10 Tips for Utilizing Ecommerce Analytics to Grow Sales

Ecommerce analytics are like the key to a map. They help retailers plot a satisfying and effective buying path for shoppers with robust data that can be used to optimize experiences across the entire shopping journey. The data you collect from various touchpoints and interactions tells a story – one that can help you improve your experiences for distinct audiences in the best possible way. 

What is Ecommerce Analytics?

Ecommerce analytics is the process of collecting, measuring, and analyzing data from your online store so that you can better understand customer behavior and optimize the buying experience for shoppers. The goal of optimization is to improve your website’s performance (e.g., increase sales, raise average order values, etc.) and provide an experience that’s so delightful, customers will want to return again and again.

Types of Data Used in Ecommerce Analytics

Ecommerce analytics relies on various types of data to provide comprehensive insights about customer behavior and business performance. Here’s a breakdown of the different types of data used in ecommerce to help you understand how it’s collected and what it tracks.

• First-party data is collected directly from your customers through interactions with your website, forms, and purchases. Think of it as the base of the analytics pyramid – the foundation of ecommerce analytics. It includes demographics, purchase history, and website interactions. 
• Zero-party data is information voluntarily provided by customers. It’s also foundational, but forms the center of the pyramid as well as it helps strengthen and support your first-party data. Some ways to collect this type of data include surveys,  feedback forms, chatbots, and service interactions. 
• Second-party data is information purchased from other companies. It is essentially another company’s first-party data that has been packaged and sold. This type of data can be valuable for new or growing companies to help supplement your owned customer data. It can also help build your customer base if acquired from a trustworthy source.
• Third-party data is data that has been collected from external sources like government agencies, nonprofits, and marketing agencies. Data like this might include demographic and psychographic information, economic data and forecasts, supplier data. This data is often aggregated into one dataset, then packaged and sold by companies that typically did not collect the data themselves. While third-party data can offer a significant scale advantage, it may come with exclusivity issues and potential concerns about data quality or compliance.

How Data is Used in Ecommerce Analytics

While the term “analytics” isn’t interchangeable with the term “data”, you can’t have one without the other. Analytics uses data including metrics, reports, and key performance indicators (KPIs) to predict future performance and identify areas for improvement in processes and strategies.

Here are the different types of ecommerce analytics you can use to optimize retail customer experience (CX):

1. Audience Analytics

Audience analytics data includes demographics, device preferences, and customer interests, all of which help retailers segment customers so they can better target marketing campaigns and personalize shopping experiences. 

2. Acquisition Analytics

Acquisition data looks at the various traffic sources that refer visitors and performance by channel (e.g., cost per acquisition, cost per sale, etc.) This gives you a deep understanding about where visitors are coming from and which channels are the most effective for attracting and acquiring customers.

3. Behavior Analytics

Behavioral data is incredibly useful for optimizing website design and content and personalizing shopping experiences. The data used for behavior analytics include metrics like pageviews, bounce rates, average time on page, and product interactions. 

4. Conversion Analytics

Conversion analytics are focused on helping retailers identify opportunities to improve the conversion funnel. Tangible metrics like website conversion rates, average order value, and customer lifetime value help you understand what’s effectively selling products, motivating customers to return, and what’s not working. 

5. Paid Analytics

Your paid marketing efforts contain a wealth of data about what channels your customers use and which channels are most effective for driving referrals and sales. Paid analytics looks at metrics like return on ad spend, clickthrough rates, and conversion rates which can be sliced and diced by campaign, creative, keyword, and channel. 

Ecommerce Analytics Example

It’s helpful to understand ecommerce industry benchmarks around key performance indicators (KPIs) like conversion rate, add-to-cart rate, and average order value so you can focus on where to improve your own website performance. Monetate analyzes billions of customer sessions across hundreds of retail globally to provide this important intel.

Some choice ecommerce benchmarks from Q1 2024 include: 

• Sessions by device: 62.5% mobile, 35% desktop, 2.5% tablet
• Global ecommerce conversion rate: 2% mobile, 3.20% desktop 
• Add-to-cart rate: 8.05% mobile, 8.85% desktop 
• Average order value: $100.93 mobile, $130.76 desktop

You can drill down by industry, region, and source for most of the benchmarks. So dig in. Our ecommerce industry benchmarks provide valuable insights about how your brand stacks up against other retailers and where you should focus your ecommerce strategy.

