Customer data is the behavioral, demographic and personal information about customers collected by businesses and marketing companies to understand, communicate and engage with customers.
In this installment of MarTech 101, we look at the basics of customer data. We will start by understanding its definition and types. We then delve into how you can collect, validate, and analyze customer data.
Table of Content
- What Is Customer Data?
- Types of Customer Data
- How to Collect Customer Data?
- Validating Customer Data
- Analyzing Customer Data
- Benefits of Customer Data Analysis
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.
An organization collects a myriad of customer data points throughout the buyer’s journey. The volume of these data points is vast, and for ease of understanding, we have segregated them in different categories.
Let’s look at the different types of customer data you need to collect to enhance your business strategy.
Note: Collecting and storing customer data is an intricate topic that is largely dictated by the rules and regulations (such as GDPR) of the country your organization operates from and/or that of your target audience. Make sure to study and follow these regulations to avoid legal consequences. It’s safe to seek legal help if you’re unsure.
1. Personal Data (PII and Non-PII)
Personal data can be divided into two categories, Personally Identifiable Information (PII) and Non-Personally Identifiable Information (Non-PII).
Personally Identifiable Information (PII): PII is any information that can be used to recognize an individual’s identity. It is further divided into two categories
1. Linked Information:
Linked information is information that can be used to identify an individual without requiring additional information/data point. Examples of linked information are:
- Full name
- Physical address
- Email address
- Login details
- Driver’s license number
- Social security number
- Passport number
- Credit/debit card details
- Date of birth
- Phone number
2. Linkable Information: Linkable information is any information that can’t identify a person on its own, but it can do so when it’s clubbed with another piece of information. Examples of linkable information include:
- First or last name
- Location â€” Country, state, city, ZIP code
- Race and ethnicity
- Age group
- Job details
Non-Personally Identifiable Information (Non-PII): Non-PII is the opposite of PII, which is anonymous information and can’t be used to identify any one person. Examples of non-PII include:
- IP address
- Device IDs
Note: You could be wondering why we chose to include non-PII in the personal data section even though it doesn’t help to identify individuals. The reason being, various laws treat these data points differently. For instance, according to GDPR, non-PII such as cookies can get categorized as personal data. So, to avoid any potential confusion, we’ve grouped them under one umbrella.
2. Engagement Data
Engagement data tells you how your customers interact with your brand via various marketing avenues. This data includes information such as the customer’s behavior on the website, their interaction with you on social media and through customer service, and so on. Here are the inclusions of each channel:
- Website and Mobile App Interactions: Website visits, App stickiness, Most viewed pages, User flow, Traffic sources, etc.
- Social Media Engagement: Post likes, Post shares, Post replies, Native video views, etc.
- Email Engagement: Open rate, Click-through rate, Bounce rate, Email forwards, etc.
- Customer Service Information: Number of tickets, Complaint/Query details, Feedback, etc.
- Paid Ad Engagement: Impressions, Click-through rate, Cost per click/mille, Conversions, etc.
3. Behavioral Data
Behavioral data helps you uncover underlying patterns that your customers reveal during their purchase journey. Engagement data may or may not be a part of behavioral data. Here’s how you can gather this data:
1. Transactional Data: Subscription details, Purchase details, Previous purchases, Average order value, Cart abandonment data, Average customer lifetime valueOpens a new window , Customer loyalty program details, etc.
2. Product Usage: Repeated actions, Feature usage, Feature duration, Task completion, Devices, etc.
3. Qualitative Data: User attention, Heatmaps (clicks, scroll, mouse movement data), etc.
4. Attitudinal Data:
Attitudinal data is driven by the feelings and emotions of your customers. It’s how they perceive your brand and offerings. Since attitudinal data is mostly qualitative and subjective, to get concrete outputs, it’s wise to combine it with quantitative data.
