How well do you know your customers? Well enough to predict their future behavior and leverage that behavior to grow your business?
Imagine a customer buys a yoga mat from your ecommerce fitness store. A few hours later, they get an email with personalized accessory recommendations for their practice. The next time they go to your site, your data-driven insights help you anticipate their needs (more yoga routine enhancements) and offer the perfect solution—your foam rollers and resistance bands.
This is just one way customer analytics can help you improve your customer’s journey using data-based insights. Here’s what customer analytics is, its benefits, its categories, and best practices for collecting and using customer data.
What are customer analytics?
Customer analytics involve collecting and analyzing customer data to better understand the customer journey. It can help you make smarter business decisions, optimize marketing strategies, improve customer experiences, and drive growth. This data can come from website metrics (like the number of visits or conversion data), product reviews, social media comments, customer feedback surveys, and more.
Customer analytics can uncover insights about customer lifetime value, purchasing behavior, customer segmentation, and customer churn. By analyzing this data, you can create products that better address customers’ needs, increase customer satisfaction, build more effective marketing campaigns, and drive business growth with increased predictability.
Benefits of customer analytics
The more you learn about the customer journey using customer data analytics, the better online experience you can create. This provides key benefits such as:
- Improved customer engagement. By analyzing customer data and trends, you can customize your website content and other communications, like emails, to more effectively engage users.
- Better conversion rate. Accurate understanding and predicting user behavior can help you address common pain points and customer concerns, creating a more seamless customer journey.
- Increased customer retention. When you understand your customer lifecycle, you can resolve issues that lead to customer churn, or when customers stop buying from you.
- More revenue. All of the above benefits drive more conversions and foster customer loyalty, increasing your overall revenue.
Customer analytics categories
Not all customer analytics data provides the same insights. A combination of these types can give you a comprehensive perspective for actionable decisions:
Descriptive analytics
Descriptive analytics summarize historical customer data to understand past customer behavior and trends. Think of descriptive analytics as your performance scorecard: it tells you what your customers do and how happy they are, but it doesn’t tell you why.
Example: 25% of new customers buy one product on their first purchase.
Diagnostic analytics
Diagnostic analytics look deeper to determine the cause of a trend or why the user behavior occurs. This helps you identify the underlying factors driving customer actions.
Diagnostic analytics are typically qualitative. You can build your diagnostic analytics by asking open-ended survey questions, looking at screen recordings of users’ visits to your site, reviewing customer support tickets, or reading social media comments.
Example: Customer support tickets indicate that 30% of customers abandon their cart because the checkout process is confusing and frustrating.
Predictive analytics
Descriptive and diagnostic analytics help generate predictive analytics, which forecast future customer behavior. Predictive analytics are instrumental for predicting future revenue, planning inventory needs, developing new products, and preparing product launches.
Example: Based on prior years’ sales, we expect customer purchases to increase 25% in the last three months of the year.
Prescriptive analytics
Whereas other analytics focus on insight, prescriptive customer analytics use all the above categories to make actionable recommendations. They use data to address customer needs and achieve business outcomes.
Example: Based on purchase patterns and customer behavior, we should target ads for protective gear to customers who have recently bought skateboards.
6 best practices for customer analytics
- Use qualitative and quantitative data
- Automate data collection
- Integrate data for multiple sources
- Identify trends
- Use customer segmentation
- Predict, test, analyze, repeat
Effective customer analytics requires ethical data collection and detailed analysis to generate actionable insights. Here are six best practices for using customer data analytics:
1. Use qualitative and quantitative data
Be sure you’re collecting data from various channels and demographics, including qualitative (descriptive) and quantitative (numerical) data. You also have a legal obligation to let customers know you’re tracking their behavior online, typically through a cookie consent banner. Laws vary according to jurisdiction—for example, GDPR in Europe, CCPA in California, and PIPEDA in Canada.
Quantitative data can include Google Analytics data, revenue reports, sales data, and click tracking. It’s measured statistically and represents objective numbers that fluctuate over time.
