Bring sales, stores, products, customers, costs, reviews, and sustainability data together. This creates a unified view of commercial performance, operations, and results across channels, locations, and countries. It enables retail, operations, finance, and management teams to better compare and explain performance and make adjustments based on revenue, profitability, quality, and development.
Retail brings together sales, product ranges, stores, online channels, inventory, costs, and customer behavior. Rising revenue may seem positive, but it means little when discounts increase, operating costs rise faster, visitors are less likely to make a purchase, or differences between stores and countries cannot be properly explained.
Store chains, omnichannel retailers, franchise organizations, e-commerce companies, and international retail organizations each have different priorities. But their questions overlap. Which stores, countries, channels, and products are performing well? Where is real profitability generated? What costs are incurred to serve customers and markets? And where does execution deviate from the agreements in place?
The necessary data is often scattered across point-of-sale systems, ERP, finance, CRM, e-commerce, product management, reviews, visitor tracking, sustainability reporting, and Excel. Bringing this data together creates a unified view of commercial performance, costs, operations, quality, and development across stores, countries, and channels.
Retail organizations usually have a wealth of data, but that data does not automatically connect. Sales, visitors, products, costs, reviews, and sustainability data are often recorded in different systems and at different levels. As a result, reporting remains fragmented and differences are mainly explained after the fact.
Higher sales do not automatically lead to better results. Discounts, returns, logistics costs, staffing, local exceptions, and differences in product mix can mean that revenue growth contributes little to the margin.
Differences in store type, product range, currency, opening hours, market size, visitor numbers, and local conditions affect the outcome. Without consistent definitions and comparable benchmarks, you get a ranking, but not a meaningful explanation.
High visitor numbers may seem positive, but they only become meaningful when combined with conversion, average spend, product mix, and repeat visits. When traffic and sales data come from different sources, it remains unclear where store or channel performance is actually changing.
Sales, orders, and costs are often recorded at different levels of aggregation. This makes it difficult to determine the cost of serving a country, store, product group, order flow, or customer segment—and which areas truly contribute to the result.
Audits, store reviews, quality checks, and self-assessments are conducted periodically, but the results are scattered across years, locations, and topics. This makes it difficult to track recurring issues, differences between locations, and follow-up on improvement actions.
Data on energy, emissions, water, waste, and other sustainability topics often comes from multiple systems and manually maintained files. Different definitions, measurement periods, and missing records make comparisons between locations and years time-consuming.
Effective retail analysis does not start with a single standard dashboard. The relevant questions depend on the retail format, channels, and organizational structure, but usually cover the same topics: commercial performance, customer behavior, execution, costs, quality, and sustainability.
An ERP, POS system, or e-commerce platform contains a great deal of relevant data, but rarely the complete picture. Sales may take place across multiple channels, product information is managed elsewhere, visitor data comes from separate sources, and costs are recorded by finance at a different level. Reviews, audits, and sustainability data are often even more disconnected.
For reliable management, stores, countries, channels, products, product groups, periods, customers, visits, orders, and cost structures must be aligned. Currencies, fiscal years, organizational structures, and historical changes must also be handled consistently.
The sources required vary by retailer. In omnichannel retail, online and in-store sales operate side by side. For organizations focused on online sales and conversion, our approach to E-commerce is also relevant. International retailers need to pay close attention to countries, currencies, local organizations, and comparability. For organizations with many locations, reviews, audits, consumption, and sustainability data are often part of the picture as well.
Transactions, order lines, store visits, online sessions, customers, returns, discounts, and payment methods show how demand and sales are developing. Combining traffic and sales reveals conversion, average spend, and differences between stores and channels.
Locations, store types, regions, countries, sales organizations, and channels determine the level at which performance can be compared. Historical changes, openings, closures, and reorganizations must be handled correctly.
Products, categories, collections, product groups, prices, promotions, and assortments explain where sales and margin come from. Harmonizing product structures across systems and periods keeps analyses comparable even when changes occur.