Attitudinal data is usually scouted via surveys, interviews, focus groups, feedback, customer complaints, reviews, etc. Here are a few examples of attitudinal data:
- Customer satisfaction
- Product desirability
- Motivations and challenges
- Purchase criteria
Marketers can collect data from every channel that the customer interacts with the brand on. Although there are probably hundreds of ways to collect customer data, in this section, we will look at the most essential avenues that you can use to get to know your customers better.
Before we delve into how you can collect customer data, answer the following five questions:
- What are the different data points you should be collecting?
- How should you organize the data? What tools would you require to store it?
- What measures should you take to protect customer data? And are you transparent with your customers on how you collect their information?
- Have you ensured that your data collection methods are compliant with your country’s laws and regulations?
- How are you going to use the data for the organization’s benefit?
Once you have decided on these questions, you can look at how to go about collecting customer data.
1. Website Analytics
Your website is often the primary channel that your customers interact with. You can collect customer data such as their demographic and geographic characteristics along with engagement and behavioral data.
Tools such as Google Analytics, Mixpanel, Piwik PRO, and Matomo help you understand their interests, referral sources, conversion details, along with their real-time behavior on your website.
While these tools have shortcomings such as the inability to collect qualitative information, you can compensate for them by using visual/experimentation tools such as Crazy Egg, Optimizely, VWO, and Hotjar. These tools help you understand user behavior through heatmaps, session recordings, and conversion funnel visualization.
2. Social Media
You can know a lot about your customers based on how they interact with you on social media. Apart from using basic engagement metrics (such as likes, comments, and shares), you can get to know a lot about your customers through the native analytics/insights section of each social media platform.
Through online reputation management (ORM) efforts, you can gather customer data that lets you understand the general sentiment surrounding your brand and offerings.
You can up the ante of your customer data collection activities by investing in social media ads. Through the targeting capabilities of social media platforms, you can understand the interests and other characteristics of your customers. By uploading your email list on social media platforms using the custom audience feature, you can uncover their behavior on a specific social media channel to know more about them.
3. Tracking Pixels
Through tracking pixels, marketers can also get to know the conversion activities of their customers.
4. Contact Information
Contact information is perhaps the most important information from the perspective of communicating with your customers. It is unlikely that your customers will share all the information from the get-go. It’s wise to collect their details considering the stage of the buyer’s journey. For instance, longer forms will be ineffective early in the stage. Make sure to provide appropriate rewards/incentives when your customers provide their data.
5. Customer Feedback and Surveys
Customer feedback and surveys are effective to gather interests, tastes, and preferences of your customers. By asking the right questions, surveys can help you collect qualitative, attitudinal data.
You can receive feedback on your offerings, services, sales and marketing activities through surveys. Using Net Promoter Score (NPS), you can comprehend your products’ avidity among your customers.
6. Customer Service Software
A customer service software helps you understand the instances when your customers seek help, problems existing in your product, the complexity of those problems, the medium your customers choose to connect with you, how long it takes to resolve a query and how it can be optimized.
Based on this data, marketers can gauge customer satisfaction.
7. Transactional Information
Depending on your business model, there are different ways to collect transactional customer data. For a SaaS business, it is often entirely through online means, and it typically consists of the standard data such as the subscription details of the customer.
For example, for an e-commerce business, it will include cart abandonment data, while for a retail brand with brick-and-mortar stores, it will primarily rely on PoS (Point of sale) system to collect purchase data.
Apart from these seven ways, you can collect customer data through focus groups, customer interviews, data management platform (DMP), to name a few.
Ensuring the accuracy of your customer data is essential for the success of your marketing efforts. Accurate customer data not only boosts your marketing efforts but also prevents waste of time and monetary resources and further prevents a bad CX.
Therefore, validating key customer data points â€” name, email address, physical address, contact number, etc. is crucial for the accuracy and completeness of data.
Here is how you can go about validating your customer data:
- Having a plan for data validation helps you set the right expectations from the beginning. The plan should lay out your milestones to measure progress. It should also consider the impact it might have on the existing operations and make sure that there’s enough time to resolve any potential hurdles that may arise.
