E-commerce companies have access to a wealth of data. Yet it remains difficult to see which campaigns, customers, products, and channels truly contribute to growth and margin. Online store behavior, orders, marketing costs, inventory, fulfillment, returns, service, and finance are often spread across different systems.
We bring this data together so you can better manage acquisition, conversion, customer value, assortment performance, availability, marketing returns, and profitability.
An online store or e-commerce organization wants to know more than how much revenue it has generated. You also want to see where that revenue comes from, which campaigns and channels are profitable, which customers return, and which products sell mainly because of discounts or high advertising spend.
Then comes the operational side. Is enough inventory available? Are orders processed and delivered on time? Which products frequently lead to returns or service requests? And what remains on the bottom line after purchasing, discounts, marketing costs, fulfillment, and returns?
These questions span multiple systems and departments. Marketing focuses on acquisition, campaigns, and conversion; e-commerce on customer behavior and funnel performance; operations on inventory and fulfillment; customer service on reasons for contact and resolution; and finance on revenue, margin, budget, and forecasts.
Without a shared data foundation, each department manages a different part of the customer journey. As a result, revenue can grow while margins lag, inventory is allocated incorrectly, or marketing budgets go to channels that mainly reach existing customers.
E-commerce data is spread across advertising platforms, analytics software, the online store, ERP, CRM, inventory management, fulfillment, customer service, and finance. When this data is not aligned in meaning, it remains unclear where growth comes from, where profitability is being lost, and which improvements have a real impact.
Campaigns are often evaluated based on clicks, conversions, and attributed revenue. But without purchasing costs, discounts, returns, fulfillment costs, and customer value, it remains unclear which campaigns actually contribute to the bottom line.
Online store analytics shows where visitors drop off, while order and customer data resides in other systems. This makes it difficult to connect pre-purchase behavior with products, customer segments, returns, and repeat purchases.
Popular products unexpectedly go out of stock, while other items remain in inventory for too long. Without a combined analysis of traffic, sales velocity, open orders, reservations, and expected inventory, action is often taken only after the problem has already become apparent.
A product may generate high revenue and conversion rates while also leading to many returns, complaints, or repeat customer contacts. If this data is not linked to orders and products, performance appears better than it actually is.
More orders do not automatically lead to better results. Discounts, advertising costs, payment and platform fees, shipping, returns processing, and customer service can significantly affect margins. Without full cost allocation, it is difficult to see which customers, products, and channels are truly profitable.
Effective e-commerce analysis does not start with a list of KPIs, but with the questions that different teams try to answer every day.
For a deeper look at campaign performance, attribution, and the link between media spend and commercial results, see also our approach to Media & Advertising.
The online store contains product views, shopping carts, checkouts, and orders. To manage performance effectively, this information must be connected to marketing platforms, customer data, inventory, fulfillment, returns, service, and finance.
It is important for the definitions used across these sources to align. An order in the online store is not always the same as a paid, shipped, or invoiced order. Revenue may be gross or net, returns may be processed later, and the same customer may return through multiple devices, online stores, or channels.
Sessions, traffic sources, landing pages, product views, searches, shopping carts, checkout steps, and conversions form the basis for funnel analysis. Connecting this information to order lines, payment statuses, cancellations, discounts, vouchers, and customer data provides a more complete view of what visitors ultimately order and which customers return.
Data from advertising platforms, email marketing, affiliate channels, and other campaigns makes it possible to compare reach, clicks, costs, and attributed revenue. Linking this data to orders, returns, and margin provides insight beyond ROAS or revenue alone.
Product categories, brands, variants, prices, costs, promotions, and product attributes form the basis for assortment analysis. This makes it possible to analyze revenue, conversion, returns, and margin down to the product or SKU level.
Available inventory, reservations, open purchase orders, warehouse statuses, shipping times, and delivery information determine whether the customer promise can be met. Combining inventory with sales velocity makes potential shortages and excess inventory visible sooner.
Reasons for returns, refunds, service requests, conversations, and customer interactions provide insight into what happens after an order is placed. When linked to the product, order, supplier, or carrier, this data helps identify systemic causes.
General ledger data, purchasing costs, payment and platform fees, fulfillment costs, budgets and forecasts show the financial returns generated by revenue growth. This connects commercial and operational performance to the financial reality.
Bringing together online store behavior, marketing, orders, customers, inventory, fulfillment, returns, service and finance creates a single foundation for commercial, operational and financial decision-making.
Connecting campaign costs to orders, new and returning customers, returns and margins reveals which channels truly contribute to growth. This allows you to reallocate budget based on returns rather than just clicks, conversions or attributed revenue.