10 Ways to Utilize Ecommerce Data Analytics to Grow Sales

Now that you understand the different types of ecommerce analytics and how data can be used to improve customer experience, we’re ready to explore some best practices for growing your ecommerce sales. Here are our top 10 recommendations.

1. Personalize Every Experience for Every Shopper

Audience and behavior analytics can help you create targeted, personalized experiences for your customers. When you understand a shopper’s preferences, interests, and past interactions, you can tailor product recommendations, content, and promotions for each individual shopper. For example, a shoe retailer like Clark’s might feature sale items front and center on the home page for budget conscious shoppers versus new and trending (and not on sale) items for customers who are browsing higher end items.

2. Direct Paid Media to Channels That Drive Sales

Acquisition analytics from paid marketing campaigns provide important insight about the channels, platforms, and approaches that sell products. Go beyond clickthrough-rates to understand the most effective channels for conversion rates, return on ad spend, and average order value. Once you identify the top-performing campaigns and channels, you can allocate your budget to get the best return. So, if TikTok consistently drives high-quality traffic (based on sales and AOV) versus Facebook and Instagram, you can spend more there and pull back spend on the underperforming platforms.

3. Study Shopper Behavior to Prevent Cart Abandonment

Analyzing shopper behavior data can help you understand the “why” of cart abandonment. Where are the roadblocks? Is your checkout process clunky or are surprise shipping costs causing visitors to leave before completing a sale? Look to behavioral insights to unearth patterns that point to poor digital experiences, then tweak your content, design, and overall experience to smooth out the bumps.

4. Let Customers Be Your Product Gurus

With every click and purchase, your customers provide valuable intel about what they do (and don’t) want. Analyze their purchase history, read their reviews (the good and bad), and use this data to inform your inventory strategy by stocking up on popular items and ditching the duds. When you align your merchandising approach with your customers’ needs and wants, sales naturally improve. 

5. Use Data to Keep Your Virtual Shelves Stocked 

Inventory analytics are your secret weapon for keeping your online store running like a well-oiled machine. By monitoring stock levels and product demand, you’re in a good position to have best-selling products available when customers come knocking. Inventory analytics help you avoid “out of stock” messages which are frustrating for customers and sale killers for you. With the right inventory data at your fingertips, you can confidently meet customer expectations and keep those orders rolling in.

6. Make the Entire Shopping Journey Better

Journey analytics analyze the entire end-to-end customer shopping journey. Use this information to closely examine customer experiences across all interactions and touchpoints including testing variants. This is what we mean when advise retailers to have a holistic view of the customer. Journey analytics help you understand trends and changes in customer behavior so you can identify new opportunities and customer needs then shift your strategy accordingly.

7. Make Shopping Experiences More Meaningful 

AI-powered audience discovery automatically creates new customer segments using first and zero-party data (e.g., behavior, preferences, past purchases, etc.) It’s an approach that provides retailers with rich customer insights that help you better understand your various customer segments time. Once you have segments defined, you can create meaningful personalized shopping experiences on your website through tailored messaging and specialized offers that resonate with each unique segment.

8. Use Real-Time Analytics to Act Quickly

Real-time analytics allow you to monitor and act on insights when they matter most, while they’re happening. For example, Monetate’s measurement tools analyze experience results instantly, so you can make quick strategy adjustments based on current trends and customer behaviors. Customers crave relevance, so making data-driven decisions in the moment helps you recognize (and capitalize on) emerging opportunities.

9. Break Down Data Silos to Facilitate Collaboration

It’s impossible to understand everything about your customers, much less maintain a consistent CX, when data and departments are siloed. Make it easy for teams to collaborate by combining information from CRM systems, POS systems, and offline tracking data. Unifying all your various data sources allows you to share information easily between teams like marketing, sales, service, and ecommerce so that experiences are personalized, but also (importantly) consistent regardless of who a customer connects with or how they reach out.

10. Improve Customer Retention and Brand Loyalty

In today’s vast selection, what’s the secret to retaining your clientele? When your brand genuinely understands and values your customers’ requirements, they become loyal. Utilizing ecommerce data enables you to create deals and experiences that consistently succeed based on customers’ personalized browsing journeys.