Introduction
Modern e-commerce has access to powerful analytical tools – from Google Analytics 4, through Google Ads, Meta Ads, to data from sales platforms such as Shopify, WooCommerce, or PrestaShop. In the e commerce industry, e-commerce analytics is essential for optimizing business operations, as it relies on collecting data from various sources and ensuring data quality to support accurate and efficient processes. E-commerce analytics plays a key role in modern e-commerce business, supporting marketing strategies, optimizing marketing campaigns, and enabling decision-making based on reliable data. These are excellent sources of information about the health of your business.
These analytics tools help businesses leverage ecommerce data and raw data for data driven decision making, allowing for more strategic and informed actions across all areas of the business.
The problem is that most stores stop at a basic level of analysis: revenue, number of transactions, conversions, campaign costs. Based on this, we calculate ROI or ROAS and… often that’s all. These are valuable data but provide only a general picture of the situation. However, e-commerce analytics allows obtaining precise data that enables effective business decisions and better marketing budget management.
If you want to actually increase cart value and improve the profitability of your activities, you need to dig deeper. It is worth focusing on marketing strategies and optimizing activities in the context of conducted marketing campaigns to more effectively analyze performance and increase ROI. Below you will find 5 key areas worth analyzing to make decisions that translate into profit – not just reports.
E-commerce analytics includes four analytical frameworks: Descriptive, Diagnostic, Predictive, and Prescriptive.
Customer Lifetime Value (LTV) vs. Acquisition Cost – The long-term game
Most advertising campaigns are settled based on short-term effects: “did the customer pay off within a week?”. This is a mistake that can cost you hundreds of thousands of zlotys in lost potential. It is worth including customer acquisition cost (CAC) here – a metric that defines how much you spend on marketing and sales activities, including ad spend, to acquire a new customer. Analyzing this cost allows better planning of new customer acquisition strategies and assessing which activities are most effective. LTV (Customer Lifetime Value) refers to the total amount of money the average customer spends with a business over their lifetime, while CAC is the average amount a business spends to acquire a new customer.
Not every customer needs to “pay off” immediately. Often the greatest value comes from customers who shop cyclically – after weeks or months. Therefore, it is worth analyzing LTV (Customer Lifetime Value) and comparing it with CAC (Cost of Acquisition) not only over 7 days but for example 30, 90, or 180 days. Customer Lifetime Value (CLV) is the total revenue generated by a customer during the entire cooperation period. Understanding the average customer lifespan is crucial for accurate financial analysis and for projecting long-term profitability and cash flow considerations. A healthy LTV to CAC ratio is generally considered to be around 3:1, and if the ratio is less than 1, it indicates the business is spending more on acquiring customers than it earns from them.
Technically, you can do this by segmenting customers by acquisition source (campaign channel, campaign, ad group) and tracking their behavior over time. This allows discovering which channels attract loyal customers and which generate only one-time traffic. By analyzing costs related to customer acquisition, including operational, logistics, marketing expenses, and especially ad spend, you can better assess the profitability of individual channels and optimize the budget for long-term profit. A significantly positive LTV to CAC ratio indicates that a business is on a sustainable path to profitability, but a high ratio may also indicate missed opportunities for growth if the business is not reinvesting in customer acquisition. Improving conversion rates can help reduce CAC by ensuring that more visitors become paying customers. Loyalty programs can increase customer lifetime value by encouraging repeat purchases.
Revenue vs. margin – ROAS 2.0
Standard ROAS shows how much revenue a given campaign generated relative to cost. But is that enough? Not necessarily – because if you promote a low-margin product, you may have a great ROAS but still break even (or worse). Therefore, in e-commerce analytics, it is crucial to track the most important indicators as well as those worth monitoring in the context of marketing investments to effectively assess performance and optimize marketing investment. E-commerce analytics also helps businesses refine their marketing strategies by tracking the effectiveness of marketing campaigns in real time.
Therefore, it is worth calculating profitability ROAS – an indicator that, instead of revenue, considers net profit (revenue – cost of acquisition/production of the product). Pricing strategies play a significant role here, as they directly influence both margin and ROAS, making them a key consideration in e-commerce analytics. Measuring ROI allows quick identification of effective marketing activities and optimization of the marketing budget, which translates into better management of marketing investments.
In practice, this means the need to pull margin data from the ERP system or sales platform and combine it with campaign data (e.g., from Google Ads). This allows discovering which campaigns are truly profitable – and which only generate “nice numbers”. In summary, pay special attention to cost analysis and marketing investment effectiveness to make accurate business decisions.
Products that attract vs. products that convert
Key performance indicators (KPIs) and sales data are essential for evaluating product performance in e commerce analytics. Not every product that attracts users to the page actually sells. Sometimes a highly clicked lead product generates only traffic, but conversions happen on completely different items. Analyzing viewed products on the product page allows understanding which offers truly engage customers and lead to desired actions such as adding to cart or purchase. Interpreting data related to product views and sales helps in understanding customer behavior and optimizing strategies accordingly. Thanks to this, you can better personalize marketing campaigns and optimize content marketing in e-commerce stores.