- Next, check the size of the data and whether the data is available in its entirety. Also, measure the number of customer records, size of data, and unique IDs.
- Data enrichment helps marketers validate and refine customer data by verifying its in-house/first-party data against trusted third-party data sources.
Data enrichment also helps you eliminate data redundancies and update existing records.
- The aim of data validation is to establish a golden record or a single source of truth. With the help of customer data integration (CDI), you can collect, organize, and unify customer data to get a 360-degree view of your customers.
Tip: Read The Basics of Customer Data Management Part I Opens a new window and Part IIOpens a new window from our MarTech101 series to understand these concepts thoroughly.
Customer data analysis is a major undertaking. It’s one thing to collect customer data, but it’s a whole new ballgame to derive actionable insights from it.
One of the biggest challenges with analyzing large sets of customer data is to analyze qualitative information as it is subjective and varies from person-to-person. But before we get into how to you can analyze qualitative information, let’s understand how data mining can help analyze quantitative data.
1. Analyzing Quantitative Customer Data Using Data Mining
Data mining uses the concepts of statistics, artificial intelligence, and machine learning to analyze large sets of data and identify underlying patterns. You can use the following data mining techniques to extrapolate actionable insights:
- Classification: This technique requires you to categorize data into a given set of categories (classes). For example, based on the income groups and purchase history of your customers, you can make them customized product offers
- Association Rule Mining: Association uses correlation to identify patterns in a given data set. It uses the â€˜if thisâ€¦then thatâ€¦’ reasoning to predict outcomes. Recommendation engines use association rule mining to recommend products or content.
- Outlier Detection: You can use this technique to identify anomalies or unexpected patterns in the data. For example, if you see an unexpected rise during the product sales in a period, you can find the root cause of it and take the necessary decision.
- Clustering: Cluster analysis is used to classify data into homogenous categories based on a characteristic/feature
- Regression Analysis: Regression is used to identify the relationship between different data points. It is useful to understand how the presence of a specific characteristic impacts other characteristics in the set.
- Prediction: With the help of prediction, you can forecast the future behavior of your customers based on their history.
Along with data mining, marketers can also use data visualization techniques and business intelligence to extract meaningful information from quantitative data.
2. Analyzing Qualitative Data
Information collected through customer service software, interviews, feedback, surveys, etc. tends to be qualitative in nature, and hence the traditional data mining techniques would not be effective on them. However, you can use the following methods to extract revelations from them:
- Content Analysis: In content analysis, you highlight relevant keywords, ideas, or themes to find their occurrences in your data. For example, while analyzing surveys, you can create a list of problems identified by your in-house team and discover different keywords that a customer would use to describe them. Now, by analyzing the survey you can understand how you can improve your product.
- Narrative Analysis: People communicate through stories. With a narrative analysis, you can identify how customers communicate stories and ideas, which can further help you understand how customers feel about your brand and offerings.
Here are five ways in which analyzing customer data can help you know more about your customers, brands, and offerings:
- If you have already created buyer persona templates, based on the customer data analysis, you can make the necessary tweaks in these templates to keep them updated.
- You can segment your customers based on their geographic, demographic, or psychographic characteristics.
- It helps you understand the needs and pain points of your customers and tailor your product messaging accordingly. You can also improve your narrative to justify the price-benefit aspect of your product.
- It can assist you in streamlining your marketing campaigns.
- Customer Data Analysis can also help you increase customer lifetime value and reduce churn.
To summarize, we looked at the concept of customer data, its various types including Personal (PII and Non-PII), Engagement, Behavioral and Attitudinal. We covered seven ways you can collect customer data and learned all about analyzing and validating this data plus the benefits of doing that.
Hope this article has helped you understand the basics of customer data. You can now start collecting and analyzing your data to improve your business strategy and ROI.