Analyzing landing pages, search behavior, product views, shopping carts and checkout steps shows where visitors drop off. Combining this with order value, customer segments and product characteristics allows you to focus improvements on the areas with the greatest commercial impact.
New customers are not automatically valuable customers. Tracking purchase frequency, repeat purchases, average order value, return behavior and margins over time reveals which customers and initial purchases lead to sustainable revenue.
Sales velocity, product views, outstanding orders, available inventory and expected deliveries together provide a clearer picture of demand. This helps you identify potential shortages earlier, limit excess inventory and make better-informed product range decisions.
For organizations that combine online store data with stores, locations and in-store inventory, the same challenge applies more broadly within retail.
Connecting order statuses, shipping times, delivery performance, reasons for returns and service requests reveals recurring problems. You can then investigate which products, suppliers, carriers or processes lead to delays, complaints and additional costs.
When the focus is on inventory flows, warehouse processes, transportation and delivery reliability, this directly relates to supply chain & logistics.
Revenue and ROAS do not show what ultimately remains. By accounting for purchasing, discounts, marketing, payments, platform fees, fulfillment, returns and service, you can assess the net contribution of each product, customer, campaign and sales channel.
These insights can be translated into daily dashboards, weekly trading reports, management reports, budget monitoring and forecasts. This enables marketing, e-commerce, operations, customer service, finance and management to work from the same definitions and figures.
We’ll discuss the challenges you currently face, the data available and the biggest opportunities to improve decision-making around conversion, customer value, inventory, fulfillment and margins.
For Topgeschenken Nederland, we developed a Data & Analytics environment for multiple consumer and business brands, including Topgeschenken.nl, Topbloemen.nl, Toptaarten.nl, and Topfruit.nl. This environment brings together sales, order, inventory, marketing, service, and financial data.
The organization serves both consumers and business customers. Seasonality, peak days, delivery dates, and availability all play a major role. With Qlik Cloud Analytics and Inphinity, we support operations and management with daily performance management, planning, and demand forecasting.
Management and operational teams track revenue, orders, purchases, average order value, and margins by week, product group, brand, and customer segment. From these overviews, they can drill down into individual orders, products, customers, partners, order statuses, and delivery locations.
This reveals not only how total revenue is developing, but also which products, brands, and customer groups are behind a deviation and where action is needed.
Planned inventory is compared with actual and expected sales. For each product and SKU, teams can see how much inventory is available, how much has already been sold, and when the safety stock level will be reached.
This helps identify potential shortages earlier, especially around holidays and other peak periods. Checks on product and inventory data also reveal where items from different sources do not match correctly.
Marketing performance is tracked in terms of revenue, targets, vouchers, orders, and repeat purchases. This makes it possible to examine which promotions are actually used, which customers return, and how revenue and order value develop by brand, product group, and customer type.
This takes the analysis beyond campaign reach or discount use alone. Marketing can connect results to orders actually placed, customer growth, and revenue trends.
Revenue, discounts, purchase prices, and product quantities are combined to track margin trends by week, month, product, and product category. Distinctions can be made between B2B and B2C and between different types of products and costs.
This reveals which categories generate significant revenue but contribute less to the bottom line, and where discounts or purchasing costs put pressure on margins.
Data from Robin HQ is linked to conversations, service requests, orders, products, brands, and employees. This makes it possible to track contact volumes, First Time Right, open requests, and service rates, among other metrics.
By comparing service data with sales and order data, teams can see which products, partners, or processes generate relatively high levels of customer contact and where structural improvements are possible.
With Inphinity, forecasts and planning data can be entered and adjusted from within the analytics environment. This input is immediately compared with actual orders, revenue, and inventory.
This means management and operations do not work with separate spreadsheets alongside their reporting, but instead manage planning and actual results using the same data foundation.
E-commerce touches almost every department. Marketing, e-commerce, operations, customer service, finance, and management each view the same visitors, customers, orders, products, inventory, and costs from a different perspective. We bring this information together and ensure that teams can manage performance using the same definitions.
We start with the questions that need better answers. We then map the processes, definitions and data sources and determine what data is needed to connect marketing, online store behavior, orders, customers, inventory, fulfillment, returns, service and costs.
If the question extends beyond a single application, we can help with strategic advice, data strategy, AI Readiness or an independent BI tool selection.
We connect data from online stores, advertising platforms, CRM, ERP, payment providers, warehouse and fulfillment systems, customer service and financial administration.
We account for differences in customer identification, order statuses, payment timing, returns processing and revenue definitions. This allows behavior before an order to be linked to the final order, delivery, return, service and margin.
