Ecommerce Analytics: How to Leverage the Power of Data for Your Business

Successful ecommerce strategies require careful pre-planning, and successful businesses learn from previous efforts to ensure a brighter future. 

And all of this requires one thing: ecommerce analytics. 

Ecommerce analytics is the process of accumulating data from all of the areas that have an impact on your store. You can then use this data to comprehend shifts in customer behavior and online shopping trends — ultimately leading to better business decisions, wiser budget allocations, a frictionless customer experience (by identifying where customers drop off in the funnel), and more personalized marketing. 

In fact, Gartner predicts that 65% of B2B businesses will be making data-driven decisions by 2026.

If you want to be one of those businesses, keep reading. We’ll explore the key areas for ecommerce success, which metrics you should be tracking, and how BigCommerce can support your data analytics goals.

Analytics vs. metrics vs. KPIs 

Many people tend to use these terms interchangeably, but they actually have different meanings. The key to a successful ecommerce strategy is being able to use all four in harmony so that you can drive the best results. 

Analytics.

Analytics helps answer the question “How can our business improve?” and can be used to predict future business performance. Business owners use analytics to identify trends and relationships to extract meaningful information from data to drive strategic actions — for example, analyzing the overall sentiment of customer reviews or calculating the return on investment (ROI) of a marketing campaign. 

Metrics. 

Metrics are specific, quantifiable measurements that assess the performance of specific business initiatives, such as an email campaign or marketing event. Ecommerce stores can use metrics to compare their performance against industry standards. For example, marketers can compare email open rates against the industry benchmark to determine if a campaign was effective or compare average order value before and after a promotion. Other examples of metrics include conversion rate, customer acquisition cost, bounce rate, cart abandonment rate, and website traffic. 

KPIs. 

Key Performance Indicators (KPIs) are used for the purpose of measuring the success of business objectives and reaching targeted goals. Typically, KPIs include a threshold that companies must meet or exceed, such as a sales quota or customer retention rate. Some examples of KPIs include customer lifetime value, sales conversion rate, and return on investment. 

Why is ecommerce analytics important for enterprise businesses? 

Running an ecommerce business without regularly consulting your data is like spearfishing blindfolded or throwing ideas at the wall, hoping one will stick. But with ecommerce analytics, enterprises can make better decisions, A/B test strategies, and gauge progress against industry benchmarks or historical data. 

Let’s look at a handful of ways that ecommerce analytics can benefit your online business.

Measure the effectiveness of marketing campaigns. 

Marketing analytics software can assist ecommerce companies in measuring how successful their marketing campaigns are, as well as improving decision-making, gaining more omnichannel traction, and informing holistic marketing efforts. You can track the performance of Google Ads, marketing automations, and email blasts, and you can even view real-time stats to quickly determine where you should be focusing your budget.   

Discover trends so you can forecast accurately. 

Ecommerce analytics also give you the power to better understand how your business is performing — both now and in the future. This forecasting will inform everything from hiring goals and sales goals to making sure that the right products are accessible at the right time so that your customers’ expectations are met. 

Optimize pricing and inventory performance. 

With ecommerce analytics, you’ll be able to benefit from a granular picture of what drives pricing for every customer segment. You can use this insight to discover the best price points at the product level, rather than category level, so you can earn optimal revenue.

Offer personalization at every touch point. 

Understanding how customers interact with your business is imperative to inform what sort of formats, content, and channels resonate with your target demographics. Using ecommerce data analytics, you can deliver custom experiences for each shopper through dynamic content, product recommendations, discounts and offers, and  more.

Enhance strategy with data-driven insights. 

One of the biggest benefits of ecommerce analytics is their ability to inform business strategies. In fact, organizations who leverage data-driven strategies have seen five to eight times as much ROI as those who don’t. 

These insights not only help in refining existing strategies but also in anticipating future market demands, ensuring that enterprises stay ahead of the competition.

5 key areas to utilize ecommerce analytics 

In this section, we’ll break down the most important ecommerce metrics into five crucial areas across your customer lifecycle, allowing you to gain a comprehensive view of your store’s performance. 

Discovery.

In order for your business to generate sales, your customers first need to be able to find your site. Brand recognition, search engine optimization (SEO), and paid advertising are all prime examples of how retailers can help boost visibility. Discovery metrics measure the number of customers who see your ads, visit your website, or react to your social media content. 

Impressions.

Impressions refer to the number of times an ad, product listing, or webpage has been displayed or shown to users. These are counted each time the content is viewed, regardless of whether users click or interact with it.

Reach.

