Retail Analytics: Benefits, Tools, and Frameworks

We can’t talk about retail analytics without talking about Amazon. 

If you take a step back and think about it, Amazon is a ridiculously effective company. It started out in books, but has managed to achieve dominance in a remarkable number of markets: in e-commerce, cloud computing, video streaming, ebooks, smart home hardware, and in last-mile delivery. Presumably, over the next decade, it will continue to strengthen its positions in many of these markets; it will also likely also expand into new ones.

They’re able to do so because they are obsessed with metrics. 

They have something called a ‘Weekly Business Review’ meeting, or a WBR meeting — which is fractal: top leadership does a full-company WBR every week, followed by every department and operational team on down. The WBR covers all the most important metrics in the company in a metrics ‘deck’ — a presentation that contains hundreds of graphs, charts, and tables. And they do this religiously. 

We’ve written in-depth about how Amazon measures here. The biggest takeaway that you’ll get from the blog post is the idea that good operators must instrument the organizations they are running. If you don’t instrument, you won't know what’s going on. And if you don’t know what’s going on, you can’t possibly be a good operator — you don’t know what to focus on to get the outcomes you desire. To do so, you’ll need data and analytics.

So how can you be the next Amazon? If you’re here, you’re at the right place. By the end of this post, you’ll know:

• The types of analysis that every retailer should look into. 
• How to set up a modern retail analytics stack. 
• The best retail analytics software to help you with your goal. 

Why Retail Analytics Matters 

Retailers today can’t rely on guesswork. To stay competitive, they need data to make smarter decisions—like knowing what products to stock, how to market effectively, and how to boost profits. Analytics provides the tools to achieve all of this with precision. As Clive Humby famously said, “Data is the new oil,” but like oil, it only becomes valuable when refined.

Here are the common analysis retailers might want to look into. 

1.1  Trade/Promotional Strategies Analysis

Analytics empowers retailers to design trade and promotional campaigns with optimal impact. By analyzing customer purchase data and historical sales trends, businesses can identify which promotions work best and when to run them. 

1.2 Customer Willingness-to-Pay (WTP)

Understanding customer WTP helps retailers price products optimally, balancing profitability with customer satisfaction. For example, premium brands often use WTP analysis to justify higher pricing for products that customers perceive as high-value.

1.3 Revenue Growth Management (RGM)

RGM analysis helps retailers identify growth opportunities by evaluating pricing strategies, product categories, and market potential. This analysis ensures businesses capitalize on the highest-growth areas.

1.4 Price Pack Architecture

Price Pack Architecture (PPA) ensures retailers strike the perfect balance between price, size, and customer demand. Brands like Coca-Cola use PPA to create packaging that caters to different markets, like single servings for convenience and bulk packs for families.

This strategy helps align your products with customer expectations, ensuring they feel they’re getting value for their money.

1.5 Market Basket Analysis

Noticed how Amazon recommends products frequently bought together? That’s Market Basket Analysis in action.

This technique identifies product combinations customers commonly purchase, helping retailers design better promotions, optimize store layouts, or suggest add-ons at checkout. For brick-and-mortar stores, it’s the secret behind grouping chips and soda together on supermarket shelves.

1.6 Inventory Analytics: Avoiding Stock Issues

Inventory challenges can cripple a retailer. 

Remember when Nike faced backlash for running out of Air Jordans on launch day? Their stockout wasn’t just a supply chain hiccup, it was a revenue and loyalty loss. Inventory analytics, powered by demand forecasting, prevents such disasters by analyzing historical data, seasonal trends, and even external factors like weather.

1.8 Customer Segmentation

Customer segmentation divides shoppers into meaningful groups, helping retailers target them with precision. Retailers can do the same by offering exclusive deals to VIP customers or targeting discounts to budget-conscious shoppers. By understanding who your customers are, you can craft offers they simply can’t refuse.

How to Build A Retail Analytics Stack 

Building an analytics stack for retail companies is no different from building on for a real estate company, or a financial company. You always start with the same principles. 

Every data analytics system does three basic things.

1. You have to load data into a central repository of data, usually a data warehouse.
2. You have to transform or model your data so that it ends up nice sparkly and ready for consumption in your data warehouse.
3. You have to get that data to business decision-makers, where it may be used.

Step 1: Collecting, Consolidating and Storing Data

Before you analyze your organization’s data, raw data from multiple sources must be pulled into a central location in your analytics stack.

In the past, this might have been a chaotic staging area, but today, we recommend using a modern data warehouse optimized for analytical workloads. This process, commonly called ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform), syncs data into a single, manageable hub. Consolidation simplifies analysis, improves organization, and makes data more accessible.

Step 2: Processing Data

This is when your raw source data is loaded into a central database.

Raw data isn’t ready for analysis—it needs to be cleaned, transformed, and modeled. This step removes duplicates, test records, and irrelevant metadata, while applying business logic and summarizing data for reporting. 

