Customer Data Hubspot
  5. Who Owns Customer Data? Signal
  6. 13 ways to make the best out of your customer data EduCBA
  7. The Importance of Customer Data and Why it Matters to your Business Marketing and Growth Hacking
  8. Challenges with using customer data Twilio Segment