Reach counts the total number of unique users exposed to a particular piece of content, advertisement, or message within a given timeframe. Unlike impressions (which count every instance the content is displayed, even if the same user sees it multiple times), reach measures unique views.

Engagement.

Engagement tallies the number of user interactions in response to an ad, social media post, or landing page. Engagement includes likes, comments, shares, clicks, downloads, time spent on a page, video views, form submissions, or any other action that indicates active interaction with the content.

Customer acquisition.

Another type of ecommerce analytics that can power your business forward is data relating to the acquisition of customers. This is highly valuable because you’ll learn about how your visitors found you online and how they ended up on your website to begin with. 

When using acquisition data, you’ll discover more about the sort of online marketing channels that are bringing the most visitors to your website. You’ll also learn what channels are driving the greatest sales or conversions. 

Cost per acquisition (CPA).

Cost per Acquisition (CPA) measures the cost of converting a lead into a paying customer. To calculate CPA, divide the total cost of a marketing campaign or initiative by the number of customers acquired or conversions generated. For instance, if a campaign costs $1,000 and results in 50 new customers, the CPA would be $20 per customer.

Click-through rate (CTR).

Click-through rate (CTR) indicates what proportion of the audience clicks a link after viewing your ad, social media post, or landing page. In other words, it tells you how many people responded to your CTA. 

CTR is given as a percentage and calculated using the following formula:

CTR= (Clicks÷Impressions) x 100

For instance, if an ad was shown 1,000 times and received 50 clicks, the CTR would be 5%. A high CTR suggests the audience finds the content engaging and relevant. 

Cost per lead (CPL).

CPL calculates the cost incurred for acquiring a lead — a potential customer who has shown interest in a product or service — through a specific marketing campaign or initiative. It measures the cost-effectiveness of lead generation efforts by dividing the total cost of the campaign by the number of leads generated.

CPL = Total marketing spend÷Total number of leads generated

For example, if a campaign costs $1,000 and generates 100 leads, the CPL would be $10 per lead.

Conversions.

When do online users convert into actual customers? How do online users convert into actual customers?

These are the two questions to look at when it comes to conversion analytics. Conversions measure the percentage of users who took a desired action in response to a campaign, ad, or landing page. This is a foundational element of ecommerce analytics, as it directly reflects your website’s success in turning visitors into customers or leads.

Average order value (AOV).

AOV refers to the average amount customers spend per transaction. Businesses use strategies like cross-selling, upselling, and personalized product recommendations to increase AOV. A high AOV increases customer lifetime value (the total amount a customer spends during the business relationship) while providing the business with consistent cash flow. 

AOV = Total revenue÷ Total orders 

For instance, if an online store generated $10,000 in revenue from 500 orders, the AOV would be $20.

Cart abandon rate.

Cart abandonment occurs when users add items to their shopping cart, only to skip the purchase. Unexpected costs at checkout, payment security concerns, or mandatory account creation can stifle would-be buyers from sealing the deal. 

The cart abandonment rate compares the number of abandoned transactions to the total number of transactions initiated. For example, if there were 500 initiated carts and 150 of them were abandoned before completing the purchase, the cart abandonment rate would be 30%.

Conversion Rate.

Conversion rate measures the proportion of users who take a desired action. For example, if you run ads to promote an event and link to a sign-up page, conversions would measure how many users saw the ad, clicked the link, and registered for the event. 

Other conversion goals include making a purchase, signing up for a newsletter, filling out a form, or downloading a resource. For instance, if a website had 500 visitors and 50 of them made a purchase, the conversion rate would be 10%.

Customer retention.

Retention determines the business’ ability to keep existing customers over a given time frame — typically monthly, quarterly, or yearly. Retention comes from repeat purchases, providing a source of recurring revenue without additional spending on new customer acquisition.

Customer lifetime value (CLV).

CLV measures the long-term profitability of the business and the extent to which each customer provides a source of recurring revenue. This metric helps businesses decide how much to spend on customer acquisition and segment the highest-value customers to target with exclusive promotions or loyalty programs. 

CLV = Average purchase value x average purchase frequency x Average lifespan 

For example, if the average purchase value is $50, the average purchase frequency is 4 times a year, and the estimated customer lifespan is 5 years, the CLV would be $1,000 ($50 x 4 x 5).

Retention rate.

Retention rate yields the total number of customers who continue to purchase from or engage with a business over a given period. Retention is a critical metric for subscription-based businesses that rely on customer loyalty to recoup their initial customer acquisition costs. 