Therefore, it is worth analyzing: which products or categories generate the most traffic, how this translates into conversion, what is the average order value (AOV), and what return on investment they provide (ROAS). Average order value (AOV) is a key metric that measures the average amount spent by customers per transaction. To calculate AOV, simply divide total revenue by the number of orders. Tracking AOV helps you understand how much customers typically spend per transaction. Raising your average order value doesn't require big changes; simple tweaks can nudge customers to spend more. Customer expectations around shipping costs are a major consideration when it comes to increasing average order value. A good average order value depends on your industry and products. Based on this data, you can adjust your promotional strategy by investing budget in products that not only attract attention but also really earn. It is also worth monitoring how many users added products to the cart but did not complete the purchase to better understand at which stage of the purchase process barriers appear.
Additionally, by analyzing promotion data, you can assess whether discounts actually translate into sales increase – or just spoil margins and teach customers to wait for deals. Purchase process analysis also allows identifying moments when it is worth offering customers additional products or special offers, increasing the chance of higher cart value. Thanks to e-commerce analytics, you can understand why customers abandon carts and at which point of the purchase process they stop.
Upsell, cross-sell, and customer behavior purchase path analysis
Increasing average order value often happens through classic upselling (encouraging higher-priced products) and cross-selling (adding complementary products). Upselling and cross-selling are effective methods to increase average order value in your store. Product bundling can also enhance perceived value, making offers more attractive and encouraging larger purchases. But how to check if it works?
You can measure upsell/cross-sell conversion, e.g., by analyzing the user’s purchase sequence: what was initially in the cart vs. what was finally bought. By tracking data on product combinations, you can identify those most often bought together – and automatically suggest them in the store or campaigns. Additionally, you can implement a loyalty program as a strategy to increase customer loyalty and increase customer lifetime value. Loyalty programs can increase customer lifetime value by encouraging repeat purchases and fostering long-term engagement.
Moreover, you can segment users by first cart value and check which groups are receptive to suggestions. These are valuable insights that translate into real AOV growth.
Finally, consider time-limited offers that create a sense of urgency among buyers and can significantly increase sales in your store.
Abandoned carts in online store – understand before you recover
Not every abandoned cart is a lost opportunity – but each carries some information. By analyzing abandoned carts, you can check:
- which products are most often left behind,
- what was the value of abandoned carts,
- whether users were close to the free shipping threshold,
- whether there were promotional products in the cart,
- whether customers react to fast delivery as a factor influencing purchase decisions.
Maintaining appropriate inventory levels is crucial to prevent stockouts that can contribute to cart abandonment. The average shopping cart abandonment rate is roughly 69.8%.
Thanks to this data, you can tailor your cart recovery strategy – e.g., dynamically suggest products to increase cart value above the free shipping threshold or offer a discount only to those who left a cart above a certain value. Optimizing the checkout process can also help recover lost online sales.
Remember that free shipping may not be profitable for all e-commerce store owners, but it often effectively encourages customers to buy more.
How to do all this?
To measure all these indicators reliably, it is worth investing in data integration in one place, i.e., in a data warehouse (e.g., Google BigQuery). E-commerce ETL processes allow businesses to collect data from multiple sources, transform it into a usable format, and load it into a centralized database for analysis. However, e-commerce businesses often face challenges in creating in-house data pipelines, including high development costs and the complexity of integrating various systems, as integrating data from multiple sources into a unified pipeline requires complex coding and configuration, which can be time-consuming and error-prone. Thanks to this, you create a single source of truth, where data flows from:
- GA4 (user behavior, integration with other systems such as advertising or analytics systems, and tracking website traffic and user demographics),
- advertising campaigns (Google Ads, Meta, etc., including analysis of marketing channels and campaign effectiveness),
- internal systems (e.g., margins, prices, stock levels, and customer demographics).
Based on this, we create transformations and reports in BI tools (e.g., Looker Studio, Power BI) that provide you with a complete picture of what really works.
Integrating e-commerce systems allows for process automation, which reduces errors in communication and order fulfillment. By integrating various sales channels in one place, information about products, prices, availability, orders, and payments can be updated in real time across all platforms. Integration with sales platforms enables reaching a wider audience, increasing the chances of higher sales and acquiring new customers. E-commerce integration facilitates international expansion by providing easy access to new, foreign consumers and allows business scalability, enabling you to reach thousands of new consumers. Thanks to e-commerce integration, companies can better manage their operations, automate processes, increase efficiency and effectiveness of activities, and save time and money.
In e-commerce analytics, it is worth using tools for monitoring competition, such as Senuto and SEMstorm, which are used for keyword analysis and search engine visibility. Google Search Console monitors search visibility and indexing errors, while Google Analytics (GA4) analyzes traffic, user demographics, and the effectiveness of marketing campaigns. Tools like Hotjar and Crazy Egg visualize user behavior through heatmaps and session recordings. Brand24 monitors mentions of the brand and products on the internet and social media. For visualizing complex data and key indicators, it is worth using Tableau and Microsoft Power BI, which enable the creation of advanced dashboards.
Summary
Want to increase average cart value? Stop acting “by feel” – start analyzing data wisely, focusing on optimization and decision-making based on the most important indicators in your e-commerce store. Leveraging e-commerce analytics to generate actionable insights is crucial for informing business decisions and driving continuous improvements. It is not a matter of another promotional pop-up but conscious management of the offer, marketing strategies, campaigns, and user experience.
If you want to learn how data can work for your organization’s success – contact us.