For retail organizations with seasonal collections, sizes, colors, and high return volumes, this closely relates to our experience in fashion.
Sales, orders, personnel costs, logistics, handling, payment flows, returns, marketing, and other costs determine what a market, location, or order flow actually delivers. Connecting this data provides insight into cost to serve and profitability.
When inventory flows, fulfillment, delivery reliability, and operating costs predominate, there is clear overlap with supply chain & logistics.
Reviews, questions, scores, checks, audits, and improvement actions show how locations and processes are operating. Bringing together results across years, topics, and organizational units makes patterns and recurring deviations easier to identify sooner.
Energy, water, waste, emissions, refrigerants, and location characteristics form the basis for sustainability reporting. Combining them across stores, countries, and periods provides a comparable overview of developments, deviations, and data quality.
When sales, visitor, product, cost, review, and sustainability data come together, they provide a single foundation for daily management, periodic reviews, and longer-term decisions.
Combine revenue, volumes, product mix, store type, country, currency, and historical periods. This reveals which differences are structural, which can be explained by local circumstances, and where further investigation is needed.
Bring together traffic, transactions, conversion, average spend, and sales trends. This reveals whether growth is driven by more visitors, higher conversion, a different product mix, or higher spend per customer.
Connect revenue and order volume with operating costs, logistics flows, staffing, returns, and financial results. This reveals which countries, locations, product groups, or order flows contribute to results and where costs are growing faster than revenue.
Bring together assessments, questions, scores, and actions across stores, regions, and periods. This provides insight into recurring deviations, differences in execution, and outstanding improvement actions without relying on separate exports.
Combine consumption, emissions, waste, water, and other sustainability data with locations and calendar periods. This creates a consistent overview of performance, trends, deviations, and missing records.
By combining central definitions with access controls and appropriate reporting, local teams, subject-matter owners, and management can each view the same data from their own perspective.
These insights can be translated into operational dashboards, management reports, mobile reports, periodic reviews, data quality checks, forecasts, and alerts. The right format depends on the user, the process, and how current the information needs to be.
We’ll discuss the challenges you currently face, what data is available, and where the greatest opportunities lie to improve how you manage sales, stores, costs, quality, sustainability, and results.
Our retail experience ranges from commercial performance and cost-to-serve analysis to quality reviews, sustainability reporting, and migrations from Qlik to Power BI. The solutions vary, but they have one thing in common: data from multiple sources, countries, and parts of the organization must come together in an environment that is reliable, manageable, and usable.
We developed solutions that bring together sales, visitors, customers, products, stores, countries, and currencies. Users can analyze performance at different levels and have separate applications for management, mobile use, and operational presentation, among other purposes.
Cost-to-serve solutions combine sales, orders, costs, logistics flows, and P&L data. They can account for differences between countries, retail organizations, product groups, and other organizational levels.
We built reports that allow different types of reviews, assessments, and actions to be compared across locations, countries, and years. This provides a more coherent view than the standard reporting from the record-keeping system alone.
For sustainability reporting, we brought together energy, emissions, water, waste, and location attributes, among other data. This makes it possible to track performance across locations and periods and identify missing or anomalous records more quickly.
Retail involves sales, operations, finance, IT, quality, and sustainability. Each discipline views stores, products, customers, costs, and performance from a different perspective. We bring this information together and ensure that teams can work from the same definitions.
We start with the questions that need better answers. We then map the processes, definitions, users and data sources, and determine what data is needed to connect sales, stores, products, customers, costs, reviews and sustainability.
If the question extends beyond a single application, we can help with strategic consulting, a data strategy, AI Readiness or an independent BI tool selection.
For example, we connect point-of-sale systems, ERP, e-commerce, CRM, finance, product management, visitor data, review processes, sustainability records and supplementary files.
We account for differences between stores, countries, currencies, fiscal years, channels, product structures and organizations, as well as historical changes. This ensures that sales, visits, products, locations, costs and ratings are aligned in terms of meaning.