This step usually involves two kinds of operations:

• Modeling data: apply business logic and formulae onto the data
• Transforming data: clean up, summarize, and pre-calculate data

Step 3: Presenting & Using Data

With transformed data in hand, it’s time to visualize and use it. This involves connecting a BI tool, or in your case, retail analytics software to create dashboards, reports, and exploratory tools for end users

Most people think of this step as just being about dashboarding and visualization, but it involves quite a bit more than that, there are a few applications of using data:

• Ad-hoc reporting, which is what happens throughout the lifecycle of the company.
• Data reporting, which we’ve already covered.
• Data exploration: how letting end users freely explore your data lightens the load on the data department.

Of course, we couldn’t cover everything about the modern analytics stack in this blog post. We’ve written a short guidebook on this topic, it has been downloaded by more than 30,000 data professionals, many of whom recommend it to their friends and colleagues. This book aims to:

• Give you a high-level framework and understanding of a proper modern analytics setup, and how each component interacts with each other.
• Go into enough practical detail on each of the components mentioned above. Explain the best practices, and help you understand the role of each component in the entire pipeline. 
• Show readers how to get started quickly on an analytics setup, yet remain able to scale it as time passes.

How to Evaluate Retail Analytics Software

Choosing the right analytics tool can feel like picking a needle out of a haystack—especially when there are dozens of options, all claiming to be the best.

But not all tools are created equal, and selecting the wrong one can lead to wasted resources and unmet expectations. This section walks you through the criteria for evaluating analytics tools tailored specifically for retail businesses.

• Integration: Your retail analytics software must play well with others. Look for native integrations with your existing tools—POS systems, inventory management, and customer relationship management (CRM) platforms. The easier it is to connect data sources, the faster you’ll get actionable insights.
• Scalability and performance: Your data grows as your business grows. Can the tool handle massive amounts of data during peak periods like Black Friday? For example, some tools are known for scaling effortlessly during high-volume traffic.
• Self-Service Analytics: Not everyone on your team is a data scientist. Choose tools that allow non-technical users to create dashboards by themselves or run reports with minimal training. 
• Real-Time Insights: Retail moves fast, and decisions can’t wait. Real-time analytic tools provide real-time analytics or threshold-based alerts, allowing managers to respond instantly to changes in spending, customer behavior or inventory levels.
• Metric Sheet: A robust metric sheet allows retailers to centralize key performance indicators (KPIs) and track them consistently across teams. Whether it's revenue per square foot, average order value (AOV), or cart abandonment rates, having a single source of truth for metrics helps streamline reporting and decision-making. 
• Geospatial Analytics: As a retail business, you always have to manage multiple stores at multiple locations. Geospatial analytics can pinpoint store performance and help optimize site selection, localized marketing, and regional inventory allocation.
• Advanced Predictive Capabilities: When evaluating analytics tools for retail, it’s essential to consider their ability to move beyond historical data and provide actionable forecasts. Advanced predictive capabilities enable retailers to anticipate trends, customer demands, and potential disruptions in the supply chain. 
• Robust Data Security: Retailers handle sensitive customer data, so security is critical. Ensure the tool complies with regulations like GDPR and CCPA. 

When looking for retail analytics software, use these criteria as your starting point, but don’t just take vendors’ word for it—ask for demos, test the software, and see if it truly meets your needs.

6 Best Analytics Tools for Retail Companies

The retail analytics landscape is packed with tools, each designed to tackle specific challenges. To save you the headache of evaluating every option, we’ve curated a list of six tools that stand out for their features, usability, and value for retail businesses.

1. Holistics

Holistics is a self-service BI tool known for its semantic modeling layer and ease of use. 

Using Holistics’ semantic layer, analysts can define business metrics once, re-use across dashboards, and manage them centrally for a golden source of truth. Its drag-and-drop self-service interface enables retail managers to explore data without writing code. 

Holistics is also designed for scalability, allowing you to cache query results on demand for configurable durations, or automatically pick the most optimal aggregated tables per each query for maximal query performance. This allows it to handle large datasets during high-traffic periods, provided your data warehouse is optimized. It also offers strong customization capabilities, allowing teams to create custom map-based visualizations, or custom dashboards for board reporting. 

However, Holistics integration with specialized retail systems like POS or CRM tools is limited compared to more retail-specific platforms. If your retail setup heavily relies on industry-specific tools, this may require additional connectors or middleware.

Holistics’ predictive capabilities are also limited, requiring external tools or custom work for forecasting.

2. Power BI

Power BI is part of the Microsoft ecosystem, making it a natural choice for teams already using tools like Excel, Azure, or SharePoint. 

It boasts a wide range of interactive and highly customizable visualizations, including heatmaps, waterfall charts, and drill-through reports, empowering users to uncover insights at a granular level. Its scalability ensures the smooth handling of massive datasets, making it well-suited for large retail organizations managing complex data streams during peak seasons like Black Friday. 

It also offers robust real-time analytics through streaming datasets, providing up-to-the-minute insights crucial for fast-moving retail environments. 

However, Power BI is incompatible with non-Microsoft operating systems like Linux or MacOS, and users cannot create reports or perform data analysis without installing the desktop application. Additionally, Power BI can pose a steep learning curve for non-technical users, as customizing dashboards or embedding reports often requires technical expertise. Retail teams seeking quick adoption may need additional training or developer support. 

