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.

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.

Types of Customer Data

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

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.

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

• 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.

• 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.