On that basis, we develop dashboards, analyses and reports for areas including:
Where needed, we add budgeting, forecasting, planning, controls and user input. This enables day-to-day operations, commercial management and financial reporting to work from the same data foundation.
Depending on the existing environment, we work with Qlik, Power BI, Microsoft Fabric, TimeXtender or a combination of these. For storage and data processing, we can also integrate with existing platforms such as Snowflake, Databricks and Google BigQuery.
If you already have an analytics environment, we build on it where that makes sense. We can add sources, improve models and dashboards, harmonize definitions or support a migration. If you have doubts about the current approach, a Second Opinion Data & Analytics can be a good first step.
After delivery, we can support and manage the environment, so that new online stores, countries, sales channels, campaigns and data sources can be added in a controlled manner.
We start with the decisions that need to be better informed. These may concern marketing ROI, conversion, customer value, inventory, fulfillment, returns, service, or net margin.
We then determine which definitions, processes, and data sources are needed to answer those questions reliably. This prevents a broad data platform from being built before there is a clear use case.
We can connect data from online stores, advertising platforms, web analytics, CRM, ERP, payment providers, inventory management, warehouse and fulfillment systems, carriers, customer service, and financial accounting.
The exact approach depends on the available APIs, databases, exports, historical data, and access rights. We also assess whether the data aligns sufficiently in terms of content, for example by customer, order, product, campaign, and transaction.
No. Differences between systems, missing definitions, and manual files are often precisely what prompts an engagement.
We identify what is usable, where the greatest risks lie, and which improvements are necessary. Not every data issue needs to be resolved beforehand, but critical differences must be made visible and manageable.
No. The size of the online store matters less than the complexity of managing the business. A mid-sized e-commerce organization may also operate multiple online stores, advertising channels, fulfillment partners, countries, and data sources.
An engagement should, however, be proportionate to the organization. Sometimes a targeted solution for a few important questions makes more sense than immediately implementing a broad data platform.
This is possible when the available data provides sufficient points of connection. These may include customer accounts, order numbers, session or campaign data, and other identifiers.
We account for anonymous visitors, multiple devices, cookie consent, different attribution models, and the distinction between an order being placed, paid, shipped, and invoiced. It is not always possible to link every visitor reliably to an order or customer.
Together, we define what terms such as revenue, order, new customer, return, margin, and marketing costs mean. We also determine which system is the source of truth for each data point and when a transaction is included.
This allows marketing to continue making quick operational decisions while also reconciling results with controlled financial definitions. Differences between attribution and actual financial results remain visible and explainable.
That depends on how customers are identified across different online stores, devices, and sales channels. We first determine which data is available and which links can be established responsibly.
We can then analyze metrics including purchase frequency, repeat purchases, average order value, return behavior, acquisition costs, and realized margin over time. We make all assumptions and limitations explicit.
Yes. We can connect inventory positions, order statuses, warehouse processing, shipping times, delivery performance, reasons for returns, and service requests.
This reveals which products, suppliers, carriers, or processes are relatively likely to cause delays, returns, customer inquiries, and additional costs. The possibilities depend on the matching keys available across the systems.
Not always. It depends on the number of sources, the historical data required, the refresh frequency, and the complexity of the analyses.
For a limited use case, a lighter setup may be sufficient. When many systems need to be combined on an ongoing basis, definitions need to be managed centrally, and multiple teams use the same data, a data warehouse or broader data platform usually becomes more relevant.
Yes. We first map out the existing data sources, models, dashboards, definitions, and management processes.
We then determine what can remain, what needs improvement, and which components would be better replaced. This can range from adding a few sources to restructuring a data model or supporting a migration.
If you first want an independent assessment of whether your current approach, architecture, or choice of vendor makes sense, you can request a Second Opinion Data & Analytics.
When the real problem is primarily a poorly designed operational process, missing records, or a system that captures hardly any usable data. A dashboard will not solve that on its own.
It also makes little sense to start building right away when there is no clear owner, priority, or decision to be supported. In such situations, we prefer to start with an analysis, data strategy, or a focused improvement plan.
We need subject-matter input from employees who understand the processes and figures, such as people in marketing, e-commerce, operations, customer service, and finance.
They help establish definitions, validate results, and set priorities. A small group of subject-matter owners and decision-makers is usually more effective than a large project team.
Want to better understand which customers, campaigns, products, and processes truly contribute to growth and margins? Barry and Eric would be happy to discuss your current environment, information needs, and a logical next step. In half an hour, you will know whether we are a good fit. Send us an email, call us, or schedule a call right away.