What Is Customer Analytics? Customer Data Analysis 101

It's easy for brick-and-mortar stores to know their customers and anticipate their needs, but it’s a lot harder to understand customer behavior, especially when their only contact with you is through a digital touchpoint like your website.

Luckily, it’s still possible to learn more about your customers through customer analytics.

That said, it’s important to collect and analyze customer data the right way. Identifying trends in customer behavior and staying compliant with data privacy regulations can be challenging without the right tools.

When you collect and analyze your customer data using a digital analytics tool, you can provide personalized customer experiences, make product decisions, and monitor customer sentiment toward your company.

Introduction to customer analytics

Customer analytics is the practice of analyzing customer data across channels to better understand customer behavior and make more informed business decisions.

With data from behavioral analytics—which involves collecting customer behavior data through surveys, interviews, focus groups, and analytics tools—businesses get valuable insights into customer preferences, buying habits, and in-product experiences.

Using customer analytics helps organizations develop more informed marketing and product strategies, optimize the customer experience, and improve customer retention.

Types of customer data analysis

To get the information you need to make sales, marketing, and product decisions, you’ll need to run different types of customer data analysis.

Demographic analysis

A demographic analysis focuses on the characteristics of your customers, like age, gender, income, and location. You can use this information to help identify customer segments and target markets, tailor marketing campaigns, and create products or services for specific audience needs.

Behavioral analysis

With behavioral analysis, you can closely examine how your customers interact with your company. For example, you might analyze user behavior to see how your audience uses your mobile app or product or what makes people abandon their shopping carts. You can also see how often customers purchase and how long they remain customers. This type of analysis helps you find patterns in customer loyalty and purchasing behavior.

Attitudinal analysis

Attitudinal analysis, or sentiment analysis, provides insights into the emotions or beliefs that customers have about your company and its products. It uses natural language processing tools and machine learning to gain insight into overall customer satisfaction to help shape your marketing and customer service strategies. For example, if one of your products gets negative feedback, you can analyze customer reviews for patterns to see why they’re dissatisfied.

This type of analysis also helps you be proactive about customer churn. If you notice negative patterns in how a customer communicates with your company, you’ll know it’s time to step in and address any issues before they end their customer journey with you.

How customer analytics works

To begin analyzing information about your customers, you’ll need to collect data from various sources and consolidate that information into a database

Gather first-party data from your customers, whether it’s through surveys, questionnaires, feedback forms, or your social media pages. You’ll also want to collect information from your digital analytics platform to look at website metrics (bounce rates, traffic sources, and new signups) and product metrics (activation rates, monthly active users, and customer lifetime value).

Make sure you also have a customer database ready to store your customer data in one spot. These databases help you comply with data privacy laws and secure customer data. Additionally, they provide data visualization tools so you can easily spot trends and correlations without sifting through volumes of data.

Once you’ve identified your key customer data points, you’d normalize the data across all data sources to ensure it’s consistent and comparable. This could involve standardizing data, such as changing all dates to a single format or merging data from many sources into a single database. Then it’s time to analyze your customer data to understand their needs and preferences.

Take your analysis further by using customer segmentation to organize customers into groups (“cohorts”) based on certain characteristics, like demographics, devices, or in-product behaviors. Using customer segments makes it easier to personalize your marketing and provide a seamless omnichannel experience.

Common data analysis tools

Data analysis tools can help you perform accurate and effective customer analytics. Some of these tools include:

• Amplitude: This Digital Analytics Platform helps companies understand user behavior across mobile apps, websites, and other digital touchpoints. It also enables you to build customer profiles and create segments to target your audience with the most relevant content.
• Tableau: Use this data visualization and business intelligence software to create interactive visualizations and dashboards.
• Microsoft Power BI: This business intelligence platform helps build interactive dashboards and reports from data sources. It offers features like data modeling, data visualization, and data exploration.

Applications for customer data analytics

Customer data analytics provides your business with powerful knowledge, but what can you do with that information once you have it? Your analysis can help you make data-driven decisions across your organization, including:

• Marketing: Use customer segmentation to build personalized marketing campaigns that resonate with each segment of your customer base. For example, you might send customers who are least likely to make a repeat purchase a discount to encourage loyalty and conversion.
• Products: Use product analytics data to gain insight into how to improve your product or service. For example, you might find users consistently dropping off before completing onboarding. You could use this insight to A/B test new onboarding journeys that increase activation.
• Customer service: Say you do a customer churn analysis and find several of your customers are at risk of leaving. This is a sign that your customer service agents or customer success managers need to intervene.

Challenges in customer data analytics

While customer analytics gives you valuable insights, it’s not without challenges.

If you’re not using a data analytics tool to help you comply with data privacy laws, then you have a lot of data and personally identifiable information (PII) to manage on your own. Some of that can easily slip through the cracks, and you’ll face stiff penalties.

Laws like the General Data Protection Regulation (GDPR), which is a data privacy law in the European Union, help protect customer privacy and require organizations to take appropriate measures to protect the personal data they process. Each GDPR violation can cost you up to 20 million euros, or 4% of your annual revenue, depending on which is greater. Data privacy will likely keep evolving, so you need a solution that can pivot with your company when that happens.

Analyze customer data securely and effectively with Amplitude

With Amplitude, you’ll gain the insights you need to make smarter, data-driven decisions about your business, products, and customers. Easily visualize trends in your customer data and run analyses to improve your bottom line. Our platform also provides the tools to comply with GDPR and other data protection laws. Sign up for a free account today to start analyzing your customer data.