3. Tableau

Tableau is renowned for its stunning, interactive visualizations. For retailers wanting to tell compelling data stories—whether to internal teams or external stakeholders—Tableau is an excellent choice.

Tableau integrates well with a wide range of data sources, from traditional databases to cloud platforms like AWS, Google Cloud, and Snowflake, ensuring seamless data connectivity for diverse retail environments. 

One of Tableau’s strengths is its ability to handle geospatial analytics, offering robust mapping features that allow retailers to analyze store performance by location, optimize regional marketing strategies, and manage inventory allocation effectively. 

However, Tableau’s predictive analytics capabilities are not built-in and often require integration with R or Python for advanced forecasting. It also lacks a centralized analytics management system similar to Holistics, which can lead to inconsistencies if metrics aren’t carefully standardized by teams. Additionally, Tableau Server requires significant technical expertise for deployment and maintenance.

4. Looker

Looker is a cloud-based business intelligence platform known for its semantic layer and powerful analytical capability. 

Its standout feature is its LookML modeling layer, which allows technical teams to define centralized data models that ensure consistent metrics across dashboards and reports, minimizing data discrepancies.

Looker integrates seamlessly with cloud-based systems and supports real-time analytics through live connections to data warehouses. This makes it highly effective for retail environments requiring up-to-date insights into inventory levels, customer behaviors, or sales performance. 

Looker also offers self-service capabilities, enabling non-technical users to explore data without SQL expertise. Additionally, it integrates well with marketing and CRM tools, supporting retail-specific use cases like customer segmentation or campaign analysis.

However, Looker has its limitations. 

While powerful for analytics, it lacks built-in geospatial features or advanced visual customization compared to tools like Tableau. Additionally, its licensing costs, estimated at $146,995 annually, can be prohibitively expensive for smaller retail teams. Another challenge lies in its reliance on LookML, which, while powerful, can be complex and time-consuming to set up and maintain for some users.

5. RetailNext

RetailNext specializes in brick-and-mortar analytics, offering insights into in-store traffic, customer behavior, and layout optimization.

RetailNext unique selling point is its ability to integrate with in-store technologies such as cameras, sensors, and POS systems making it a powerful tool for understanding customer behavior, mapping traffic patterns, and improving sales conversion rates.

The platform also excels in real-time insights, delivering instant notifications for metrics like sales conversions or occupancy thresholds, helping managers respond proactively to dynamic store conditions.

However, RetailNext has some limitations. 

Compared to other analytics software in this list, RetailNext’s focus on in-store data limits its versatility. While it excels in its niche, it lacks more sophisticated features such as centralized metric management, which ensures consistent KPIs across reports, or a robust self-service interface that allows non-technical users to explore data independently. It also lacks the broader integration capabilities of platforms like Power BI or Tableau for unified analytics across channels. 

6. Crisp

Crisp is a cloud-based analytics platform tailored for supply chain and retail businesses, offering real-time data visibility to optimize operations and reduce waste

Its standout feature is its ability to aggregate data from diverse sources such as wholesalers, distributors, and retailers, making it highly effective for businesses managing complex supply chains. With Crisp, users can access live data feeds, enabling quicker decision-making on inventory management, demand forecasting, and supplier performance.

Crisp excels in integration, connecting seamlessly with platforms like Shopify, Amazon, and various ERP systems to unify supply chain data. Its real-time dashboards provide insights into inventory levels, sales trends, and supply chain bottlenecks, empowering retailers to act proactively. The platform also offers predictive capabilities, helping businesses anticipate demand fluctuations and optimize stock levels, particularly useful during seasonal peaks or promotions.

However, compared to analytics tools like Looker or Holistics, Crisp’s focus on supply chain analytics limits its scope, making it not suitable for retail organizations with diverse analytics needs. 

It lacks advanced self-service analytics, requiring users to rely on pre-built dashboards rather than creating custom visualizations. Additionally, geospatial analytics is underdeveloped, offering limited tools for location-based insights. Crisp also doesn’t provide robust centralized metric management, which makes it less suitable for organizations needing consistent metric definitions across departments.

Final Words 

Building an effective retail analytics stack is no easy feat. It requires careful planning, thoughtful integration of tools, and alignment with your business needs. To help you navigate this complex process, make sure to check out our comprehensive guide on building an analytics stack.

Evaluating retail analytics software is equally challenging, given the wide variety of options and features available. It’s easy to get lost in the technical jargon or be swayed by flashy demos. To simplify this, we’ve created this RFP template - distilled from over 100 RFP (Request for Proposal) and criteria lists we’ve received over the years - that makes it easier to evaluate tools systematically based on your unique requirements.

That said, tooling is just one piece of the puzzle.

Becoming a truly data-driven organization requires the right processes and a culture that values data. Companies like Amazon have set the gold standard for this, as described in Working Backwards, showcasing how they combine technology, and processes to achieve a rigorously data-driven culture. If you’re a retailer, it’s a must read.

We can’t recommend it enough.