Today’s e-commerce is an ocean of information, in which every customer interaction – from click to search to final purchase – generates valuable data. In this digital ecosystem, the term ‘Big Data’ has ceased to be just a fashionable buzzword and has become a fundamental pillar of business strategy. Success no longer depends on the ability to collect data, but on the strategic ability to interpret it using key metrics. These are the ones that enable deep business optimisation, from customer segmentation to maximising return on investment.
The financial backbone of e-commerce
Before delving into complex behavioural analysis, it is crucial to understand the financial backbone of any e-commerce business. Three interrelated metrics form an early warning system and navigational compass that indicates a shop’s financial health and growth potential.
The first is Customer Lifetime Value (CLV), which measures the total revenue a company can expect to receive from the average customer over the course of their entire relationship with the brand. Its importance lies in the shift in perspective from single transactions to long-term profitability. Understanding CLV allows informed decisions to be made about marketing budgets and identifying the most profitable segments.
In opposition to CLV stands the Customer Acquisition Cost (CAC), i.e. the total cost incurred on sales and marketing activities to acquire one new buyer. For this indicator to be meaningful, its calculation must take into account all related expenses, from the cost of advertising campaigns to the salaries of the marketing team.
The true analytical power of these two metrics is revealed when they are analysed together. The ratio of CLV to CAC is one of the most important indicators of business health. It is widely accepted that a healthy business model should have a ratio of at least 3:1 – meaning that each customer acquired generates three times as much revenue as the cost of acquiring that customer.
The third pillar is Average Order Value (AOV), which determines how much money the average customer spends during a single transaction. Increasing AOV is one of the fastest ways to increase revenue without increasing website traffic. This can be achieved through strategies such as cross-selling, product bundling or setting free delivery thresholds. These three metrics create a dynamic feedback loop. Higher CLV justifies higher CAC, which in turn allows for more aggressive campaigns, opening the way to more expensive but potentially more valuable marketing channels.
Voice of the customer in the data
While financial metrics describe the bottom line, behavioural metrics explain why this result was achieved. They are the voice of the customer expressed in the data, being a direct reflection of their satisfaction and experience.
The conversion rate (CR) is the percentage of users who performed the desired action – most often making a purchase. It is a fundamental performance measure for any online shop. Its interpretation requires context, so it is crucial to compare it to industry benchmarks, which range from 1.6 per cent in fashion to as high as 6.8 per cent in the health and beauty industry.
Its natural complement is the Cart Abandonment Ratio (CAR), i.e. the percentage of shoppers who add products to their basket but leave the site without completing the transaction. Its average value is alarmingly high at around 70%. A high CAR is a clear indication of the existence of barriers to the shopping process, such as hidden costs, the requirement to create an account or the complicated process of finalising an order.
A negative trend in behavioural metrics is an early warning sign of a future decline in financial indicators. An increase in CAR directly causes a decrease in CR, which lowers total revenue and CLV. At the same time, with constant marketing expenditure, the CLV to CAC ratio deteriorates. Thus, a minor flaw in the user experience can trigger a cascade of negative financial impacts.
Map to the treasure: Analysis of purchase paths
Individual metrics provide only a fragmented picture of reality. To fully understand how and why customers make decisions, it is necessary to put these metrics into a broader context, which is the customer’s purchase path (Customer Journey). Analysing this journey involves deconstructing it into key stages – from awareness, to consideration, to conversion, to service and building loyalty – to understand the needs and barriers at each stage.
The aim is to identify friction points, i.e. places where customers encounter difficulties, and ‘moments of truth’ – key interactions that disproportionately influence the final decision. Modern analytics tools, such as Google Analytics 4, make it possible to visualise these journeys and track where users are most likely to leave the site.
The purchase path is not a metric in itself, but a narrative framework that answers the “where?” and “why?” questions behind the numbers. An e-commerce manager, seeing a low conversion rate, sees only the effect. Path mapping allows him to identify where in the process the biggest drop-off is occurring, and further analysis can reveal why this is happening. In this way, path analysis links an abstract number to a concrete, fixable problem.
A data-driven future
Analysis of key Big Data metrics in e-commerce leads to one conclusion: these metrics are not isolated numbers, but elements of a complex, interconnected system. Success is not about optimising a single KPI in a vacuum, but understanding how improvements in one area trigger a cascade of positive effects across the ecosystem.
Looking to the future, the role of data will only grow, driven by advances in artificial intelligence and predictive analytics. These technologies will shift the paradigm from reactive analysis (“what happened?”) to proactive prediction (“what will happen?”). Those who not only learn to listen to the voice of their customers in the data, but who can predict their next words, will win. The future of e-commerce is not about having the most data, but about asking them the smartest questions.