Qualitative datahelps uncover customer sentiment and the “why” behind your quantitative data points. It can also show whether your quantitative data is impacted by a one-time occurrence (like a user’s credit card being declined) or indicative of a more significant problem (like a product page missing critical information).
2. Automate data collection
Many customer analytics tools can help collect and sort customer behavior data. Manually collecting and inputting this customer data is time-consuming and error-prone; automating the process is a more effective solution.
For example, instead of asking customers to review their purchase on your site, send an automated email two weeks later with a link for submitting their review. Apps like Yotpo can streamline this process. Or, instead of manually analyzing behavior funnels, integrate a heatmap tool like Hotjar to visualize customer interactions on your site.
3. Integrate data for multiple sources
Gather data from multiple platforms and stages of the buying cycle for the most well-rounded insights. This includes customer interactions across your website, online store, and social media.
To understand how a customer moves from discovering your brand to making a purchase, track their behavior throughout the buying cycle. This can help you create content and product recommendation options to increase sales and encourage customer loyalty.
Examples of data sources include:
- Google Analytics
- Email platforms like Klaviyo or Mailchimp
- Facebook Insights and Instagram analytics
- Client relationship management (CRM) systems like Salesforce
4. Identify trends
It’s easy to get overwhelmed by the amount of customer analytics data you can collect. And since user behavior varies and fluctuates, a small data snapshot is often insufficient.
For a more comprehensive customer data analysis, look for trends over time rather than individual data points. Plotting your quantitative data on a line chart is a great way to start.
Once you have identified a trend, look for what may have influenced the results. For example, increased sales trends through the holiday season may indicate people buying your product as holiday gifts. Or, you may notice customer satisfaction dropped sharply last week but returned to normal this week, aligning with the downtime of your online store’s payment processing system.
5. Use customer segmentation
As your data collection volume increases, segment your results into demographics or conditions for more specific analysis. For example, you can look at sales or customer satisfaction trends and commonalities based on where the customer lives, their purchase history, or how long they’ve been a customer.
If you own an ecommerce clothing store, segmenting customers by location can help target those who need warm clothing in winter and those who prefer light clothing year-round.
Cohort analytics is an advanced segmentation method that can help you measure and understand how the user experience changes over time.
For instance, you might group all customers who subscribed in December of last year, then review this segment a year later to analyze churn rates and reasons. In Shopify, you can segment users and subscribers to increase customer lifetime value, reconnect with repeat buyers, and convert abandoned carts.
6. Predict, test, analyze, repeat
A solid customer data analytics program allows you to make effective business predictions. For example, you can predict customer lifetime value, purchasing trends, and likely upsells and cross-sells.
Testing can mean running a formalized test with a tool like VWO or making a change and monitoring performance over time. Here are some ideas to test, depending on your goals or the customer experiences you want to optimize:
- Add customized product recommendations based on user browser history
- Include one-click upsells during checkout
- Send personalized post-purchase emails
- Run A/B tests for product images and descriptions
Test changes one at a time to see if they impact sales volume, customer retention, and the customer experience. This helps you determine which changes had the desired effect. Change can be reflected by quantitative or qualitative data. Simply asking customers how they feel about their experience can sometimes tell you more than a number ever could.
Customer analytics FAQ
What are customer analytics tools?
Customer analytics tools help you collect, sort, interpret, analyze, and predict customer behavior. Examples of customer analytics software and tools include:
- Shopify Analytics
Native analytics in your social media or email marketing tools
What are applications for customer analytics?
Marketing, product, and customer service teams most commonly use customer analytics. This practice can effectively apply to a wide range of activities, including customer segmentation, personalization, retention, and acquisition, as well as product and service improvement.
What are the types of customer analytics?
Customer analytics can be descriptive (customer data about past behavior), diagnostic (data-based insights into “why”), predictive (using past data and trends to predict future customer behavior), and prescriptive (data-based suggestions to improve the customer experience.
