Advanced eCommerce analytics – which data really shows your store’s profitability?

eCommerce analytics – conversion rate alone is not enough

Conversion rate shows what percentage of users visiting your store complete a purchase. It is an important metric, but when analyzed without a broader context, it can lead to oversimplified conclusions.

A much more interesting question is: how much real value does the traffic coming to your store generate?

You may have a million users visiting your website every month, but if most of them are not interested in your product range, high traffic alone has little business value. What is more, acquiring that traffic costs money, while handling larger volumes of visitors can also increase the infrastructure requirements of your store.

On the other hand, a smaller but highly relevant audience can generate significant sales.

That is why you should analyze not only the number of visitors, but above all traffic quality and how effectively it translates into sales.

Data segmentation in eCommerce analytics

One of the most important elements of advanced analytics is segmentation. The more precisely you divide your data, the easier it becomes to identify which areas of the business are actually generating value.

Data can be analyzed by, among other things:

• country,
• product group,
• traffic source,
• customer,
• sales channel,
• individual transaction.

For example, you may discover that out of a thousand products, only 20% generate the majority of your sales. In that case, these products may deserve particular attention in your marketing activities.

Similarly, it is worth analyzing sales from Google Ads, social media, organic traffic, and other acquisition sources separately.

The deeper the segmentation, the lower the risk of making decisions based on averages that hide important differences.

Customer Lifetime Value – how much is a customer really worth?

A single order does not always reflect the true value of a customer.

Let us assume that acquiring a customer costs PLN 70 and their first order is worth PLN 100. At first glance, the relationship between acquisition cost and sales value may not look particularly attractive.

However, if the same customer returns during the year and spends another PLN 300, their total value increases to PLN 400.

That is why it is worth analyzing Customer Lifetime Value, which shows the value a customer generates over a given period of time.

This is particularly important for businesses where customers make regular purchases. An acquisition campaign may look unattractive after the first transaction, but turn out to be highly profitable if a significant percentage of acquired customers return to make further purchases.

ROAS and sales profitability – why is it not enough?

ROAS shows the relationship between advertising spend and the revenue generated. The problem is that it does not take into account many of the costs associated with fulfilling a particular sale.

Products can have different margins, and their logistics costs can also vary significantly.

Packing a small parcel requires a different amount of work than preparing a large pallet order. In the latter case, additional costs may include:

• pallets,
• protective packaging materials,
• employee time,
• equipment usage,
• forklift operation,
• transportation.

That is why profitability analysis should take into account not only advertising costs and sales value, but also product margins and the actual costs of fulfilling an order.

The most detailed analytical model can go all the way down to the level of an individual transaction and show how much the company actually earned on that particular order.

How to calculate the minimum profitable order value

This is one of the metrics online stores often overlook.

If processing and fulfilling an order costs the company a certain amount, it is possible to calculate the minimum order value at which the transaction becomes profitable.

For example, if the costs associated with fulfilling an order amount to PLN 50 and the margin means that the store only starts making a profit once the order value reaches PLN 200, processing orders worth PLN 150 may result in a real loss.

To calculate this properly, you need to combine:

• customer acquisition cost,
• product margins,
• logistics costs,
• operating costs,
• order fulfillment costs,
• potential returns.

Only then can you determine the actual profitability of your sales.

How to analyze promotion profitability in an online store

Reducing a price is not a strategy in itself. Every promotion should have a specific business objective.

For example, it may be designed to:

• clear excess inventory,
• sell products with a limited shelf life,
• increase average order value,
• drive sales of complementary products.

That is why the effectiveness of a promotion should not be evaluated solely by the number of discounted products sold.

If a discounted product encourages a customer to add other items to their cart, the primary effect of the promotion may actually be an increase in the total order value.

How to combine data from your store, advertising, and ERP

There are advanced Business Intelligence systems available on the market, but for small and medium-sized online stores, their cost may be disproportionate to the scale of the business.

An alternative is to build a custom analytics model that collects information from several different sources.

A single database can include, for example:

• store transactions,
• customer acquisition sources,
• campaign costs,
• actual sales data,
• invoice data,
• margins,
• returns,
• logistics costs.

Data connectors can play a key role here, allowing information to be retrieved from different systems and combined within a single analytical model.

However, there is no universal solution that works for every eCommerce business. The analytics model should reflect the company’s cost structure, product range, and business model.

How to use sales data for inventory planning

Sales analytics does not have to stop at evaluating what has already happened in your store.

Companies that regularly replenish inventory should also analyze the seasonality of individual product groups.

Garden products, for example, will follow a very different sales pattern from Christmas decorations.

Historical sales data can therefore support decisions about when to order the next batch of products and how much stock to purchase.

Summary

Advanced eCommerce analytics is not about adding more charts to a dashboard. Its purpose is to answer a much more important question:

where does your store actually make money, and where does it simply generate revenue?

Only by combining information about sales, acquisition costs, margins, logistics, returns, traffic sources, and customer value can you see the true financial health of the business.

The most important point is that such an analytics model should be designed individually. A B2B wholesaler will need to focus on different data than a retail store, while a business operating across several markets may require yet another analytical approach.