How To Get Data of Customers? A Guide To Customer Data

If you own an online business, you know much work goes into finding new leads and convincing them to buy your products. Some say you must work hard and be patient to succeed in e-commerce. Of course, this is the absolute truth about any business, but you can choose to work hard or smart.

Collecting and analyzing customer data allows you to scale your business and reach people interested in what you have to sell. And the best thing is, even if you’re a small business owner, you don’t have to hire an entire team to analyze data, you can do it all yourself!

In this article, you’ll learn customer data basics and how to collect information about consumers quickly and effortlessly.

What is customer data?

Customer data is the information your customers leave when interacting with your business. This data can be collected through your website, mobile applications, surveys, social media, marketing campaigns, and other places a potential customer can find your business. Customer data includes the personal information and behavioral and demographic data their audience provides. 

Brands collect customer data to understand their audience better. It also allows for improving digital marketing communications and engaging with target customers. Customer data allows businesses to understand what customers need, what kind of products they want, and how they want to engage with a brand.

Customer data is a cornerstone of 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.

The sources of customer data include: 

• Social media platforms 
• Website analytics tools 
• Customer surveys 
• Loyalty programs

Benefits of collecting customer data

Customer data types

Several types of customer data help to distinguish the types of interactions someone has with your company. These types are segmented depending on the engagement or behavior of customers on your website or social media accounts. Here are the most popular types of customer data:

Personal data

Personal data include your personal information such as name or email address. This type of data is usually given intentionally, meaning it’s zero-party data. You can collect this information through a newsletter, survey, or form. Because customers share zero-party data with brands, it’s considered more trustworthy than other customer data types.

However, there is also personal data that a customer doesn’t give you directly – this data includes IP address or cookies.

Engagement data

Engagement data shows how your customers engage with your brand. This data includes information such as the actions on the website, interaction with you on social media and customer service, and so on. Here are engagement data divided into different channels:

Website/mobile app interaction data include website visits, most viewed pages, app stickiness, traffic sources, etc.

Social media engagement data include post likes, number of followers, post shares, video views, etc.

Email engagement data include open rate, click-through rate, bounce rate, etc.

Customer service information includes the number of tickets, query details, customer feedback, customer complaints, etc.

Paid ads engagement information includes impressions, cost per click, cost per mile, click-through rate, conversions, etc.

Behavioral data

Behavioral data helps you determine patterns that your customers reveal during their purchase journey. Sometimes engagement data is included in behavioral data. Customer behavior data includes:

Transactional customer data includes subscription details, purchase details, previous purchases, cart abandonment data, average order value, customer loyalty program details, average customer lifetime value, etc.

Product usage data includes repeated actions, devices, feature usage, feature duration, task completion, etc.

Qualitative data includes user attention, heatmaps (clicks, scroll, mouse movement data), etc.

Attitudinal data

Attitudinal data is information on a customer’s direct opinion of a company. It helps businesses to gather insight into customers’ feedback on a product and the public opinion of the brand. The most popular method of collecting attitudinal data is the option for online reviews on your website. Examples of attitudinal customer data include:

• Customer satisfaction
• Product desirability
• Preferences
• Purchase criteria

First-party data

First-party data is the information that a company gathers directly from its customers. This kind of data can come in the form of a number of website visitors, newsletter subscribers, and customers. Customers don’t give this information by filling out a form or setting up an account but through their interaction with the brand.

Second-party data

This data is collected through the brand’s partners, such as affiliates, loyalty programs, cashback apps, and more.

Trusted business partners share this data with the company so that both parties would benefit from it. For example, a wedding dress store can share its customer data with other wedding vendors, such as catering services.

Third-party data

Third-party data is the information you can acquire through a middleman. It’s collected by an external organization that doesn’t have a direct connection with the visitor or customer. The upside of using third-party data is that it’s usually easy to buy and often comes in large quantities. However, nowadays, because of data protection regulations, people are more conscious of data privacy, and practices such as data mining are impeded.

How to collect customer data?

Now that we know the types and benefits of customer data, it’s time to focus on how you can collect this data. Here are our tips for collecting customer data:

Create sign-up forms

Sign-up forms are a great way to get the data directly from the source – your customer. To encourage people to fill them in, you should offer value to people, for example, a newsletter with the latest insights, a discount, or a free sample. Make sure the sign-up forms aren’t too elaborate – just require some basic information.

Offer loyalty programs

Loyalty programs are great because not only do you have access to personal data but also transactional data. Sometimes people make purchases from different accounts or without even signing in; this makes it hard to track the information about a specific customer effectively. Creating a loyalty program adds the incentive to make all purchases from a dedicated account because it comes with a prize at the end.

Host online surveys

To effectively run online surveys, offering something in return, such as an additional discount or exclusive materials, is also a good idea. In online surveys, you can ask several questions that are more elaborate. These questions can regard customer feedback, user experience, and so on.

Ask customers for reviews

After they make a purchase, ask customers to review the products. It allows you to gather information about your products and the customers who bought them. Online reviews also strengthen the trust between customers and the brand.

Analyzing customer data

Once you have collected the information, it’s time to analyze customer data to see what conclusions you can draw. Here are some questions you can ask yourself when looking at customer data:

• Who is my target audience?
• What do my target customers like?
• What do my target customers need?

Answering these questions can make it easier for you to work with the data later.

There are several techniques to analyze the data:

• Classification – grouping data into a given set of categories
• Association Rule Mining – identifying patterns with correlation in a given data set
• Outlier Detection – identifying anomalies in the data.
• Clustering – classifying data into homogenous categories based on a characteristic/feature
• Regression Analysis – helps understand how the presence of a specific characteristic impacts other characteristics in the set.
• Prediction – forecasting the future behavior of your customers based on their history.

Segmenting customer data

Data segmentation is grouping previously collected data into categories that help utilize it. For example, you separate your customers into groups based on their shared traits. Segmentation offers a simple way of customer data management and organization. Segmentation is used to create targeted campaigns and ads to resonate with and convert segments of customers. It can also improve your customer service and customer support efforts.

Conclusion

Customer data is essential to businesses as it helps them understand their customer base, what they need and want, and how they can better serve them. There are several ways to collect customer data, such as sign-up forms, loyalty programs, online surveys, and customer reviews. Once you have collected the data, it’s time to analyze it using techniques such as classification, association rule mining, outlier detection, clustering, regression analysis, prediction, and segmentation. Finally, customer data segmentation is used to create targeted campaigns and ads. Understanding customer data is essential to businesses in order to improve their customer service and support efforts. Thanks for reading!