Retention rate = (Number of customers at end of period - Number of new customers acquired) ÷ Number of customers at start of period x 100

Companies can improve retention rates by providing ongoing value and offering incentives for continued engagement, such as periodic upgrades or exclusive promotions. 

Churn rate.

Churn rate measures the rate at which customers discontinue their relationship with the business over a given timeframe. It represents the percentage of customers who stop using a service or cancel a subscription.  

Churn rate = (Lost customers ÷ total customers at the start of period) x 100

Advocacy.

Satisfied customers become your brand advocates — those who enthusiastically promote a brand or product through word of mouth (WOM), social media posts, or referrals. They may write reviews, share testimonials, or tell their friends and family. Brands can actively encourage advocacy through WOM triggers.

Net promoter score.

NPS is a customer satisfaction metric based on a single question: "On a scale of 0 to 10, how likely are you to recommend our product/service to a friend or colleague?" Customers are categorized into three groups: 

• Promoters (scoring 9-10)

• Passives (scoring 7-8)

• Detractors (scoring 0-6)

The NPS is calculated by subtracting the percentage of Detractors from the percentage of Promoters, resulting in a score between -100 and 100.

Key metrics for enterprise business success

As we’ve seen, there is a laundry list of types of metrics you can use to inform your ecommerce marketing strategy and make data-driven decisions. But if you’re just beginning your ecommerce analytics journey, these metrics might be a good place to start. 

Pages per visit. 

Pages per visit is an essential metric that measures the average number of pages a visitor views during a single session on a website. A higher pages-per-visit average often indicates greater user interest, exploration, and engagement with the website's content or offerings.

Returning visitors. 

Returning visitors counts the number of users who visit a website or platform more than once within a specific timeframe. Returning visitors are a valuable segment as they demonstrate continued interest, trust, and loyalty to the brand. 

Time on site. 

Time on site measures the average duration of a single session per user. Longer session duration indicates the user finds the website engaging and user-friendly and is more likely to purchase. A website bounce occurs when a user leaves a page within 10 seconds. Heatmaps reveal areas of the website where users tend to linger or click. 

Customer lifetime value. 

The customer lifetime value (CLV) measures how much your average customer will spend with your business over their lifetime:

CLV = customer value x average customer lifespan

For example, if a customer purchases $30 worth of product over 50 years, and your profit margin is 5%, then their CLV is $75.

Page load time. 

Page load time is a crucial website performance metric indicating how long it takes a single webpage to load. Fast-loading pages provide a superior user experience and increase the likelihood of visitors interacting with your site. According to Google, the probability of bounce increases 32% as page load time increases from 1-3 seconds. 

Customer acquisition cost. 

Customer Acquisition Cost (CAC) represents the total expense incurred in acquiring a single customer. It involves the sum of all marketing and sales costs, including advertising expenses, salaries of the sales team, marketing campaigns, and other associated costs, divided by the number of customers acquired within a specific timeframe. A lower CAC signifies the company is acquiring customers at a reasonable cost relative to the revenue they generate.

Predictive analytics. 

Predictive analytics involves using statistical algorithms, machine learning techniques, and data mining to analyze historical data and make predictions about future outcomes or shopping behaviors. This helps businesses forecast trends, identify potential risks or opportunities, and make data-driven decisions. 

How BigCommerce supports your ecommerce analytics

At BigCommerce, our Open SaaS ecommerce platform combines the best of SaaS and API-enabled openness and flexibility, giving you the enterprise integrations and tools you need to customize faster — and that includes ecommerce analytics tools.

With our Big Open Data Solutions, you have the robust tools needed to collect, analyze, and act on data across all aspects of your online store. By integrating seamlessly with leading data technology partners, BigCommerce gives you the freedom to assemble a tech stack that’s unique to your business, choosing from best-of-breed data warehouses, BI tools, and customer data platforms. 

In turn, you can easily connect and securely transfer your BigCommerce store data to any partner technology solution, allowing you to break down data silos and give you a unified view of your business operations and customer journeys — right from your BigCommerce dashboard. As a result, you’ll be able to gain actionable insights, create personalized shopping experiences, and make data-informed decisions that foster growth and drive your business forward.

Chat with one of our ecommerce experts today to find out how BigCommerce can help you ignite the power of your data.

The final word

Ecommerce success is the product of strategic decision-making, fine-tuning based on data insights, and a commitment to understanding the customer journey. With ecommerce analytics as the guiding light, businesses don't just react; they anticipate, innovate, and flourish in a landscape that rewards those who can decipher their data.

FAQs about ecommerce analytics