Based on this foundation, we develop dashboards, analyses and reports for areas including:
Where needed, we add data quality checks, alerts, mobile reporting, forecasts, user input and different access profiles.
Depending on the existing environment, we work with Qlik, Power BI, Microsoft Fabric, TimeXtender or a combination of these.
Within retail, we have developed both Qlik and Power BI solutions and migrated existing Qlik applications to Power BI. In the process, we assess which definitions, models and business logic can be reused and where redesign is needed.
If you already have an analytics environment, we build on it where that makes sense. We can add sources, improve models, redesign reports or support a phased 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, allowing new stores, countries, data sources, users and reports to be added in a controlled manner.
We start with the decisions that need to be better informed. These may relate to sales, store performance, visitors, products, costs, reviews, sustainability, or results.
We then determine which definitions, processes, users, and data sources are needed to answer those questions reliably. This prevents a broad platform from being built before it is clear which retail challenge it needs to solve.
We can connect to point-of-sale systems, ERP, e-commerce platforms, CRM, finance systems, product management systems, visitor tracking systems, review systems, sustainability data sources, and central data platforms. Excel and other supplementary files can also be included.
The available integration options depend on the API, database, export, or other means of accessing the data. We also consider how the data for stores, countries, channels, products, customers, periods, and costs aligns from a business perspective.
Yes, provided that differences are made explicit. These may include currencies, fiscal years, store types, local product ranges, market size, opening hours, and historical changes.
We configure models so that performance can be assessed at comparable levels and users can see which differences are explained by context.
Yes. Visitor data can be combined with transactions, customer data, conversion rates, average spend, and product sales.
This shows whether changes are driven by traffic, conversion, spending, price, promotions, or product mix. The possibilities depend on the available level of detail and the quality of the connections between sources.
Yes, provided that sufficiently detailed data is available on sales, orders, costs, and organizational structures.
For example, we can examine the cost of serving countries, stores, product groups, order flows, or customer segments. We clearly distinguish between costs that are available directly and those allocated using allocation keys.
Yes. We can consolidate review results, questions, scores, topics, and improvement actions across locations, countries, and periods.
This reveals which issues recur, where differences arise between parts of the organization, and which actions still require follow-up. Data from record-keeping systems such as Salesforce can also be accessed for this purpose.
Yes. Data on energy, water, waste, emissions, and refrigerants, for example, can be linked to locations, countries, and calendar periods.
This makes it possible to track trends, anomalies, and missing records. The available indicators depend on the sources used and how data is recorded locally.
Yes. We have developed and managed retail solutions in both Qlik and Power BI.
The right technology depends on the existing environment, architecture, user needs, and management organization. We can also work with Microsoft Fabric, TimeXtender, and other components of the existing data platform.
Yes. We have experience migrating retail solutions from Qlik to Power BI.
We first map the existing applications, data sources, definitions, models, user groups, and management processes. We then determine what can be reused and where redesign is needed. We do not treat a migration as a technical conversion, but as a controlled transition to a new analytics environment.
Read more about our approach in the guide to migrating from Qlik to Power BI.
Yes. We first map the existing sources, models, dashboards, definitions, and management processes.
We then determine what can remain, what needs improvement, and where additional applications or sources are needed. This can range from adding a new data source to restructuring a model or supporting a migration.
If you would first like an independent assessment of whether the current approach, architecture, or choice of supplier makes sense, you can request a Second Opinion Data & Analytics.
Subject-matter involvement is needed from employees who understand the processes and figures. This includes people from retail, sales, finance, operations, IT, quality, and sustainability.
They help establish definitions, assess exceptions, verify 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 know what insights you can gain from your current sales, store, customer, cost, and other data sources? Barry and Eric would be happy to discuss retail performance, cost to serve, reviews, sustainability, and your existing analytics environment. In half an hour, you will know whether we are a good fit. Email us, call us, or schedule a call right away.
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Available on weekdays between 8:00 AM and 6:00 PM (Amsterdam time)
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